This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration.
A probabilistic multi-objective approach for FACTS devices allocation with di...IJECEIAES
This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.
stability of power flow analysis of different resources both on and off gridrehman1oo
This document presents a power flow optimization strategy model for a distribution network that considers source, load, and storage. The model aims to minimize total cost, voltage deviation, and power losses over time periods determined through k-means clustering of an equivalent load curve. A particle swarm optimization algorithm is used to solve the multi-objective optimization model subject to power flow, voltage, and other constraints. The model is tested on an IEEE 33-node system and is shown to improve economic and reliability performance compared to a fixed weighting approach.
Multi-objective distributed generation integration in radial distribution sy...IJECEIAES
This paper introduces a new approach based on a chaotic strategy and a neural network algorithm (NNA), called chaotic-based NNA (CNNA), to solve the optimal distributed generation allocation (ODGA), in the radial distribution system (RDS). This consists of determining the optimal locations and sizes of one or several distributed generations (DGs) to be inserted into the RDS to minimize one or multiple objectives while meeting a set of security limits. The robustness of the proposed method is demonstrated by applying it to two different typical RDSs, namely IEEE 33bus and 69-bus. In this regard, simulations are performed for three DGs in the cases of unity power factor (UPF) and optimal power factor (OPF), considering single and multi-objective optimization, by minimizing the total active losses and improving the voltage profile, voltage deviation (VD) and voltage stability index (VSI). Compared to its original version and recently reported methods, the CNNA solutions are more competitive without increasing the complexity of the optimization algorithm, especially when the RDS size and problem dimension are extended.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
MODELING AND OPTIMIZATION OF PIEZOELECTRIC ENERGY HARVESTING adeij1
In this paper, the modeling, optimization and simulation results of the piezoelectric energy harvesting using bond graph approach are presented. Firstly, a lightweight equivalent model derived from the bond graph is proposed. It’s a comprehensive model, which is suitable for piezoelectric seismic energy harvester investigation and power optimization. The optimal charge impedance for both the resistive load and complex load are given and analysed. Finally a bond graph approach is proposed to allow optimization of the extracted energy while keeping simplicity and standalone capability. The proposed model does not rely on any inductor and is constructed with a simple switch. The power harvested is more than twice the conventional technique one on a wide band of resistive load. The bond graph model is valid close to the analysed mode centre frequency and delivers results compared to experimental and analytical data. Furthermore, we also show that the harvester can be electrically tuned to match the excitation frequency. This makes it possible to maximize the power output for both linear and non-linear loads.
The document presents a multi-objective optimization model for allocating thyristor-controlled series capacitors (TCSCs) in power systems with wind power and load randomness. It first describes existing literature on modeling renewable energy and load uncertainty and optimizing power system objectives. It then introduces a method to generate and reduce scenarios for wind power and load. Next, it establishes a multi-objective optimization model with available transmission capacity and voltage stability as objectives. Finally, it proposes an improved multi-objective particle swarm optimization algorithm to solve the established model.
Impact of compressed air energy storage system into diesel power plant with w...IJECEIAES
The wind energy plays an important role in power system because of its renewable, clean and free energy. However, the penetration of wind power (WP) into the power grid system (PGS) requires an efficient energy storage systems (ESS). compressed air energy storage (CAES) system is one of the most ESS technologies which can alleviate the intermittent nature of the renewable energy sources (RES). Nyala city power plant in Sudan has been chosen as a case study because the power supply by the existing power plant is expensive due to high costs for fuel transport and the reliability of power supply is low due to uncertain fuel provision. This paper presents a formulation of security-constrained unit commitment (SCUC) of diesel power plant (DPP) with the integration of CAES and PW. The optimization problem is modeled and coded in MATLAB which solved with solver GORUBI 8.0. The results show that the proposed model is suitable for integration of renewable energy sources (RES) into PGS with ESS and helpful in power system operation management.
This article presents the system design and prediction performance of a 1kW capacity grid-tied photovoltaic inverter applicable for low or medium-voltage electrical distri-bution networks. System parameters, for instance, the longitude and latitude of the solar plant location, panel orientation, tilt and azimuth angle calculation, feasibility testing, optimal sizing of installment are analyzed in the model and the utility is sim-ulated precisely to construct an efficient solar power plant for residential applications. In this paper, meteorological data are computed to discuss the impact of environmen-tal variables. As regards ensuring reliability and sustenance, a simulation model of the system of interest is tested in the PVsyst software package. Simulation results yield that the optimum energy injected to the national grid from the solar plant, specific pro-duction, and performance ratio are 1676kWh/year, 1552kWh/kWp/year, and 79.29% respectively. Moreover, the predicted carbon footprint reduction is 23.467 tons during the 30 years lifetime of the system. Therefore, the performance assessments affirm the effectiveness of the proposed research.
A probabilistic multi-objective approach for FACTS devices allocation with di...IJECEIAES
This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.
stability of power flow analysis of different resources both on and off gridrehman1oo
This document presents a power flow optimization strategy model for a distribution network that considers source, load, and storage. The model aims to minimize total cost, voltage deviation, and power losses over time periods determined through k-means clustering of an equivalent load curve. A particle swarm optimization algorithm is used to solve the multi-objective optimization model subject to power flow, voltage, and other constraints. The model is tested on an IEEE 33-node system and is shown to improve economic and reliability performance compared to a fixed weighting approach.
Multi-objective distributed generation integration in radial distribution sy...IJECEIAES
This paper introduces a new approach based on a chaotic strategy and a neural network algorithm (NNA), called chaotic-based NNA (CNNA), to solve the optimal distributed generation allocation (ODGA), in the radial distribution system (RDS). This consists of determining the optimal locations and sizes of one or several distributed generations (DGs) to be inserted into the RDS to minimize one or multiple objectives while meeting a set of security limits. The robustness of the proposed method is demonstrated by applying it to two different typical RDSs, namely IEEE 33bus and 69-bus. In this regard, simulations are performed for three DGs in the cases of unity power factor (UPF) and optimal power factor (OPF), considering single and multi-objective optimization, by minimizing the total active losses and improving the voltage profile, voltage deviation (VD) and voltage stability index (VSI). Compared to its original version and recently reported methods, the CNNA solutions are more competitive without increasing the complexity of the optimization algorithm, especially when the RDS size and problem dimension are extended.
Two-way Load Flow Analysis using Newton-Raphson and Neural Network MethodsIRJET Journal
The document presents a study comparing two-way load flow analysis using the Newton-Raphson method and a neural network method for networked microgrids. The optimal power flow problem is solved using both a conventional Newton-Raphson method and an artificial intelligence neural network method. Results show that the neural network method achieves minimum losses and higher efficiency compared to the Newton-Raphson method, with efficiencies of 99.3% and 97% respectively for the test networked microgrid system.
MODELING AND OPTIMIZATION OF PIEZOELECTRIC ENERGY HARVESTING adeij1
In this paper, the modeling, optimization and simulation results of the piezoelectric energy harvesting using bond graph approach are presented. Firstly, a lightweight equivalent model derived from the bond graph is proposed. It’s a comprehensive model, which is suitable for piezoelectric seismic energy harvester investigation and power optimization. The optimal charge impedance for both the resistive load and complex load are given and analysed. Finally a bond graph approach is proposed to allow optimization of the extracted energy while keeping simplicity and standalone capability. The proposed model does not rely on any inductor and is constructed with a simple switch. The power harvested is more than twice the conventional technique one on a wide band of resistive load. The bond graph model is valid close to the analysed mode centre frequency and delivers results compared to experimental and analytical data. Furthermore, we also show that the harvester can be electrically tuned to match the excitation frequency. This makes it possible to maximize the power output for both linear and non-linear loads.
The document presents a multi-objective optimization model for allocating thyristor-controlled series capacitors (TCSCs) in power systems with wind power and load randomness. It first describes existing literature on modeling renewable energy and load uncertainty and optimizing power system objectives. It then introduces a method to generate and reduce scenarios for wind power and load. Next, it establishes a multi-objective optimization model with available transmission capacity and voltage stability as objectives. Finally, it proposes an improved multi-objective particle swarm optimization algorithm to solve the established model.
Impact of compressed air energy storage system into diesel power plant with w...IJECEIAES
The wind energy plays an important role in power system because of its renewable, clean and free energy. However, the penetration of wind power (WP) into the power grid system (PGS) requires an efficient energy storage systems (ESS). compressed air energy storage (CAES) system is one of the most ESS technologies which can alleviate the intermittent nature of the renewable energy sources (RES). Nyala city power plant in Sudan has been chosen as a case study because the power supply by the existing power plant is expensive due to high costs for fuel transport and the reliability of power supply is low due to uncertain fuel provision. This paper presents a formulation of security-constrained unit commitment (SCUC) of diesel power plant (DPP) with the integration of CAES and PW. The optimization problem is modeled and coded in MATLAB which solved with solver GORUBI 8.0. The results show that the proposed model is suitable for integration of renewable energy sources (RES) into PGS with ESS and helpful in power system operation management.
This article presents the system design and prediction performance of a 1kW capacity grid-tied photovoltaic inverter applicable for low or medium-voltage electrical distri-bution networks. System parameters, for instance, the longitude and latitude of the solar plant location, panel orientation, tilt and azimuth angle calculation, feasibility testing, optimal sizing of installment are analyzed in the model and the utility is sim-ulated precisely to construct an efficient solar power plant for residential applications. In this paper, meteorological data are computed to discuss the impact of environmen-tal variables. As regards ensuring reliability and sustenance, a simulation model of the system of interest is tested in the PVsyst software package. Simulation results yield that the optimum energy injected to the national grid from the solar plant, specific pro-duction, and performance ratio are 1676kWh/year, 1552kWh/kWp/year, and 79.29% respectively. Moreover, the predicted carbon footprint reduction is 23.467 tons during the 30 years lifetime of the system. Therefore, the performance assessments affirm the effectiveness of the proposed research.
A Technique for Shunt Active Filter meld micro grid SystemIJERA Editor
The proposed system presents a control technique for a micro grid connected hybrid generation system ith case study interfaced with a three phase shunt active filter to suppress the current harmonics and reactive power present in the load using PQ Theory with ANN controller. This Hybrid Micro Grid is developed using freely renewable energy resources like Solar Photovoltaic (SPV) and Wind Energy (WE). To extract the maximum available power from PV panels and wind turbines, Maximum power point Tracker (MPPT) has been included. This MPPT uses the “Standard Perturbs and Observe” technique. By using PQ Theory with ANN Controller, the Reference currents are generated which are to be injected by Shunt active power filter (SAPF)to compensate the current harmonics in the non linear load. Simulation studies shows that the proposed control technique performs non-linear load current harmonic compensation maintaining the load current in phase with the source voltage.
Optimal Configuration of Wind Farms in Radial Distribution System Using Parti...journalBEEI
Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.
A grid connected hybrid generation system (HGS) consisting of wind energy conversion System (WECS)/Photo voltaic (PV) System/solid oxide fuel cell (SOFC) is designed and simulated by using Matlab/Simulink. SOFC is the replacement of battery, attached to produce the clean energy when these renewable energy sources are unable to produce required amount of electric power. A controller is used to regulate the flow of H2 through the valveto the SOFC and the rest amount of H2 is stored in storage tank. Also, an operational control strategy (OCS) is developed to utilize maximum amount of power of PV to the required load and rest amount of power is coming from wind to fulfill the load demand. Hence, the electrolyzer is supplied by the wind power to convert the water in to H2 and oxygen. Also the power quality factor (PQF) analysis is exercised to measure the quality of power transmission.
Stochastic renewable energy resources integrated multi-objective optimal powe...TELKOMNIKA JOURNAL
This document proposes a method for solving single and multi-objective optimal power flow problems that integrate stochastic wind and solar power with traditional coal-based power plants. It models the uncertainties in wind and solar output using probability distribution functions. A multi-objective moth flame optimization technique is used to solve the optimization problems. The results are validated on an adapted IEEE 30-bus test system incorporating wind and solar plants.
Fractal representation of the power demand based on topological properties of...IJECEIAES
In a power system, the load demand considers two components such as the real power (P) because of resistive elements, and the reactive power (Q) because inductive or capacitive elements. This paper presents a graphical representation of the electric power demand based on the topological properties of the Julia Sets, with the purpose of observing the different graphic patterns and relationship with the hourly load consumptions. An algorithm that iterates complex numbers related to power is used to represent each fractal diagram of the load demand. The results show some representative patterns related to each value of the power consumption and similar behaviour in the fractal diagrams, which allows to understand consumption behaviours from the different hours of the day. This study allows to make a relation among the different consumptions of the day to create relationships that lead to the prediction of different behaviour patterns of the curves.
Wind power prediction using a nonlinear autoregressive exogenous model netwo...IJECEIAES
The monitoring of wind installations is key for predicting their future behavior, due to the strong dependence on weather conditions and the stochastic nature of the wind. However, in some places, in situ measurements are not always available. In this paper, active power predictions for the city of Santa Marta-Colombia using a nonlinear autoregressive exogenous model (NARX) network were performed. The network was trained with a reliable dataset from a wind farm located in Turkey, because the meteorological data from the city of Santa Marta are unavailable or unreliable on certain dates. Three training and testing cases were designed, with different input variables and varying the network target between active power and wind speed. The dataset was obtained from the Kaggle platform, and is made up of five variables: date, active power, wind speed, theoretical power, and wind direction; each with 50,530 samples, which were preprocessed, and in some cases, normalized, to facilitate the neural network learning. For the training, testing and validation processes, a correlation coefficient of 0.9589 was obtained for the best scenario with the data from Turkey, while the best correlation coefficient for the data from Santa Marta was 0.8537.
Feasibility Study of a Grid Connected Hybrid Wind/PV SystemIJAPEJOURNAL
This paper investigates the feasibility of a grid connected, large-scale hybrid wind/PV system. From data available an area called RasElnaqab in Jordan is chosen because it enjoys both high average wind speed of 6.13 m/s and high average solar radiation of 5.9KWhr/m2 /day. MATLAB and HOMER software’s are used for sizing and economical analysis respectively. Results show that76124 SUNTECH PV panels and 38 GW87-1.5MW wind turbines are the optimal choice. The net present cost (NPC) is 130,115,936$, the cost of energy (COE) is 0.049$/KWhr with a renewable fraction of 74.1%.A stepby-step process to determine the optimal sizing of Hybrid Wind/PV system is presented and it can be applied anywhere.
Evaluation of wind-solar hybrid power generation system based on Monte Carlo...IJECEIAES
The application of wind-photovoltaic complementary power generation systems is becoming more and more widespread, but its intermittent and fluctuating characteristics may have a certain impact on the system's reliability. To better evaluate the reliability of stand-alone power generation systems with wind and photovoltaic generators, a reliability assessment model for stand-alone power generation systems with wind and photovoltaic generators was developed based on the analysis of the impact of wind and photovoltaic generator outages and derating on reliability. A sequential Monte Carlo method was used to evaluate the impact of the wind turbine, photovoltaic (PV) turbine, wind/photovoltaic complementary system, the randomness of wind turbine/photovoltaic outage status and penetration rate on the reliability of Independent photovoltaic power generation system (IPPS) under the reliability test system (RBTS). The results show that this reliability assessment method can provide some reference for planning the actual IPP system with wind and complementary solar systems.
To increase energy yield from an installed photovoltaic (PV) array, particularly during partial shading condition (PSC), a new technique based on reconfigurable PV array interconnection is proposed in this work. The proposed technique works by dynamically changing the interconnection of PV modules to form a new configuration using a switching matrix inside the array. The criteria of good reconfigurable PV array interconnection techniques depend on the efficiency and accuracy of the control algorithm to optimally reconfigure the PV array to maximize the total output power. Hence, this paper proposes a new control algorithm using differential evolution (DE) for photovoltaic array reconfiguration (PVAR). To verify the superiority of the proposed algorithm, DE is compared with the particle swarm optimization (PSO) algorithm. Results confirm that DE performs well in terms of the amount of energy production during PSC. For all the nine shading patterns tested on a 3 × 3 PV array, DE yields 1% to 5% more power than PSO.
Comparative analysis of optimal power flow in renewable energy sources based...IJECEIAES
Adaptation of renewable energy is inevitable. The idea of microgrid offers integration of renewable energy sources with conventional power generation sources. In this research, an operative approach was proposed for microgrids comprising of four different power generation sources. The microgrid is a way that mixes energy locally and empowers the end-users to add useful power to the network. IEEE-14 bus system-based microgrid was developed in MATLAB/Simulink to demonstrate the optimal power flow. Two cases of battery charging and discharging were also simulated to evaluate its realization. The solution of power flow analysis was obtained from the Newton–Raphson method and particle swarm optimization method. A comparison was drawn between these methods for the proposed model of the microgrid on the basis of transmission line losses and voltage profile. Transmission line losses are reduced to about 17% in the case of battery charging and 19 to 20% in the case of battery discharging when system was analyzed with the particle swarm optimization. Particle swarm optimization was found more promising for the deliverance of optimal power flow in the renewable energy sources-based microgrid.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Optimal Power Generation in Energy-Deficient Scenarios Using Bagging EnsemblesKashif Mehmood
This paper presents an improved technique for optimal power generation using ensemble
artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor
computing rather than traditional serial computation to reduce bias and variance in machine learning. The
load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an
IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN)
with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and
authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton
Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework
to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint
partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power
generation. The results of MATLAB simulations are analyzed and compared along with computational
complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed
to LR
Investigate the maximum power point of photovoltaic system at different envi...IJECEIAES
The main objective of this work is to implement a circuit-based simulation model of a photovoltaic (PV) cell in order to investigate the electrical behavior of the practical cell with respect to some changes in weather parameters. The simulation model consists of three subsystems: photovoltaic cells, DC/DC converter and MPPT controller based logic fuzzy control. The maximum power control function is achieved with the appropriate power control of the power inverter. Fuzzy logic controller has been used to perform MPPT functions to get maximum power from the PV panel. The proposed circuit was implemented in MATLAB/Simulink. The obtained results show that the output sequence is non-linear and almost constant current to the open circuit voltage and the power has maximum motion to voltage for certain environmental conditions.
This paper presents application and control of the gate-controlled series capacitor (GCSC) for series compensation and subsynchronous resonance (SSR) damping in doubly-fed induction generator (DFIG)-based wind farms. The GCSC is a new series FACTS device composed of a fixed capacitor in parallel with a pair of antiparallel gate-commuted switches. The study considers a DFIG-based wind farm, which is connected to a series-compensated transmission line whose parameters are derived from the IEEE first benchmark model for computer simulation of the SSR. The small-signal stability analysis of the system is presented, and the eigenvalues of the system are obtained. Using both modal analysis and time-domain simulation, it is shown that the system is potentially unstable due to the SSR mode. Therefore, the wind farm is equipped with a GCSC to solve the instability of the wind farm resulting from the SSR mode, and an SSR damping controller (SSRDC) is designed for this device using residue-based analysis and root locus diagrams. Using residue-based analysis, the optimal input control signal to the SSRDC is identified, which can damp the SSR mode without destabilizing other modes, and using root-locus analysis, the required gain for the SSRDC is determined. MATLAB/Simulink is used as a tool for modeling, design, and time-domain simulations.
Optimal Generation Scheduling of Power System for Maximum Renewable Energy...IJECEIAES
This paper proposes an optimal generation scheduling method for a power system integrated with renewable energy sources (RES) based distributed generations (DG) and energy storage systems (ESS) considering maximum harvesting of RES outputs and minimum power system operating losses. The main contribution aims at economically employing RES in a power system. In particular, maximum harvesting of renewable energy is achieved by the mean of ESS management. In addition, minimum power system operating losses can be obtained by properly scheduling operating of ESS and controllable generations. Particle Swam Optimization (PSO) algorithm is applied to search for a near global optimal solutions. The optimization problem is formulated and evaluated taking into account power system operating constraints. The different operation scenarios have been used to investigate the effective of the proposed method via DIgSILENT PowerFactory software. The proposed method is examined with IEEE standard 14-bus and 30-bus test systems.
A novel efficient adaptive-neuro fuzzy inference system control based smart ...IJECEIAES
This document presents a novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm for a smart grid integrating solar, wind, and grid power sources. The proposed ANFIS controller is used to improve the steady-state and transient response of the hybrid power system. Fuzzy logic maximum power point tracking algorithms are used to extract maximum power from solar photovoltaic panels and a permanent magnet synchronous generator is used for wind power generation. Back-to-back voltage source converters operated by the ANFIS controller are used to maximize both renewable power generations. Simulation results under different operating conditions and nonlinear faults show the proposed ANFIS control algorithm improves the overall system performance.
Modelling and simulation for energy management of a hybrid microgrid with dro...IJECEIAES
The most efficient and connected alternative for increasing the use of local renewable energy sources is a hybrid microgrid, these systems face additional challenges due to the integration of power electronics, energy storage technologies and traditional power plants. The hybrid alternating currentdirect current (AC-DC) microgrid that is the subject of this research uses a primary-droop control system to regulate state variables and auxiliary services, thus, it is composed of batteries, solar panels and a miniature wind turbine (PDC) and controls how each energy source in a microgrid contributes to the final product. To achieve the given objectives, this paper will create appropriate models for each part of the microgrid design and define, among them, the energy storage batteries and power electronic converters required for each level of each of these systems. Finally, the dynamic nature of the system will be critically evaluated and characterized, to distribute the load and reduce imbalances, modify the primary drop of the resulting microgrid using MATLAB simulation.
Micropower system optimization for the telecommunication towers based on var...IJECEIAES
This study investigates the technical and cost-effective performance of options renewable energy sources to develop a green off-grid telecommunication tower to replace diesel generators in Malaysia. For this purpose, the solar, wind, pico-hydro energy, along with diesel generators, were examined to compare. In addition, the modeling of hybrid powering systems was conducted using hybrid optimization model for energy (HOMER) simulation based on techno-economic analysis to determine the optimal economically feasible system. The optimization findings showed that the hybrid high-efficiency fixed photovoltaic (PV) system with battery followed by 2 kW pico-hydropower and battery are the optimal configurations for powering off-grid telecommunication towers in Malaysia with the lowest net present cost (NPC) and cost of energy (COE). These costs of NPC and COE are more down than diesel generator costs with battery by 17.45%, 16.45%, 15.9%, and 15.5%, respectively. Furthermore, the economic evaluation of the high-efficiency solar fixed PV panels system annual cash flow compared to the diesel generator with the battery system indicated a ten-year payback period.
Bulk power system availability assessment with multiple wind power plants IJECEIAES
The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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A Technique for Shunt Active Filter meld micro grid SystemIJERA Editor
The proposed system presents a control technique for a micro grid connected hybrid generation system ith case study interfaced with a three phase shunt active filter to suppress the current harmonics and reactive power present in the load using PQ Theory with ANN controller. This Hybrid Micro Grid is developed using freely renewable energy resources like Solar Photovoltaic (SPV) and Wind Energy (WE). To extract the maximum available power from PV panels and wind turbines, Maximum power point Tracker (MPPT) has been included. This MPPT uses the “Standard Perturbs and Observe” technique. By using PQ Theory with ANN Controller, the Reference currents are generated which are to be injected by Shunt active power filter (SAPF)to compensate the current harmonics in the non linear load. Simulation studies shows that the proposed control technique performs non-linear load current harmonic compensation maintaining the load current in phase with the source voltage.
Optimal Configuration of Wind Farms in Radial Distribution System Using Parti...journalBEEI
Recently, a wide range of wind farm based distributed generations (DGs) are being integrated into distribution systems to fulfill energy demands and to reduce the burden on transmission corridors. The non-optimal configuration of DGs could severely affect the distribution system operations and control. Hence, the aim of this paper is to analyze the wind data in order to build a mathematical model for power output and pinpoint the optimal location. The overall objective is minimization of power loss reduction in distribution system. The five years of wind data was taken from 24o 44’ 29” North, 67o 35’ 9” East coordinates in Pakistan. The optimal location for these wind farms were pinpointed via particle swarm optimization (PSO) algorithm using standard IEEE 33 radial distribution system. The result reveals that the proposed method helps in improving renewable energy near to load centers, reduce power losses and improve voltage profile of the system. Moreover, the validity and performance of the proposed model were also compared with other optimization algorithms.
A grid connected hybrid generation system (HGS) consisting of wind energy conversion System (WECS)/Photo voltaic (PV) System/solid oxide fuel cell (SOFC) is designed and simulated by using Matlab/Simulink. SOFC is the replacement of battery, attached to produce the clean energy when these renewable energy sources are unable to produce required amount of electric power. A controller is used to regulate the flow of H2 through the valveto the SOFC and the rest amount of H2 is stored in storage tank. Also, an operational control strategy (OCS) is developed to utilize maximum amount of power of PV to the required load and rest amount of power is coming from wind to fulfill the load demand. Hence, the electrolyzer is supplied by the wind power to convert the water in to H2 and oxygen. Also the power quality factor (PQF) analysis is exercised to measure the quality of power transmission.
Stochastic renewable energy resources integrated multi-objective optimal powe...TELKOMNIKA JOURNAL
This document proposes a method for solving single and multi-objective optimal power flow problems that integrate stochastic wind and solar power with traditional coal-based power plants. It models the uncertainties in wind and solar output using probability distribution functions. A multi-objective moth flame optimization technique is used to solve the optimization problems. The results are validated on an adapted IEEE 30-bus test system incorporating wind and solar plants.
Fractal representation of the power demand based on topological properties of...IJECEIAES
In a power system, the load demand considers two components such as the real power (P) because of resistive elements, and the reactive power (Q) because inductive or capacitive elements. This paper presents a graphical representation of the electric power demand based on the topological properties of the Julia Sets, with the purpose of observing the different graphic patterns and relationship with the hourly load consumptions. An algorithm that iterates complex numbers related to power is used to represent each fractal diagram of the load demand. The results show some representative patterns related to each value of the power consumption and similar behaviour in the fractal diagrams, which allows to understand consumption behaviours from the different hours of the day. This study allows to make a relation among the different consumptions of the day to create relationships that lead to the prediction of different behaviour patterns of the curves.
Wind power prediction using a nonlinear autoregressive exogenous model netwo...IJECEIAES
The monitoring of wind installations is key for predicting their future behavior, due to the strong dependence on weather conditions and the stochastic nature of the wind. However, in some places, in situ measurements are not always available. In this paper, active power predictions for the city of Santa Marta-Colombia using a nonlinear autoregressive exogenous model (NARX) network were performed. The network was trained with a reliable dataset from a wind farm located in Turkey, because the meteorological data from the city of Santa Marta are unavailable or unreliable on certain dates. Three training and testing cases were designed, with different input variables and varying the network target between active power and wind speed. The dataset was obtained from the Kaggle platform, and is made up of five variables: date, active power, wind speed, theoretical power, and wind direction; each with 50,530 samples, which were preprocessed, and in some cases, normalized, to facilitate the neural network learning. For the training, testing and validation processes, a correlation coefficient of 0.9589 was obtained for the best scenario with the data from Turkey, while the best correlation coefficient for the data from Santa Marta was 0.8537.
Feasibility Study of a Grid Connected Hybrid Wind/PV SystemIJAPEJOURNAL
This paper investigates the feasibility of a grid connected, large-scale hybrid wind/PV system. From data available an area called RasElnaqab in Jordan is chosen because it enjoys both high average wind speed of 6.13 m/s and high average solar radiation of 5.9KWhr/m2 /day. MATLAB and HOMER software’s are used for sizing and economical analysis respectively. Results show that76124 SUNTECH PV panels and 38 GW87-1.5MW wind turbines are the optimal choice. The net present cost (NPC) is 130,115,936$, the cost of energy (COE) is 0.049$/KWhr with a renewable fraction of 74.1%.A stepby-step process to determine the optimal sizing of Hybrid Wind/PV system is presented and it can be applied anywhere.
Evaluation of wind-solar hybrid power generation system based on Monte Carlo...IJECEIAES
The application of wind-photovoltaic complementary power generation systems is becoming more and more widespread, but its intermittent and fluctuating characteristics may have a certain impact on the system's reliability. To better evaluate the reliability of stand-alone power generation systems with wind and photovoltaic generators, a reliability assessment model for stand-alone power generation systems with wind and photovoltaic generators was developed based on the analysis of the impact of wind and photovoltaic generator outages and derating on reliability. A sequential Monte Carlo method was used to evaluate the impact of the wind turbine, photovoltaic (PV) turbine, wind/photovoltaic complementary system, the randomness of wind turbine/photovoltaic outage status and penetration rate on the reliability of Independent photovoltaic power generation system (IPPS) under the reliability test system (RBTS). The results show that this reliability assessment method can provide some reference for planning the actual IPP system with wind and complementary solar systems.
To increase energy yield from an installed photovoltaic (PV) array, particularly during partial shading condition (PSC), a new technique based on reconfigurable PV array interconnection is proposed in this work. The proposed technique works by dynamically changing the interconnection of PV modules to form a new configuration using a switching matrix inside the array. The criteria of good reconfigurable PV array interconnection techniques depend on the efficiency and accuracy of the control algorithm to optimally reconfigure the PV array to maximize the total output power. Hence, this paper proposes a new control algorithm using differential evolution (DE) for photovoltaic array reconfiguration (PVAR). To verify the superiority of the proposed algorithm, DE is compared with the particle swarm optimization (PSO) algorithm. Results confirm that DE performs well in terms of the amount of energy production during PSC. For all the nine shading patterns tested on a 3 × 3 PV array, DE yields 1% to 5% more power than PSO.
Comparative analysis of optimal power flow in renewable energy sources based...IJECEIAES
Adaptation of renewable energy is inevitable. The idea of microgrid offers integration of renewable energy sources with conventional power generation sources. In this research, an operative approach was proposed for microgrids comprising of four different power generation sources. The microgrid is a way that mixes energy locally and empowers the end-users to add useful power to the network. IEEE-14 bus system-based microgrid was developed in MATLAB/Simulink to demonstrate the optimal power flow. Two cases of battery charging and discharging were also simulated to evaluate its realization. The solution of power flow analysis was obtained from the Newton–Raphson method and particle swarm optimization method. A comparison was drawn between these methods for the proposed model of the microgrid on the basis of transmission line losses and voltage profile. Transmission line losses are reduced to about 17% in the case of battery charging and 19 to 20% in the case of battery discharging when system was analyzed with the particle swarm optimization. Particle swarm optimization was found more promising for the deliverance of optimal power flow in the renewable energy sources-based microgrid.
Stochastic control for optimal power flow in islanded microgridIJECEIAES
The problem of optimal power flow (OPF) in an islanded mircrogrid (MG) for hybrid power system is described. Clearly, it deals with a formulation of an analytical control model for OPF. The MG consists of wind turbine generator, photovoltaic generator, and diesel engine generator (DEG), and is in stochastic environment such as load change, wind power fluctuation, and sun irradiation power disturbance. In fact, the DEG fails and is repaired at random times so that the MG can significantly influence the power flow, and the power flow control faces the main difficulty that how to maintain the balance of power flow? The solution is that a DEG needs to be scheduled. The objective of the control problem is to find the DEG output power by minimizing the total cost of energy. Adopting the Rishel’s famework and using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation. Finally, numerical examples and sensitivity analyses are included to illustrate the importance and effectiveness of the proposed model.
Optimal Power Generation in Energy-Deficient Scenarios Using Bagging EnsemblesKashif Mehmood
This paper presents an improved technique for optimal power generation using ensemble
artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor
computing rather than traditional serial computation to reduce bias and variance in machine learning. The
load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an
IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN)
with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and
authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton
Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework
to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint
partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power
generation. The results of MATLAB simulations are analyzed and compared along with computational
complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed
to LR
Investigate the maximum power point of photovoltaic system at different envi...IJECEIAES
The main objective of this work is to implement a circuit-based simulation model of a photovoltaic (PV) cell in order to investigate the electrical behavior of the practical cell with respect to some changes in weather parameters. The simulation model consists of three subsystems: photovoltaic cells, DC/DC converter and MPPT controller based logic fuzzy control. The maximum power control function is achieved with the appropriate power control of the power inverter. Fuzzy logic controller has been used to perform MPPT functions to get maximum power from the PV panel. The proposed circuit was implemented in MATLAB/Simulink. The obtained results show that the output sequence is non-linear and almost constant current to the open circuit voltage and the power has maximum motion to voltage for certain environmental conditions.
This paper presents application and control of the gate-controlled series capacitor (GCSC) for series compensation and subsynchronous resonance (SSR) damping in doubly-fed induction generator (DFIG)-based wind farms. The GCSC is a new series FACTS device composed of a fixed capacitor in parallel with a pair of antiparallel gate-commuted switches. The study considers a DFIG-based wind farm, which is connected to a series-compensated transmission line whose parameters are derived from the IEEE first benchmark model for computer simulation of the SSR. The small-signal stability analysis of the system is presented, and the eigenvalues of the system are obtained. Using both modal analysis and time-domain simulation, it is shown that the system is potentially unstable due to the SSR mode. Therefore, the wind farm is equipped with a GCSC to solve the instability of the wind farm resulting from the SSR mode, and an SSR damping controller (SSRDC) is designed for this device using residue-based analysis and root locus diagrams. Using residue-based analysis, the optimal input control signal to the SSRDC is identified, which can damp the SSR mode without destabilizing other modes, and using root-locus analysis, the required gain for the SSRDC is determined. MATLAB/Simulink is used as a tool for modeling, design, and time-domain simulations.
Optimal Generation Scheduling of Power System for Maximum Renewable Energy...IJECEIAES
This paper proposes an optimal generation scheduling method for a power system integrated with renewable energy sources (RES) based distributed generations (DG) and energy storage systems (ESS) considering maximum harvesting of RES outputs and minimum power system operating losses. The main contribution aims at economically employing RES in a power system. In particular, maximum harvesting of renewable energy is achieved by the mean of ESS management. In addition, minimum power system operating losses can be obtained by properly scheduling operating of ESS and controllable generations. Particle Swam Optimization (PSO) algorithm is applied to search for a near global optimal solutions. The optimization problem is formulated and evaluated taking into account power system operating constraints. The different operation scenarios have been used to investigate the effective of the proposed method via DIgSILENT PowerFactory software. The proposed method is examined with IEEE standard 14-bus and 30-bus test systems.
A novel efficient adaptive-neuro fuzzy inference system control based smart ...IJECEIAES
This document presents a novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm for a smart grid integrating solar, wind, and grid power sources. The proposed ANFIS controller is used to improve the steady-state and transient response of the hybrid power system. Fuzzy logic maximum power point tracking algorithms are used to extract maximum power from solar photovoltaic panels and a permanent magnet synchronous generator is used for wind power generation. Back-to-back voltage source converters operated by the ANFIS controller are used to maximize both renewable power generations. Simulation results under different operating conditions and nonlinear faults show the proposed ANFIS control algorithm improves the overall system performance.
Modelling and simulation for energy management of a hybrid microgrid with dro...IJECEIAES
The most efficient and connected alternative for increasing the use of local renewable energy sources is a hybrid microgrid, these systems face additional challenges due to the integration of power electronics, energy storage technologies and traditional power plants. The hybrid alternating currentdirect current (AC-DC) microgrid that is the subject of this research uses a primary-droop control system to regulate state variables and auxiliary services, thus, it is composed of batteries, solar panels and a miniature wind turbine (PDC) and controls how each energy source in a microgrid contributes to the final product. To achieve the given objectives, this paper will create appropriate models for each part of the microgrid design and define, among them, the energy storage batteries and power electronic converters required for each level of each of these systems. Finally, the dynamic nature of the system will be critically evaluated and characterized, to distribute the load and reduce imbalances, modify the primary drop of the resulting microgrid using MATLAB simulation.
Micropower system optimization for the telecommunication towers based on var...IJECEIAES
This study investigates the technical and cost-effective performance of options renewable energy sources to develop a green off-grid telecommunication tower to replace diesel generators in Malaysia. For this purpose, the solar, wind, pico-hydro energy, along with diesel generators, were examined to compare. In addition, the modeling of hybrid powering systems was conducted using hybrid optimization model for energy (HOMER) simulation based on techno-economic analysis to determine the optimal economically feasible system. The optimization findings showed that the hybrid high-efficiency fixed photovoltaic (PV) system with battery followed by 2 kW pico-hydropower and battery are the optimal configurations for powering off-grid telecommunication towers in Malaysia with the lowest net present cost (NPC) and cost of energy (COE). These costs of NPC and COE are more down than diesel generator costs with battery by 17.45%, 16.45%, 15.9%, and 15.5%, respectively. Furthermore, the economic evaluation of the high-efficiency solar fixed PV panels system annual cash flow compared to the diesel generator with the battery system indicated a ten-year payback period.
Bulk power system availability assessment with multiple wind power plants IJECEIAES
The use of renewable non-conventional energy sources, as wind electric power energy and photovoltaic solar energy, has introduced uncertainties in the performance of bulk power systems. The power system availability has been employed as a useful tool for planning power systems; however, traditional methodologies model generation units as a component with two states: in service or out of service. Nevertheless, this model is not useful to model wind power plants for availability assessment of the power system. This paper used a statistical representation to model the uncertainty of power injection of wind power plants based on the central moments: mean value, variance, skewness and kurtosis. In addition, this paper proposed an availability assessment methodology based on application of this statistical model, and based on the 2m+1 point estimate method the availability assessment is performed. The methodology was tested on the IEEE-RTS assuming the connection of two wind power plants and different correlation among the behavior of these plants.
The quality of data and the accuracy of energy generation forecast by artific...IJECEIAES
The paper presents the issues related to predicting the amount of energy generation, in a particular wind power plant comprising five generators located in south-eastern Poland. Thelocation of wind power plant, the distribution and type of applied generators, and topographical conditions were given and the correlation between selected weather parameters and the volume of energy generation was discussed. The primary objective of the paper was to select learning data and perform forecasts using artificial neural networks. For comparison, conservative forecasts were also presented. Forecasts results obtained shaw that Artificial Neural Networks are more universal than conservative method. However their forecast accuracy of forecasts strongly depends on the selection of explanatory data.
Similar to New typical power curves generation approach for accurate renewable distributed generation placement in the radial distribution system (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
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New typical power curves generation approach for accurate renewable distributed generation placement in the radial distribution system
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 5, October 2023, pp. 4909~4918
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp4909-4918 4909
Journal homepage: http://ijece.iaescore.com
New typical power curves generation approach for accurate
renewable distributed generation placement in the radial
distribution system
Ali Tarraq, Faissal El Mariami, Abdelaziz Belfqih
Laboratory of Energy and Electrical Systems, Ecole Nationale Supérieure d’Electricité et de Mécanique (ENSEM),
Hassan II University of Casablanca, Casablanca, Morocco
Article Info ABSTRACT
Article history:
Received Dec 30, 2022
Revised Mar 13, 2023
Accepted Apr 7, 2023
This paper investigates, for the first time, the accuracy of normalized power
curves (NPCs), often used to incorporate uncertainties related to wind and
solar power generation, when integrating renewable distributed generation
(RDG), in the radial distribution system (RDS). In this regard, the present
study proposes a comprehensive, simple, and more accurate model, for
estimating the expected hourly solar and wind power generation, by adopting
a purely probabilistic approach. Actually, in the case of solar RDG, the
proposed model allows the calculation of the expected power, without going
through a specific probability density function (PDF). The validation of this
model is performed through a case study comparing between the classical and
the proposed model. The results show that the proposed model generates
seasonal NPCs in a less complex and more relevant way compared to the
discrete classical model. Furthermore, the margin of error of the classical
model for estimating the expected supplied energy is about 12.6% for the
photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This
introduces an offset of about 10% when calculating the total active losses of
the RDS after two RDGs integration.
Keywords:
Distributed generation
Radial distribution system
Solar power generation
Uncertainty modeling
Wind power generation
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ali Tarraq
Laboratory of Energy and Electrical Systems, Department of Electrical Engineering, Ecole Nationale
Supérieure d’Electricité et de Mécanique (ENSEM), Hassan II University of Casablanca
B.P 8118, Oasis, Casablanca, Morocco
Email: ali.tarraq@gmail.com
1. INTRODUCTION
The 26th session of the Conference of the Parties COP26 held in Glasgow concluded that the most
sustainable and ecological solution for the green transition is to integrate renewable energy sources into the
electricity grid, leading to the launch of the "Green Grids" initiative with the vision of "One sun, one world,
one grid" [1]. Previous research has demonstrated that the appropriate integration of renewable distributed
generators (RDGs) into the electrical distribution network can have significant economic, technical, social, and
environmental benefits [2]. However, the integration of RDGs, particularly wind turbines and solar panels, is
challenging due to their intermittency and dependence on climate change. Recent studies recommend
incorporating uncertainties in the power generated by these units, which depend on uncertain and continuous
variables such as wind speed and solar irradiance [3], [4].
Considering these two variables are stochastic in nature, and continuously follow the weather
conditions, constructing a predictive model for a quantity that depends on them, remains a task that is not
obvious enough. According to the authors' knowledge, most of the existing long-term predictive models are
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 5, October 2023: 4909-4918
4910
still limited in terms of predictive capacity, concerning the studies of the optimal planning of RDGs [5], [6].
In most cases, these models only simulate the future evolution of the total daily or monthly power supplied by
the wind turbine or solar module. That is, when optimizing the grid placement of wind turbines and/or
photovoltaic modules, most authors used a probabilistic approach to predict the possible long-term evolution
of the hourly power supplied by these two systems [7], [8].
Consequently, to investigate the optimal integration of RDGs into the RDS for a long-term planning
period, most researchers assume only the knowledge of the typical hourly variation in generated power during
the day, often illustrated by normalized power curves (NPCs) [9]. On this basis, a probabilistic approach is
often conducted to generate NPCs for a predefined geographical location. This provides quasi-realistic
information on the future evolution of the overall power system parameters, caused by the RDG insertion. In
this way, the electrical distribution system can be more and more controllable, despite its future expansion.
Furthermore, to consider weather conditions, researchers propose to generate seasonal or monthly NPCs [10].
However, this approach is still based on the expected power estimation by a discrete probabilistic model,
whose accuracy depends on the chosen step size and the available computing capabilities. Others have proposed
to aggregate historical data by adopting a multi-scenario approach, such as the K-mean method [9], or the
hierarchical agglomerative method [11]. This constitutes a preliminary study aimed at reducing the number of
samples by detecting the same events. Nevertheless, this method is also based on the discrete model, which only
aims to simplify the computational complexity, without worrying about the accuracy of the generated results [12].
The study presents several significant contributions related to the modeling and optimization of
RDGs. Firstly, it addresses the accuracy issue related to NPCs for the first time. Secondly, it introduces a new
probabilistic integral formula that can model the uncertainties in wind or solar power, leading to NPCs that are
more accurate than the discrete model. Thirdly, it computes the expected power output of a solar system without
relying on a specific probability density function (PDF) like the Beta PDF or the log-normal PDF. Finally, the
study uses the new integral model to optimize the placement of both photovoltaic distributed generation
(PV-DG) and wind turbine distributed generation (WT-DG) in the IEEE 33-bus system, reducing the total daily
active power losses of the system.
Apart from this introduction, the rest of this article is composed of four other sections. The second
one briefly describes the mathematical basis of the study and the classical documented framework. While the
third section presents the proposed new mathematical model of the hourly expected power provided by a
photovoltaic (PV) module and that of a wind turbine (WT). Then, the validation of this model is properly
described in the fourth section of the paper, through a comparative study with the classical discrete model.
Firstly, the comparison is made in terms of the expected total energy delivered by a PV module and that
delivered by a WT. Secondly, it is done through an optimization study of the location and size of two different
RDGs in the typical IEEE 33-bus RDS of Baran and Wu [13]. The main results and perspectives of the study
are presented in the conclusion section.
2. MATHEMATICAL BASIS OF THE STUDY
Let us suppose that the continuous uncertain variable x follows the probability distribution ℒ, and f
denotes the probability density function PDF related to ℒ. Hence, each value of x is associated with a theoretical
maximum power Po(x), according to a predefined empirical or theoretical formula. Consequently, the expected
total power output during a time segment t can be expressed as (1) [14],
Pt
= ∫ Po(x)ft
(x)dx
Xmax
Xmin
(1)
where Xmin and Xmax, are respectively the minimum and maximum values of the variable x.
Generally, the PDF computation involves the mean and the standard deviation of the observed values
of x, in a predefined interval called class, such as (2), (3):
μt
=
∑ xj
Nk
j
Ns
(2)
σt
=√
∑ (xj-μ)2
Nk
j
Ns-1
xj ϵ [xi ; xi+1] (3)
where µt
, σt
, and Nk are respectively the mean, the standard deviation, and the number of observed values in each
interval [xi; xi+1], for a time segment t. While, Ns is the total number of available observed values, during t. Ns
corresponds to the total number of days in each season sa, throughout the period of historical data (HD) collection.
3. Int J Elec & Comp Eng ISSN: 2088-8708
New typical power curves generation approach for accurate renewable distributed … (Ali Tarraq)
4911
3. GENERAL FRAMEWORK OF THE PROPOSED COMPREHENSIVE MODEL
3.1. Solar power modeling
3.1.1. Maximum solar power calculation
The maximum alternating current (AC) power output of a PV module can be expressed as (4) [15],
Popv
h
(s)=ηconv
×Prpv×
s
sstc
× [1+ηp
×(Tc(s)-Tstc)] (4)
where Prpv is the rated power in (Wp), that a PV module can provide under the standard test conditions (STC),
i.e., at Tstc=25 °C, and sstc=1 kW/m². The coefficients ηp and ηconv are respectively the power-related temperature
factor expressed in (%/K), and the PV conversion efficiency. Tc denotes the PV cell temperature in °C, which
can be computed by (5), (6):
Tc(s)=Tamb+s×KNOCT (5)
KNOCT=
NOCT-20
0,8
(6)
where NOCT presents the normal operating cell temperature of the PV panel at the STC and the irradiation
0.8 kW/m², and Tamb is the ambient temperature, which is assumed to be equal to Tstc.
It should be noted that the conversion efficiency ηconv is obtained by multiplying all factors
characterizing the efficiency of the conversion system, which are the mismatch factor, the dirt or soiling
coefficient, and the efficiency of the chosen inverter [16]. In this study, ηconv is set to 0.9.
3.1.2. Expected solar power output estimation
According to (1), the expected power that a PV module can produce during the hour number h is
expressed as (7),
Ppv
h
= ∫ Popv
h
(s)×fβ
h
(s) ds
1
0
(7)
Substituting (7) into (10), we get:
Ppv
h
=μpv
h
× (A+B×μpv
h
) +B×(σpv
h
)
2
(8)
The parameters A and B could be computed by the following expressions:
A=Prpv×ηconv
(9)
B=A×KNOCT×ηP
(10)
where μpv
h
and σpv
h
are respectively the mean and standard deviation of all available values of solar irradiance
during the hth
hour. From (8), the expected solar power does not depend, explicitly, on the PDF from which the
solar irradiation can be predicted.
3.2. Wind power modeling
3.2.1. Maximum wind power calculation
The maximum power a wind turbine can provide depends on the wind speed measured at the height h
of its hub. However, the historical wind speed data are collected from an anemometer installed at an altitude
called ho, above the earth. That is, the correction of the hub effect is done through Hellman's exponential law,
such that [17]:
v=vo× (
h
ho
)
α
(11)
where α is the friction coefficient which depends on the type of land where the installation of the WT is planned,
and vo is the wind speed measured at the altitude ho.
According to [18], the maximum power that a wind turbine can provide is determined based on the
coupling speed vcin, decoupling speed vcout, rated speed vr, and rated power Prw of the wind turbine, such that:
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 5, October 2023: 4909-4918
4912
Pow
h
(v)= {
0 v≤vcin, v≥vcout
Prw×
v-vcin
vr-vcin
vcin≤v≤vr
Prw vr≤v≤vcout
(12)
Because of its reduced order, (12) greatly simplifies the development and calculation of the integral in (1).
Consequently, the hourly-expected power output of a WT can be easily computed. More details on different
reported models adopted in literature are documented in [19].
3.2.2. Expected wind power output estimation
Based on (1), the hourly-expected wind power can be expressed as (13),
Pw
h
= ∫ Pow
h
(v)×fwbl
h
(v) dv
vcout
vcin
(13)
Substituting (15) into (16), we obtain:
Pw
h
=
Prw
vn-vcin
× (μvcin≤v≤vn
h
+vn×C-vcin×D) (14)
where C and D are two probabilistic coefficients, which can be expressed as (15) and (16),
C=Fwbl
h
(vcout)-Fwbl
h
(vn) (15)
D=Fwbl
h
(vcout)-Fwbl
h
(vcin) (16)
where the quantity μvcin≤v≤vn
h
is the mean of all available samples comprised between vcin and vn at the hth
hour,
and Fwbl
h
presents the CDF of the chosen distribution for modeling the wind speed, corresponding to the hour
number h.
Thus, the generation of NPCs can be achieved through the use of formulas (8) and (14), providing a
simple and accurate alternative to classical methods that require the discretization of formula (1) into finite
elements [20]. This involves calculating the sum or average of expected powers across each element, which is
a more complex and less precise approach. While the generation of expected wind power model expressed by
(13) depends on the selected probability density function (PDF), the majority of studies recommend the use of
the Weibull PDF for modeling wind speed.
3.2.3. Weibull distribution
The performance of wind turbines is greatly affected by the variability of wind speed, which is a
stochastic continuous variable. It has been widely acknowledged by previous research works that the Weibull
probability distribution accurately describes the wind speed variability [21]. The Weibull PDF with two
parameters can be mathematically represented by (17), which is commonly used to model the wind speed
probability distribution for wind energy applications.
fwbl
t
(v)=
k
λ
× (
v
λ
)
k-1
×e-(
v
λ
)
k
v,k,λ>0 (17)
The corresponding CDF is deduced as (18),
Fwbl
h
(v)=1-e-(
v
λ
)
k
(18)
where k and λ respectively represent the shape end the scale factors, and their estimation is obtained through
the use of the following two formulas [9]:
k= (
σ
μ
)
-1,086
(19)
λ=
μ
Γ(1+
1
k
)
(20)
5. Int J Elec & Comp Eng ISSN: 2088-8708
New typical power curves generation approach for accurate renewable distributed … (Ali Tarraq)
4913
The computation of µ and σ is carried out using historical data that is non-zero and corresponds to the
wind speed of each season. These parameters are estimated by (3) and (2).
4. VALIDATION OF THE PROPOSED MODEL
4.1. Historical data processing and data settings
The present study uses data collected from an urban site located in the city of Basel, situated in the
northern part of Switzerland, having a latitude of 47.546944 and a longitude of 7.568918. The data consist of
hourly records of wind speed and solar irradiance, which were obtained for a period of six years, from January
1, 2015, to December 31, 2020. The data can be accessed free of cost through an online portal [22].
To facilitate the analysis, the hourly data for each variable (wind speed and solar irradiance) are
arranged in a matrix M, where each row corresponds to an hour of the day, and each column represents a
particular day. Matrix M is then divided into four smaller matrices, one for each season, named S. Each matrix
S has 24 rows (corresponding to the 24 hours in a day) and [(6 years) x (number of days per year of each
season)] columns. The adopted approach is described in detail through the pseudocode of algorithm 1,
which is presented in Figure 1. Additionally, the technical specifications of the solar panel and wind turbine
used in the study are listed in Table 1, and the value of Hellman's α exponent is estimated to be 0.1, as reported
in [16].
Algorithm 1: Pseudocode of the adopted approach
1: Begin the procedure
2: Parameter setting
3: Extraction, processing and seasonal arrangement of HD
4: Set X = = (Solar HD) & Y = = (Wind HD)
6: If X==1 && Y
̅==1
7: sa=1
8: For sa = 1:4 do
9: h=1
10: While h ≤ 24 do
11: μpv
h
, σpv
h
and Ppv
h
estimation according to (2), (3) and (8), h=h+1
13: End
14: End
15: Else
16: sa=1
17: For sa = 1:4 do
18: h=1
19: While h ≤ 24 do
20: μvcin≤v≤vn
h
,Fwbl
h (vcin), Fwbl
h (vcout),Fwbl
h (vn) and Pwbl
h
assessement according to (2), (14) and
(18), h=h+1
22: End
23: End
24: End
25: Results displaying and NPCs plotting
26: End of the procedure
Figure 1. Pseudocode of the adopted approach for NPCs generation
Table 1. Technical specifications of the PV module EMMVEE Pvt and the WT, Enercon E-53
PV module specifications WT specifications
Parameter Value Parameter Value
Rated power (Prpv) 290 Wc Rated power (Prw) 800 kW
Temperature coefficient rated power
(ηp)
0.43 %/K Hub height (h) 60 m
NOCT 47 °C Rated wind speed (vr) 13 m/s
Decoupling speed (vcout) 34 m/s
Coupling speed (vcin) 3 m/s
4.2. Comparative study
4.2.1. Comparison of the generated NPCs
In Figure 2, the difference between the seasonal NPCs generated by the proposed and classical discrete
models is depicted. The proposed model shows a significant difference in terms of the generated daily energy
(Ed) as compared to the classical model. Figures 2(a) and 2(b) illustrate the seasonal NPCs of the solar panel
generated by the proposed and classical models, respectively. On the other hand, Figures 2(c) and 2(d)
demonstrate the seasonal NPCs of the wind turbine generated by the proposed and classical models,
respectively.
6. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 5, October 2023: 4909-4918
4914
Furthermore, Table 2 exhibits the energy estimation gap between the proposed and classical models.
The table shows the average daily energy (Em) deduced by dividing the total annual energy (Ea) over 365 days.
The energy estimation gap between the models becomes more visible when comparing the energy estimated
by each model. For instance, the proposed model records an annual overestimation of 47.6 kWh/year for solar
energy, which translates to a margin of error εm of approximately 12.6%. Similarly, for wind energy, the
proposed model introduces an underestimation of annual energy, with a margin of error of 9%. Therefore, an
optimal planning study of renewable distributed generation integration in a power system based on the classical
discrete model may lead to inaccurate results due to the significant mismatch in the obtained results.
(a) (b)
(c) (d)
Figure 2. Seasonal NPCs of the solar panel, (a) generated by the proposed model, (b) the classical model, and
seasonal NPCs of the WT, (c) generated by the proposed model, and (d) the classical model
Table 2. Solar and wind expected energy estimated by the proposed model and by the classical model
Solar expected energy (kWh) WT expected energy (MWh)
Proposed model Classical model Proposed model Classical model
win spr sum aut win spr sum Aut win spr sum aut win spr sum aut
Ed 0.8 1.3 1.6 0.5 0.9 1.4 1.8 0.6 12.9 13.4 11.6 13.4 11.8 12.4 11.3 11.6
Ea 378.3 425.9 4680 4297.8
Em 1 1.2 12.8 11.8
εm 12.6 % 9 %
4.3.2. Numerical example: optimal RDG’s grid integration
In order to provide evidence for the previous statements, an optimization study is presented in which
the optimal location and size of two different RDGs are searched for in a power system. The system under
consideration is the IEEE 33 bus system of Baran and Wu [23], which is a commonly used benchmark system
in the field of power systems. By performing this study, the accuracy of the proposed model can be evaluated
in comparison to the classical discrete model.
7. Int J Elec & Comp Eng ISSN: 2088-8708
New typical power curves generation approach for accurate renewable distributed … (Ali Tarraq)
4915
a. Problem formulation and resolution techniques
The first RDG is a PV-based system with a number of Npv solar panels, of the brand EMMVEE Pvt
290 Wp, while the second is based on the Enercon E-53 wind turbine. Both RDGs are selected as Type I, i.e.,
they are only capable of providing active power. This optimization study aims at estimating the offset in the
real loss reduction index RLI that can be committed using the discrete model, compared to the proposed
comprehensive model. Only, the uncertainties related to renewable sources are taken into account.
The objective function chosen for this study can be expressed as (30),
min RLI=
Plwdvc
Plwodvc
(30)
where Plwodvc
and Plwdvc
are respectively the total active losses without and with RDGs, computed by (31), (32):
Plwdvc
=
∑ [(∑ Plsa
h
24
h=1 )×Ndsa]
4
sa=1
8760
(31)
Plwodvc
= ∑ Rl×Il
2
Nl
l=1 (32)
where Plsa
h
is the sum of active losses during hour h at season sa, and Ndsa
presents the number of days
corresponding to the season number sa during the whole HD collection period. Similarly, Rl and Il, are
respectively the resistance and current per branch.
The study presented in this paper employs a direct approach algorithm for the computation of load
flow to estimate the current value of Il [24]. The method used in this study is known to be efficient in solving
power system problems. The authors have used this method to evaluate the basic case, where the total real
losses Plwodvc
are estimated to be 202.6 kW. The direct approach algorithm has been preferred over other
conventional methods owing to its superior accuracy and computational efficiency in solving complex power
system problems.
The set of inequality constraints chosen include: the nodal voltage limit according to (33), and the
RDG capacity limit according to (34), while the power balance (35), presents an equality constraint.
0.95 pu ≤ Vb ≤ 1.05 pu (33)
Ppv+Pwt ≤ 0.25 × ∑ PL
N
L=1 (34)
Ps+Ppv
+Pwt = ∑ PL
N
L=1 +Pl (35)
where N is the number of system busses, and Ps, Pl and PL are respectively the power taking from the system
at the slack bus, the total real losses, and the power demand of the Lth
node. The powers Ppv and Pwt are
respectively, the size of the PV based RDG and the wind turbine based RDG. The locations of the two RDGs
(PVloc and WTloc) have to lie between 2 and N.
In this study, the chosen optimization algorithm is the famous particle swarm optimization (PSO).
According to [25], this algorithm and its new versions are among the most adopted in the literature. More
details on this algorithm and on its implementation for a power system can be found in [26]. In this paper, the
PSO algorithm is applied for a set of 20 populations with 100 iterations for 10 trials, in the case of both models.
Each population Pop is a vector of four elements such that:
Pop = [PVloc , WTloc , Ppv , Pwt] (36)
b. Results discussion
In this study, the simulations are done with an Intel ® Core™ i5-3320M CPU @ 2.60GHz (4 CPUs),
and a RAM memory of 6 GB, in the MATLAB programming environment. According to Table 3, the discrete
model introduces a shift of about 10% in terms of RLI. According to this table, the PSO with the classical
model refuses the integration of the WT into the grid. Herein, location 15 and size 31 kW are randomly
generated. Moreover, the algorithm proposes the installation of 3097 solar panels in node number 8. With land
constraints, the PV system will be very bulky and the operating cost could be very penalizing. Consequently,
the PSO gives inconsistent and impractical results with the classical discrete model.
Using the comprehensive model, the generated results are more concrete. To reach a minimal RLI,
the algorithm proposes the integration of one Enercon E-53 wind turbine at bus 30. Moreover, a PV system
with 445 EMMVEE solar panels is proposed to be incorporated at node number 17.
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Accordingly, Figure 3 how’s the difference between the hourly seasonal profiles of the total active
losses in the IEEE 33 bus system, by adopting the classical model (cf. Figure 3(a)), and by adopting the
proposed model (cf. Figure 3(b)).
Thus, by adopting the discrete model, the total active losses are overestimated by about 10%. After a
large number of runs, it is obvious that this gap is mainly due to the discretization of the formula (1). In fact,
this shift is intolerable when it concerns studies dealing with the optimal integration of RDGs in the electrical
distribution network, taking into account the uncertainties related to the wind or solar power generation.
Table 3. Obtained results for both models
Solar DG WT DG
RLI
PVloc Ppv (kW) Number WTloc Pwt (kW) Number
Proposed model 17 129 445 30 800 1 0.61
Classical model 8 898 3097 15 31 0 0.71
(a) (b)
Figure 3. Hourly profiles of total active losses by (a) applying the proposed model and (b) the classical model
5. CONCLUSION
The present study proposes a comprehensive probabilistic model for the generation of seasonal NPCs
of PV and WT systems, which considers uncertainty associated with these renewable sources. The model is
validated through a numerical example, and the results indicate that the expected solar power does not explicitly
depend on a specific PDF.
The case study further demonstrates that the discrete expected power model introduced a considerable
shift in the estimation of the energy supplied by a solar panel or a wind turbine, with an average error of 12.9%
and 9%, respectively. These findings highlight the importance of using a comprehensive probabilistic model
that captures the stochastic nature of renewable energy sources and incorporates the relevant uncertainties in
the system. Furthermore, the results suggest that the use of a discrete model can have a significant impact on
the accuracy of studies aimed at the optimal integration of RDGs in the RDS, particularly when considering
uncertainties related to wind speed and solar irradiation.
As the authors note, climate change introduces new challenges to the accurate modeling of wind
speed, and a more precise distribution could replace the widely used Weibull's distribution. The authors
recommend the adoption of the proposed comprehensive model in future studies for the optimal integration of
RDG into the RDS, particularly when incorporating uncertainties related to the WT and PV power output. The
use of such a model could improve the accuracy of predictions and facilitate the efficient integration of RDG
into the grid, ultimately contributing to a more sustainable and resilient power system.
REFERENCES
[1] S. Pye, A. Shivakumar, and J. Price, “Climate finance for grid investments in emerging and developing : COP26 Glasgow 2021,”
Glasgow, 2021. https://climatecompatiblegrowth.com/green-grids-initiative/ (accessed Apr. 20, 2023).
[2] A. Tarraq, F. Elmariami, A. Belfqih, T. Haidi, N. Agouzoul, and R. Gadal, “Meta-heuristics applied to multiple DG allocation in
radial distribution network: A comparative study,” in 2022 International Conference on Intelligent Systems and Computer Vision
9. Int J Elec & Comp Eng ISSN: 2088-8708
New typical power curves generation approach for accurate renewable distributed … (Ali Tarraq)
4917
(ISCV), May 2022, pp. 1–8, doi: 10.1109/ISCV54655.2022.9806131.
[3] M. Ebeed and S. H. E. A. Aleem, “Overview of uncertainties in modern power systems: uncertainty models and methods,” in
Uncertainties in Modern Power Systems, Elsevier, 2021, pp. 1–34.
[4] A. Zakaria, F. B. Ismail, M. S. H. Lipu, and M. A. Hannan, “Uncertainty models for stochastic optimization in renewable energy
applications,” Renewable Energy, vol. 145, pp. 1543–1571, Jan. 2020, doi: 10.1016/j.renene.2019.07.081.
[5] Y. Wang, R. Zou, F. Liu, L. Zhang, and Q. Liu, “A review of wind speed and wind power forecasting with deep neural networks,”
Applied Energy, vol. 304, Dec. 2021, doi: 10.1016/j.apenergy.2021.117766.
[6] H. Eom, Y. Son, and S. Choi, “Feature-selective ensemble learning-based long-term regional PV generation forecasting,” IEEE
Access, vol. 8, pp. 54620–54630, 2020, doi: 10.1109/ACCESS.2020.2981819.
[7] A. Ahmed, M. F. Nadeem, I. A. Sajjad, R. Bo, I. A. Khan, and A. Raza, “Probabilistic generation model for optimal allocation of
wind DG in distribution systems with time varying load models,” Sustainable Energy, Grids and Networks, vol. 22, Jun. 2020, doi:
10.1016/j.segan.2020.100358.
[8] E. S. Oda, A. M. A. El Hamed, A. Ali, A. A. Elbaset, M. A. El Sattar, and M. Ebeed, “Stochastic optimal planning of distribution
system considering integrated photovoltaic-based DG and DSTATCOM under uncertainties of loads and solar irradiance,” IEEE
Access, vol. 9, pp. 26541–26555, 2021, doi: 10.1109/ACCESS.2021.3058589.
[9] S. Sannigrahi, S. R. Ghatak, and P. Acharjee, “Multi-scenario based bi-level coordinated planning of active distribution system
under uncertain environment,” IEEE Transactions on Industry Applications, vol. 56, no. 1, pp. 850–863, Jan. 2020, doi:
10.1109/TIA.2019.2951118.
[10] G. Wang, Q. Wang, Z. Qiao, J. Wang, and S. Anderson, “Optimal planning of multi-micro grids based-on networks reliability,”
Energy Reports, vol. 6, pp. 1233–1249, Nov. 2020, doi: 10.1016/j.egyr.2020.05.007.
[11] S. Roy Ghatak, S. Sannigrahi, and P. Acharjee, “Multi-objective approach for strategic incorporation of solar energy source, battery
storage system, and DSTATCOM in a smart grid environment,” IEEE Systems Journal, vol. 13, no. 3, pp. 3038–3049, Sep. 2019,
doi: 10.1109/JSYST.2018.2875177.
[12] V. Lenzi, A. Ulbig, and G. Andersson, “Impacts of forecast accuracy on grid integration of renewable energy sources,” in 2013
IEEE Grenoble Conference, Jun. 2013, pp. 1–6, doi: 10.1109/PTC.2013.6652486.
[13] M. E. Baran and F. F. Wu, “Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE
Transactions on Power Delivery, vol. 4, no. 2, pp. 1401–1407, Apr. 1989, doi: 10.1109/61.25627.
[14] S. Darrin and C. Bryan, Probability, statistics, and data. Boca Raton: Chapman and Hall/CRC, 2021.
[15] H. Z. Al Garni, A. Awasthi, and M. A. M. Ramli, “Optimal design and analysis of grid-connected photovoltaic under different
tracking systems using HOMER,” Energy Conversion and Management, vol. 155, pp. 42–57, Jan. 2018, doi:
10.1016/j.enconman.2017.10.090.
[16] G. M. Masters, Renewable and efficient electric power systems. Wiley, 2004.
[17] G. K. Suman, J. M. Guerrero, and O. P. Roy, “Optimisation of solar/wind/bio-generator/diesel/battery based microgrids for rural
areas: A PSO-GWO approach,” Sustainable Cities and Society, vol. 67, Apr. 2021, doi: 10.1016/j.scs.2021.102723.
[18] N. Cherkaoui, A. Belfqih, F. El Mariami, J. Boukherouaa, and A. Berdai, “Active power ouptut optimization for wind farms and
thermal units by minimizing the operating cost and emissions,” International Journal of Electrical and Computer Engineering
(IJECE), vol. 10, no. 4, pp. 3412–3422, Aug. 2020, doi: 10.11591/ijece.v10i4.pp3412-3422.
[19] W. L. Theo, J. S. Lim, W. S. Ho, H. Hashim, and C. T. Lee, “Review of distributed generation (DG) system planning and
optimisation techniques: Comparison of numerical and mathematical modelling methods,” Renewable and Sustainable Energy
Reviews, vol. 67, pp. 531–573, Jan. 2017, doi: 10.1016/j.rser.2016.09.063.
[20] M. S. M. L., S. S., and M. G. R. S., “Impact analysis of time‐varying voltage‐dependent load models on hybrid DG planning in a
radial distribution system using analytical approach,” IET Renewable Power Generation, vol. 15, no. 1, pp. 153–172, Jan. 2021,
doi: 10.1049/rpg2.12013.
[21] A. Ramadan, M. Ebeed, S. Kamel, A. M. Agwa, and M. Tostado-Véliz, “The probabilistic optimal integration of renewable
distributed generators considering the time-varying load based on an artificial gorilla troops optimizer,” Energies, vol. 15, no. 4,
Feb. 2022, doi: 10.3390/en15041302.
[22] Meteoblue, “Historical weather data for Basel.” 2023, Accessed: May 16, 2021. [Online]. Available:
https://www.meteoblue.com/en/weather/archive/export.
[23] S. A. Taher and S. A. Afsari, “Optimal location and sizing of DSTATCOM in distribution systems by immune algorithm,”
International Journal of Electrical Power & Energy Systems, vol. 60, pp. 34–44, Sep. 2014, doi: 10.1016/j.ijepes.2014.02.020.
[24] J.-H. Teng, “A direct approach for distribution system load flow solutions,” IEEE Transactions on Power Delivery, vol. 18, no. 3,
pp. 882–887, Jul. 2003, doi: 10.1109/TPWRD.2003.813818.
[25] Z. Abdmouleh, A. Gastli, L. Ben-Brahim, M. Haouari, and N. A. Al-Emadi, “Review of optimization techniques applied for the
integration of distributed generation from renewable energy sources,” Renewable Energy, vol. 113, pp. 266–280, Dec. 2017, doi:
10.1016/j.renene.2017.05.087.
[26] E. Cuevas, E. B. Espejo, and A. C. Enríquez, Metaheuristics algorithms in power systems, vol. 822. Cham: Springer International
Publishing, 2019.
BIOGRAPHIES OF AUTHORS
Ali Tarraq was born in Safi, Morocco. He received the M. Eng. degree in
industrial engineering from the University of Cadi Ayyad (UCA), National School of
Applied Sciences, ENSA of Safi, Morocco, in 2010. Currently he is a PhD student in
electrical engineering at the National School of Electricity and Mechanics, ENSEM of
Casablanca, Morocco. His current research interests include distributed generation based
renewable energy, and smart grids applications. He can be contacted at email:
ali.tarraq@gmail.com.
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Faissal El Mariami is an engineer, PhD and holder of university HDR
accreditation. He is a professor qualified to direct research and member of the RECS research
team at the National School of Electricity and Mechanics ENSEM (Hassan II University).
Electrical networks stability and protection coordination are the center of his interest. He can
be contacted atemail: f_elmariami@yahoo.fr.
Abdelaziz Belfqih is an engineer, PhD and holder of university HDR
accreditation. He is a professor qualified to direct research and member of the RECS research
team at the National School of Electricity and Mechanics ENSEM (Hassan II University).
His research subjects focus on electrical networks and smart grids. He can be contacted at
email: a-belfqih@hotmail.com.