In the day-ahead dispatching of network-constrained electricity markets, renewable energy and distributed resources are dispatched together with conventional generation. The uncertainty and volatility associated to renewable resources represents a new paradigm to be faced for power system operation. Moreover, in various electricity markets there are mechanisms to allow the demand participation through demand response (DR) strategies. Under operational and economic restrictions, the operator each day, or even in intra-day markets, dispatchs an optimal power flow to find a feasible state of operation. The operation decisions in power markets use an optimal power flow considering unit commitment to dispatch economically generation and DR resources under security restrictions. This paper constructs a model to include demand response in the optimal power flow under wind power uncertainty. The model is formulated as a mixed-integer linear quadratic problem and evaluated through Monte-Carlo simulations. A large number of scenarios around a trajectory bid captures the uncertainty in wind power forecasting. The proposed integrated OPF model is tested on the standard IEEE 39-bus system.
Analysis of the role of energy storages in Germany with TIMES PanEU – methodo...IEA-ETSAP
This document discusses a methodology and results from analyzing the role of energy storage in Germany using the TIMES energy systems model. The methodology improves the model's temporal resolution for Germany and adds modeling of storage technologies. Scenario analysis examines the optimal configuration of storage and flexibility options through 2050 under increasing renewable energy and emissions reduction targets. Results show a significant increase in electricity storage, particularly lithium-ion batteries paired with solar, to balance rising variable renewable supply and meet flexibility needs.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
Load types, estimation, grwoth, forecasting and duration curvesAzfar Rasool
It includes the detail analysis of the various types electrical load, how to estimatate the load, methods of load forecasting and explanation of the load duration curves.
Multi-Objective based Optimal Energy and Reactive Power Dispatch in Deregulat...IJECEIAES
This paper presents a day-ahead (DA) multi-objective based joint energy and reactive power dispatch in the deregulated electricity markets. The traditional social welfare in the centralized electricity markets comprises of customers benefit function and the cost function of active power generation. In this paper, the traditional social welfare is modified to incorporate the cost of both active and reactive power generation. Here, the voltage dependent load modeling is used. This paper brings out the unsuitability of traditional single objective functions, e.g., social welfare maximization (SWM), loss minimization (LM) due to the reduction of amount of load served. Therefore, a multi-objective based optimization is required. This paper proposes four objectives, i.e., SWM, load served maximization (LSM), LM and voltage stability enhancement index (VSEI); and these objectives can be combined as per the operating condition. The simulation studies are performed on IEEE 30 bus test system by considering the both traditional constant load modeling and the proposed voltage dependent load modeling.
This document presents a strategic design optimization model for microgrids with multiple energy storage technologies and demand response aggregation. The model (1) uses game theory to model the strategic behavior of utilities, aggregators, and consumers, (2) determines optimal tradeoffs between power imported from the grid and demand response resources, and (3) allocates various storage technologies cost-optimally. The model was tested on a 100% renewable energy microgrid in New Zealand, reducing lifetime costs by an estimated 21% compared to a business-as-usual approach without demand response.
Analysis of the role of energy storages in Germany with TIMES PanEU – methodo...IEA-ETSAP
This document discusses a methodology and results from analyzing the role of energy storage in Germany using the TIMES energy systems model. The methodology improves the model's temporal resolution for Germany and adds modeling of storage technologies. Scenario analysis examines the optimal configuration of storage and flexibility options through 2050 under increasing renewable energy and emissions reduction targets. Results show a significant increase in electricity storage, particularly lithium-ion batteries paired with solar, to balance rising variable renewable supply and meet flexibility needs.
Optimal power flow based congestion management using enhanced genetic algorithmsIJECEIAES
Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.
Load types, estimation, grwoth, forecasting and duration curvesAzfar Rasool
It includes the detail analysis of the various types electrical load, how to estimatate the load, methods of load forecasting and explanation of the load duration curves.
Multi-Objective based Optimal Energy and Reactive Power Dispatch in Deregulat...IJECEIAES
This paper presents a day-ahead (DA) multi-objective based joint energy and reactive power dispatch in the deregulated electricity markets. The traditional social welfare in the centralized electricity markets comprises of customers benefit function and the cost function of active power generation. In this paper, the traditional social welfare is modified to incorporate the cost of both active and reactive power generation. Here, the voltage dependent load modeling is used. This paper brings out the unsuitability of traditional single objective functions, e.g., social welfare maximization (SWM), loss minimization (LM) due to the reduction of amount of load served. Therefore, a multi-objective based optimization is required. This paper proposes four objectives, i.e., SWM, load served maximization (LSM), LM and voltage stability enhancement index (VSEI); and these objectives can be combined as per the operating condition. The simulation studies are performed on IEEE 30 bus test system by considering the both traditional constant load modeling and the proposed voltage dependent load modeling.
This document presents a strategic design optimization model for microgrids with multiple energy storage technologies and demand response aggregation. The model (1) uses game theory to model the strategic behavior of utilities, aggregators, and consumers, (2) determines optimal tradeoffs between power imported from the grid and demand response resources, and (3) allocates various storage technologies cost-optimally. The model was tested on a 100% renewable energy microgrid in New Zealand, reducing lifetime costs by an estimated 21% compared to a business-as-usual approach without demand response.
This document summarizes research on integrating thermal energy storage with cogeneration systems. It discusses using a firefly algorithm to optimize the scheduling of cogeneration units with thermal storage systems. The algorithm aims to minimize operation costs while meeting demand and constraints. It models the behavior and constraints of the thermal storage systems and cogeneration units. The algorithm is shown to effectively balance local and global search for optimization. A comparison shows it performs better than other algorithms for this application.
A Response Surface Based Wind Farm Cost (RS-WFC) model, is developed to evaluate the economics of wind farms. The RS-WFC model is developed using Extended Radial Basis Functions (E-RBF) for onshore wind farms in the U.S.. This model is then used to explore the in uence of di erent design and economic parameters, including number of turbines, rotor diameter and labor cost, on the cost of a wind farm. The RS-WFC model is composed of three parts that estimate (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The accuracy of the cost model is favorably established through comparison with pertinent commercial data. Moreover, the RS-WFC model is integrated with an analytical power generation model of a wind farm. A recently developed Unrestricted Wind Farm Layout Optimization (UWFLO) model is used to determine the power generated by a farm. The ratio of the total annual cost and the energy generated by the wind farm in one year (commonly known as the Cost of Energy, COE) is minimized in this paper. The results show that the COE could decreasesigni cantlythroughlayoutoptimization,toobtainmillionsofannualcostsavings.
This document proposes a multi-objective framework for short-term scheduling of a microgrid considering cost minimization and emission minimization objectives. It formulates the problem as a mixed integer nonlinear program with constraints including power balance and unit generation limits. The Normal Boundary Intersection method is employed to solve the multi-objective problem and generate a Pareto front of optimal solutions. Simulation results are presented comparing the proposed approach to other methods.
Should the focus be on broader policy goals or on specific technology targets?IEA-ETSAP
This document provides an overview of the Swiss energy system and scenarios analyzed using the Swiss TIMES Energy Model (STEM). STEM is a whole energy system optimization model of Switzerland that examines the Swiss energy system from primary energy supply to end use across sectors. The document describes scenarios that focus on either achieving a 40% or 60% reduction in transport sector CO2 emissions by 2050 or achieving a system-wide 60-67% CO2 reduction. The results show that a transport sector target alone shifts emissions to other sectors while a system-wide target leads to greater electrification and use of renewable electricity to reduce overall CO2 emissions.
Status ETSAP_TIAM Git project and starting up ETSAP-TIAM updateIEA-ETSAP
The document discusses two projects related to improving collaboration on and updating the ETSAP-TIAM energy systems model. The ETSAP_TIAM Git project aims to enhance collaboration through a version control system to track model changes. The 2-year ETSAP-TIAM Update Project aims to ensure the model remains relevant by updating technologies, data, and scenarios through workshops and collaborative development among members. It will deliver an updated model, documentation, and standard scenarios in a new VEDA-BE database. A reviewer group was also announced to review proposed model changes.
ETOU electricity tariff for manufacturing load shifting strategy using ACO al...journalBEEI
This paper presents load shifting strategy for cost reduction on manufacturing electricity demand side, by which a real test load profile had been used to prove the concept. Superior bio-inspired algorithm, Ant Colony Optimization (ACO) had been implemented to optimize the upright load profile of load shifting strategy in the Malaysia Enhance Time of Use (ETOU) tariff condition. Subsequently, significant simulation results of operation profit gain through 24 hours electricity consumption had been analyzed properly. The proposed method had shown reduction of approximately 6% of the electricity cost at peak and mid peak zones, when 20%, 40%, 60%, 80% and 100% load shifting weightages were applied to the identified 10% controlled loads consequently. It is hoped that the finding of this study can help poise the manufacturers to switch to ETOU tariff as well as support the national Demand Side Management (DSM) program
The document discusses plans to update the ETSAP-TIAM energy-economic model through a collaborative process. It proposes organizing workshops with ETSAP-TIAM users to identify needed updates, revising model data and structures, recalibrating the model, and documenting results. The goals are to facilitate analysis of deep decarbonization scenarios and improve linking of ETSAP-TIAM with other models. A two-year, €100,000 budget is presented to fund updating energy balances and technologies, workshops, recalibration, documentation, and scenario development through biannual phases.
A Generalized Multistage Economic Planning Model for Distribution System Cont...IJERD Editor
This document presents a generalized multistage economic planning model for distribution systems containing distributed generation (DG) units. The model minimizes total investment and operation costs over a planning horizon divided into multiple periods, taking into account load growth, equipment capacities and voltages limits. Constraints include power flow equations and logical constraints relating planning periods. The model is applied to a sample 11kV distribution network with one substation, 23 load buses and 32 feeders over 4 annual periods. The mixed integer nonlinear optimization problem is solved using LINGO software to obtain the least-cost expansion plan.
Base load power plants run continuously to meet minimum electricity demand. Peak load plants operate intermittently to meet spikes in demand. Less flexible base load plants are better for providing base energy needs, while more flexible peak load plants can adjust quickly to fill peak demand. The economics of power generation involve calculating the cost per unit of electricity based on fixed, semi-fixed, and running costs which include capital costs, interest, depreciation, fuel, and maintenance. Depreciation methods like straight line, diminishing value, and sinking fund determine annual charges to fund future replacement of equipment over its useful lifetime.
Evaluation of the role of energy storages in Europe with TIMES PanEUIEA-ETSAP
This document summarizes the results of scenario analyses conducted using the TIMES PanEU energy system model and ESTMAP storage database to evaluate the role of energy storage in Europe. The analyses found that increased electricity demand and electrification of the energy system are needed to meet EU GHG reduction targets. Additional electricity storage capacity investments from 2030 onward are also needed to integrate more variable renewable energy from wind and solar. First investments are in diabatic CAES and battery storage, shifting later to pump storage and adiabatic CAES as costs decrease. Energy storage, along with other flexibility options, helps reduce GHG emissions compared to scenarios relying more on natural gas storage.
Grid Features in the TIMES-based Japan ModelIEA-ETSAP
1) The document describes updates made to the Japan Multi-regional Transmission (JMRT) model to include grid features, allowing for analysis of high renewable energy penetration scenarios.
2) The model was modified to include 351 grid nodes to represent Japan's 47 prefectures, with renewable energy potential and demand allocated to nodes based on location.
3) Simulations examined scenarios with 25-55% variable renewable energy (VRE) shares by 2050, and the impact of grid infrastructure expansion. Without expansion, high VRE led to increased marginal electricity costs between regions.
The document analyzes the current energy consumption patterns and forecasts the future energy demand of Jaya Container Terminal (JCT) in Sri Lanka by 2020. It models the current energy use using LEAP software and evaluates the per TEU energy consumption for different container types handled by JCT. It then forecasts JCT's energy demand by 2020 based on projections for container throughput. Finally, it analyzes potential demand side management options to reduce energy costs and consumption at JCT in the future.
Emissions reduction potential in regions of Kazakhstan using TIMES-16RKZ modelIEA-ETSAP
The TIMES-16RKZ model was used to assess emissions reduction potential in Kazakhstan's 16 regions under different scenarios. The model found that meeting Kazakhstan's INDC target of reducing emissions 15% below 1990 levels would require reducing coal consumption 21% compared to business as usual. Most reductions would come from the energy supply sector through improved efficiency rather than reduced demand. The key emitting regions of Almaty, Karaganda, and Pavlodar would see the largest decreases in emissions through retirement of old coal plants, increased gas generation, and new capacities in high demand growth areas. Regional policies will be important to realize differences in energy demand and prices across Kazakhstan.
Fractal Energy Consulting- Microgrid Feasibility Model PDFLouis Monteagudo
Fractal Energy Consulting developed an integrated capital budgeting model to understand the tradeoffs between carbon emissions reduction targets and financial targets like levelized cost of electricity and investment payback period for a microgrid project in Ithaca, NY. The model considers technology and cost parameters, demand projections over 30 years in 4 phases, equipment sizing, revenues, costs, incentives, and creates 5 scenarios to analyze environmental and financial tradeoffs to select an optimal case.
Wind Solar Hybrid Power Project Investigation For Theme Parks A Case Studychittaranjang
The document investigates the feasibility of a wind-solar hybrid power project for a theme park in California. Based on wind maps, the site has average wind speeds of 6-7.5 m/s suitable for 1.5-1.65 MW wind turbines that could generate 3.14-5.64 GWh annually. Available land could accommodate a 112 kW solar farm estimated to generate 176 MWh annually.
A hybrid system with two 1.5 MW turbines and 112 kW solar is recommended. Further technical studies are required to obtain permits, which can take 3-18 months. The project has potential but detailed commercial assessments are needed regarding costs, expenses, and power purchase agreements.
Incorporating uncertainties in the transition towards a clean European energy...IEA-ETSAP
Incorporating uncertainties in the transition towards a clean European energy system: a stochastic approach for decarbonization paths in the transport sector.
A framework for dynamic pricing electricity consumption patterns via time ser...Asoka Korale
Clustering individual household electricity consumption patterns enables a utility to design pricing plans catered to groups of households in a particular locality to more accurately reflect the cost of supply at a particular time of day.
In this paper we model each time series as an Autoregressive Moving Average (ARMA) process with an optimal model order determined by the Akaike Information Criterion when the parameters estimated by the Hannan-Rissanen algorithm converge. The estimated model has the representation of a transfer function with a frequency response defined by the ARMA parameters. We use the frequency response as the means to further refine the within cluster profiling and classification of the objects.
Through our modeling we are also able to identify instances where the consumption behavior exhibits patterns that are uncharacteristic or not in line with the behavior or consumption profiles of the other households in a particular locality providing insights in to potential faults, fraud or illegal activity.
Economic Impacts of Behind the Meter Distributed Energy Resources on Transmis...Power System Operation
The increasing penetration of customer-owned Distribution Energy Resources (DERs) will have an impact on the economics that govern market operation. Visibility and control of local Independent System Operators (ISOs) over these resources are currently restricted or available in some form of aggregation. Additionally, non-curtailable resources pose a serious problem while balancing the market with eminent risks of over-generation and added congestion to the system. This study attempts to decouple the model at the Transmission-Distribution interface and demonstrate the following: 1) economic implications of such resources under two control strategies, 2) aspects of market dynamics affected by several DER penetration levels, 3) Potential benefits of increased ISO visibility beyond the Transmission-Distribution(T-D) interface.
Cost development of renewable energy technologiesLeonardo ENERGY
This course covers the cost development of renewable energy technologies, which includes the analysis of technological change, in particular with regard to technological learning, the assessment of learning rates of renewable energy technologies available in literature and forecasting studies. For many (energy) technologies, a log-linear relation was found between the accumulated experience and the technical (e.g. efficiency) and economic performance (e.g. investment costs). The rate at which cost decline for each doubling of cumulative production is expressed by the progress ratio (PR). A progress ratio of 90% results in a learning Rate (LR) of 10% and similar cost reduction per doubling of cumulative production (IEA 2000; Junginger, Sark et al. 2010). Learning curves for the renewable energy technologies as well as levelised cost of electricity will be presented. The latter also include the impact of resource conditions (e.g. wind and solar yield) at different locations as well as operation and maintenance costs and fuel expenditures in the case of biomass technologies.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
Quantification of operating reserves with high penetration of wind power cons...IJECEIAES
The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore, the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty in wind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated as a mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching.
This document summarizes research on integrating thermal energy storage with cogeneration systems. It discusses using a firefly algorithm to optimize the scheduling of cogeneration units with thermal storage systems. The algorithm aims to minimize operation costs while meeting demand and constraints. It models the behavior and constraints of the thermal storage systems and cogeneration units. The algorithm is shown to effectively balance local and global search for optimization. A comparison shows it performs better than other algorithms for this application.
A Response Surface Based Wind Farm Cost (RS-WFC) model, is developed to evaluate the economics of wind farms. The RS-WFC model is developed using Extended Radial Basis Functions (E-RBF) for onshore wind farms in the U.S.. This model is then used to explore the in uence of di erent design and economic parameters, including number of turbines, rotor diameter and labor cost, on the cost of a wind farm. The RS-WFC model is composed of three parts that estimate (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The accuracy of the cost model is favorably established through comparison with pertinent commercial data. Moreover, the RS-WFC model is integrated with an analytical power generation model of a wind farm. A recently developed Unrestricted Wind Farm Layout Optimization (UWFLO) model is used to determine the power generated by a farm. The ratio of the total annual cost and the energy generated by the wind farm in one year (commonly known as the Cost of Energy, COE) is minimized in this paper. The results show that the COE could decreasesigni cantlythroughlayoutoptimization,toobtainmillionsofannualcostsavings.
This document proposes a multi-objective framework for short-term scheduling of a microgrid considering cost minimization and emission minimization objectives. It formulates the problem as a mixed integer nonlinear program with constraints including power balance and unit generation limits. The Normal Boundary Intersection method is employed to solve the multi-objective problem and generate a Pareto front of optimal solutions. Simulation results are presented comparing the proposed approach to other methods.
Should the focus be on broader policy goals or on specific technology targets?IEA-ETSAP
This document provides an overview of the Swiss energy system and scenarios analyzed using the Swiss TIMES Energy Model (STEM). STEM is a whole energy system optimization model of Switzerland that examines the Swiss energy system from primary energy supply to end use across sectors. The document describes scenarios that focus on either achieving a 40% or 60% reduction in transport sector CO2 emissions by 2050 or achieving a system-wide 60-67% CO2 reduction. The results show that a transport sector target alone shifts emissions to other sectors while a system-wide target leads to greater electrification and use of renewable electricity to reduce overall CO2 emissions.
Status ETSAP_TIAM Git project and starting up ETSAP-TIAM updateIEA-ETSAP
The document discusses two projects related to improving collaboration on and updating the ETSAP-TIAM energy systems model. The ETSAP_TIAM Git project aims to enhance collaboration through a version control system to track model changes. The 2-year ETSAP-TIAM Update Project aims to ensure the model remains relevant by updating technologies, data, and scenarios through workshops and collaborative development among members. It will deliver an updated model, documentation, and standard scenarios in a new VEDA-BE database. A reviewer group was also announced to review proposed model changes.
ETOU electricity tariff for manufacturing load shifting strategy using ACO al...journalBEEI
This paper presents load shifting strategy for cost reduction on manufacturing electricity demand side, by which a real test load profile had been used to prove the concept. Superior bio-inspired algorithm, Ant Colony Optimization (ACO) had been implemented to optimize the upright load profile of load shifting strategy in the Malaysia Enhance Time of Use (ETOU) tariff condition. Subsequently, significant simulation results of operation profit gain through 24 hours electricity consumption had been analyzed properly. The proposed method had shown reduction of approximately 6% of the electricity cost at peak and mid peak zones, when 20%, 40%, 60%, 80% and 100% load shifting weightages were applied to the identified 10% controlled loads consequently. It is hoped that the finding of this study can help poise the manufacturers to switch to ETOU tariff as well as support the national Demand Side Management (DSM) program
The document discusses plans to update the ETSAP-TIAM energy-economic model through a collaborative process. It proposes organizing workshops with ETSAP-TIAM users to identify needed updates, revising model data and structures, recalibrating the model, and documenting results. The goals are to facilitate analysis of deep decarbonization scenarios and improve linking of ETSAP-TIAM with other models. A two-year, €100,000 budget is presented to fund updating energy balances and technologies, workshops, recalibration, documentation, and scenario development through biannual phases.
A Generalized Multistage Economic Planning Model for Distribution System Cont...IJERD Editor
This document presents a generalized multistage economic planning model for distribution systems containing distributed generation (DG) units. The model minimizes total investment and operation costs over a planning horizon divided into multiple periods, taking into account load growth, equipment capacities and voltages limits. Constraints include power flow equations and logical constraints relating planning periods. The model is applied to a sample 11kV distribution network with one substation, 23 load buses and 32 feeders over 4 annual periods. The mixed integer nonlinear optimization problem is solved using LINGO software to obtain the least-cost expansion plan.
Base load power plants run continuously to meet minimum electricity demand. Peak load plants operate intermittently to meet spikes in demand. Less flexible base load plants are better for providing base energy needs, while more flexible peak load plants can adjust quickly to fill peak demand. The economics of power generation involve calculating the cost per unit of electricity based on fixed, semi-fixed, and running costs which include capital costs, interest, depreciation, fuel, and maintenance. Depreciation methods like straight line, diminishing value, and sinking fund determine annual charges to fund future replacement of equipment over its useful lifetime.
Evaluation of the role of energy storages in Europe with TIMES PanEUIEA-ETSAP
This document summarizes the results of scenario analyses conducted using the TIMES PanEU energy system model and ESTMAP storage database to evaluate the role of energy storage in Europe. The analyses found that increased electricity demand and electrification of the energy system are needed to meet EU GHG reduction targets. Additional electricity storage capacity investments from 2030 onward are also needed to integrate more variable renewable energy from wind and solar. First investments are in diabatic CAES and battery storage, shifting later to pump storage and adiabatic CAES as costs decrease. Energy storage, along with other flexibility options, helps reduce GHG emissions compared to scenarios relying more on natural gas storage.
Grid Features in the TIMES-based Japan ModelIEA-ETSAP
1) The document describes updates made to the Japan Multi-regional Transmission (JMRT) model to include grid features, allowing for analysis of high renewable energy penetration scenarios.
2) The model was modified to include 351 grid nodes to represent Japan's 47 prefectures, with renewable energy potential and demand allocated to nodes based on location.
3) Simulations examined scenarios with 25-55% variable renewable energy (VRE) shares by 2050, and the impact of grid infrastructure expansion. Without expansion, high VRE led to increased marginal electricity costs between regions.
The document analyzes the current energy consumption patterns and forecasts the future energy demand of Jaya Container Terminal (JCT) in Sri Lanka by 2020. It models the current energy use using LEAP software and evaluates the per TEU energy consumption for different container types handled by JCT. It then forecasts JCT's energy demand by 2020 based on projections for container throughput. Finally, it analyzes potential demand side management options to reduce energy costs and consumption at JCT in the future.
Emissions reduction potential in regions of Kazakhstan using TIMES-16RKZ modelIEA-ETSAP
The TIMES-16RKZ model was used to assess emissions reduction potential in Kazakhstan's 16 regions under different scenarios. The model found that meeting Kazakhstan's INDC target of reducing emissions 15% below 1990 levels would require reducing coal consumption 21% compared to business as usual. Most reductions would come from the energy supply sector through improved efficiency rather than reduced demand. The key emitting regions of Almaty, Karaganda, and Pavlodar would see the largest decreases in emissions through retirement of old coal plants, increased gas generation, and new capacities in high demand growth areas. Regional policies will be important to realize differences in energy demand and prices across Kazakhstan.
Fractal Energy Consulting- Microgrid Feasibility Model PDFLouis Monteagudo
Fractal Energy Consulting developed an integrated capital budgeting model to understand the tradeoffs between carbon emissions reduction targets and financial targets like levelized cost of electricity and investment payback period for a microgrid project in Ithaca, NY. The model considers technology and cost parameters, demand projections over 30 years in 4 phases, equipment sizing, revenues, costs, incentives, and creates 5 scenarios to analyze environmental and financial tradeoffs to select an optimal case.
Wind Solar Hybrid Power Project Investigation For Theme Parks A Case Studychittaranjang
The document investigates the feasibility of a wind-solar hybrid power project for a theme park in California. Based on wind maps, the site has average wind speeds of 6-7.5 m/s suitable for 1.5-1.65 MW wind turbines that could generate 3.14-5.64 GWh annually. Available land could accommodate a 112 kW solar farm estimated to generate 176 MWh annually.
A hybrid system with two 1.5 MW turbines and 112 kW solar is recommended. Further technical studies are required to obtain permits, which can take 3-18 months. The project has potential but detailed commercial assessments are needed regarding costs, expenses, and power purchase agreements.
Incorporating uncertainties in the transition towards a clean European energy...IEA-ETSAP
Incorporating uncertainties in the transition towards a clean European energy system: a stochastic approach for decarbonization paths in the transport sector.
A framework for dynamic pricing electricity consumption patterns via time ser...Asoka Korale
Clustering individual household electricity consumption patterns enables a utility to design pricing plans catered to groups of households in a particular locality to more accurately reflect the cost of supply at a particular time of day.
In this paper we model each time series as an Autoregressive Moving Average (ARMA) process with an optimal model order determined by the Akaike Information Criterion when the parameters estimated by the Hannan-Rissanen algorithm converge. The estimated model has the representation of a transfer function with a frequency response defined by the ARMA parameters. We use the frequency response as the means to further refine the within cluster profiling and classification of the objects.
Through our modeling we are also able to identify instances where the consumption behavior exhibits patterns that are uncharacteristic or not in line with the behavior or consumption profiles of the other households in a particular locality providing insights in to potential faults, fraud or illegal activity.
Economic Impacts of Behind the Meter Distributed Energy Resources on Transmis...Power System Operation
The increasing penetration of customer-owned Distribution Energy Resources (DERs) will have an impact on the economics that govern market operation. Visibility and control of local Independent System Operators (ISOs) over these resources are currently restricted or available in some form of aggregation. Additionally, non-curtailable resources pose a serious problem while balancing the market with eminent risks of over-generation and added congestion to the system. This study attempts to decouple the model at the Transmission-Distribution interface and demonstrate the following: 1) economic implications of such resources under two control strategies, 2) aspects of market dynamics affected by several DER penetration levels, 3) Potential benefits of increased ISO visibility beyond the Transmission-Distribution(T-D) interface.
Cost development of renewable energy technologiesLeonardo ENERGY
This course covers the cost development of renewable energy technologies, which includes the analysis of technological change, in particular with regard to technological learning, the assessment of learning rates of renewable energy technologies available in literature and forecasting studies. For many (energy) technologies, a log-linear relation was found between the accumulated experience and the technical (e.g. efficiency) and economic performance (e.g. investment costs). The rate at which cost decline for each doubling of cumulative production is expressed by the progress ratio (PR). A progress ratio of 90% results in a learning Rate (LR) of 10% and similar cost reduction per doubling of cumulative production (IEA 2000; Junginger, Sark et al. 2010). Learning curves for the renewable energy technologies as well as levelised cost of electricity will be presented. The latter also include the impact of resource conditions (e.g. wind and solar yield) at different locations as well as operation and maintenance costs and fuel expenditures in the case of biomass technologies.
Reliability Constrained Unit Commitment Considering the Effect of DG and DR P...IJECEIAES
Due to increase in energy prices at peak periods and increase in fuel cost, involving Distributed Generation (DG) and consumption management by Demand Response (DR) will be unavoidable options for optimal system operations. Also, with high penetration of DGs and DR programs into power system operation, the reliability criterion is taken into account as one of the most important concerns of system operators in management of power system. In this paper, a Reliability Constrained Unit Commitment (RCUC) at presence of time-based DR program and DGs integrated with conventional units is proposed and executed to reach a reliable and economic operation. Designated cost function has been minimized considering reliability constraint in prevailing UC formulation. The UC scheduling is accomplished in short-term so that the reliability is maintained in acceptable level. Because of complex nature of RCUC problem and full AC load flow constraints, the hybrid algorithm included Simulated Annealing (SA) and Binary Particle Swarm Optimization (BPSO) has been proposed to optimize the problem. Numerical results demonstrate the effectiveness of the proposed method and considerable efficacy of the time-based DR program in reducing operational costs by implementing it on IEEE-RTS79.
Quantification of operating reserves with high penetration of wind power cons...IJECEIAES
The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore, the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty in wind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated as a mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching.
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.
The document summarizes the optimal configuration of a wind/solar/diesel/battery hybrid energy system for electrifying a rural area in Jhiri village, Madhya Pradesh, India. Load profile analysis was conducted based on population and standard electricity consumption. Meteorological data on solar radiation, wind speed, and temperature was collected from a local weather station. Component models for the PV array, wind turbine, battery, and diesel generator were developed. Homer software was then used to optimally size the hybrid energy system components to reliably meet the village's electricity needs at minimum cost. The proposed system aims to improve lives in the rural community through a more economical and environmentally-friendly energy solution.
This document summarizes a research paper that presents a model for optimizing merchant investments in energy storage units that can compete in both energy and reserve markets. The model uses a bilevel programming framework to maximize the expected lifetime profit for the energy storage investor while considering market clearing decisions over characteristic operating days. The bilevel model is converted to a single-level mixed-integer linear program using Karush-Kuhn-Tucker conditions and solved using Benders' decomposition. A case study on the ISO New England test system provides insights into how optimal energy storage siting and sizing is affected by changing capital costs and reserve requirements.
Utility ownership of combined heat and power an economic model based approacheSAT Journals
Abstract This paper proposes, and reviews, an Excel based model to evaluate the cost effectiveness for utility ownership of Combined Heat and Power (CHP) plants. CHP plants provide highly efficient use of fuel for production of electric power and useful heat. This efficient use of fuel can lead to lower energy bills for a company, or group of companies, utilizing a CHP plant, though at a large upfront cost. In order to provide the needed capital, cash reserves or debt can be used. This large capital experiments leads to longer payback periods, which may make CHP plant unattractive investments for end users, but fits perfectly into Electric power utilities investment strategy. This model was developed as a tool for an electric utility to determine if an investment into a CHP plant and selling the waste heat to an end user, would be an attractive investment worthy of further engineering investigation. For this paper, simulations where ran at seven different power hubs in five different power markets in the United States. Overall, the model showed that CHP plant would be an attractive investment in the New York City region and Boston regions, but not in Midwest. These differences are driven by lower power and lower capacity prices in the Midwest compared to the North East. Key Words: combined heat and power, economic approach, energy, modelling, simulation, Waste Heat
A NOVEL SYSTEM OPTIMIZATION OF A GRID INDEPENDENT HYBRID RENEWABLE ENERGY SYS...ijscmcj
Hybrid renewable energy based off-grid or distribute power supply has customarily thought to be a solitary
innovation based restricted level of supply to meet the essential needs, without considering dependable
energy procurement to rural or remote commercial enterprises. The aim of the paper is to propose a design
idea off-grid hybrid system to fulfil the load demand of the telecom base station by using renewable energy
resources for rural regions. HOMER software tool is used for simulation and optimization and it also
analysis the total net present cost (TNPC) $100,757, carbon emission is zero percent, initial cost $70,920,
operating cost $2,334, Capacity Shortage 0.17% and the cost of energy (COE) $0.502. The HOMER
simulation outcome gives the most feasible hybrid system configuration for electric power supply to the
remote location telecom base station.
IRJET- Modelling of a PMSG Wind Turbine with Voltage ControlIRJET Journal
This document summarizes a study that models a wind energy conversion system using a permanent magnet synchronous generator (PMSG) with variable-speed control. It describes the system components, including the wind turbine, PMSG, maximum power point tracking (MPPT) algorithm, and inverters. Mathematical models are presented for the wind turbine, PMSG in a d-q reference frame, and MPPT control. Simulations were performed in MATLAB to verify the system design models for the generator-side inverter, grid-side inverter, MPPT controller, and pitch angle control of the wind turbine. The simulation results validate the autonomous control system design for the PMSG-based wind energy conversion system.
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.
Heuristic Optimization Technique for CHP-Wind Power Dispatchidescitation
The document describes applying a differential evolution algorithm to solve an economic dispatch problem for a combined heat and power system with wind turbines. It establishes a mathematical model that minimizes total generation cost subject to constraints involving power output, heat output, and wind power. The model considers the probabilistic nature of wind power based on a Weibull distribution. The differential evolution algorithm is tested on a sample system and able to find optimal dispatch schedules that meet all constraints while minimizing cost for different levels of allowed wind power shortage.
Implementation effects of economics and market operations based model for tra...nooriasukmaningtyas
The main objective of this paper is to introduce power system economic operations in traditionally integrated power systems and market operations in deregulated power systems and study its effects. The power system economic operation is mathematically treated as an optimization problem. Also, a function of economic operation is to minimize generation cost, transmission losses, and so on, subject to power system operation constraints. In this paper, we start from generation cost formulations and introduce traditional economic dispatch model, optimal power flow model, and unit commitment model. With the deregulation of the power industry, integrated power system is unbundled to generation, transmission, and distribution. Electricity is traded in the wholesale market. Small customers purchase energy from electricity retailers through the retail market. The electricity market is operated for energytrading while satisfying power system operation requirements. Electricity market is mathematically modelled as an optimization problem that is subject to power system operation constraints and market operation constraints.
The following article presents the control of the power generated by the Doubly Fed Induction Generator, integrated into the wind system, whose rotor is linked to the power converters (Rotor Side Convert (RSC) and Grid Side Converter (GSC)) interfaced by the DC-BUS and connected to the grid via a filter (Rf, Lf) in order to obtain an optimal power to the grid and to ensure system stability. The objective of this study is to understand and to make the comparison between Sliding mode Control technique and the Flux Oriented Control in order to control the Doubly Fed Induction Generator powers exchanged with the grid, it also aims at maintaining the DC-BUS voltage constant and a unit power factor at the grid connection point.The results of simulation show the performance of the Sliding mode Control in terms of monitoring, and robustness with regard to the parametric variations, compared to the Flux Oriented Control. The performance of the systems was tested and compared with the use of MATLAB/Simulink software.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses using real options theory to value power generation assets. It summarizes that deregulation of energy markets has increased risk exposure for power producers from volatile electricity prices. Real options theory accounts for flexibility in operating assets, often leading to higher asset values than discounted cash flow analysis. The document then describes using stochastic dynamic programming to model operating characteristics like minimum on/off times, ramp times, and calculate optimal operating policies and asset values over time under uncertainty.
Optimal Unit Commitment Based on Economic Dispatch Using Improved Particle Sw...paperpublications3
The document presents an improved particle swarm optimization (IPSO) algorithm for solving the optimal unit commitment problem in power systems. The IPSO algorithm extends the standard PSO algorithm by using additional particle information to control mutation and mimic social behaviors. The algorithm was implemented on the IEEE 14 bus test system in MATLAB. Results showed the IPSO approach committed units to meet load demand over 24 hours while satisfying constraints, with bus voltages maintained between 1.0017 and 1.0751 per unit. Total costs including fuel, startup, and shutdown costs were minimized at each hour.
Improved particle swarm optimization algorithms for economic load dispatch co...IJECEIAES
Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation of constraints. These methods are tested on three and ten-unit systems considering payment model for power delivered and different constraints. Results obtained from the PSO methods are compared with each other to evaluate the effectiveness and robustness. As results, IWPSO method is superior to other methods. Besides, comparing the PSO methods with other reported methods also gives a conclusion that IWPSO method is a very strong tool for solving ELDCEM problem because it can obtain the highest profit, fast converge speed and simulation time.
Numerical simulation of Hybrid Generation System: a case studyIRJET Journal
This document summarizes a study that simulates a hybrid power generation system for an area in Tamanrasset, Algeria using HOMER software. The system combines wind turbines, photovoltaic panels, diesel generators, and batteries. Solar radiation, wind speed, and load data for the area are presented. The simulation process and components of the hybrid system are defined in HOMER. Simulation results will validate the technical and economic feasibility of the hybrid system to reduce dependence on diesel generators and lower emissions.
A Comparison Study of Reactive Power Control Strategies in Wind Farms with SV...IJECEIAES
In the recent years, the integration of the wind farms into the electrical grids has increased rapidly. Especially, the wind power plants made up with doubly fed induction generators due to its many advatanges, such as being able to control its reactive power. Hence, some countries have published grid code requirements related to the reactive power that the wind turbines have to satisfy. This paper presents a coordinated reactive power control strategy in which STATCOM and doubly fed induction generators in wind power plants are used in order to bring back the voltage at the point of common coupling in the allowable range. First, reactive power requirements that the wind farms have to fulfill in some European countries are introduced. Second, the reactive power limitations of 2MW doubly fed induction generator are determined. Then, the static synchronous compensator (STATCOM) and the synchronous var compensator (SVC) FACTS (Flexible AC Transmission Systems) devices are presented. Finaly, various reactive power control strategies are applied to 10 MW wind farm, and the simulation results are analysed and compared.
optimization of the managed electrical energy within a hybrid renewable energ...INFOGAIN PUBLICATION
Hybrid energy applications based on renewable energy sources are becoming more and more desirable every day. They have increased the economic attractiveness of renewable electric energy generation. Because of the sudden fluctuations of the load requirements, the main attribute of such Hybrid Systems is to be able to generate energy at any time by optimally using each source. In this article, we have proposed a combination between a sizing study and a control one for the aim of solving the complex optimization problem of finding the optimal combination of size and storage to make the best use of the renewable power generations and to become more independent of rising electricity costs. Additionally, an improvement in the induced optimization algorithm is introduced in this paper so as to compute the optimal size and the operation control of the system with the aim of minimizing as much as possible the cost while responding to the load energy requirements taking into account the environmental factors.
Enhancement of reactive power capability of doubly fed induction generator 2-3IAEME Publication
With the growing integration into power grids, wind power plants are very important for
power system. According to the grid codes wind power plants should have the ability to perform
voltage control and reactive power compensation at the Point of Common Coupling (PCC). In
general, the entire wind farm operates within a power factor range of 0.95 leading and lagging. This
operation drastically under utilizes the reactive output of the machine. The results offered in this
paper demonstrates enhancement of reactive power capability of Doubly Fed Induction Generator
(DFIG). This additional reactive power supports to improve the post fault voltage and reduces the
overall system losses and also reduces the cost of the generation. The utilization of extended reactive
limits in voltage control may prevent system collapse.
Similar to An integrated OPF dispatching model with wind power and demand response for day-ahead markets (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
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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ramps. The generation system reliability of the system may be reduced in case of unpredicted decreases in
wind power because the available ramping capability of the system may not be sufficient to accommodate
those changes. The day-ahead dispatching with wind power has been addressed widely. The optimal power
flow (OPF) formulation have been extended to account for the variable nature of wind power generation.
For instance, in [5-8], the intermittent nature of wind power generation is captured using probabilistic
techniques. A stochastic unit commitment model is presented in [9], where authors propose a framework to
quantify the impact of large-scale wind power integration into power systems. The uncertainty in the wind
generation is addressed by means of scenarios and demands are considered to be fixed. Others authors have
suggested stochastic optimization (SO) based on scenarios to cope with wind power uncertainty in the unit
commitment problem [10-12]. SO employs several scenarios along with their associated probabilities to
simulate possible uncertainties during the period. In [13], an optimal generation scheduling method including
renewable energy, distributed resorues and storage systems is solved using a particle swan optimization
algorithm. In addition, in [14], the auhors propose an enhanced genetic algorithm to solve the optimal power
flow.
On the other hand, the integration of demand side resources into electricity markets has drawn a lot of
attention. DR is a strategy to utilize electricity demand as a distributed resource with real possibilities to
improve efficiency and reliability of electricity networks. Usually, the demand in power system is considered
inelastic to the prices. However, a substantial amount of electricity demand is elastic such as plug-in electric
vehicle (PEV) [15] charging batteries, heating ventilation, air conditioning, and this report [16] indicates that
one third of residential demand in U.S. is flexible.
Several studies are researching about the participation of demand side resources in the procurement of
energy and reserve services. Seminal studies [17-19] have developed pool based market structures considering
the participation of demand side resources into the energy and reserve markets. The demand side resources
(i.e., DR resources) are technically capable of providing ancillary services given the flexibility and
the possibility to alleviate large and unexpected wind ramp events [20, 21]. Distribution companies or
aggregators usually manage DR resources [22]. The aggregators represent technically and financially various
users in order to bid DR reductions in electricity markets. This paper addresses the day-ahead dispatching
including wind power bids and DR bids.
The paper is organized as follows. The problem formulation is presented in Section II. In Section III,
the proposed procedure is tested using the IEEE 39-bus test system. The results are analyzed and discussed.
Section IV provides some concluding remarks.
2. PROBLEM FORMULATION
The notation for the OPF dispatching model including wind power generations and DR resources is
expressed in terms of power generation for each unit, the load following reserves and the binary commitment
variable for thermal units. The complete set of variables are described as follows:
2.1. Notation
t Index over time periods.
T Set of indices of time periods in the planning horizon, typically 1 ... tn .
i Index over injections (generation units, dispatchable or curtailable loads).
j Index over scenarios.
t
I Indices of all units (generators) available for dispatch in any time t .
f Index of wind farms.
FN Set of indices of all units (wind farms and generators) available for dispatch in any time t
b Index of loads.
MAXDP Max., power demand for unit i at time t .
BN Set of indices of all loads at time t .
tiF Load flexibility of demand for unit i at time t .
tijp Active injection for unit i of scenario j at time t .
tf
wp Wind power forecast as offered in the market for unit f at time t
ti
DP Real power demand for unit i of scenario j at time t .
ti
PC Cost function for active injection i at time t .
ti
DC Cost function of upward and downward regulation of the demand from unit i at time t .
tbD Demand power at time t .
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,ti ti Upward/downward load-following ramping reserves needed from unit i at time t for transition to
time 1t .
ti
C , ti
C Cost of upward and downward load-following ramp. Reserve for unit i at time t .
tij
MINP , tij
MAXP Limits on active injection for unit i in the scenario j at time t .
MAX
i
, MAX
i
Upward/downward load-following ramping reserve limits for unit i .
tiu Binary commitment state for unit i in period t .
,ti tiv w Binary startup and shutdown states for unit i in period t .
,ti ti
v wC C Startup and shutdown costs for unit i at time t
,ti ti
Startup and shutdown costs for unit i at time t
2.2. Formulation
The problem formulation is expressed as a mixed-integer linear quadratic optimization problem
(MILP), where the optimization variable x is comprised of all the ,p ,ti
- ,ti ,u v and w variables
corresponding to power generation for each unit, the load following reserves and the binary commitment
variable for thermal units.
Objective Function: The objective is expressed as the minimization ( )f x
min ( )
x
f x
(1)
Subject to
( ) 0g x (2)
( ) 0h x (3)
min maxx x x (4)
where ( )f x is comprised of three components.
( ) ( ) ( , ) ( , ) ( , )p lf uc drf x f p f f u w f u w (5)
Cost of active power dispatch
( ) ( )
t
ti tij
p P
t T i I
f p C p
(6)
Cost of load-following ramp reserves
( , )
t
ti ti ti ti
lf
t T i I
f C C
(7)
Startup and shutdown cost
( , )
t
ti titi ti
uc v w
t T i I
f v w C v C w
(8)
Cost of demand response
( , )
t
tijti
dr D D
t T i I
f v w C P
(9)
This minimization is subject to the following constraints, for all: all and all :T tt T j J i I
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Constraints:
Power balance constraints,
T T F B
tij tfjtij tb
wD
i I i I f N b N
p P p D
(10)
Nonlinear transmission flow and voltage limits as inequality constraints,
( , ) 0tj tj tjh V p (11)
Load-following ramping limits and reserves,
max0 ti ti
(12)
max0 titi
(13)
Injection limits and commitments,
maxmin
tij tijti tij tiu P p u P
(14)
Startup and shutdown events,
( 1)ti t i ti tiu u v w (15)
Integer constraints,
0,1 , 0,1 , 0,1ti ti tiu v w
(16)
Flexibility interval of the demand
max0 ti DF P (17)
3. SIMULATIONS RESULTS
The IEEE 39-bus test system is examined in this section to test the integrated model that considers
wind power generation DR resources. The day-ahead dispatching framework proposed as a MILP problem is
solved using GUROBI 7.5.1 [23] under the Matpower platform [24]. The case IEEE 39-bus test system
includes 10 generators; the data is listed in Table 1. The cost data are equal to report in [25] and [26]. Table 2
lists the quadratic cost functions for each generator in the IEEE 39-bus system according to [27]. The system
daily load curve is shown in Figure 1 with a maximum peak of 4531 MW at hour 20 and a minimum of 1840
at hour 3.
Table 1. Generator data for the IEEE 39 bus system
Gen.
#
C C vC wC minP maxP
1 6.9 6.9 920 736 0 250
2 6.9 6.9 920 736 0 678
3 6.9 6.9 920 736 0 650
4 6.9 6.9 920 736 0 632
5 6.9 6.9 920 736 0 508
6 6.9 6.9 920 736 0 650
7 6.9 6.9 920 736 0 560
8 6.9 6.9 920 736 0 540
9 6.9 6.9 920 736 0 830
10 6.9 6.9 920 736 0 1000
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Table 2. Cost functions
Gen. Cost Function [$]
1 2
1 0.00194 7.85 310C P P
2 2
2 0.0035 8.5 260C P P
3 2
3 0.00482 7 78C P P
4 2
4 0.00128 6.4 459C P P
5 2
5 0.0024 6 80C P P
6 2
6 0.0032 5.8 400C P P
7 2
7 0.0053 6.24 120C P P
8 2
8 0.00185 8.4 60C P P
9 2
9 0.0025 5.75 450C P P
10 2
10 0.00142 8.2 510C P P
Figure 1. System daily load curve
This model considers a wind power integration level of 20% with respect to the peak load level.
A large number of scenarios around a trajectory bid captures the uncertainty in wind power forecasting by
the wind power generator as shown in Figure 2.
Figure 2. Wind power generation profile
DR offers incentives designed to induce lower electricity use at times of high market price. For this
simulation, four loads provide demand response services, and the power quantity, which they are willing to
reduce in a certain time, is 1295 MW. The incentives represented as cost functions are shown in Table 3.
Table 3. Load data for the demand response
Load Bus Demand Power [MW] Cost Function [$]
1 8 522 2
1 0.00128 6.4 459C P P
2 15 320 2
2 0.00128 6.4 459C P P
3 23 247.5 2
3 0.00128 6.4 459C P P
4 28 206 2
4 0.00128 6.4 459C P P
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For five hundred (500) scenarios, this paper quantifies the demand response frequency and
he magnitude in MW for each load. Each scenario runs over 24 hours according to the system daily load and it
considers a forecasted trajectory for the wind integration. In Figure 3, can observe the frequency and quantity
of flexibility for the load in the bus 8. It is obeserved the demand response is maximum all the time. Each time
that this load is shed, it is shed at 522 MW. While that for the load in the bus 15, Figure 4 shows sometimes
the quantity of DR required is around 270 MW, 295 MW, and 305 MW, although the 75% of time the DR
required is at the maximum value. The results for demand response in the bus 23, see Figure 5, are similar to
the results in the Figure 4, almost all the time the DR is dispatched at maximum. In Figure 6, the load in
the bus 28 exhibit a similar dispatching to the loads in the buses 15 and 23.
Figure 3. Demand response in the bus 8 Figure 4. Demand response in the bus 15
Figure 5. Demand response in the bus 23 Figure 6. Demand response in the bus 28
Now, in Figure 7 shows the percentage of DR with respect to the available capacity. It is observed
that in the hour 3, 13, 16 and 17 for the 24 horizon planning, the DR is not dispatched at maximum.
For instance, in the hour 13, the load in the 28 responds at 40% for the maximum value available. At the peak
demand for the system, at hour 20, all loads are dispatched as DR resources.
Figure 7. Demand response for all loads in the power system
In order to quantify the benefits for DR participation in the power system operation, the probability
density function for the power generation cost is calculated in the 24-hour horizon. The realizations correspond
to the trajectories generated around the bid made by the wind power generator. Figure 8 shows the power
generation cost under 500 scenarios with DR available for dispatching while the Figure 9 shows the power
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2800
generation cost without DR available. In order to compare the results for both cases, mean and standard
deviation for each case are calculated. The savings for the power generation cost is $8271. The result
represents the savings can be made in the power generation cost if the demand is flexible. Table 4 compares
the mean and variance parameters for both cases, with DR and without DR.
Figure 8. Cost objective function with DR Figure 9. Cost objective function without DR
Table 4. Gaussian parameters for the cost objective function with DR and without DR
Parameter
Estimate
without DR
Estimate
with DR
Difference
Mean $577,400 $569,120 $8,271
standard deviation $3,190 $3,360 -
In order to quantify DR benefits, others cost functions are set up to provide insight about
the dispatching cost. Table 5 provides six cost functions for demand response. This cost functions represents
the willing of each load to provide DR. The plot for those cost functions are shown in Figure 10.
Table 5. Incentive based demand response
Ref. Cost Function [$]
A 20.000128 1.4 439AC P P
B 20.00248 2.4 449BC P P
C 20.00128 6.4 459CC P P
D 20.00528 6.4 469DC P P
E 20.00928 6.4 479EC P P
F 20.015221 6.4 489FC P P
Figure 10. Cost functions for demand response
The power generation cost varies according to different incentives for DR as shown in Figure 11.
For instance, the case F, in Figure 10, shows a cost function with high bid for DR. The case A corresponeds to
a situation with a lower cost function, however, the dispatching is not the lower. For the cases evaluated,
the optimal solution corresponds to the case C, the cost function C is between the cases A and F.
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Figure 11. Cost objective function for different incentives to DR
4. CONCLUSION
In this paper, presented an integrated OPF model that explicitly includes wind power generation and
demand response resources for day-ahead dispatching in constrained electricity markets. Demand response is
integrated into the model as flexible loads with willing to bid day-ahead. Observed that considerable savings in
power generation cost could be achieved if the demand participates in the markets. The numerical results show
that the wind power uncertainty can be captured using trajectories from a Monte-Carlo simulation.
ACKNOWLEDGEMENTS
The authors acknowledge the support of the Universidad Autónoma de Occidente in Colombia.
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