Price formation is critical to efficient wholesale electricity markets that support reliable operation and efficient investment. The Midcontinent Independent System Operator (MISO) developed the Extended Locational Marginal Pricing (ELMP) with the goal of more completely reflecting resource costs and generally improving price formation to better incent market participation. MISO developed ELMP based on the mathematical concept of convex hull. However, considering the computational challenges and the existing market structure, MISO implemented an approximate version of ELMP. This paper presents enhancements to ELMP to bring the practical implementation of ELMP closer to the theoretical ideal and to achieve greater benefits of ELMP in production. The Special Ordered Set of Type Two (SOS2) piece-wise linear cost function formulation is used to tighten the approximation of, and under certain conditions exactly match, the convex hull of the cost function. Regulation commitment logic is also enhanced to maintain optimality under degeneracy conditions while providing flexibility for real-time regulation scheduling and pricing. Simulation results on the MISO system illustrate expected benefits. With the increasing interests in inter-temporal constraints, the on-going work on ELMP ramp modelling is also discussed.
This document summarizes research on approaches to managing congestion in deregulated electricity markets. It reviews various congestion management methods that have been proposed, including nodal pricing, price area congestion management, available transfer capability based approaches, using thyristor controlled phase shifting transformers, and flexible AC transmission systems devices. It also discusses optimization techniques that have been applied to congestion management problems, such as genetic algorithms and particle swarm optimization. The document provides examples of research on applying these different congestion management methods and optimization techniques to address transmission network congestion issues in deregulated power systems.
This document proposes a generalized quadratic model for optimal power flow problems. It formulates the optimal power flow problem in a quadratic form and develops the necessary conditions for feasibility and optimality. It also describes a generalized algorithm that uses sensitivity analysis and optimal adjustments to constraints to find a global optimal solution. The algorithm can accommodate multiple objective functions like losses, costs, voltages and flows. It was tested on actual power systems and found to reach optimal solutions in few iterations, showing potential for online applications.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
This document summarizes two methods for allocating transmission line costs: the Generation Shift Distribution Factor (GSDF) method and the Bialek Tracing method. The GSDF method uses linear power flow approximations to calculate distribution factors that measure the incremental use of transmission lines by generators and loads. These factors can then be used to allocate total fixed transmission costs. The Bialek Tracing method is based on the proportional sharing principle and uses a topological approach to determine the contribution of individual generators or loads to every line flow. Both methods aim to generate appropriate economic signals to recover fixed transmission costs from market participants.
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.
Genetic algorithm based Different Re-dispatching Scheduling of Generator Unit...IDES Editor
Proper pricing of active power is an important issue
in deregulated power environment. This paper presents a
flexible formulation for determining short run marginal cost
of synchronous generators using genetic algorithm based
different re-dispatching scheduling considering economic load
dispatch as well as optimized loss condition. By integrating
genetic algorithm based solution, problem formulation became
easier. The solution obtained from this methodology is quite
encouraging and useful in the economic point of view and it
has been observed that for calculating short run marginal
cost, generator re-dispatching solution is better than classical
method solution. The proposed approach is efficient in the
real time application and allows for carrying out active power
pricing independently. The paper includes test result of IEEE
30 bus standard test system.
A MULTIPURPOSE MATRICES METHODOLOGY FOR TRANSMISSION USAGE, LOSS AND RELIABIL...ecij
In the era of power system restructuring there is a need of simplified method which provides a complete allocation of usage, transmission losses and transmission reliability margin. In this paper, authors presents a combined multipurpose matrices methodology for Transmission usage, transmission loss and transmission reliability margin allocation. Proposed methodology is simple and easy to implement on large power system. A modified Kirchhoff matrix is used for allocation purpose. A sample 6 bus system is used to demonstrate the feasibility of proposed methodology.
This document informs Masoud Yadollahi zadeh that his paper titled "Profit Maximization in Competitive Electricity Markets" has been accepted for oral presentation at the IEEE 3rd International Power and Energy Conference (PECon2010) in Kuala Lumpur, Malaysia from November 29 to December 1, 2010. The author is asked to address comments from reviewers to improve the paper and pre-register for the conference. The acceptance is conditional on at least one author attending to present the paper.
This document summarizes research on approaches to managing congestion in deregulated electricity markets. It reviews various congestion management methods that have been proposed, including nodal pricing, price area congestion management, available transfer capability based approaches, using thyristor controlled phase shifting transformers, and flexible AC transmission systems devices. It also discusses optimization techniques that have been applied to congestion management problems, such as genetic algorithms and particle swarm optimization. The document provides examples of research on applying these different congestion management methods and optimization techniques to address transmission network congestion issues in deregulated power systems.
This document proposes a generalized quadratic model for optimal power flow problems. It formulates the optimal power flow problem in a quadratic form and develops the necessary conditions for feasibility and optimality. It also describes a generalized algorithm that uses sensitivity analysis and optimal adjustments to constraints to find a global optimal solution. The algorithm can accommodate multiple objective functions like losses, costs, voltages and flows. It was tested on actual power systems and found to reach optimal solutions in few iterations, showing potential for online applications.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
This document summarizes two methods for allocating transmission line costs: the Generation Shift Distribution Factor (GSDF) method and the Bialek Tracing method. The GSDF method uses linear power flow approximations to calculate distribution factors that measure the incremental use of transmission lines by generators and loads. These factors can then be used to allocate total fixed transmission costs. The Bialek Tracing method is based on the proportional sharing principle and uses a topological approach to determine the contribution of individual generators or loads to every line flow. Both methods aim to generate appropriate economic signals to recover fixed transmission costs from market participants.
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.
Genetic algorithm based Different Re-dispatching Scheduling of Generator Unit...IDES Editor
Proper pricing of active power is an important issue
in deregulated power environment. This paper presents a
flexible formulation for determining short run marginal cost
of synchronous generators using genetic algorithm based
different re-dispatching scheduling considering economic load
dispatch as well as optimized loss condition. By integrating
genetic algorithm based solution, problem formulation became
easier. The solution obtained from this methodology is quite
encouraging and useful in the economic point of view and it
has been observed that for calculating short run marginal
cost, generator re-dispatching solution is better than classical
method solution. The proposed approach is efficient in the
real time application and allows for carrying out active power
pricing independently. The paper includes test result of IEEE
30 bus standard test system.
A MULTIPURPOSE MATRICES METHODOLOGY FOR TRANSMISSION USAGE, LOSS AND RELIABIL...ecij
In the era of power system restructuring there is a need of simplified method which provides a complete allocation of usage, transmission losses and transmission reliability margin. In this paper, authors presents a combined multipurpose matrices methodology for Transmission usage, transmission loss and transmission reliability margin allocation. Proposed methodology is simple and easy to implement on large power system. A modified Kirchhoff matrix is used for allocation purpose. A sample 6 bus system is used to demonstrate the feasibility of proposed methodology.
This document informs Masoud Yadollahi zadeh that his paper titled "Profit Maximization in Competitive Electricity Markets" has been accepted for oral presentation at the IEEE 3rd International Power and Energy Conference (PECon2010) in Kuala Lumpur, Malaysia from November 29 to December 1, 2010. The author is asked to address comments from reviewers to improve the paper and pre-register for the conference. The acceptance is conditional on at least one author attending to present the paper.
The document is an invitation letter from the general chair of the IEEE EPEC 2011 Conference to Mr. Masoud Yadollahi Zadeh. It invites him to participate in the conference from October 3-5, 2011 in Winnipeg, Canada, where he will be presenting his paper titled "Nash Equilibrium In competitive Electricity Markets". It provides details about the conference objectives, acknowledges that all expenses will be covered by the participant or their company, and provides contact information for the registration chair if he has any other questions.
Integrating the TDBU-ETSAP models in MCP formatIEA-ETSAP
The document discusses integrating energy system optimization models like TIMES with macroeconomic models like MSA using the Mathematical Programming with Complementarity Constraints (MCP) format. Key points:
1) An operational version of MSA has been developed in MCP format and linked/tested with TIMES, producing the same results as the original optimization.
2) Formulating both TIMES and MSA in MCP allows them to be fully integrated while maintaining consistency. This has advantages like introducing multiple objective sectors.
3) Next steps include testing the integrated TIMES-MSA MCP model with other TIAM models and the decomposition algorithm, as well as exploring other integration approaches like linking CGE and T
The challenges of cross-border participation in CRMsLeonardo ENERGY
CRMs are tools set up to remunerate directly generation (or demand side management) capacity.
In the European Union several member states are implementing markets for CRMs, in a rather uncoordinated manner. In an integrated energy market framework, this poses several challenges to both the electricity market design and the treatment of cross border capacity.
Scope of this webinar is to review the challenges and opportunities posed by the opening of CRMs to external participation. External participation is defined; three different models of external participation are specified and the pro and cons of cross-border participation to CRMs are compared.
Bi-Level Optimization based Coordinated Bidding Strategy of a Supplier in Ele...IJERD Editor
This document presents a bi-level optimization problem formulation to develop an optimal coordinated bidding strategy for a supplier participating in the Day-Ahead Energy Market and Balancing Energy Market. The lower level problem represents the market clearing process of these markets, while the upper level problem represents the supplier's profit maximization objective. An Artificial Bee Colony algorithm is used to solve the non-linear bi-level optimization problem. The approach is tested on a 5-bus system and results are compared to a Genetic Algorithm approach.
Power Loss Allocation in Deregulated Electricity MarketsIJERD Editor
The restructuring of Electricity Supply Industry (ESI) all over the world thatstartedmainlyinthe 20th
century introduces an open electricity marketfor trading electricity betweengenerators and suppliers in
competitive environments. Market participants utilize thenetwork differently to maximize their profits. This
transformation consists of two aspects that are related with each other; restructuring and privatization.
However, dueto this change, some problems and challenges have risen. One of it is theissue of power losses
allocation. When electrical power is transmitted throughanetwork, it will cause power losses. The generators
must compensate this lossbygenerating more power. Under competitive electricity market environment, no
generators would want to generate more to compensate this loss asit will increase their production cost.
Logically both generators and consumers are supposed topayfor the losses because they both use the network
and thus are responsible for the lossesincurred. If there is no specified method to handle this problem, there is a
probability that the Independent System Operator (ISO) which is a non-profit entity and does not have source of
income will be responsible for this losses. However, if ISO paid forthe losses, itis considered unfair. Thus, this
analysis focuses on some existing allocating transmissionlosses.The selected methods are pro rata, postage
stamp, and Current Adjustment Factor (CAF) and these methods have been tested using simple bus network and
the IEEE standard 14 test bus system.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
This document discusses enhancing available transfer capability (ATC) in deregulated electricity markets using flexible AC transmission system (FACTS) devices. It analyzes using thyristor controlled series compensator (TCSC), static VAR compensator (SVC), and unified power flow controller (UPFC) individually and in combinations to boost single area ATC and multi-area ATC. Particle swarm optimization is employed to determine the optimal device settings. The study evaluates ATC enhancement on the IEEE 30 bus and 118 bus test systems for selected bilateral, multilateral, and area transactions, and also calculates the installation costs.
This document discusses four methods for calculating locational marginal pricing (LMP) for optimal power flow under lossy conditions: 1) using a DC optimal power flow (OPF) model with loss factors and delivery factors, 2) using an AC OPF model, 3) using an iterative DC OPF formulation with fictitious nodal demands, and 4) using a genetic algorithm technique. The methods are applied to the IEEE 30-bus test system and the results are compared.
About This Training Course
Load forecasting is a central and integral process for planning periodical operations and facility expansion in the electricity sector. Demand pattern is almost very complex due to the deregulation of energy markets. Therefore, finding
an appropriate forecasting model for a specific electricity network is not an easy task. Although many forecasting
methods were developed, none can be generalized for all demand patterns. This training presents a pragmatic
methodology that can be used as a guide to construct Electric Power Load Forecasting models. The trainer brings with
him real case studies and examples from his direct experience in this industry.
Learning Outcomes
Participants will be able to understand and put into the practice the following key learnings
Significance and implementation of Load Forecast
Accuracy vs. Sensitivity of Load Flow assessment
Data mining and information requirement for the analysis
Methodology
Building a benchmark model for different utilities and examples from practice
Practical implementation, best practice and continuous updates
Who Should Attend
Load/price forecasters, energy traders, quantitative/business analysts in the utility industry, power system planners,
power system operators, load research analysts, and rate design analysts
GENCO Optimal Bidding Strategy and Profit Based Unit Commitment using Evolutio...IJECEIAES
In deregulated electricity markets, generation companies (GENCOs) make unit com- mitment (UC) decisions based on a profit maximization objective in what is termed profit based unit commitment (PBUC). PBUC is done for the GENCO’s demand which is a summation of its bilateral demand and allocations from the spot energy market. While the bilateral demand is known, allocations from the spot energy market depend on the GENCO’s bidding strategy. A GENCO thus requires an optimal bidding strategy (OBS) which when combined with a PBUC approach would maximize operating profits. In this paper, a solution of the combined OBS-PBUC problem is presented. An evolutionary particle swarm optimization (EPSO) algorithm is implemented for solving the optimization problem. Simulation results carried out for a test power system with GENCOs of differing market strengths show that the optimal bidding strategy depends on the GENCO’s market power. Larger GENCOs with significant market power would typically bid higher to raise market clearing prices while smaller GENCOs would typically bid lower to capture a larger portion of the spot market demand. It is also illustrated that the proposed EPSO algorithm has a better performance in terms of solution quality than the classical PSO algorithm.
Summary of “Efficient large-scale fleet management via multi-agent deep reinf...MauroRubieri
The document summarizes a student's thesis on using multi-agent deep reinforcement learning for large-scale fleet management. Two algorithms are introduced: contextual DQN (cDQN) and contextual actor-critic (cA2C). Simulation results show cA2C and cDQN improve gross merchandise volume and order response rate over baseline methods. cA2C performs best by considering geographic and collaborative context between agents, coordinating their actions, and factoring in vehicle repositioning costs.
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.
Benchmarking medium voltage feeders using data envelopment analysis: a case s...TELKOMNIKA JOURNAL
Feeder performance evaluation is a key component in improving the power system network.
Currently there is no proper method to find the performance of Medium Voltage Feeders (MVF) except the
number of feeder failures. Performance benchmarking may be used to identify actual performance of
feeders. The results of such benchmarking studies allow the organization to compare feeders with
themselves and identify poorly performing feeders. This paper focuses on prominent benchmarking
techniques used in international regulatory regime and analyses the applicability to MVFs.
Data Envelopment Analysis (DEA) method is selected to analyze the MVFs. Correlation analysis and DEA
analysis are carried out on different models and then the base model is selected for the analysis.
The relative performance of the 32 MVFs of Western Province, Sri Lanka is evaluated using the DEA.
Relative efficiency scores are identified for each feeder. Also the feeders are classified according to the
sensitivity analysis. The results indicate that the DEA analysis may be conveniently employed to evaluate
the performance of the MVFs. The evaluation is carried out once or twice a year with the MV distribution
development plan in order to identify the performance of the feeders and to utilize the available limited
resources efficiently.
Modular Multi-Objective Genetic Algorithm for Large Scale Bi-level ProblemsStefano Costanzo
A genetic algorithm is used to solve the Centralised Peak-Load Pricing model on the European Air Traffic Management system. The Stackelberg equilibrium is obtained by means of an optimisation problem formulated as a bilevel linear programming model where the Central Planner sets one peak and one off-peak en-route charge and the Airspace Users choose the route among the available alternatives.
This document discusses line-by-line embedded transmission pricing methodologies. It introduces concepts of deregulating the electric power industry and defines wheeling as transmitting electricity from a seller to buyer through a third party transmission network. It discusses different wheeling cost computation methodologies, including embedded and incremental cost approaches. It focuses on explaining the "line-by-line" embedded methodology in detail and how it can be used to calculate wheeling costs by allocating all existing and new transmission system costs to wheeling customers.
This document discusses natural monopolies and provides several definitions and examples. A natural monopoly is characterized by decreasing long-run average costs over all levels of output, meaning a single large firm can produce at lower costs than smaller firms. Industries with high fixed and overhead costs relative to variable costs, like utilities maintaining infrastructure networks, often resemble natural monopolies. While allowing one dominant firm may be most efficient, monopoly power could exploit consumers. Policy options include nationalization, price regulation, or introducing competition by separating infrastructure from services.
Due to limited availability of coal and gases, optimization plays an important factor in thermal
generation problems. The economic dispatch problems are dynamic in nature as demand varies with time.
These problems are complex since they are large dimensional, involving hundreds of variables, and have
a number of constraints such as spinning reserve and group constraints. Particle Swarm Optimization
(PSO) method is used to solve these challenging optimization problems. Three test cases are studied
where PSO technique is successfully applied.
Application of a new constraint handling method for economic dispatch conside...journalBEEI
In this paper, optimal load dispatch problem under competitive electric market (OLDCEM) is solved by the combination of cuckoo search algorithm (CSA) and a new constraint handling approach, called modified cuckoo search algorithm (MCSA). In addition, we also employ the constraint handling method for salp swarm algorithm (SSA) and particle swarm optimization algorithm (PSO) to form modified SSA (MSSA) and modified PSO (MPSO). The three methods have been tested on 3-unit system and 10-unit system under the consideration of payment model for power reserve allocated, and constraints of system and generators. Result comparisons among MCSA and CSA indicate that the proposed constraint handling method is very useful for metaheuristic algorithms when solving OLDCEM problem. As compared to MSSA, MPSO as well as other previous methods, MCSA is more effective by finding higher total benefit for the two systems with faster manner and lower oscillations. Consequently, MCSA method is a very effective technique for OLDCEM problem in power systems.
Performance of CBR Traffic on Node Overutilization in MANETsRSIS International
Mobile Ad hoc networks (MANETs) are power constrained since nodes are operated with limited battery supply. The important technical challenge is to avoid the node overutilization and increase the energy efficiency of each node with increasing traffic. If a node runs out of battery, its ability to route the traffic gets affected and hence, the network lifetime. There has been considerable progress in the battery technology, but not in par with the semiconductor technology. There are various techniques adopt the different approaches to achieve energy efficiency. The proposed approach uses a cost metric for path selection, which is a function of residual battery and current traffic load at a node. Further, the simulation and performance is carried through Qualnet network simulator. From the simulation results, it is observed that the proposed scheme has lower node overutilization with the less CBR connections.
This paper presents a new method using quadratic programming to solve economic dispatch problems that minimize fuel costs and emission dispatch problems that minimize pollutant emissions from power plants, while meeting demand. The method transforms variables to linearize constraints and applies quadratic programming recursively until convergence. It is shown to find the global minimum for economic load dispatch, minimum emission dispatch, combined economic and emission dispatch, and emission-constrained economic dispatch problems, and performs better than genetic algorithms. The algorithm is tested on a system and results demonstrate the effectiveness of the proposed quadratic programming method.
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.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document is an invitation letter from the general chair of the IEEE EPEC 2011 Conference to Mr. Masoud Yadollahi Zadeh. It invites him to participate in the conference from October 3-5, 2011 in Winnipeg, Canada, where he will be presenting his paper titled "Nash Equilibrium In competitive Electricity Markets". It provides details about the conference objectives, acknowledges that all expenses will be covered by the participant or their company, and provides contact information for the registration chair if he has any other questions.
Integrating the TDBU-ETSAP models in MCP formatIEA-ETSAP
The document discusses integrating energy system optimization models like TIMES with macroeconomic models like MSA using the Mathematical Programming with Complementarity Constraints (MCP) format. Key points:
1) An operational version of MSA has been developed in MCP format and linked/tested with TIMES, producing the same results as the original optimization.
2) Formulating both TIMES and MSA in MCP allows them to be fully integrated while maintaining consistency. This has advantages like introducing multiple objective sectors.
3) Next steps include testing the integrated TIMES-MSA MCP model with other TIAM models and the decomposition algorithm, as well as exploring other integration approaches like linking CGE and T
The challenges of cross-border participation in CRMsLeonardo ENERGY
CRMs are tools set up to remunerate directly generation (or demand side management) capacity.
In the European Union several member states are implementing markets for CRMs, in a rather uncoordinated manner. In an integrated energy market framework, this poses several challenges to both the electricity market design and the treatment of cross border capacity.
Scope of this webinar is to review the challenges and opportunities posed by the opening of CRMs to external participation. External participation is defined; three different models of external participation are specified and the pro and cons of cross-border participation to CRMs are compared.
Bi-Level Optimization based Coordinated Bidding Strategy of a Supplier in Ele...IJERD Editor
This document presents a bi-level optimization problem formulation to develop an optimal coordinated bidding strategy for a supplier participating in the Day-Ahead Energy Market and Balancing Energy Market. The lower level problem represents the market clearing process of these markets, while the upper level problem represents the supplier's profit maximization objective. An Artificial Bee Colony algorithm is used to solve the non-linear bi-level optimization problem. The approach is tested on a 5-bus system and results are compared to a Genetic Algorithm approach.
Power Loss Allocation in Deregulated Electricity MarketsIJERD Editor
The restructuring of Electricity Supply Industry (ESI) all over the world thatstartedmainlyinthe 20th
century introduces an open electricity marketfor trading electricity betweengenerators and suppliers in
competitive environments. Market participants utilize thenetwork differently to maximize their profits. This
transformation consists of two aspects that are related with each other; restructuring and privatization.
However, dueto this change, some problems and challenges have risen. One of it is theissue of power losses
allocation. When electrical power is transmitted throughanetwork, it will cause power losses. The generators
must compensate this lossbygenerating more power. Under competitive electricity market environment, no
generators would want to generate more to compensate this loss asit will increase their production cost.
Logically both generators and consumers are supposed topayfor the losses because they both use the network
and thus are responsible for the lossesincurred. If there is no specified method to handle this problem, there is a
probability that the Independent System Operator (ISO) which is a non-profit entity and does not have source of
income will be responsible for this losses. However, if ISO paid forthe losses, itis considered unfair. Thus, this
analysis focuses on some existing allocating transmissionlosses.The selected methods are pro rata, postage
stamp, and Current Adjustment Factor (CAF) and these methods have been tested using simple bus network and
the IEEE standard 14 test bus system.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
This document discusses enhancing available transfer capability (ATC) in deregulated electricity markets using flexible AC transmission system (FACTS) devices. It analyzes using thyristor controlled series compensator (TCSC), static VAR compensator (SVC), and unified power flow controller (UPFC) individually and in combinations to boost single area ATC and multi-area ATC. Particle swarm optimization is employed to determine the optimal device settings. The study evaluates ATC enhancement on the IEEE 30 bus and 118 bus test systems for selected bilateral, multilateral, and area transactions, and also calculates the installation costs.
This document discusses four methods for calculating locational marginal pricing (LMP) for optimal power flow under lossy conditions: 1) using a DC optimal power flow (OPF) model with loss factors and delivery factors, 2) using an AC OPF model, 3) using an iterative DC OPF formulation with fictitious nodal demands, and 4) using a genetic algorithm technique. The methods are applied to the IEEE 30-bus test system and the results are compared.
About This Training Course
Load forecasting is a central and integral process for planning periodical operations and facility expansion in the electricity sector. Demand pattern is almost very complex due to the deregulation of energy markets. Therefore, finding
an appropriate forecasting model for a specific electricity network is not an easy task. Although many forecasting
methods were developed, none can be generalized for all demand patterns. This training presents a pragmatic
methodology that can be used as a guide to construct Electric Power Load Forecasting models. The trainer brings with
him real case studies and examples from his direct experience in this industry.
Learning Outcomes
Participants will be able to understand and put into the practice the following key learnings
Significance and implementation of Load Forecast
Accuracy vs. Sensitivity of Load Flow assessment
Data mining and information requirement for the analysis
Methodology
Building a benchmark model for different utilities and examples from practice
Practical implementation, best practice and continuous updates
Who Should Attend
Load/price forecasters, energy traders, quantitative/business analysts in the utility industry, power system planners,
power system operators, load research analysts, and rate design analysts
GENCO Optimal Bidding Strategy and Profit Based Unit Commitment using Evolutio...IJECEIAES
In deregulated electricity markets, generation companies (GENCOs) make unit com- mitment (UC) decisions based on a profit maximization objective in what is termed profit based unit commitment (PBUC). PBUC is done for the GENCO’s demand which is a summation of its bilateral demand and allocations from the spot energy market. While the bilateral demand is known, allocations from the spot energy market depend on the GENCO’s bidding strategy. A GENCO thus requires an optimal bidding strategy (OBS) which when combined with a PBUC approach would maximize operating profits. In this paper, a solution of the combined OBS-PBUC problem is presented. An evolutionary particle swarm optimization (EPSO) algorithm is implemented for solving the optimization problem. Simulation results carried out for a test power system with GENCOs of differing market strengths show that the optimal bidding strategy depends on the GENCO’s market power. Larger GENCOs with significant market power would typically bid higher to raise market clearing prices while smaller GENCOs would typically bid lower to capture a larger portion of the spot market demand. It is also illustrated that the proposed EPSO algorithm has a better performance in terms of solution quality than the classical PSO algorithm.
Summary of “Efficient large-scale fleet management via multi-agent deep reinf...MauroRubieri
The document summarizes a student's thesis on using multi-agent deep reinforcement learning for large-scale fleet management. Two algorithms are introduced: contextual DQN (cDQN) and contextual actor-critic (cA2C). Simulation results show cA2C and cDQN improve gross merchandise volume and order response rate over baseline methods. cA2C performs best by considering geographic and collaborative context between agents, coordinating their actions, and factoring in vehicle repositioning costs.
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.
Benchmarking medium voltage feeders using data envelopment analysis: a case s...TELKOMNIKA JOURNAL
Feeder performance evaluation is a key component in improving the power system network.
Currently there is no proper method to find the performance of Medium Voltage Feeders (MVF) except the
number of feeder failures. Performance benchmarking may be used to identify actual performance of
feeders. The results of such benchmarking studies allow the organization to compare feeders with
themselves and identify poorly performing feeders. This paper focuses on prominent benchmarking
techniques used in international regulatory regime and analyses the applicability to MVFs.
Data Envelopment Analysis (DEA) method is selected to analyze the MVFs. Correlation analysis and DEA
analysis are carried out on different models and then the base model is selected for the analysis.
The relative performance of the 32 MVFs of Western Province, Sri Lanka is evaluated using the DEA.
Relative efficiency scores are identified for each feeder. Also the feeders are classified according to the
sensitivity analysis. The results indicate that the DEA analysis may be conveniently employed to evaluate
the performance of the MVFs. The evaluation is carried out once or twice a year with the MV distribution
development plan in order to identify the performance of the feeders and to utilize the available limited
resources efficiently.
Modular Multi-Objective Genetic Algorithm for Large Scale Bi-level ProblemsStefano Costanzo
A genetic algorithm is used to solve the Centralised Peak-Load Pricing model on the European Air Traffic Management system. The Stackelberg equilibrium is obtained by means of an optimisation problem formulated as a bilevel linear programming model where the Central Planner sets one peak and one off-peak en-route charge and the Airspace Users choose the route among the available alternatives.
This document discusses line-by-line embedded transmission pricing methodologies. It introduces concepts of deregulating the electric power industry and defines wheeling as transmitting electricity from a seller to buyer through a third party transmission network. It discusses different wheeling cost computation methodologies, including embedded and incremental cost approaches. It focuses on explaining the "line-by-line" embedded methodology in detail and how it can be used to calculate wheeling costs by allocating all existing and new transmission system costs to wheeling customers.
This document discusses natural monopolies and provides several definitions and examples. A natural monopoly is characterized by decreasing long-run average costs over all levels of output, meaning a single large firm can produce at lower costs than smaller firms. Industries with high fixed and overhead costs relative to variable costs, like utilities maintaining infrastructure networks, often resemble natural monopolies. While allowing one dominant firm may be most efficient, monopoly power could exploit consumers. Policy options include nationalization, price regulation, or introducing competition by separating infrastructure from services.
Due to limited availability of coal and gases, optimization plays an important factor in thermal
generation problems. The economic dispatch problems are dynamic in nature as demand varies with time.
These problems are complex since they are large dimensional, involving hundreds of variables, and have
a number of constraints such as spinning reserve and group constraints. Particle Swarm Optimization
(PSO) method is used to solve these challenging optimization problems. Three test cases are studied
where PSO technique is successfully applied.
Application of a new constraint handling method for economic dispatch conside...journalBEEI
In this paper, optimal load dispatch problem under competitive electric market (OLDCEM) is solved by the combination of cuckoo search algorithm (CSA) and a new constraint handling approach, called modified cuckoo search algorithm (MCSA). In addition, we also employ the constraint handling method for salp swarm algorithm (SSA) and particle swarm optimization algorithm (PSO) to form modified SSA (MSSA) and modified PSO (MPSO). The three methods have been tested on 3-unit system and 10-unit system under the consideration of payment model for power reserve allocated, and constraints of system and generators. Result comparisons among MCSA and CSA indicate that the proposed constraint handling method is very useful for metaheuristic algorithms when solving OLDCEM problem. As compared to MSSA, MPSO as well as other previous methods, MCSA is more effective by finding higher total benefit for the two systems with faster manner and lower oscillations. Consequently, MCSA method is a very effective technique for OLDCEM problem in power systems.
Performance of CBR Traffic on Node Overutilization in MANETsRSIS International
Mobile Ad hoc networks (MANETs) are power constrained since nodes are operated with limited battery supply. The important technical challenge is to avoid the node overutilization and increase the energy efficiency of each node with increasing traffic. If a node runs out of battery, its ability to route the traffic gets affected and hence, the network lifetime. There has been considerable progress in the battery technology, but not in par with the semiconductor technology. There are various techniques adopt the different approaches to achieve energy efficiency. The proposed approach uses a cost metric for path selection, which is a function of residual battery and current traffic load at a node. Further, the simulation and performance is carried through Qualnet network simulator. From the simulation results, it is observed that the proposed scheme has lower node overutilization with the less CBR connections.
This paper presents a new method using quadratic programming to solve economic dispatch problems that minimize fuel costs and emission dispatch problems that minimize pollutant emissions from power plants, while meeting demand. The method transforms variables to linearize constraints and applies quadratic programming recursively until convergence. It is shown to find the global minimum for economic load dispatch, minimum emission dispatch, combined economic and emission dispatch, and emission-constrained economic dispatch problems, and performs better than genetic algorithms. The algorithm is tested on a system and results demonstrate the effectiveness of the proposed quadratic programming method.
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.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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.
IRJET- Swarm Optimization Technique for Economic Load DispatchIRJET Journal
This document discusses using particle swarm optimization (PSO) technique to solve the economic load dispatch (ELD) problem in power systems. The ELD problem aims to minimize the total generation cost while satisfying constraints. PSO is applied to determine the optimal power outputs of generators. The key steps are: (1) Initialize a swarm of particles randomly within generator limits, (2) Evaluate fitness of each particle using a cost function, (3) Update particles' velocities and positions based on individual and global best positions, (4) Repeat steps 2-3 until convergence criteria is met. The method is tested on 3-unit and 6-unit systems and shown to find lower cost solutions than other algorithms like cuckoo search. PSO
A hybrid approach for ipfc location and parameters optimization for congestio...eSAT Journals
Abstract
The deregulated power system operation with competitive electricity market environment has been created many challenging tasks to the system operator. The competition with strategic bidding has been resulted for randomness in generation schedule, load withdrawal and power flows across the network. The economic efficiency of electricity market is mainly dependent on network support. In the event of congestion, it is required to alter the base case market settlement and hence the economic inefficiency in terms of congestion cost can occur. In order to anticipate congestion and its consequences in operation, this paper has been considered Interline Power Flow Controller (IPFC).This article proposed a tactical approach for optimal location and then its parameters in Decoupled Power Injection Modeling (DPIM) are optimized using Gravitational Search Algorithm (GSA). The case studies are performed on IEEE 30-bus test system and the results obtained are validating the proposed approach for practical implementations.
Keywords: Deregulated power system, competitive electricity market, congestion management, IPFC, Gravitational Search Algorithm (GSA)
Economic Load Dispatch for Multi-Generator Systems with Units Having Nonlinea...IJAPEJOURNAL
This document presents an economic load dispatch problem that uses the Gravity Search Algorithm to minimize total generation costs for multi-generator power systems. It discusses how practical constraints like valve point loading, multi-fuel operation, and forbidden zones result in non-ideal, non-continuous generator cost curves. The Gravity Search Algorithm is applied to find the optimal dispatch schedule that accounts for these realistic cost functions and minimizes the total cost of generation while satisfying demand. The algorithm is tested on sample power systems and able to find solutions within acceptable timeframes that outperform traditional optimization methods for large, complex problems.
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.
This document describes a flexible software-based distributed energy management system (DEMS) designed to investigate how controllable distributed energy units (CDEs) can be aggregated and integrated into the electric grid. The DEMS uses a hierarchical agent-based model to control different CDEs, including a wind turbine, combined heat and power plant, electric vehicle charging station, and industrial load. An experiment was conducted using the DEMS to demonstrate how it can aggregate these CDEs in different communication configurations to meet a secondary frequency control signal while maximizing profit from energy generation. Results showed the DEMS was able to successfully control the CDEs to closely track the required active power output.
This document presents derivations of Locational Marginal Prices (LMPs) using different optimal power flow models. It first derives LMPs using a full AC optimal power flow model, considering both active and reactive power constraints. It then derives LMPs from a full structured DC optimal power flow model, and further from a reduced form DC model. As a byproduct, it provides a rigorous explanation of basic LMP formulas presented without derivation in industry manuals. The goal is to increase transparency of LMP calculations in restructured wholesale power markets.
Multi objective economic load dispatch using hybrid fuzzy, bacterialIAEME Publication
The document summarizes a research paper that proposes a new approach for solving the economic load dispatch problem using a hybrid fuzzy, bacterial foraging-Nelder–Mead algorithm. The economic load dispatch problem minimizes generation costs while satisfying load demand under system constraints. The proposed approach considers generation costs, spinning reserve costs, and emission costs simultaneously. It also accounts for valve-point effects, prohibited operating zones, and other practical constraints. A hybrid bacterial foraging and Nelder–Mead algorithm combined with fuzzy logic is used to solve the optimization problem. Simulation results show the advantages of the proposed method in reducing total system costs.
A Review on Various Techniques Used for Economic Load Dispatch in Power Systemijtsrd
The document discusses various techniques used to solve the economic load dispatch (ELD) problem in power systems. The ELD problem involves determining the optimal power output of generators to minimize generation costs while meeting demand and operating constraints. The document reviews several methods that have been used to solve the ELD problem, including lambda iteration, gradient search, Newton's method, linear programming, dynamic programming, neural networks, evolutionary algorithms, particle swarm optimization, and other metaheuristic techniques. It provides details on how each method approaches solving the optimization problem posed by economic load dispatch.
A new approach to the solution of economic dispatch using particle Swarm opt...ijcsa
This document presents a new approach to solving the economic dispatch problem using particle swarm optimization combined with simulated annealing (PSO-SA). The economic dispatch problem aims to minimize the total generation cost while satisfying constraints like power demand and generator limits. Previous solutions had limitations. The authors propose using PSO-SA to find high quality solutions more efficiently. PSO is able to find global optima but can get trapped in local optima. SA helps avoid this through probabilistic jumping. The authors combine PSO and SA techniques to leverage their benefits while overcoming individual limitations. They test the PSO-SA method on three generator systems and find it provides better results than traditional and other computational methods.
This document summarizes literature on economic load dispatch problems. It begins with an introduction to economic load dispatch and its goal of generating required power at minimum cost. It then provides a literature survey summarizing 12 papers on optimization techniques applied to economic load dispatch, including methods like particle swarm optimization and grey wolf optimization. The document also discusses India's overall power generation scenario and Tripura's scenario. It defines the economic load dispatch problem formulation and constraints considered like power balance, generator capacity, and prohibited operating zones. The document concludes that the proposed enhanced colliding bodies optimization technique efficiently solves the economic load dispatch problem.
This paper presents a novel approach for static transmission expansion planning and
allocation of the associated expansion costs to individual market entities in a restructured power
system. The approach seeks the optimal addition of transmission lines among the possible candidate
transmission lines minimizing the overall system costs and at the same time satisfying the system
operational and security constraints. Novelty of the approach lies in applying a widely known
technique used for overload security analysis to an area such as Transmission expansion planning.
Transmission expansion costs are allocated using distribution factors to the individual entities in a
fair and transparent manner. The results for modified Garver Test system demonstrate that the
approach with the advantage of its simplicity can be applied to transmission expansion planning and
cost allocation in restructured power system
This document summarizes an article from the International Journal of Electrical Engineering and Technology (IJEET) that presents a novel approach for transmission expansion planning and cost allocation in deregulated power systems. The approach seeks to optimally add transmission lines to minimize costs while satisfying operational and security constraints. It applies an overload security analysis technique to transmission expansion planning. Transmission expansion costs are allocated to individual market participants using distribution factors in a fair manner. The approach is demonstrated on the modified Garver test system and is shown to be effective for transmission expansion planning and cost allocation in restructured power systems.
This document discusses optimization methods for solving the optimal power flow (OPF) problem in electric power systems. It provides an overview of OPF, describing its formulation as an optimization problem that minimizes objectives like generation costs while satisfying operational and physical constraints. Common objectives mentioned include minimizing costs associated with reactive power sources and real power losses. Constraints include power flow equations and limits on variables like generator outputs and voltages. The document reviews stochastic optimization methods that have been applied to solve the OPF problem and presents three real applications of OPF.
This document summarizes a research paper that proposes a new approach for solving the economic dispatch problem in power systems using a hybrid particle swarm optimization and simulated annealing algorithm. The paper introduces economic dispatch and describes previous solution methods. It then presents the new hybrid algorithm, which combines the global search capabilities of particle swarm optimization with the probabilistic jumping of simulated annealing to find high-quality solutions faster. The paper applies the method to test cases and finds it performs better than traditional and other computational techniques at determining low-cost generation schedules that satisfy operational constraints.
The document proposes a new approach for solving the economic dispatch problem in power systems using a hybrid particle swarm optimization and simulated annealing algorithm. It begins with introductions to economic dispatch and optimization techniques like particle swarm optimization and simulated annealing. It then describes the economic dispatch problem formulation, including the objective of minimizing generation cost while satisfying constraints. The document proposes a novel hybrid algorithm that combines the salient features of particle swarm optimization and simulated annealing to generate high-quality solutions efficiently. It presents the particle swarm optimization, simulated annealing and hybrid algorithms in detail. The effectiveness of the proposed approach is demonstrated through case studies on different power systems.
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
Multi-Objective Optimization Based Design of High Efficiency DC-DC Switching ...IJPEDS-IAES
In this paper we explore the feasibility of applying multi objective stochastic
optimization algorithms to the optimal design of switching DC-DC
converters, in this way allowing the direct determination of the Pareto
optimal front of the problem. This approach provides the designer, at
affordable computational cost, a complete optimal set of choices, and a more
general insight in the objectives and parameters space, as compared to other
design procedures. As simple but significant study case we consider a low
power DC-DC hybrid control buck converter. Its optimal design is fully
analyzed basing on a Matlab public domain implementations for the
considered algorithms, the GODLIKE package implementing Genetic
Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated
Annealing (SA). In this way, in a unique optimization environment, three
different optimization approaches are easily implemented and compared.
Basic assumptions for the Matlab model of the converter are briefly
discussed, and the optimal design choice is validated “a-posteriori” with
SPICE simulations.
Similar to Enhancements of Extended Locational Marginal Pricing – Advancing Practical Implementation (20)
The document provides highlights and key insights from the DNV Energy Transition Outlook 2021 report. It finds that:
1) Global emissions are not decreasing fast enough to meet Paris Agreement goals, and warming is projected to reach 2.3°C by 2100 despite renewable growth.
2) Electrification is surging, with renewables like solar and wind outcompeting other sources by 2030 and providing over 80% of power by 2050, supported by technologies like storage.
3) Energy efficiency gains lead to flat global energy demand after the 2030s, with a 2.4% annual improvement in energy intensity outpacing economic growth.
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SVC PLUS Frequency Stabilizer Frequency and voltage support for dynamic grid...Power System Operation
SVC PLUS
Frequency Stabilizer
Frequency and voltage support for dynamic grid stability
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Balancing services help maintain the frequency of the power grid by providing short-term energy or capacity reserves. They include balancing energy, which system operators use to maintain grid frequency, and balancing capacity, which providers agree to keep available. Different balancing services have varying activation speeds to respond to frequency deviations. Harmonization efforts in Europe are working to establish common balancing markets and platforms for cross-border exchange of reserves.
The Need for Enhanced Power System Modelling Techniques & Simulation Tools Power System Operation
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Power Quality Trends in the Transition to Carbon-Free Electrical Energy SystemPower System Operation
Power Quality
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A Power Purchase Agreement (PPA) is a long-term contract between an electricity generator and purchaser that defines the conditions for the sale of electricity. PPAs provide price stability and help finance renewable energy projects by guaranteeing revenue. There are physical PPAs, which deliver electricity directly, and virtual PPAs, which financially settle the contract without physical delivery. PPAs benefit both renewable developers by enabling project financing, and buyers seeking long-term electricity price certainty and renewable attributes.
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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
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Enhancements of Extended Locational Marginal Pricing – Advancing Practical Implementation
1. ychen@misoenergy.org
21, rue d’Artois, F-75008 PARIS CIGRE US National Committee
http://www.cigre.org 2019 Grid of the Future Symposium
Enhancements of Extended Locational Marginal Pricing –
Advancing Practical Implementation
Y. CHEN, C. WANG
Midcontinent Independent System Operator (MISO)
USA
SUMMARY
Price formation is critical to efficient wholesale electricity markets that support reliable
operation and efficient investment. The Midcontinent Independent System Operator (MISO)
developed the Extended Locational Marginal Pricing (ELMP) with the goal of more
completely reflecting resource costs and generally improving price formation to better incent
market participation. MISO developed ELMP based on the mathematical concept of convex
hull. However, considering the computational challenges and the existing market structure,
MISO implemented an approximate version of ELMP. This paper presents enhancements to
ELMP to bring the practical implementation of ELMP closer to the theoretical ideal and to
achieve greater benefits of ELMP in production. The Special Ordered Set of Type Two
(SOS2) piece-wise linear cost function formulation is used to tighten the approximation of,
and under certain conditions exactly match, the convex hull of the cost function. Regulation
commitment logic is also enhanced to maintain optimality under degeneracy conditions while
providing flexibility for real-time regulation scheduling and pricing. Simulation results on the
MISO system illustrate expected benefits. With the increasing interests in inter-temporal
constraints, the on-going work on ELMP ramp modelling is also discussed.
KEYWORDS
Convex hull, convex envelope, electricity market, extended locational marginal pricing, ramp
modeling, regulation commitment
2. 2
1. INTRODUCTION
The bid-based, security-constrained, economic dispatch model with locational prices provides the
foundation of electricity market design in the organized markets of the United States. These prices
incentivize market participants to follow the Regional Transmission Organization or Independent
System Operator (RTO/ISO) commitment and dispatch instructions so that the power grid is operated
reliably and efficiently. These prices also serve as important signals for bilateral contracts and long-
term investment decisions. However, given the practical realities of operating a complex electric
system subject to non-convexity and constantly changing conditions, price suppression and resulting
uplift have posed fundamental challenges to price formation. In particular, market clearing prices do
not typically reflect certain components of a resource’s operating costs (e.g., startup costs) or the costs
of resources that are dispatched at their operating limits. In theory, unit commitment and other lumpy
decisions can create situations where locational prices alone cannot fully support the commitment
solution. Reducing price suppression and uplift has been a key focus area of the Federal Energy
Regulatory Commission (FERC) and the subject of recent price-formation proceedings [1].
The Midcontinent Independent System Operator (MISO) manages one of the largest electricity
markets in the world. At the core of its day ahead and real time markets are the Security Constrained
Unit Commitment (SCUC) and Security Constrained Economic Dispatch (SCED) calculations which
co-optimize energy and ancillary services. Initially, the dual solutions from SCED were used to
calculate locational marginal price (LMP) for energy and market clearing prices (MCP) for ancillary
services. In the SCED calculation, all commitment variables were fixed, and fixed startup and no-load
costs were not reflected in prices. As a result, LMPs and MCPs would not necessarily fully cover the
commitment and dispatch costs for some generators, even if all committed generation was needed to
meet demand. This issue is fundamentally due to the non-convexity of the SCUC problem. Make-
whole uplift payments have been used to compensate for generation costs not covered by market
prices, but these payment settlements are not transparent and can mute efficient locational market
price signals.
The extended locational marginal price (ELMP) [4] restores the support of an economic solution
model with minimum reliance on uplift payments. Here, uplift payments include both opportunity
costs and make whole payments. A convex hull price is the slope of the convex hull of the total cost
function, and is thus non-decreasing with respect to demand. Such a price minimizes the total uplift
payment, defined by the duality gap between the SCUC problem and its Lagrangian dual.
Nevertheless, solving the full convex hull price is computationally expensive, and no commercial
solvers are currently available. There are also market design complexities in its application to real-
time prices which are executed on a rolling window basis every five minutes. MISO implemented an
approximation of convex hull pricing, or ELMPs [5]. Under MISO’s ELMP implementation, fixed
costs from quick-start resources can be incorporated into the clearing prices through partial
commitment [6]. Many variants of convex hull pricing have been explored in academic literature. The
accumulated experiences from different RTO/ISO implementations have also been a significant source
of advancement, such as PJM’s “Proposed Enhancements to Energy Price Formation” [2], [3].
This paper presents two improvements based on MISO ELMP production experiences. The first
improvement is a re-formulation of the piecewise linear (PWL) incremental energy curve. Between
2013 and 2016, MISO evaluated various options for the performance of its day ahead market clearing
engine. The reformulation of the PWL energy offer curve brought about a 30% reduction in mixed
integer programming SCUC problem solve times in some of the hardest cases [8][9]. Subsequently,
this PWL enhancement was found to be equivalent to the convex envelope total cost formulation in
[7][10]. As proven in [9], this formulation produces the convex envelope of the single interval PWL
cost function, and is also the tightest modeling of the PWL cost function. MISO thus explored its
application to the single interval ELMP implementation [10] and simulated the enhancement in its
production system.
3. 3
The second improvement is related to regulation commitment. In reality, a resource’s dispatch range
can be narrower when it is committed to provide regulating reserve, and a regulation commitment
variable is used for each regulation qualified resource in SCUC engine to decide whether a unit is
committed for regulation. SCED regulation clearing is then restricted to SCUC regulation committed
units which could be a very limited pool of resources. When resources are re-dispatched in ELMP or
under varying real-time system conditions, artificial regulation scarcities may be encountered within
this limited pool, resulting in regulating reserve price spikes. An improved regulation clearing process
was thus developed to allow more units for regulation clearing in the dispatch and pricing runs while
maintaining optimality.
The rest of the paper is organized as follows. Section II presents a MIP formulation to tighten the
piecewise linear function formulation. Section III introduces the improvement on ELMP regulation
commitment. Section IV discusses the on-going work of ELMP ramp modeling and Section V
concludes this paper.
2. PIECEWISE LINEAR INCREMENTAL ENERGY CURVE
MISO implemented a single interval approximation of the convex hull pricing to achieve a practical
approximation that incorporates fixed costs. The initial method developed in 2011 allocates startup
cost into individual intervals for a single interval implementation, and use a partial commitment
variable to smooth out the fixed costs in approximation to the convex hull as shown in Fig.1. This
approximation may result in more uplift and less efficient prices relative to a true convex hull price.
The re-formulation of the piecewise linear (PWL) incremental energy curve is used to improve the
approximation below.
Fig. 1 Convex hull of energy offer curve
Consider a single-interval ELMP model. For simplicity of presentation without affecting the idea of
the improved PWL formulation, reserves are not considered.
(1)
Subject to Power balance constraints, Transmission constraints, System wide reserve constraints and
zonal reserve constraints, and Resource level constraints including
≤ (2)
In this formulation, is the commitment variable for resource j. is the energy dispatch variable.
and are the minimum and maximum limits, respectively. Startup costs are amortized over the
minimum run time. is the allocated startup cost plus the non-load cost at interval t. ELMP solves
the problem as a linear programming (LP) relaxation by treating as continuous variables between 0
and 1 (i.e., binary relaxation).
4. 4
The incremental energy cost function is modeled as a monotonically non-decreasing piece-wise linear
function (i.e. convex PWL function). In [8][9], a special order type 2 (SOS2) model was introduced to
model this piece-wise linear (PWL) cost function . It has greatly improved the performance of
day-ahead unit commitment. The typical SOS2 PWL function can be formulated as follows:
Between pre-determined fixed points , define a set of nonnegative
continuous variables , , …, and the constraints:
=1 (3)
(4)
The incremental energy cost function is represented by:
(5)
The incremental energy offer is convex and satisfies:
. (6)
In this model, at most two consecutive variables in (3) can be nonzero for convex cost functions. The
piecewise linear incremental energy offer formulation in MISO’s ELMP was formulated differently
but is mathematically equivalent. However, considering constraints (3) and (4), and the fact that cost
curves always start at with , constraints (3)-(6) can be reformulated as:
(3a)
(4a)
(5a)
Note for some systems, may be non-zero, but this can be easily adjusted by moving to
no-load cost. Constraint (3a) links generation dispatch variables to commitment variables.
In [7], a convex primal formulation for convex hull pricing was introduced. It proves that a binary
relaxation approach can achieve convex hull pricing if (1) individual generator cost functions have
convex envelope formulations, and (2) individual generator constraints have convex hull formulations.
In [9], it is proven that the least cost function from LP relaxation of the MIP problem under constraints
(3a)-(5a) is the convex envelop of the original PWL cost function.
With this approach, the ELMP implemented by MISO, which is equivalent to (3)-(6), may be
improved to a convex envelope formulation (3a)-(5a). It can be illustrated by a simple example.
Consider a generator unit, G1, with three segments on its incremental energy offer: $1/MWh between
[0,30MW], $5/MWh between (30MW,50MW], and $9/MWh between (50MW, 65MW). Assume the
no load cost is $100/h, the minimum limit Pmin is 35MW, and maximum limit is 65MW. Assume
another generator unit, G2, has a $0/MWh energy offer with a dispatch range between 0MW and
60MW. The cost function is (0, 0) and the solid blue line in Fig. 2 is between 35MW and 65MW. It is
non-convex due to fixed costs and Pmin.
Figure 2 compares the LP relaxation solution under the convex envelope SOS2 PWL formulation (3a)-
(5a) (red line) and the traditional LP relaxation solution under formulation (3)-(6) (green line). As
shown in Table I, under the SOS2 formulation, the price below the minimum limit (35MW) is the cost
$155 at 35MW averaged over the 35MW range. Above 35MW, it overlaps with the original cost
curve. Together, the SOS2 and the original cost curves form the convex envelope of the original cost
function. The LP relaxation implemented in MISO for quick-start resources [6] as shown in the green
line and Table II is not as tight as the convex envelope formulation. The fixed cost is averaged over
the maximum limit 65MW under current ELMP implementation. When load is between 60MW and
90MW, the ELMP price from LP relaxation is $2.54/MWh under the current MISO implementation.
5. 5
The cost for G1 at its minimum limit of 35MW is not covered by this price, and as a result the
generator still requires a make whole payment. The price from the LP relaxation under the convex
envelope formulation is $4.43, and this price does cover the cost at 35MW for G1. When load is above
95MW, u1 is solved below 1 under current ELMP. This is because the no load cost is averaged over
the maximum limit of G1. The ELMP price is higher than the LMP derived from true marginal cost of
G1, even though the LMP can cover the unit’s total cost. Importantly, the ELMP price incentivizes G1
to deviate and move up from its dispatch target. With the convex envelope formulation, u1 is solved at
1 when load is above 95MW. ELMP equals to LMP, and there is no incentive for G1 to deviate from
its dispatch target.
Table I LP relaxation from convex envelope PWL formulation Table II LP relaxation from non-convex envelope PWL formulation
Total Load Objective
Shadow Price of
Power Balace
Equation
65 5 0.143 0.107 0.036 0 $22.14 $4.43
77.5 17.5 0.5 0.375 0.125 0 $77.50 $4.43
80 20 0.572 0.429 0.143 0 $88.57 $4.43
85 25 0.715 0.536 0.179 0 $110.71 $4.43
87.5 27.5 0.785 0.589 0.196 0 $121.79 $4.43
90 30 0.857 0.643 0.214 0 $132.86 $4.43
95 35 1 0.75 0.25 0 $155.00 $4.43
100 40 1 0.5 0.5 0 $180.00 $5.00
110 50 1 0 1 0 $230.00 $5.00
125 65 1 0 0 1 $365.00 $9.00
Total Load Objective
Shadow Price of
Power Balace
Equation
65 5 0.077 0.167 0 0 $12.69 $2.54
77.5 17.5 0.269 0.583 0 0 $44.42 $2.54
80 20 0.308 0.667 0 0 $50.77 $2.54
85 25 0.833 0.833 0 0 $63.46 $2.54
87.5 27.5 0.423 0.917 0 0 $69.81 $2.54
90 30 0.462 1 0 0 $76.15 $6.54
95 35 0.538 0.75 0.25 0 $108.85 $6.54
100 40 0.615 0.5 0.5 0 $141.54 $6.54
110 50 0.769 0 1 0 $206.92 $6.54
125 65 1 0 0 1 $365.00 $10.53
Fig. 2 Comparison of LP Relaxation Total Cost under Convex Envelope and
Non-Convex Envelope PWL Formulation
The convex envelope formulation is further solved for practical large-scale system. We prototyped the
enhancement in the ELMP engine and simulated the pricing impact against 1,152 production cases
representing various system conditions. As expected, simulation results show that ex-post prices under
the convex envelope formulation can be both higher and lower relative to ELMP II. The average daily
price difference varies from $0/MWh to a $0.08/MWh reduction. A close review of the cases with
price differences showed that when prices decrease, there were usually fast-start resources partially
committed above the tangent point ( = 1), while when prices increase, there were usually fast-start
resources partially committed below the tangent point ( < 1). Uplift payments are evaluated as the
difference between maximum profit and actual profit, including both make-whole payments and lost
opportunity costs:
+ - (7)
– - - (8)
Where and are the ex-post ELMP energy and reserve prices, and
are the ex-ante LMP energy and reserve prices, and are ex-post energy and reserve
clearing MW, and are ex-ante energy and reserve clearing MW, and
6. 6
are ex-post and ex-ante reserve costs. As expected, uplift payments trended down under
the convex envelope formulation.
Figure 3. Price Difference between Convex Envelope and Production ELMP over the 5-minute
Intervals of Sampled Days
Table III Uplift Reduction under the Convex Envelope Formulation as
Compared to ELMP II Production
Sample Day 1 2 3 4
Uplift reduction 0 -$814 -$116 -$1,112
3. IMPROVEMENT ON REGULATION COMMITMENT LOGIC
In reality, a resource’s dispatch range can be narrower when it is committed to provide regulating
reserve. In MISO practice, a regulation commitment variable is used for each regulation-qualified
resource in SCUC engine to decide whether a unit is committed for regulation.
≤ (9)
Where and are binary variables in SCUC and is for regulation commitment. A unit can
have different operational limits depending on whether or not it is on regulation (i.e. ≤ and
≤ ). This operating characteristic is further illustrated below.
(10)
Currently after the SCUC run, binary variables are fixed at SCUC solved values, either at 0 or at 1,
within SCED run. If is fixed at 0 for a resource, it cannot clear for regulation, i.e., =0.
Theoretically, SCED and SCUC should have identical models except that in SCED, all binary
variables are fixed at SCUC solution. Clearing results between the two models should be identical. By
fixing regulation commitment binary variables at SCUC solution values, total capacity is guaranteed
not to drop in SCED clearing. Nevertheless, in practice, there can be modeling differences such as in
in ELMP pricing where we relax EconMin, and system conditions changes such as load or wind
forecast errors. With regulation committed resources fixed at the SCUC solution, SCED regulation
clearing has been limited to “REG-Commit” resources (usually a small pool of about 30-40 units).
This logic can be too conservative, since the capacity from resources with equivalent regulating and
economic limits is not impacted by regulation selection, i.e., there is a degeneracy when
are both optimal solutions. A set of resources has the same economic and regulating dispatch range,
7. 7
i.e., = and = . These resources could be committed for energy but not economic to
clear regulation, i.e., with in SCUC optimal solution. For those resources, the SCUC
solution is not impacted by and the SCUC objective is identical under (11) or (12) when
in SCUC optimal solution:
= , = (11)
= , = (12)
By setting to 1 on resources with equivalent regulating and economic limits, SCED will continue
to clear so long as the underlying SCED and SCUC models remain consistent. When
conditions between SCUC and SCED differ, this difference may cause regulation clearing on this set
of resources to diverge. By fixing these resources at , the SCED solution is unnecessarily
restrictive, and could occasionally cause artificial regulation MCP spikes when the system has
adequate resources to satisfy (12) in SCUC and to clear regulation on these resources in SCED due to
the slight difference between SCUC and SCED.
This problem occurred more frequently under ELMP. In the ELMP pricing run, the binary variables of
quick-start resources are relaxed to solve binary relaxation and derive an approximate ELMP. In the
initial ELMP implementation, if a resource has in SCUC, it was not able to clear regulation
under ELMP. Quick-start-resource commitment variables are allowed to be solved at fractional values
in ELMP. The range for clearing is . If in ELMP
solution, the range for clearing in ELMP is less than the range in SCED. Hence,
regulation MCP can be higher under ELMP than under SCED. These higher regulation prices usually
represent a desired outcome when the price increases reflect the fixed commitment cost for regulation.
However, price spikes may not be appropriate if the system actually has a surplus of regulation
qualified resources not committed for regulation in SCUC and are economic for regulation under the
higher price. Under the convex hull primal formulation approach in [8], both and should be
relaxed in the LP relaxation under multi-interval SCUC formulation. Under the current single interval
ELMP implementation, only fast-start resources are allowed to be partially commitment (i.e., allowed
to have solved as a continuous variable). Regulation commitment variables of other resources
are fixed at the SCUC solution. For resources with = and = , setting to 1 allows these
resources to clear in SCED and ELMP run when economic. The resulting prices can more
accurately reflect online regulation availability and are closer to convex hull pricing.
The enhanced regulation clearing logic is thus developed to make the set of units satisfying (12)
eligible to clear regulation in SCED and ELMP, i.e., set =1 to allow clearing regulation, if =1,
= and = for regulation qualified resource j.
The new logic has been studied in production day-ahead cases to examine impacts on dispatch and
pricing. Pricing results ($/MWh) shown in Fig. 4 for six sample days verified that the artificial
RegMCP spikes under “Orig RegMCP” were effectively eliminated under the new regulation clearing
logic (“New RegMCP”). The New RegMCP from the ELMP run is closer to ex-ante SCED RegMCP.
Energy prices and RegMCP in other days were mostly unchanged.
The improved regulation clearing logic is further used in the real-time market to replace a manual
regulation management tool that operators used to manually designate units as “REG-Commit” for
regulation clearing as system conditions change in real-time. In addition, the regulation clearing
process is more complicated in real-time. For instance, a unit offers three ramp rates in real-time: an
up ramp rate, a down ramp rate, and a bi-directional ramp rate. The bi-directional ramp rate (
up/down ramp rate) is used when the unit is designated as “REG-Commit”. Another complication
8. 8
involves the 5 minute real-time interval versus an hourly regulation selection process. The improved
logic further considers ramp rate when adding units on regulation to the same capacity and flexibility
as seen by SCUC. The enhanced logic was tested on the production MISO system, and simulation
results demonstrate that more units are designated as “REG-Commit” under the enhanced logic,
capturing most of the units that are currently manually designated as “REG-Commit” by the regulation
management tool. This improvement also addressed stranded capacity or flexibility when units with
narrower dispatch range or lower ramp rates are inappropriately designated as “REG-Commit”. As a
result of the more efficient regulation clearing logic, system wide production costs were reduced,
especially during regulation constrained intervals. Average production cost was reduced by
$1.8k~$20k for the simulated days.
Fig.4. Comparison of DA RegMCP
4. ELMP RAMP MODELING
Under the Fast Start Pricing scheme, fast-start resources can be partially committed instead of using a
pure on / off decision, so that fast-start resource can set prices in the ex-post process. A variation also
allows relaxing the minimum generation limit to zero for fast-start resources. Nevertheless even with
this method, some fast-start resources may not be able to set prices if constrained by ramp. The ramp
modeling under ELMP thus needs to be improved for fast-start resources to more effectively set
prices. In particular, it is important to differentiate between two sets of ramp rate constraints: 1) Inter-
temporal Ramp, and 2) Startup / Shut-Down Ramp. Typically, a unit is ramp-constrained across
intervals when it is online for dispatch.
(13)
During startup or shutdown periods, a different ramp limit is used to allow the unit to ramp from 0 to
EconMin or EconMin to 0, where in-between the unit is in the starting or shut down process and is not
for the RTO’s dispatch. Specifically, when an online fast-start resource is partially committed toward
zero, it is essentially a shutdown and the ramp constraint for shut down is:
(14)
or combining (1) and (2), the ramp down constraint can be uniformly formulated as:
(15)
9. 9
The lumpiness or non-convexity thus arises associated with the shutdown intervals, and the fast-start
resource would not be able to set price if constrained by the normal ramp limit from being further
dispatched down below EconMin. The nature of inter-temporal ramping in constraint (13) is different
from the lumpiness issue in constraint (15), and the ELMP ramp modeling needs to be carefully
developed to avoid any unintended consequences. Inappropriate relaxation of ramp-down limits may
result in unnecessary divergence between dispatch and pricing. Solution options are explored to
address the shutdown ramp issue without inadvertently affecting the inter-temporal ramp.
Option 1: Utilizing Partial Commitment Variable
ELMP allows fast-start resources to relax their dispatch minimums to zero by allowing the partial
commitment of such resources for pricing purposes. That is, instead of an on (1) or off (0)
commitment decision in reality, ELMP allows a fast-start resource to be partially committed between
0 and 1. When a fast-start resource is partially committed down from Oni,t-1 to Oni,t, it can be
interpreted as that the resource is shut down by a fraction of (Oni,t-1 - Oni,t), and has a fraction of Oni,t
remaining committed. Therefore, the shutdown ramp limit can be used for the shutdown fraction, and
the normal limit can be used for the remaining fraction. That is, by re-writing ramp down constraint
(3), it can be obtained that:
(16)
Compared to the ramp down constraint (13) that is used in the current ELMP model, the ramp limit
can be relaxed to larger value that accounts for the shutdown. For example, a fast-start resource can
generate between 100MW to 200MW and its Ramp Rate is 10MW/min. Under the existing ramp
model (13), it can only ramp down 50MW over a 5-minute interval and will be ramp constrained even
though EconMin is relaxed to 0. By using (16), the ex-post pricing can further dispatch the unit down
by pushing the partial commitment variable Oni,t toward 0 so that the ramp limit is pushed toward the
larger value of . The costs associated with the dispatch and partial commitment will
be able to eligible to participate in price setting. In addition, if the resource is ramping normally
between two consecutive online intervals, i.e., Oni,t toward 1, the ramp limit will be pushed toward
.
The shutdown ramp is usually a larger limit than normal ramp to ensure that the unit can be dispatched
down from anywhere to zero in shutdown periods. In the current unit commitment problem, it is set at
EconMax. However, real time dispatch intervals are much shorter. Assuming a large shutdown ramp
may cause significant divergence between ex-ante and ex-post even under the scenario when fixed
cost is near zero. Other possibilities include or max {EconMin, }. Further studies are
needed to determine the appropriate value for shutdown ramp.
Another challenge is related to the single-interval pricing model. To calculate price at t in Real-Time,
in (16) will be a known parameter based on the latest resource output. If the resource is
partially committed or dispatched down in ex post pricing to , in the next interval t+1 the unit
will be ramping from which can be different from . For example, a unit that has low
incremental energy cost and high no-load cost may be dispatched at EconMax. The ex-post pricing
would try to dispatch the unit down toward zero at t, but in the next interval it will have to ramp from
EconMax again. This can affect a unit being dispatched down to zero in ex post pricing if the down
ramping process takes more than one interval. A large can force the unit ramp to
zero in one interval but may result in significant deviation if it takes several intervals to ramp the unit
to zero in ex ante. Further studies are needed to understand the pricing impact in coordination with the
value selection of .
Option 2: Utilizing information from ex ante
This option is to leverage the information from ex ante to detect the issue when the ramp-down limit
should be relaxed. For example,
10. 10
1) If the dispatch ex ante is close to EconMin, then the ramp-down limit may be relaxed to shutdown
ramp to allow the unit to be dispatched down to zero when EconMin is relaxed to zero in ex post.
Nevertheless, this approach may be limited in its effectiveness if a resource is shut down from a
dispatch level above EconMin. For example, resources with high start-up and no-load costs but low
incremental cost may be dispatched well above EconMin in ex ante where commitment costs are not
considered, but could be dispatched toward zero when those costs are considered in ex-post pricing.
2) If the ramp rate constraint is binding in ex ante, it indicates an inter-temporal ramping situation and
ramp rate may not be relaxed in ex post.
MISO continues to study this problem. The multi-interval pricing research could provide guidelines on
the appropriate ramp modeling for current single-interval ELMP implementation.
5. CONCLUSION
This paper introduces two improvements for the single-interval approximation of ELMP. The first
improvement is a tighter formulation of the PWL energy offer curve. The second improvement is
better handling of regulation commitment. Both enhancements further improve price efficiency under
the single-interval approximation of ELMP and bring the practical implementation closer to the ELMP
theoretical ideal. The on-going work in addressing pricing complication caused by ramp constraints is
also introduced.
BIBLIOGRAPHY
[1] FERC Price Formation proceedings AD14-14, 2014.
https://www.ferc.gov/industries/electric/indus-act/rto/AD14-14-000.pdf
[2] PJM Price Formation proposal, 2017, http://www.pjm.com/-/media/library/reports-
notices/special-reports/20171115-proposed-enhancements-to-energy-price-formation.ashx
[3] U.S. Department of Energy Staff Report to the Secretary on Electricity Markets and Reliability,
DOE, August 2017.
[4] P. Gribik, W. Hogan and S. Pope, “Market-clearing electricity pices and energy uplift,”, Harvard
Univ., Cambridge, MA, USA, working paper, 2007
[5] C. Wang, P. B. Luh, P. Gribik, T. Peng, and L. Zhang, “Commitment cost allocation of fast-start
units for approximate extended locational marginal prices,” IEEE Trans. Power Syst., vol. 31,
no. 6, pp. 4176–4184, Nov. 2016
[6] MISO tariff Schedule 29A “ELMP for Energy and Operating Reserve Market: Ex-Post Pricing
Formulations”. online available: https://www.misoenergy.org/Library/Tariff/Pages/Tariff.aspx
[7] B. Hua and R. Baldick, “A Convex Primal Formulation for Convex Hull Pricing”, IEEE Trans.
Power Syst., vol. 32, no. 5, pp. 3814-3823, Sept. 2017.
[8] FERC Technical Conference “Improving Market Clearing Software Performance to Meet
Existing and Future Challenges – MISO’s Perspective,” Y. Chen, J. Bladen, A. Hoyt, D.
Savageau, R. Merring, June 2016 https://www.ferc.gov/CalendarFiles/20160804133957-3%20-
%20MISO%20FERC_M1_Chen_062016.pdf
[9] Y. Chen and F. Wang, “MIP Formulation Improvement for Large Scale Security Constrained
Unit Commitment with Configuration based Combined Cycle Modeling”, Electric Power
System Research, Vol. 148, July 2017
[10] FERC Technical Conference “Experience and Future R&D on Improving MISO DA Market
Clearing Software Performance,” Y. Chen, D. Savageau, F. Wang, R. Merring, J. Li, J.
Harrison, and J. Bladen, June 2017 https://www.ferc.gov/CalendarFiles/20170623123549-
M1_Chen.pdf?csrt=18151806463483539378.