A Simultaneous Perturbation Approach for Solving Economic Dispatch Problems With Emission, Storage, and Network Constraints

Xia, Yu; Ghiocel, Scott G.; Dotta, Daniel; Shawhan, Daniel L.; Kindle, Andrew; Chow, Joe H.

doi:10.1109/tsg.2013.2263111

Cited by 25 publications

(15 citation statements)

References 27 publications

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“…In other words, the ED problem typically optimizes a predefined objective function, such as minimizing the total generation cost and transmission loss. Many factors, such as non-smooth or non-convex objective functions considering valve-point effects [1]- [4], practical operational limits such as generator ramp-rates, system reserve requirements [5], penetration of renewable resources [6]- [8], as well as emission, storage, and network constraints [9], complicate the ED problem.…”

Section: Introductionmentioning

confidence: 99%

Game-Theoretic Formulation of Power Dispatch With Guaranteed Convergence and Prioritized BestResponse

Grijalva

Harley

2015

IEEE Trans. Sustain. Energy

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This paper formulates and solves the economic power dispatch (ED) problem with practical operation constraints using potential games. Each generator operates as an independent player in a self-optimizing manner with marginal contribution utility functions to minimize the total generation cost. The proposed distributed formulation converts inequality constraints into feasible action sets, incorporates equality constraints by penalty functions, and extends to practical cases that exhibit non-convex or non-smooth objective functions. Two learning algorithms with guaranteed convergence to Nash equilibria and/or optima are applied to solve the proposed formulation. How generators react as best responses to others is analyzed to capture the reasoning of operations. As a numerical example, the solutions obtained using the proposed ED method in a benchmark system are analyzed. Examples are provided to emphasize how priority for renewable sources are incorporated.

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Section: Introductionmentioning

confidence: 99%

Game-Theoretic Formulation of Power Dispatch With Guaranteed Convergence and Prioritized BestResponse

Grijalva

Harley

2015

IEEE Trans. Sustain. Energy

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“…Thatte et al [42] used robust optimisation to determine the optimal bidding strategy for ESS performing arbitrage in the day-ahead market. Xia et al [43] showed by simultaneous perturbation method that an ESS in unit commitment can be used for energy and emissions arbitrage. Mohsenian-Rad [44] considered using distributed ESS for price arbitrage in a coordinated way so some ESS can be buying while others are selling electricity.…”

Section: Market Arbitragementioning

confidence: 99%

Operating and Investment Models for Energy Storage Systems

2020

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In the context of climate changes and the rapid growth of energy consumption, intermittent renewable energy sources (RES) are being predominantly installed in power systems. It has been largely elucidated that challenges that RES present to the system can be mitigated with energy storage systems (ESS). However, besides providing flexibility to intermittent RES, ESS have other sources of revenue, such as price arbitrage in the markets, balancing services, and reducing the cost of electricity procurement to end consumers. In order to operate the ESS in the most profitable way, it is often necessary to make optimal siting and sizing decisions, and to determine optimal ways for the ESS to participate in a variety of energy and ancillary service markets. As a result, many publications on ESS models with various goals and operating environments are available. This paper aims at presenting the results of these papers in a structured way. A standard ESS model is first outlined, and that is followed by a literature review on operational and investment ESS models at the transmission and distribution levels. Both the price taking and price making models are elaborated on and presented in detail. Based on the examined body of work, the paper is concluded with recommendations for future research paths in the analysis of ESS.

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“…System losses are considered in the balance constraints. The system losses can be approximated as a quadratic function of the generation [7] P loss…”

Section: ) Power Balance Constraintsmentioning

confidence: 99%

“…The LR methodology is among the most successful approaches for solving large-scale UC problems. It temporarily relaxes the coupling constraints, for example, load balance constraint and intertemporal constraints, using Lagrange multipliers [7]. The original problem is then decoupled into several smaller and easierto-solve subproblems.…”

Section: Introductionmentioning

confidence: 99%

A two-stage MIP based optimization framework for unit commitment and energy pricing

Xia

Marwali²,

Chow

2015

2015 IEEE Power and Energy Conference at Illinois (PECI)

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Power system unit commitment (UC) is a nonconvex NP-complete problem, which is very complex to solve on a large scale. Solution methods for the UC problem have been explored, with Lagrangian relaxation (LR) being one of the most popular approaches in practice. The significant reduction in numerical solution times of commercial Mixed Integer Programming (MIP) solvers makes transitioning from LR to MIP possible. This paper presents a MIP based two-stage optimization approach for solving the UC problem as well as the energy price. The proposed framework has been implemented using MATLAB and tested on two test power systems. Index Terms-Mixed integer programming, Lagrangian relaxation, unit commitment, energy pricing.978-1-4799-7949-3/15/$31.00 ©2015 IEEE

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A Simultaneous Perturbation Approach for Solving Economic Dispatch Problems With Emission, Storage, and Network Constraints

Cited by 25 publications

References 27 publications

Game-Theoretic Formulation of Power Dispatch With Guaranteed Convergence and Prioritized BestResponse

Game-Theoretic Formulation of Power Dispatch With Guaranteed Convergence and Prioritized BestResponse

Operating and Investment Models for Energy Storage Systems

A two-stage MIP based optimization framework for unit commitment and energy pricing

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