Energy storage operation in the day-ahead electricity market

Pandžić, Hrvoje; Kuzle, Igor

doi:10.1109/eem.2015.7216754

Cited by 21 publications

(16 citation statements)

References 13 publications

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“…Both of the proposed models are tested on a modernized IEEE RTS-96 system, as shown in Figure 1 [19], with wind power plants and ESS devices [23,25]. The case study network consists of three areas, where the first area contains nine wind farms (w1-w9) with overall capacity of 3900 MW; the second area contains six wind farms (w10-w16) with overall capacity of 2400 MW; and the third area contains three wind farms (w17-w19) with overall capacity of 300 MW.…”

Section: Case Study and Resultsmentioning

confidence: 99%

“…Objective Function (19) contains a non-linear product of the LMP variable α n (t) and charging and discharging variables. We use some of the lower-level problem Karush-Kuhn-Tucker (KKT) conditions to rewrite the objective function in an equivalent linear way, similarly as in [23]. Since this linearization requires additional constraints, auxiliary variables φ chb (t) and φ disb (t) are used to implement the big M linearization method.…”

Section: Of 16mentioning

confidence: 99%

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Assessment of Energy Storage Operation in Vertically Integrated Utility and Electricity Market

Luburić

Pandžić

Plavšić³

2017

Energies

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The aim of this paper is to compare the operational pattern of an energy storage system (ESS) in a vertically-integrated utility and in a deregulated market environment for different levels of wind integration. As the main feature of a vertically-integrated utility is a centralized decision-making process, all of the investment and operating decisions are made with a single goal of minimizing the overall system operating costs. As a result, an ESS in such an environment is operated in a way that is optimal for the overall system economics. On the other hand, the system operator in a deregulated market has less power over the system resources, and commitment and dispatch decisions are a result of the market clearing procedure. In this setting, the ESS owner aims at maximizing its profit, which might not be in line with minimizing overall system operating costs or maximizing social welfare. To compare the ESS operation in these two environments, we analyze the storage operation in two different settings. The first one is a standard unit commitment model with the addition of centrally-controlled storage. The second one is a bilevel model, where the upper level is a coordinated ESS profit maximization problem, while the lower level a simulated market clearing. The case study is performed on a standardized IEEE RTS-96 system. The results show a reduction in the generation dispatch cost, online generation capacity and wind curtailment for both models. Moreover, ESS significantly increases social welfare in the market-based environment.

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Section: Case Study and Resultsmentioning

confidence: 99%

Section: Of 16mentioning

confidence: 99%

Assessment of Energy Storage Operation in Vertically Integrated Utility and Electricity Market

Luburić

Pandžić

Plavšić³

2017

Energies

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“…Losses materialise because of the self-discharge of the ESS over time. When modelling the ESS short-term operation, such as participation in the day-ahead or intraday markets, this term is often ignored [16,17], but it is taken into account in the long-term models [18].…”

Section: Energy Storage System Modelsmentioning

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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“…We study the dispatch and size of storage to maximize its profit. Aside from [3], [4], [5], the majority of the existing literature [6]- [10] focuses on energy storage systems as price-takers in the current market. Due to the small size and amount of storage considered, the clearing price is not affected by the operation of the storage.…”

Section: Introductionmentioning

confidence: 99%

Optimal scheduling of large-scale price-maker energy storage

Deboever

Grijalva

2016

2016 IEEE Power and Energy Conference at Illinois (PECI)

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This paper describes optimal scheduling of largescale price-maker energy storage and revenue maximization for storage owners. With the penetration of storage eminent, the sizing and scheduling of the storage system must be studied to fully benefit from this enabling technology. Energy storage systems are attractive for providing various services to the electrical grid. Energy arbitrage, being one of them, enables electricity demand to be temporally decoupled from generation. While small-scale storage systems should be considered as price-taker, large-scale aggregated storage can affect the price by increasing and decreasing the required power on the generation side. Purchasing and selling power at the spot price can create a revenue based on the power transferred and the difference in price between two periods. The paper derives a dispatch schedule based on the current load and the forecasted average demand to maximize the storage owner's revenue and discusses the effect of size on price making mechanisms.Index Terms-Energy Arbitrage, Energy Storage Systems, Price-maker. I. NOMENCLATURE, , Quadratic fuel cost function coefficients ESS Energy Storage Systems Linear ESS cost function coefficient LMP Localized Marginal Prices Demand power at period t Clearing price at period t Power generated at period t Power provided by the ESS at period t ROI Return-On-Investment SO System Operators SoC State-of-Charge II. INTRODUCTIONnergy Storage Systems (ESSs) will inevitably have a large impact on the electrical infrastructure whether is it to support non-dispatchable resources or to reduce the overall cost of energy. The current state of operation of the electrical grid in the US requires real-time balancing of the production and consumption of electricity. This characteristic of the grid creates challenges in the daily operation and increases the overall cost of electricity. ESSs, whether it is centralized or distributed, can allow the demand to be decoupled from the generation regarding instantaneous balancing requirements. Commonly known as energy arbitrage, temporal energy shifting can provide either peak-shaving or load-leveling capabilities dependent on the scale and objective of the system. Additionally, ESSs can provide a number of benefits such as different ancillary services or renewable integration support. The authors in [1] detail the various applications of ESSs both at the transmission and distribution level. One reason why ESS have not yet been largely introduced is the high capital cost of some storage technologies [2]. Thus, it becomes important to study various options for daily operation of ESSs and the revenue streams it creates, which may results in grid benefits and attractive return-on-investment to energy storage.In this paper, we discuss the scheduling of energy storage systems to reduce the global cost of energy and provide a revenue for storage-owners through energy arbitrage. Furthermore, large-scale ESSs is considered in this paper as price-maker. We study the dispatch and size of storage to max...

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Energy storage operation in the day-ahead electricity market

Cited by 21 publications

References 13 publications

Assessment of Energy Storage Operation in Vertically Integrated Utility and Electricity Market

Assessment of Energy Storage Operation in Vertically Integrated Utility and Electricity Market

Operating and Investment Models for Energy Storage Systems

Optimal scheduling of large-scale price-maker energy storage

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