Participation of an Energy Storage Aggregator in Electricity Markets

Contreras-Ocaña, Jesus E.; Ortega‐Vazquez, Miguel A.; Zhang, Baosen

doi:10.1109/tsg.2017.2736787

Cited by 76 publications

(22 citation statements)

References 26 publications

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“…Parvania et al [63] investigated how decentralised ESSs scheduled by an aggregator influence the transmission grid and concluded that this way of scheduling might be prone to rescheduling after the power flows through the network are realised. Contreras-Ocaña et al [78] modelled interactions between an aggregator and ESS units under its control and between an aggregator and a wholesale market. The interaction between the aggregator and the ESS was modelled as a Nash bargaining game.…”

Section: Distribution System-level Essmentioning

confidence: 99%

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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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Section: Distribution System-level Essmentioning

confidence: 99%

“…Planning Solving Technique [20,22,23,30,43,46,[49][50][51]53,54,57,59,73,74,78,[88][89][90]105] [ 119,125,130,156] LP [1,16-18,20,25,32,33,35,44,47,50,52, 56-58,60,61,63,65,66,70-72,76,79-83, 86,92,94-96,100-102,104,106,107] [ 14,28,[109][110][111][112][113][114][115]117,121,122,126,127,131,[133][134][135][136][137]139,148,149,155,156,<...>…”

Section: Operationmentioning

confidence: 99%

Operating and Investment Models for Energy Storage Systems

2020

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“…The conversion from per-unit value to known value should meet Equation (4). If P t wpre is the predicted wind power output (known value) at time t, moment σ t w should meet Equation (5). Therefore, the prediction error should be normally distributed according to Equation (6).…”

Section: System Uncertainly Analysismentioning

confidence: 99%

“…However, the individual participation of DES systems at the system level would not be possible for two reasons: (1) their individual capacity is below the required minimum to participate in the day-ahead (DA) market and balancing market, and (2) the scheduling organization cannot easily obtain DES systems' aggregation information and it would be difficult to manage. The distributed energy storage aggregator (DESA) [5] adopts advanced communication and control technologies to aggregate and control DES systems in the distribution network. In this way, it can form a large-energy and high-power schedulable resource pool.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling Strategies of Distributed Energy Storage Aggregator in Energy and Reserve Markets Considering Wind Power Uncertainties

Jia

Wang

et al. 2018

Energies

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With continuous technological improvement and economic development of energy storage, distributed energy storage (DES) will be widely connected to the distribution network. If fragmented DES systems are aggregated to form a distributed energy storage aggregator (DESA), the DESA will have great potential to participate in the day-ahead energy and reserve market and the balancing market. The DESA could act as a mediator between the market and DES consumers, enabling beneficial coordination for DES owners and power systems. This paper presents a bilevel optimization model for DESAs in the energy and reserve market under wind power uncertainties. In the lower-level problem, generating companies, wind power plants (WPP), and DESAs are optimized for scheduling day-ahead (DA) energy and the reserve market. In the upper-level problem, operational strategies for DES systems and DESAs are designed to deal with wind power uncertainties in the balancing market. The DESA splits its resources between the energy and reserve markets so that it can reduce total power system consumption, and mutual profit for the system and end customers is achieved. This model is formulated as a mixed-integer linear programming (MILP) program, which can be solved with commercial software. The validity of the bilevel optimization model is verified by the eight-node test transmission system and IEEE-33 bus distribution system.

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“…Different from above referred research, some literatures grasped the dynamic interaction characteristics of market participants in the process of strategy optimization and then introduced game theory into the DR mechanism [19][20][21][22]. Specifically, some literatures focused on the bidding strategy optimization of DR aggregator with game theory [23][24][25][26]. For example, research [24] employed non-cooperative game approach to analyze the bidding strategies of load aggregators in the day-ahead market.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Game-Theoretic Bidding Optimization for Aggregators Considering the Breach of Demand Response Resource

Liu

Gao

2019

Applied Sciences

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Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse to be controlled by aggregator due to the uncertainty factors of electricity consumption. Therefore, in this paper, community operator (i.e., DR aggregator) is proposed to equip auxiliary equipment, such as energy storage and gas boiler, to compensate for power shortage caused by users' breach behavior. DR aggregated resource with different auxiliary equipment will have different characteristics, such as breach rate of DR resource. In the proposed DR framework, for selling the aggregated resource, community operator has to compete the market share with other operators in day-ahead DR market. In the competition, each operator will try its best to make the optimal bidding strategy by knowing as much information of its opponents as possible. But, some information of community operator (e.g., DR resource's characteristic) belongs to privacy information, which is unknown to other operators. Accordingly, this paper focuses on the application of incomplete information game-theoretic framework to model the competition among community operators in DR bidding market. To optimize bidding strategy for the high profit with incomplete information, a Bayesian game approach is formulated. And, an effective iterative algorithm is also presented to search the equilibrium for the proposed Bayesian game model. Finally, a case study is performed to show the effectiveness of the proposed framework and Bayesian game approach.

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Participation of an Energy Storage Aggregator in Electricity Markets

Cited by 76 publications

References 26 publications

Operating and Investment Models for Energy Storage Systems

Operating and Investment Models for Energy Storage Systems

Optimal Scheduling Strategies of Distributed Energy Storage Aggregator in Energy and Reserve Markets Considering Wind Power Uncertainties

Bayesian Game-Theoretic Bidding Optimization for Aggregators Considering the Breach of Demand Response Resource

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