Operation Scheduling of Battery Storage Systems in Joint Energy and Ancillary Services Markets

Kazemi, Mostafa; Zareipour, Hamidreza; Amjady, Nima; Rosehart, William; Ehsan, Mehdi

doi:10.1109/tste.2017.2706563

Cited by 195 publications

(83 citation statements)

References 29 publications

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“…References [16] and [17], besides the formulation of the scheduling problem, describe the real-time control to implement the proposed strategies. References [11], [12], [17] propose a robust optimization approach to deal with uncertainties related to price signals and reserve deployment. Finally [11] analyses how providing multiple services simultaneously affects the BESS life time.…”

Section: B Literature Surveymentioning

confidence: 99%

“…• the formulation of a complete algorithmic toolchain to control a BESS in order to provide multiple services simultaneously. This framework differs from the existing literature in: i) the generic formulation of the scheduling problem, ii) the technical rather than revenue-driven control objective, iii) the consideration of the stochastic behaviour of the services deployment (due to the uncertainties in the forecast of the feeder prosumption as well as in the energy needed to perform PFR) and exploitation of robust optimization techniques to hedge against uncertainty and achieve reliable real-time operation (similarly to [12]). • the formulation of a control strategy to manage a BESS connected within a MV feeder, together with a set of heterogeneous resources (loads and PV generations), in order to dispatch the operation of the same feeder and exploit the remaining capacity to provide PFR.…”

Section: Paper's Contributionsmentioning

confidence: 99%

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Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services

Namor

Sossan

Cherkaoui

et al. 2019

IEEE Trans. Smart Grid

113

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In this paper, we propose a control framework for a battery energy storage system to provide simultaneously multiple services to the electrical grid. The objective is to maximise the battery exploitation from these services in the presence of uncertainty (load, stochastic distributed generation, grid frequency). The framework is structured in two phases. In a period-ahead phase, we solve an optimization problem that allocates the battery power and energy budgets to the different services. In the subsequent real-time phase the control set-points for the deployment of such services are calculated separately and superimposed. The control framework is first formulated in a general way and then casted in the problem of providing dispatchability of a medium voltage feeder in conjunction to primary frequency control. The performance of the proposed framework are validated by simulations and real-scale experiments, performed with a grid-connected 560 kWh/720 kVA Li-ion battery energy storage system.

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Section: B Literature Surveymentioning

confidence: 99%

Section: Paper's Contributionsmentioning

confidence: 99%

Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services

Namor

Sossan

Cherkaoui

et al. 2019

IEEE Trans. Smart Grid

113

View full text Add to dashboard Cite

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“…The base forecasted wind speed is a metered value of three sites in Bishop Clerks, Paxton and Bland Ford from January 1st to 7th 2013 . Two 1.5 MW wind turbine generators are assumed to be installed in each site.…”

Section: Case Studymentioning

confidence: 99%

“…In the following study, there are three wind power plants (WPP), two PV power plants, a GTG and load available to aggregate. Four uncertain variables The base forecasted wind speed is a metered value of three sites in Bishop Clerks, Paxton and Bland Ford from January 1st to 7th 2013 [21]. Two 1.5 MW wind turbine generators are assumed to be installed in each site.…”

Section: Parameters and Settingsmentioning

confidence: 99%

An optimum bidding strategy of CVPP by interval optimization

Liu

Jiang

2018

IEEJ Transactions Elec Engng

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Virtual power plant (VPP) aggregates distributed energy resources (DER) as a single profile to overcome scale disadvantage and dispersed characteristics. A commercial virtual power plant (CVPP) especially operates to improve the power‐market involvement. In this article, the CVPP medium‐term bidding strategy is optimized to obtain the profit interval mostly preferred by the CVPP's decision maker. Because the medium‐term forecast of renewable power generation and day ahead (DA) market price is far more difficult than short term, interval optimization is proposed, which does not require uncertain variable‐distribution assumption. The pessimism degree is introduced to quantify the decision maker's risk acceptance and compare intervals so as to solve the interval‐optimization model. Within the interval‐optimization framework, the bidding strategy between the bilateral contract (BC) and the DA market is figured out. A combined DA market deal and gas turbine generator strategy is proposed to cope with power shortage. Finally, an in‐depth sensitivity analysis is applied, and the characteristics and caution of interval optimization are deduced. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…Optimal charging/discharging schedule based on the battery's aging cost is achieved through an online optimal control algorithm for the operation of a BESS in regulation only [8] or regulation and energy markets [9]. Uncertainty in market price prediction and amount of energy deployment for the BESS strategic biding problem are handled by robust optimization approach in [10], and this work is expanded by adding aging cost to it in [11]. This paper falls into the second category of existing literature, where the BESS is modeled as a price-maker due to its size and specific operation capabilities [12]- [15].…”

Section: Introductionmentioning

confidence: 99%

Optimal Participation of Price-Maker Battery Energy Storage Systems in Energy, Reserve and Pay as Performance Regulation Markets

Khalilisenobari¹,

Wu²

2019

2019 North American Power Symposium (NAPS)

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Motivated by the need of assessing the optimal allocation of battery energy storage services across various markets and the corresponding impact on market operations, an optimization framework is proposed in this work to coordinate the operation of an independent utility-scale price-maker battery energy storage system (BESS) in the energy, spinning reserve and performance-based regulation markets. The entire problem is formulated as a bi-level optimization process, where the structure of all markets is modeled considering the joint operation limits. The strategic bidding behavior of a price-maker BESS in a pay as performance regulation market is investigated. Additionally, a specific approach is introduced for modeling automatic generation control (AGC) signals in the optimization. Although the formulated problem is non-linear, it is converted to mixed-integer linear programming (MILP) to find the optimum solution. The proposed framework is evaluated using test case scenarios created from real-world market data. Case study results show the impact of BESS's price-making behavior on the joint operation of energy, reserve, and regulation markets.Index Terms-Battery energy storage system (BESS), bidding strategy, price-maker, performance-based regulation market, bilevel optimization, mixed integer linear programming

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Operation Scheduling of Battery Storage Systems in Joint Energy and Ancillary Services Markets

Cited by 195 publications

References 29 publications

Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services

Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services

An optimum bidding strategy of CVPP by interval optimization

Optimal Participation of Price-Maker Battery Energy Storage Systems in Energy, Reserve and Pay as Performance Regulation Markets

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