Optimal allocation of energy storage in a co-optimized electricity market: Benefits assessment and deriving indicators for economic storage ventures

Krishnan, Venkat; Das, Trishna

doi:10.1016/j.energy.2014.12.016

Cited by 64 publications

(27 citation statements)

References 34 publications

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“…The operation of a storage system is optimized, by minimizing the total operation costs in the network, to facilitate the integration of intermittent renewable resources in power systems in [8]. Minimum (operational/installation) cost storage allocation problem for renewable integrated power systems is studied in [9]- [11] under deterministic wind models, and in [12] under a stochastic wind model. The minimum-cost storage allocation problem is studied in a bi-level problem in [13], [14], with the upper and lower levels optimizing the allocation and the operation, respectively.…”

Section: Related Workmentioning

confidence: 99%

Impact of Optimal Storage Allocation on Price Volatility in Energy-Only Electricity Markets

Masoumzadeh

Nekouei

Alpcan

et al. 2018

IEEE Trans. Power Syst.

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Abstract-Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms e.g. pump-storage hydro, large-scale or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping and price arbitrage capabilities. In this paper, we propose a stochastic bi-level optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage and transmission players is modeled at the lower level problem using an extended Cournot-based stochastic game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, is considered as the case study for the proposed storage allocation framework. Our numerical results indicate that 80% price volatility reduction in SA electricity market can be achieved by installing either 340 MWh regulated storage or 420 MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms.

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Section: Related Workmentioning

confidence: 99%

Impact of Optimal Storage Allocation on Price Volatility in Energy-Only Electricity Markets

Masoumzadeh

Nekouei

Alpcan

et al. 2018

IEEE Trans. Power Syst.

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“…Khi đó lắp đặt ES để cung cấp cho các phụ tải này. Nếu lấy giá điện theo thị trường điện giờ cao điểm giá thấp nhất là c = 98,8 $/MWh21 thì doanh thu từ nguồn ES là 7 triệu $/năm. Trong khi đó, chi phí quản lý và vận hành của ES bình quân gần 2 $/MWh 18 tương ứng gần 158 ngàn $, kết quả lợi nhuận trên 6,8 triệu $/năm.…”

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Determine the location and capacity of energy storage in the power system using the improved Min-Cut algorithm

Sang

Long

Anh

et al. 2020

Sci. Tech. Dev. J.- Eng. Tech.

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This paper aims to exhibit of optimal location and capacity of energy storage (ES) in electricity development planning, including transmission expansion planning (TEP) and generation expansion planning (GEP). Renewable energy sources are being developed in the world in order to replace the increasingly exhausting and polluting fossil energy sources. However, the energy generation of these types cannot be intentionally controlled but depends on natural conditions. To support the defects that renewable energy causes, energy storage systems have been studied and applied. Developing energy storage to charge cheap energy and provide higher prices in the electricity market is one of the issues that have being attention recently. Although choosing the proper location to place is a great challenge. A number of algorithms have been researched for a long time to find suitable locations for GEP; heuristic algorithms have been used in recent years because of their flexibility and wide range. The heuristic approaches, although being varied over time to become more and more effective search engines, but the problem is withheld into local extremes during searching, the number of too many loops when applied to large network systems... are still being researched and overcome by scientists for heuristic algorithms. On the other hand, the Max-Flow-Min-Cut (MFMC) algorithm has been applied to determine thyristor controlled series compensation (TCSC) to manage congestion that also has been of interest to many researchers but it has some limitations. The MFMC algorithm will be improved more effectively to eliminate congestion collaborate with a heuristic in the paper, and the simulation results tested to determine the position and power of ES on the 24 bus IEEE system showed the algorithm's feasibility.

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“…However, because it is likely that many of these ES systems will be deployed by private investors, we should not consider only whether they provide a social benefit in terms of reduced operating cost, but also whether they generate a sufficient return on investment [9], [10]. Since ES operators may wish to participate in multiple electricity markets to increase their profits [11]- [15], we need to consider the arbitrage jointly with the provision of ancillary services when identifying opportunities for investments in ES.…”

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confidence: 99%

Scalable Planning for Energy Storage in Energy and Reserve Markets

Wang

Dvorkin

et al. 2017

IEEE Trans. Power Syst.

110

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Abstract-Energy storage can facilitate the integration of renewable energy resources by providing arbitrage and ancillary services. Jointly optimizing energy and ancillary services in a centralized electricity market reduces the system's operating cost and enhances the profitability of energy storage systems. However, achieving these objectives requires that storage be located and sized properly. We use a bi-level formulation to optimize the location and size of energy storage systems which perform energy arbitrage and provide regulation services. Our model also ensures the profitability of investments in energy storage by enforcing a rate of return constraint. Computational tractability is achieved through the implementation of a primal decomposition and a subgradient-based cutting-plane method. We test the proposed approach on a 240-bus model of the Western Electricity Coordinating Council (WECC) system and analyze the effects of different storage technologies, rate of return requirements, and regulation market policies on ES participation on the optimal storage investment decisions. We also demonstrate that the proposed approach outperforms exact methods in terms of solution quality and computational performance.

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Optimal allocation of energy storage in a co-optimized electricity market: Benefits assessment and deriving indicators for economic storage ventures

Cited by 64 publications

References 34 publications

Impact of Optimal Storage Allocation on Price Volatility in Energy-Only Electricity Markets

Impact of Optimal Storage Allocation on Price Volatility in Energy-Only Electricity Markets

Determine the location and capacity of energy storage in the power system using the improved Min-Cut algorithm

Scalable Planning for Energy Storage in Energy and Reserve Markets

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