Cooperation of Wind Power and Battery Storage to Provide Frequency Regulation in Power Markets

He, Guannan; Chen, Qixin; Chen, Kang; Xia, Qing; Poolla, Kameshwar

doi:10.1109/tpwrs.2016.2644642

Cited by 198 publications

(117 citation statements)

References 26 publications

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“…To overcome this problem, a solution is to install BESSs for the intermittent renewable sources [6]- [8], since they can provide a faster response to absorb excessive power and compensate the insufficient power during peak generation and load periods, respectively. Thus, the active power imbalance caused by integrating high penetration wind power generation can be addressed by introducing and coordinating the BESSs in a well-designed cooperative manner.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Optimal Control of Battery Energy Storage System Under Wind Uncertainties in a Microgrid

Zhao

Ding

2018

IEEE Trans. Power Syst.

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Abstract-Since high penetration renewable sources are integrated into the future power system, energy storage systems are often installed to maintain the frequency stability in a microgrid. The operation mode of a microgrid may frequently change due to the intermittency of renewable sources, and energy storage systems will be charged/discharged accordingly to smooth and balance the generation of renewable sources. Thus, energy storage systems should be coordinated in a proper approach to ensure the supply-demand balance while increasing their profits and energy efficiency. To this end, a distributed optimal solution for energy storage systems to maintain the supplydemand balance while maximizing their welfare and energy efficiency is proposed to energy storage systems by enhancing the coordination through the communication under a multiagent system framework. Under this framework, each energy storage system is designated as an agent, and it only utilizes the local information to interact with the neighbouring agents. Additionally, since the participants in microgrid may not be willing to release their information about cost functions, or even the local gradient with other neighbouring agents, the proposed solution could be implemented without these private information to the individual agents. The simulation studies are carried out for IEEE 14-bus and 30-BESS systems to validate the effectiveness of the proposed distributed solution.

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Section: Introductionmentioning

confidence: 99%

Cooperative Optimal Control of Battery Energy Storage System Under Wind Uncertainties in a Microgrid

Zhao

Ding

2018

IEEE Trans. Power Syst.

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“…The energy constraints of storage are formulated as (5) and (6). The energy level of storage at time h , h E , is a function of the energy level at time 1 h − , 1 h E − , and the charging/discharging schedules at time h , as (5), where  is the self-discharge rate, and  is the charge/discharge efficiency.…”

Section: B Storage Operation Constraintsmentioning

confidence: 99%

Spatiotemporal Arbitrage of Large-Scale Portable Energy Storage for Grid Congestion Relief

Zhang

et al. 2019

2019 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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Energy storage has great potential in grid congestion relief. By making large-scale energy storage portable through trucking, its capability to address grid congestion can be greatly enhanced. This paper explores a business model of large-scale portable energy storage for spatiotemporal arbitrage over nodes with congestion. We propose a spatiotemporal arbitrage model to determine the optimal operation and transportation schedules of portable storage. To validate the business model, we simulate the schedules of a Tesla Semi full of Tesla Powerpack doing arbitrage over two nodes in California with local transmission congestion. The results indicate that the contributions of portable storage to congestion relief are much greater than that of stationary storage, and that trucking storage can bring net profit in energy arbitrage applications.Index Terms-Portable energy storage, spatiotemporal arbitrage, storage trucking, transmission congestion relief I.

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“…The BS charging and discharging cannot occur at the same time. The soc 12 at interval t for charging and discharging is given in (5) and (6), respectively.…”

Section: Bs Modelingmentioning

confidence: 99%

“…Usually, the forecasted values of WP are used in solving the operational or scheduling ahead of time problems. The use of BS can compensate the inaccurate/insufficient power because of overestimated/underestimated values of WP power through the coordinated operational scheme between WP and BS, and utilizing the ramping capability of BS . Hence, the BS with WP helps to handle the wind power uncertainty and improves the reliability, power quality, and arbitrage of HPS .…”

Section: Introductionmentioning

confidence: 99%

Optimal sizing and operation of battery storage for economic operation of hybrid power system using artificial bee colony algorithm

Paliwal

Singh

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper presents the optimal sizing and operation of battery storage (BS) using artificial bee colony (ABC) to maximize the revenue in a hybrid power system (HPS). The HPS consists of a wind plant (WP), hydro plant (HP), and BS integrated to main grid. Two diverse wind scenarios, namely moderate and abundant wind scenarios, are considered, in which wind speed remains in the range of 2 to 8 and 10 to 18 m/second, at majority of the hours in a day, respectively. The problem is divided into 2 stages. In stage I, the optimal sizing of BS for the installed WP capacity is performed considering various elements and operational costs of HPS with appropriate system constraints. A priority‐based power utilization scheme is used as WP, HP, and then BS to size the BS. The stage II optimizes the BS operation to maximize the HPS revenue with additionally considering BS operational costs and its aging constraint using the results of stage I as input data. And, the BS is considered as a time‐dependent source by continuously adapting to the market bids, working on the principle of buy low and sell high. The results revealed that optimal BS size varies from 20% to 40% and 50% to 65% of WP capacity for moderate and abundant wind scenarios, subsequently and efficient optimized operation of BS with its operational constraints. The viability of ABC technique is demonstrated using the fmincon's interior point method (IPM) of MATLAB® optimization toolbox, which is a numerical technique.

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Cooperation of Wind Power and Battery Storage to Provide Frequency Regulation in Power Markets

Cited by 198 publications

References 26 publications

Cooperative Optimal Control of Battery Energy Storage System Under Wind Uncertainties in a Microgrid

Cooperative Optimal Control of Battery Energy Storage System Under Wind Uncertainties in a Microgrid

Spatiotemporal Arbitrage of Large-Scale Portable Energy Storage for Grid Congestion Relief

Optimal sizing and operation of battery storage for economic operation of hybrid power system using artificial bee colony algorithm

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