Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains

Shi, Yuanyuan; Xu, Bolun; Wang, Di; Zhang, Baosen

doi:10.1109/tpwrs.2017.2749512

Cited by 329 publications

(143 citation statements)

References 21 publications

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“…Similarly, storage devices have been evaluated using power hardwarein-loop for minimizing losses and voltage fluctuations [28]. The authors in [29], [30] co-optimize storage for arbitrage, peak shaving and frequency regulation. Unlike the described prior work, we discuss storage for co-optimization of arbitrage and power factor correction.…”

Section: A Literature Reviewmentioning

confidence: 99%

Arbitrage With Power Factor Correction Using Energy Storage

Hashmi

Deka

Bušíć

et al. 2020

IEEE Trans. Power Syst.

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The importance of reactive power compensation for power factor (PF) correction will significantly increase with the large-scale integration of distributed generation interfaced via inverters producing only active power. In this work, we focus on co-optimizing energy storage for performing energy arbitrage as well as local power factor correction. The joint optimization problem is non-convex, but can be solved efficiently using a McCormick relaxation along with penalty-based schemes. Using numerical simulations on real data and realistic storage profiles, we show that energy storage can correct PF locally without reducing arbitrage profit. It is observed that active and reactive power control is largely decoupled in nature for performing arbitrage and PF correction (PFC). Furthermore, we consider a real-time implementation of the problem with uncertain load, renewable and pricing profiles. We develop a model predictive control based storage control policy using auto-regressive forecast for the uncertainty. We observe that PFC is primarily governed by the size of the converter and therefore, look-ahead in time in the online setting does not affect PFC noticeably. However, arbitrage profit are more sensitive to uncertainty for batteries with faster ramp rates compared to slow ramping batteries. TABLE I * Nomenclature ηch, ηdis Charging and discharging efficiency x i Battery charge change at time i b i Battery charge level at time i; b i = b i−1 + x i P i h Active power consumed by inelastic load Q i h Reactive power consumed by inelastic load P i r Active power generated by renewable source Q i r Reactive power output of renewable source P i Active power of inelastic load and renewable generation; P i = P i h − P i r Q i Reactive power output of inelastic load and renewable generation; Q i = Q i h − Q i r P i B Active power output of battery + converter P max B Maximum active power output of battery + converter P min B Minimum active power output of battery + converter Q i B Reactive power output of battery + converter S i B Instantaneous apparent power output of storage interfaced by converter; S i B = P i B + jQ i B S max B Maximum apparent power output of storage converter P i T Total active power seen by the grid; P i T = P i + P i B Q i T Total reactive power seen by the grid; Q i T = Q i + Q i B p i elec Price of electricity at time i

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Section: A Literature Reviewmentioning

confidence: 99%

Arbitrage With Power Factor Correction Using Energy Storage

Hashmi

Deka

Bušíć

et al. 2020

IEEE Trans. Power Syst.

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“…Several references propose specific methods for storage technologies other than BESSs: compressed air energy storage [13], fleets of thermostatically controlled loads [16], or fleets of distributed BESSs [15]. 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.…”

Section: B Literature Surveymentioning

confidence: 99%

“…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%

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

Namor

Sossan

Cherkaoui

et al. 2019

IEEE Trans. Smart Grid

112

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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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“…as demand charge (DC) management [2], [3], however, the benefits of the BESS to the utility and to the end users can be far greater. BTM energy storage systems for commercial & industrial (C&I) segments can also be available to generate additional revenues by delivering services such as frequency regulation [4], ramp rate control [5] and PV-utilization [6].…”

Section: Introductionmentioning

confidence: 99%

Model predictive BESS control for demand charge management and PV-utilization improvement

Raoufat

Asghari²,

Sharma³

2018

2018 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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Adoption of battery energy storage systems for behind-the-meters application offers valuable benefits for demand charge management as well as increasing PV-utilization.The key point is that while the benefit/cost ratio for a single application may not be favorable for economic benefits of storage systems, stacked services can provide multiple revenue streams for the same investment. Under this framework, we propose a model predictive controller to reduce demand charge cost and enhance PV-utilization level simultaneously. Different load patterns have been considered in this study and results are compared to the conventional rule-based controller. The results verified that the proposed controller provides satisfactory performance by improving the PV-utilization rate between 60% to 80% without significant changes in demand charge (DC) saving. Furthermore, our results suggest that batteries can be used for stacking multiple services to improve their benefits. Quantitative analysis for PV-utilization as a function of battery size and prediction time window has also been carried out.Index Terms-Battery energy storage, photovoltaic power generation, behind-the-meter, demand charge, PV-utilization. NOMENCLATUREη pv PV-utilization rate; P pur g , P sell g purchased/sold power from/to grid, kW; P load , P pv Load/PV power, kW; P cha b , P dis b BESS charge/discharge power, kW; P max b BESS charge/discharge power limit, kW; SOC BESS state-of-charge; SOC min ,SOC max BESS state-of-charge limits; E sell BESS , E sell N oBESS Excess energy sold to the grid with or without BESS, kWh; DCT Demand charge threshold, kW; λ DC Demand charge rate, $/kW C tp Battery throughput cost, $/kW

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Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains

Cited by 329 publications

References 21 publications

Arbitrage With Power Factor Correction Using Energy Storage

Arbitrage With Power Factor Correction Using Energy Storage

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

Model predictive BESS control for demand charge management and PV-utilization improvement

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