Exploring Economic Criteria for Energy Storage System Sizing

Liu, Jichun; Chen, Zhengbo; Xiang, Yue

doi:10.3390/en12122312

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…About the optimization problem in this paper, we should consider some aspects: in the future, the cost conditions will change; for different purposes, a different compensation degree will be required. Therefore, in this section, we will deeply discuss how these influencing factors affect the optimal result [28][29][30][31][32][33][34][35].…”

Section: The Analysis Of Influencing Factors On the Optimal Resultsmentioning

confidence: 99%

Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

Dong

Pang

et al. 2019

Energies

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To better track the planned output (forecast output), energy storage systems (ESS) are used by wind farms to compensate the forecast error of wind power and reduce the uncertainty of wind power output. When the error compensation degree is the same, the compensation interval is not unique, different compensation intervals need different ESS sizing. This paper focused on finding the optimal compensation interval not only satisfied the error compensation degree but also obtained the max profit of the wind farm. First, a mathematical model was proposed as well as a corresponding optimization method aiming at maximizing the profit of the wind farm. Second, the effect of the influencing factors (compensation degree, electricity price, ESS cost, and wind penalty cost) on the optimal result was fully analyzed and deeply discussed. Through the analysis, the complex relationship between the factors and the optimal results was found. Finally, the comparison between the proposed and traditional method was given, and the simulation results showed that the proposed method can provide a powerful decision-making basis for ESS planning in current and future market.Energies 2019, 12, 4755 2 of 21 fluctuation of wind power output [3,4], dealing with the peak shaving problem of power systems with wind power [5,6], and compensating the forecast error of wind power output, and so on.The forecast error of wind power is inevitable because of the characteristics of wind power generation. The ESS is installed at the outlet of the wind farm to compensate the forecast error of wind power and ensure that the wind farm can output the power reliably according to the pre-declared plan. In real-time dispatching, the ESS can store or release energy to balance the corresponding power error when the real-time power value of a wind farm is different from the planned output. In terms of compensating wind power forecast error, there are many references discussing this top from different aspects [7,8].The authors of [9] optimized the capacity of energy storage devices by controlling the wind power forecast error within a certain range to minimize the cost of energy storage equipment. In [10] the authors optimized the capacity of energy storage devices with the objective of minimizing wind power prediction errors. By quantifying the functional relationship between energy storage capacity and unserved energy, the minimum energy storage capacity corresponding to different unserved energy is analyzed. The authors of [11] synthetically considered the energy efficiency, environmental benefit and one investment of wind power and ESS, constructed the corresponding economic evaluation model, and evaluated the feasibility of using ESS to improve the wind power acceptance scale in different dispatching risk scenarios. The authors of [12] reduced the impacts of wind power forecast errors while prolonging the lifetime of ESS. In [13] the authors used a simple autoregressive model to capture the stochastic behavior of day-ahead forecast errors, in particular the au...

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Section: The Analysis Of Influencing Factors On the Optimal Resultsmentioning

confidence: 99%

Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

Dong

Pang

et al. 2019

Energies

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“…Moreover, Nick et al considered the multi-objective problem of achieving an optimal balance between technical and economic objectives, including the cost of voltage deviation, line loss, load supply, and storage system investment, using second-order cone programming (SOCP) to optimize the capacity and location of ESSs in distribution networks [17]. In [18], the authors proposed the optimal size of an installed BESS for storing the electrical energy during the valley load period and supplying it during the peak load period to maximize the operational benefits. Then, Ref.…”

Section: Introductionmentioning

confidence: 99%

Sizing and Sitting of Battery Energy Storage Systems in Distribution Networks with Transient Stability Consideration

2022

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In this study, the capacity and location of battery energy storage systems (BESSs) in a distribution network were evaluated to increase the stability and reliability of power systems by applying the proposed transient stability indicators. The search capability of particle swarm optimization (PSO) combined with Pareto optimality in MATLAB R2019b was used to solve multi-objective problems in steady-state conditions, including active power loss, voltage deviation, and total operating cost. The BESS capacity and location were then derived using the Manhattan distance method. Subsequently, the BESS control model was constructed using the commercial software DIgSILENT PowerFactory 2021 to evaluate generator power smoothing and short-term voltage stability index during the transient response and determine the final capacity and location of BESS in distribution networks. Finally, the accuracy of the proposed method was verified by considering the daily load and solar power generation curves using the IEEE 33-bus radial power distribution network.

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Resilience and economics of microgrids with PV, battery storage, and networked diesel generators

Marqusee

Becker

Ericson

2021

Advances in Applied Energy

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Exploring Economic Criteria for Energy Storage System Sizing

Cited by 6 publications

References 24 publications

Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

Energy Storage Sizing Optimization and Sensitivity Analysis Based on Wind Power Forecast Error Compensation

Sizing and Sitting of Battery Energy Storage Systems in Distribution Networks with Transient Stability Consideration

Resilience and economics of microgrids with PV, battery storage, and networked diesel generators

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