A stochastic distribution system planning method considering regulation services and energy storage degradation

Zhao, Xinyi; Shen, Xinwei; Guo, Qinglai; Sun, Hongbin; Oren, Shmuel S.

doi:10.1016/j.apenergy.2020.115520

Cited by 21 publications

(10 citation statements)

References 38 publications

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“…However, the integration of BESS in renewable energy generation and the corresponding battery power control strategies are unconsidered. In [27], a piecewise degradation model for the lithium battery is proposed to capture the linear and exponential two-stage degradation characteristics to improve battery capacity degradation tracking and RUL prediction. The model is more applicable to the online prediction of slow degradation systems.…”

Section: A Literature Reviewmentioning

confidence: 99%

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

Wang

et al. 2023

IEEE Access

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The nonlinear degradation effect of the fast lithium battery energy storage system (FLBESS) and the time-scale variability of peak shaving and frequency regulation both make the optimal operation of the microgrid more challenging. Therefore, this paper presents a bi-level optimization model to balance the degradation cost of lithium battery and the benefit of peak shaving and frequency regulation and improve the utilization of FLBESS while dealing with the multiple time scales optimization problems. The upper-level model mainly optimizes the day-ahead hourly power to minimize the power purchase cost and wind and solar power curtailment cost while calculating the internal parameters of the FLBESS to supply the lower-level model for the solution. The lower model optimizes the intra-hour economic cost and coordinates the frequency regulation benefit and lithium battery degradation cost. Finally, case studies are conducted using a regional microgrid to verify that the model solution satisfies the requirements for optimal system operation. In contrast, the model has the advantages of lower economic cost, more significant life extension effect, and higher energy storage utilization rate. The results show that the annual costs were reduced by 14.9%, and the battery life was increased by 19.0% than without considering degradation.INDEX TERMS Fast lithium battery energy storage system, bi-level optimization, nonlinear degradation effect, frequency regulation, multiple time scales. NOMENCLATURE A. ACRONYMS

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Section: A Literature Reviewmentioning

confidence: 99%

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

Wang

et al. 2023

IEEE Access

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“…The 33-node system from [54] was adopted as test system, as it is the same as adopted in other publications regarding BESS application [9,29,40,42,[55][56][57][58][59], see Figure 4. Regarding system size, the proposed method is reliable for a specific number of nodes, as consider a diverse group of control variables to present a compressive solution to several system issues.…”

Section: Test System and Datamentioning

confidence: 99%

Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems

Rangel

Canha

Sperandio

et al. 2021

IET Generation Trans & Dist

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This paper presents a new approach to the problem of defining an investment policy in battery energy storage systems in active distribution networks, taking into account a diversity of uncertainties. The proposed methodology allows the selection of type, capacity, and location of battery energy storage systems in distribution networks with distributed generation and electric vehicle charging stations. A mixed-integer stochastic programming problem is cunningly approached with a metaheuristic, where fitness calculation with stochastic scenarios is performed by introducing an approximation to the operation costs in the form of a polynomial neural network, generated according to the Group Method of Data Handling-GMDH method, with strong computing speeding-up. The quality of this approximation for heavy Monte Carlo simulations is assessed in a first case study using a 33-bus distribution test system. The optimization planning model is then validated in the same test system using real data collected from solar and wind sources, demand, prices, and charging stations. Four types of batteries are compared considering degradation impact. The results demonstrate the practicality and advantages of this process.

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“…For example, the loss of a single 400-kilovolt transmission line in the India blackout causes sizeable voltage deviations and around 300 million customers without power [8]. Various control actions have been performed to tackle the uncertainties in ADNs, such as network optimization, controllable loads, etc., [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Te voltage fuctuation range is 0.90-1.05 p.u. Te comprehensive objective function is shown in(9) which the weight values are set σ 1 �σ 2 �0.5.Te real-time load of one day is considered an optimization cycle. Based on the load and RDG models, Fig4show the actual hourly load demand and output power of RDGs in a certain area.…”

mentioning

confidence: 99%

An Optimization Method of Active Distribution Network Considering Time Variations in Load and Renewable Distributed Generation

Wen

Lin

et al. 2022

International Transactions on Electrical Energy Systems

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Network optimization is one of an effective ways to enhance the performance of an active distribution network (ADN). Aiming to improve the operation and power quality of the ADN considering time variations in load and renewable distributed generation (RDG) power, a multi-time period optimization model and its dynamic solution method are proposed. Considering the real time load demand and power generation variation of RDG versus input parameters like wind speed and solar irradiance, the time variation models of load and RDG power output are developed. The minimum power loss and maximum absorption of RDG power are served as the optimization indexes to construct the dynamic muti-time period optimization model. A hybrid particle swarm optimization (HPSO) algorithm is presented based on integer coding and random coding technique, which can find the most satisfactory solutions for the proposed dynamic model. Considering the time variation of load and RDG power of ADN, the optimal network structure and RDG allocation scheme at any time interval are determined by analyzing the obtained solutions. Additionally, two ADNs with time variation in load and RDG are tested to verify the effectiveness and superiority of the proposed dynamic optimization model and HPSO algorithm. The simulation results show that the proposed method can improve the operation performance and RDG optimal utilization of the ADNs through muti-time period dynamic optimization.

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A stochastic distribution system planning method considering regulation services and energy storage degradation

Cited by 21 publications

References 38 publications

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

Mixed‐integer stochastic evaluation of battery energy storage system integration strategies in distribution systems

An Optimization Method of Active Distribution Network Considering Time Variations in Load and Renewable Distributed Generation

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