Optimal location and operation of battery energy storage system in the distribution system for reducing energy cost in 24‐hour period

Ton, Trieu Ngoc; Nguyen, Thuan Thanh; Truong, Viet Anh; Vu, Phan-Tu

doi:10.1002/2050-7038.12861

Cited by 5 publications

(4 citation statements)

References 54 publications

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“…In most studies, BESSs are predominantly used for ancillary services [53] or integrated with DG [6,20]. Only a few studies recognize the minimization of power losses by means of integrating BESSs with other generation resources [70].…”

Section: Solution Approachmentioning

confidence: 99%

A Mathematical Programming Approach for the Optimal Operation of Storage Systems, Photovoltaic and Wind Power Generation

2023

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The ever-growing participation of Renewable Energy Sources (RES) in modern distribution networks is replacing an important portion of Conventional Generation (CG), which brings along new challenges in the planning and operation of distribution grids. As RES such as Photovoltaic Energy (PV) and Wind Power Generation (WPG) increase in distribution networks, studies regarding their integration and coordination become more important. In this context, the purpose of this paper is to propose a Multi-period Optimal Power Flow (MOPF) model for the optimal coordination of Battery Energy Storage Systems (BESSs) with PV, WPG, and CG in modern distribution networks. The model formulation was developed in A Modeling Language for Mathematical Programming (AMPL) and solved through the Knitro solver within a time horizon of 24 h. A distinctive feature and one of the main contributions of the proposed approach is the fact that BESSs can provide both active and reactive power. The proposed optimization model reduces power losses and improves voltage profiles. To show the applicability and effectiveness of the proposed model, several tests were carried out on the 33-bus distribution test system and a real distribution system of 141 buses located in the metropolitan area of Caracas. Power loss reductions of up to 58.4% and 77% for the test systems of 33 and 141 buses were obtained, respectively, when BESSs provided both active and reactive power. The results allow us to conclude that the proposed model for optimal coordination of BESSs with RES is suitable for real-life applications, resulting in important reductions of power losses and flattening of voltage profiles.

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Section: Solution Approachmentioning

confidence: 99%

A Mathematical Programming Approach for the Optimal Operation of Storage Systems, Photovoltaic and Wind Power Generation

2023

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“…In this article, all three types were considered, each at a specific percentage. The percentage of each type of load during a 24 h period and at each node of the IEEE 33 bus network were taken from [24]. Therefore, the load can be modeled as in ( 1) and (2).…”

Section: Load Modelingmentioning

confidence: 99%

An intelligent energy management system for optimum design and real-time operation

Zedak¹,

Belfqih²,

Boukherouaa³

et al. 2023

IJPEDS

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<span lang="EN-US">Planning and management of distribution networks has become a very difficult task, especially with the strong expansion of renewable energy sources (RES) which are intermittent in nature. Maintaining fluidity and reliability of real-time decisions while taking into consideration uncertainties related to production and increasing the profit of distribution network operators is the objective of the system proposed in this work. It is an intelligent energy management system dedicated to the management of grid-integrated RES and battery energy storage systems (BESS), composed of: i) a real-time control and data acquisition model, ii) a model for forecasting the intermittent parameters of RES based on neural networks, iii) a long-term planning model based on the optimal placement and size of RES and BESS, and iv) an hourly planning model for scheduling the energy distribution between energy sources. The non-dominated sorting genetic algorithm and the entropy-TOPSIS method (technique for order of preference by similarity to ideal solution) form the basic block of this model. To evaluate it, a modified IEEE 33 bus network was used for testing and the results, for short-term scheduling, proved that the system succeeds in maximizing profits and significantly minimizing CO<sub>2</sub> emissions, in addition to power losses and voltage drops.</span>

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“…However, the improper size of BESS may add a burden on the system's performance and operation. Additionally, in order to consider the advantage of installing both the optimal location and size of BESS simultaneously in DSs, GA and Cuckoo search (CS) meta-heuristic algorithms are proposed in [17] and [18] to reduce energy losses. The simultaneous optimal location and size of BESS with the objective of minimizing the system losses are developed using the coyote optimization (CO) algorithm [19] and WO algorithm [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…Although the aforementioned studies in [17][18][19][20][21] have efficiently addressed the BESS location and sizing problem, these studies have only covered the impact of injecting active power from BESS on power losses and the voltage profile without taking into account the capability of BESS to inject both active and reactive power. Furthermore, it is well known that meta-heuristic algorithms usually outperform mathematical optimization approaches in terms of providing optimal solutions, especially for complex DSs.…”

Section: Introductionmentioning

confidence: 99%

Optimal Integration of Battery Energy Storage Systems in Power Distribution Networks Using Hunger Game Search Algorithm

2024

IJIES

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This paper proposes an integrated method to simultaneously determine the optimal placement and sizing of battery energy storage systems (BESSs) in power distribution networks using the hunger games search algorithm (HGSA). The objective of the proposed method focuses on concurrently reducing power losses and improving the voltage deviation index to improve the distribution system performance while keeping all constraints within permissible limits. The HGSA is a nature-inspired algorithm that simulates the behavior of prey and predators in finding food. In this paper, the HGSA is used to search for the optimal solution in a multi-dimensional search space consisting of the candidate BESS locations and sizes. The proposed method is applied to modified IEEE 69-bus and IEEE 85-bus test distribution systems with five different scenarios, and the results indicate that the HGSA can efficiently determine the optimal placement and sizing of BESSs, resulting in significant power loss reduction (i.e., 69.13-98.08% for the first test system and 52.97-95.03% for the second test system) and voltage deviation improvement (i.e., 94.75-99.89% for the first test system and 12.12-93.37% for the second test system) compared to the base case. Furthermore, the performance of the HGSA is compared with other well-known metaheuristic optimization algorithms (i.e., whale optimization algorithm, chaotic neural network algorithm, genetic algorithm, grey wolf optimizer, and water cycle algorithm), and the results show that the HGSA outperforms these optimization algorithms in attaining the best optimal solution with faster convergence and less number of iterations.

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Optimal location and operation of battery energy storage system in the distribution system for reducing energy cost in 24‐hour period

Cited by 5 publications

References 54 publications

A Mathematical Programming Approach for the Optimal Operation of Storage Systems, Photovoltaic and Wind Power Generation

A Mathematical Programming Approach for the Optimal Operation of Storage Systems, Photovoltaic and Wind Power Generation

An intelligent energy management system for optimum design and real-time operation

Optimal Integration of Battery Energy Storage Systems in Power Distribution Networks Using Hunger Game Search Algorithm

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