Optimal Sizing and Management of Battery Energy Storage Systems in Microgrids for Operating Cost Minimization

Vaka, Srinivas Sandeep Kumar Reddy; Kumari, M. Sailaja

doi:10.1080/15325008.2022.2061641

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…On the other hand, the Microgrid uses its stored energy to power the load whenever there is a shortage in power generation. When operating in stand-alone mode, the major function of BESS is to regulate the Microgrid in order to prevent instability and failure in the event of abrupt changes in load or a failure of the utility grid or distributed generation [35]. Figure 3 shows the power converters, battery cells, and control components that make up the BESS structure.…”

Section: Battery Energy Storage System (Bess)mentioning

confidence: 99%

“…BESSs, which comprise mobile and static storage systems, are outfitted with RES in the microgrid to store energy during periods of high demand (serving as a load) and release it to a load during periods of low demand (act as a source). When building the optimum battery energy storage system, dependability, battery technology, power quality, changes in frequency, and environmental issues must all be taken into account [35]. The ESS rating is influenced by a number of variables, including the battery configuration, backup time, temperature, life of battery, total drainage, required reserve power, and renewable energy sources.…”

Section: Figure 3 Interconnection Diagram Of Bessmentioning

confidence: 99%

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Swarm Intelligence techniques for optimization of Microgrid with Renewable Energy System

Vaish

Tiwari

Seethalekshmi

2023

Preprint

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Recently, clean power generation has received a lot of attention in modern power systems as a means to provide sustainability and high flexibility in the power industry. Due to this, modern energy systems are now reliant on Microgrids (MGs), which can easily access and operate Renewable Energy Systems (RES) and other Distributed Generations. The use of Microgrids plays a vital role in implementing clean and renewable energy. This will enhance energy security, making considerable financial savings, and lowering greenhouse gas emissions. In this paper, a grid-connected Microgrid system is considered as test model that includes solar photovoltaic (PV), wind turbine (WT), micro gas turbine (MT), fuel cell (FC), and battery energy storage system (BESS). The developed system is presented as a multi-objective function with constraints that can be resolved by a significant optimization method. Various commonly used Swarm Intelligence (SI) based meta-heuristic optimization techniques such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Immune System (AIS), Bacteria Forging Optimization (BFO), Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony (ABC), Cuckoo Search (CS), Bat Algorithm (BA), Firefly Algorithm (FFA), Krill Herd (KH), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) can be used to optimize above stochastic multi-objective problem of Microgrid. To assist researchers in selecting the best optimization approaches for their study, a comparison of above-mentioned SI based optimization techniques is provided in this paper. The discussion also includes upcoming research issues for Microgrid operation optimization.

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Section: Battery Energy Storage System (Bess)mentioning

confidence: 99%

Section: Figure 3 Interconnection Diagram Of Bessmentioning

confidence: 99%

Swarm Intelligence techniques for optimization of Microgrid with Renewable Energy System

Vaish

Tiwari

Seethalekshmi

2023

Preprint

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“…The amount of loss is calculated using the following equations for power systems. If the total power injected into the busbar i is known, the total system loss will be equal to the complex power of all machines [31][32][33][34][35][36][37].…”

Section: First Scenariomentioning

confidence: 99%

Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

Makahleh¹,

Amer²,

Manasrah³

et al. 2023

Computers, Materials &Amp; Continua

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In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach. Ultimately, the battery charge-discharge is managed at any time during the day, considering the load consumption at each hour. The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes. This shows the significance of such matters in terms of economy and technicality.

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“…Conde et al [92] defined the operational scenarios of BMS control, highlighting the regulatory action of batteries using a power measurement unit for overall system control. Several heuristic techniques, such as accelerated particle swarm optimization (APSO), JAYA, particle swarm optimization (PSO), and linear programming-based interior point algorithms, were utilized for the microgrid EMS to reduce operational costs [93]. In another application, Khan and Singh [94] employed metaheuristic optimization techniques (PSO, GA, and DE) based on several parameters of fitness with average values and the convergence of population to optimize the battery management (charging and discharging mode of batteries) while solving the issues of operational cost minimization.…”

Section: Storage System Managementmentioning

confidence: 99%

Review of Recent Developments in Microgrid Energy Management Strategies

et al. 2022

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The grid integration of microgrids and the selection of energy management systems (EMS) based on robustness and energy efficiency in terms of generation, storage, and distribution are becoming more challenging with rising electrical power demand. The problems regarding exploring renewable energy resources with efficient and durable energy storage systems demand side management and sustainable solutions to microgrid development to maintain the power system’s stability and security. This article mainly focuses on the overview of the recent developments of microgrid EMS within the control strategies and the implementation challenges of the microgrid. First, it provides energy management strategies for the major microgrid components, including load, generation, and energy storage systems. Then, it presents the different optimization approaches employed for microgrid energy management, such as classical, metaheuristic, and artificial intelligence. Moreover, this article sheds light on the major implementation challenges of microgrids. Overall, this article provides interactive guidelines for researchers to assist them in deciding on their future research.

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Optimal Sizing and Management of Battery Energy Storage Systems in Microgrids for Operating Cost Minimization

Cited by 5 publications

References 25 publications

Swarm Intelligence techniques for optimization of Microgrid with Renewable Energy System

Swarm Intelligence techniques for optimization of Microgrid with Renewable Energy System

Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

Review of Recent Developments in Microgrid Energy Management Strategies

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