Optimal Siting and Sizing of Battery Energy Storage Systems for Distribution Network of Distribution System Operators

Boonluk, Panyawoot; Siritaratiwat, Apirat; Fuangfoo, Pradit; Khunkitti, Sirote

doi:10.3390/batteries6040056

Cited by 38 publications

(16 citation statements)

References 34 publications

(47 reference statements)

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“…Other authors have proposed multiple operative models to coordinate the daily operation of the batteries; some of these approaches are: mixed-integer linear programming [20][21][22]; second order cone optimization [23][24][25], semidefinite programming [26]; genetic algorithms [27][28][29], particle swarm optimization [30,31]; nonlinear programming [32][33][34][35][36], and reinforcement learning for energy system optimization [37,38]. The main characteristic of those researches is that the batteries are modeled through a linear relation between the state-of-charge and the amount of power injected/absorbed into the grid [11]; this linear representation allows solving efficiently the problem of the optimal dispatch of these batteries in AC and/or DC grids where these are previously located to the network.…”

Section: Introductionmentioning

confidence: 99%

On the Optimal Selection and Integration of Batteries in DC Grids through a Mixed-Integer Quadratic Convex Formulation

et al. 2021

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This paper deals with the problem of the optimal selection and location of batteries in DC distribution grids by proposing a new mixed-integer convex model. The exact mixed-integer nonlinear model is transformed into a mixed-integer quadratic convex model (MIQC) by approximating the product among voltages in the power balance equations as a hyperplane. The most important characteristic of our proposal is that the MIQC formulations ensure the global optimum reaching via branch & bound methods and quadratic programming since each combination of the binary variables generates a node with a convex optimization subproblem. The formulation of the objective function is associated with the minimization of the energy losses for a daily operation scenario considering high renewable energy penetration. Numerical simulations show the effectiveness of the proposed MIQC model to reach the global optimum of the optimization model when compared with the exact optimization model in a 21-node test feeder. All the validations are carried out in the GAMS optimization software.

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Section: Introductionmentioning

confidence: 99%

On the Optimal Selection and Integration of Batteries in DC Grids through a Mixed-Integer Quadratic Convex Formulation

et al. 2021

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“…Electricity is widely regarded as a critical factor in economic growth, as increased usage is required to serve a diverse range of load types, including industrial, commercial, and residential customers [1][2][3][4]. Renewable energy sources have been widely used to improve the capacity of electrical generation, particularly in the last few decades [5][6][7]. Power quality disturbance (PQD) has been a highly concerning problem in power systems since it can essentially degrade the system's performance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

et al. 2021

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Power quality disturbance (PQD) is an influential situation that significantly declines the reliability of electrical distribution systems. Therefore, PQD classification is an important process for preventing system reliability degradation. This paper introduces a novel algorithm called “adaptive salp swarm algorithm (SSA)” as an optimal feature selection algorithm for PQD classification. Feature extraction and classifier of the proposed classification system were based on the discrete wavelet and the probabilistic neural network, respectively. The classification was focused on the 13 types of power quality signals. The optimal number of selected features for the proposed classification system was firstly determined. Then, it demonstrated that the optimally selected features resulted in the highest classification accuracy of 98.77%. High performance of the proposed classification system in the noisy environment, as well as based on the real dataset was also verified. Furthermore, the proposed SSA indicates a very high convergence rate compared to other well-known algorithms. A comparison of the proposed classification system’s performance to existing works was also carried out, revealing that the proposed system’s accuracy is on a high-range scale. Hence, the adaptive SSA becomes another efficient optimal feature selection algorithm for PQD classification.

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“…Nowadays, RESs are widely connected to distribution grids thanks to the advantages they offer: clean energy and additional generation to address the ever increasing electricity demand [4]. Between RESs power generation technologies, solar PhotoVoltaic (PV) systems are a promising option offering a significant potential for providing energy in a sustainable way [5], directly generating it onsite [6].…”

Section: Introductionmentioning

confidence: 99%

Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods

Nespoli

Matteri

Pretto³

et al. 2021

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The increasing penetration of Renewable Energy Sources (RESs) in the energy mix is determining an energy scenario characterized by decentralized power production. Between RESs power generation technologies, solar PhotoVoltaic (PV) systems constitute a very promising option, but their production is not programmable due to the intermittent nature of solar energy. The coupling between a PV facility and a Battery Energy Storage System (BESS) allows to achieve a greater flexibility in power generation. However, the design phase of a PV+BESS hybrid plant is challenging due to the large number of possible configurations. The present paper proposes a preliminary procedure aimed at predicting a family of batteries which is suitable to be coupled with a given PV plant configuration. The proposed procedure is applied to new hypothetical plants built to fulfill the energy requirements of a commercial and an industrial load. The energy produced by the PV system is estimated on the basis of a performance analysis carried out on similar real plants. The battery operations are established through two decision-tree-like structures regulating charge and discharge respectively. Finally, an unsupervised clustering is applied to all the possible PV+BESS configurations in order to identify the family of feasible solutions.

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Optimal Siting and Sizing of Battery Energy Storage Systems for Distribution Network of Distribution System Operators

Cited by 38 publications

References 34 publications

On the Optimal Selection and Integration of Batteries in DC Grids through a Mixed-Integer Quadratic Convex Formulation

On the Optimal Selection and Integration of Batteries in DC Grids through a Mixed-Integer Quadratic Convex Formulation

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods

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