Multiobjective nested optimization framework for simultaneous integration of multiple photovoltaic and battery energy storage systems in distribution networks

Thokar, Rayees Ahmad; Gupta, Nikhil; Niazi, K. R.; Swarnkar, Anil; Meena, Nand K.

doi:10.1016/j.est.2021.102263

Cited by 40 publications

(8 citation statements)

References 71 publications

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“…Require: Obtain the sub-optimal allocation of multiple DERs suggested by the Stage-I, and deploy in the model, i.e., Objective Function O bj 2 (h) Equation ( 15) set h = 0 while h ≤ 24 do h = h + 1 Obtain the the total power produce by WTs (P h G ) using Equation ( 2) and total demand of system (P h D ) using Equation ( 4) at hth hour Determine the lower and upper limits of dispatchable DERs i.e., BESS by [P h lim j ,P h lim j ] using Equations ( 17) and (18). if P h G > P h D then Obtain the optimal power outputs of dispatchable DERs, P BESS To obtain the fitness value of the first objective function, (Obj 1 ), expressed in (5) the feasible fitnesses of individual functions of objectives F1 to F5 are determined.…”

Section: Stage-iimentioning

confidence: 99%

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Modified Dragonfly Optimisation for Distributed Energy Mix in Distribution Networks

et al. 2021

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This article presents a two-stage optimization model aiming to determine optimal energy mix in distribution networks, i.e., battery energy storage, fuel cell, and wind turbines. It aims to alleviate the impact of high renewable penetration on the systems. To solve the proposed complex optimization model, a standard variant of the dragonfly algorithm (DA) has been improved and then applied to find the optimal mix of distributed energy resources. The suggested improvements are validated before their application. A heuristic approach has also been introduced to solve the second stage problem that determines the optimal power dispatch of battery energy storage as per the size suggested by the first stage. The proposed framework was implemented on a benchmark 33-bus and a practical Indian 108-bus distribution network over different test cases. The proposed model for energy mix and modified DA technique has significantly enhanced the operational performance of the network in terms of average annual energy loss reduction, node voltage profiles, and demand fluctuation caused by renewables.

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Section: Stage-iimentioning

confidence: 99%

“…In [12], a framework is proposed for optimal accommodation of WT and BESS with an arrangement of central and distributed processes for the distribution network operators (DNO). Further, in [18] a multiobjective nested model is developed to deploy PV with BESS in a distribution system.…”

Section: Introductionmentioning

confidence: 99%

Modified Dragonfly Optimisation for Distributed Energy Mix in Distribution Networks

et al. 2021

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“…The equilibrium optimizer method was introduced by Ahmed et al 15 A highly effective algorithm was proposed for improving the size and placement of DGs within power networks and addressing the issues with microenergy management. In a study by Thokar et al, 16 a bilayer approach to the placement of energy storage systems and PV in DNs was proposed. In Biswal and Shankar, 17 the Strength Pareto Evolutionary Algorithm 2 was proposed as a solution for the problem of capacitor and DGs placement with load uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

Hachemi,

Sadaoui,

Arif

et al. 2023

Energy Science & Engineering

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This paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case.

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“…Saini and Gidwani [11] proposed a GA-based techno-economical methodology with BESS placement. Anuradha et al [12] proposed a voltage and loss sensitivity analysis based technique and Thokar et al [13] used a moth search algorithm for allocating BESS to enhance the DG capacity. Ahmadi et al [14] proposed a multi-verse optimization algorithm-based approach for optimum coordination of BESS with DGs.…”

Section: Introductionmentioning

confidence: 99%

Optimum Placement of Battery Energy Storage Systems and Solar PV Units in Distribution Networks Using Gravitational Search Algorithm

Goli¹,

Yelem²,

Jasthi³

et al. 2023

Atlantis Highlights in Intelligent Systems

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This paper proposes the optimum sizing and placement of photovoltaic (PV) units and battery energy storage systems using the Gravitational Search Algorithm (GSA) to minimize the burden on the substation. The solar PV units are allocated for daytime peak load reduction and the evening load is compensated with battery energy storage systems. Optimum placement and sizing of solar PV units and shunt capacitors is carried out to minimize the real and reactive powers drawn from the substation to a predefined value, minimize the active power losses, and improve the distribution system's voltage profile. The BESS capacity required for nighttime is also estimated by considering similar objectives. Simulation results have been presented using a 69 bustest system to demonstrate the significance of the proposed methodology.

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Multiobjective nested optimization framework for simultaneous integration of multiple photovoltaic and battery energy storage systems in distribution networks

Cited by 40 publications

References 71 publications

Modified Dragonfly Optimisation for Distributed Energy Mix in Distribution Networks

Modified Dragonfly Optimisation for Distributed Energy Mix in Distribution Networks

Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

Optimum Placement of Battery Energy Storage Systems and Solar PV Units in Distribution Networks Using Gravitational Search Algorithm

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