A cost-efficient application of different battery energy storage technologies in microgrids considering load uncertainty

Yamchi, Hamid Bakhshi; Shahsavari, Hossein; Kalantari, Navid Taghizadegan; Safari, Amin; Farrokhifar, Meisam

doi:10.1016/j.est.2019.01.023

Cited by 40 publications

(7 citation statements)

References 40 publications

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“…Even though the authors of [11,12], considered short forecasting predictions in models, these approaches were solved using a non-convex model which does not guarantee the optimal solution of the problem since this type model many local minimums [6,8]. Multiple investigations have also been proposed with the objective of improving the BESSs of MGs, such as multi-agent models based on market decisions [13], multi-objective particle swarm optimization [14,15], heuristic cost-benefit analysis [16], intelligent control methods [17], bee colony optimization [18], and genetic algorithm approaches [19]. Even these works present a good performance, and they cannot guarantee the optimal global solution to the problem.…”

Section: Introductionmentioning

confidence: 99%

Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization

et al. 2020

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A convex mathematical model based on second-order cone programming (SOCP) for the optimal operation in direct current microgrids (DCMGs) with high-level penetration of renewable energies and battery energy storage systems (BESSs) is developed in this paper. The SOCP formulation allows converting the non-convex model of economic dispatch into a convex approach that guarantees the global optimum and has an easy implementation in specialized software, i.e., CVX. This conversion is accomplished by performing a mathematical relaxation to ensure the global optimum in DCMG. The SOCP model includes changeable energy purchase prices in the DCMG operation, which makes it in a suitable formulation to be implemented in real-time operation. An energy short-term forecasting model based on a receding horizon control (RHC) plus an artificial neural network (ANN) is used to forecast primary sources of renewable energy for periods of 0.5h. The proposed mathematical approach is compared to the non-convex model and semidefinite programming (SDP) in three simulation scenarios to validate its accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization

et al. 2020

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“…Nevertheless, these approaches focused on AC grids with constant power loads. Multiple authors have previously proposed the integration and operation of a BESS system in electrical networks with optimization methods such as convex relaxation with multi-stage stochastic planning [11][12][13], multi-agent models based on market decisions [14], heuristic cost-benefit analysis [15], multi-objective genetic algorithms [16], multi-objective particle swarm optimization [17,18], and bee colony optimization [19]. A complete description of BESS allocation methods can be found in [20].…”

Section: Introductionmentioning

confidence: 99%

Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models

et al. 2019

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This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used.

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“…In the case of RESs, batteries are the most common systems used as energy storage systems [9]. Therefore, an energy storage system such a battery should be operated with RESs [10] to reduce power fluctuations. The optimal sizing of a hybrid RES-based system could maximize the self-sufficiency of the MG [11].…”

Section: T Dmentioning

confidence: 99%

Optimal energy management and sizing of renewable energy and battery systems in residential sectors via a stochastic MILP model

Farrokhifar

Aghdam

Alahyari

et al. 2020

Electric Power Systems Research

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Energy supply through integrated renewable energy sources (RESs) and battery systems will be of higher importance for future residential sectors. Optimal energy management and sizing for the components of residential systems can enhance efficiency, self-suffiency, and meanwhile can be cost-effective by reducing investment as well as operating costs. Accordingly, this paper proposes an exhaustive optimization model for determining the capacity of RESs, namely: wind turbines and photovoltaic (PV) systems. In this study, batteries and electric vehicles (EVs) are utilized in line with other sources to capture fluctuations of RESs. To model the uncertainties of RESs, energy prices, and load demands a linearized stochastic programming framework is applied. The proposed framework involves long-term and efficient resource development alongside with short-term management and utilization of these resources for supplying the demand load. In our study, we utilize the roulette wheel mechanism (RWM) method as well as proper probability distribution functions (PDFs) to generate scenarios for all sources of uncertainties, including wind turbines, PV systems, demand, and electricity market price. The approach is verified in two different cases, including an individual home and a larger micro-grid (MG). The results of multiple numerical simulations demonstrate the effectiveness of the proposed stochastic model.

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A cost-efficient application of different battery energy storage technologies in microgrids considering load uncertainty

Cited by 40 publications

References 40 publications

Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization

Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization

Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models

Optimal energy management and sizing of renewable energy and battery systems in residential sectors via a stochastic MILP model

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