Review on the optimal placement, sizing and control of an energy storage system in the distribution network

Wong, Ling Ai; Ramachandaramurthy, Vigna K.; Taylor, Phil; Ekanayake, Janaka; Walker, Sara; Padmanaban, Sanjeevikumar

doi:10.1016/j.est.2018.12.015

Cited by 190 publications

(84 citation statements)

References 78 publications

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“…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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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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“…According to the type of energy conversion and the nature of the stored energy, ESTs may be classified as electric, chemical, mechanical and thermal [30], as shown in Figure 5. The main characteristics of ESTs, relevant for clipping, are summarized in Table 3 [31][32][33]. For a detailws description of each ESTs from Figure 5, please refer to [33,34].…”

Section: Energy Storage Technology Selectionmentioning

confidence: 99%

Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations

et al. 2019

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This paper presents an analyses of an Energy Storage System (ESS) for grid-tied photovoltaic (PV) systems, in order to harness the energy usually lost due to PV array oversizing. A real case of annual PV power generation analysis is presented to illustrate the existing problem and future solutions. Three PV modeling techniques have been applied to estimate non-measured non-harnessed PV power to provide an ESS energy and power sizing strategy. Moreover, a control strategy to store or release power from the DC-link, without modifying the Maximum Power Point Tracking (MPPT) strategy, is presented. The results show an estimation of the annual power loss caused by oversizing the PV array. The ESS sizing strategy gives insight into not only the energy requirements, but also the power requirements of the system. Simulation results show that the proposed ESS control strategy is capable of harnessing the extra power without modifying the existing power converter of the PV plant nor its control strategy.

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“…The role of the systems for storing the electrical energy is gaining more and more importance in the frame of modern power systems. This is thanks to the electrical energy storage systems' (EESSs) ability to provide a number of benefits across multiple levels [1][2][3]. Focusing on the distribution level of electrical energy systems, the EESSs' benefits are mainly related to the compensation action of the intermittent effects of renewable power sources and to the support to the operation of the network by providing services aimed at regulating voltage levels, at reducing losses, and deferring the investment on the distribution system [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Planning of Distributed Energy Storage Systems in μGrids Accounting for Voltage Dips

et al. 2020

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This paper deals with the optimal planning of the electrical energy storage systems in the microgrids aimed at cost minimization. The optimization accounts for the compensation of the voltage dips performed by the energy storage systems. A multi-step procedure, at the first step, identifies a set of candidate buses where the installation of a storage device produces the maximum benefit in terms of dip compensation; then, the life cycle costs in correspondence of different alternatives in terms of size and location of the storage systems are evaluated by considering an optimized use of the energy storage systems. The simulations on a medium voltage microgrid allowed validating the effectiveness of the proposed procedure.

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Review on the optimal placement, sizing and control of an energy storage system in the distribution network

Cited by 190 publications

References 78 publications

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

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

Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations

Planning of Distributed Energy Storage Systems in μGrids Accounting for Voltage Dips

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