Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm

Grover-Silva, Etta; Girard, Robin; Kariniotakis, Georges

doi:10.1016/j.apenergy.2017.09.008

Cited by 89 publications

(57 citation statements)

References 32 publications

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“…Indeed, the OPF problem is a tool for taking into consideration constraints and objective functions. To solve the OPF problem, wide linear convex relaxations and metaheuristic algorithms have been introduced in the literature [28], including particle swarm optimization, bacterial foraging methods [29] and artificial bee colony algorithms [30]. Nevertheless, metaheuristic algorithms generally have a substantial calculation burden, creating a problem for the real-time management of power flow in power systems.…”

Section: Optimal Power Flow Problemmentioning

confidence: 99%

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

et al. 2019

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Electric vehicles (EVs) are recognized as promising options, not only for the decarbonization of urban areas and greening of the transportation sector, but also for increasing power system flexibility through demand-side management. Large-scale uncoordinated charging of EVs can impose negative impacts on the existing power system infrastructure regarding stability and security of power system operation. One solution to the severe grid overload issues derived from high penetration of EVs is to integrate local renewable power generation units as distributed generation units to the power system or to the charging infrastructure. To reduce the uncertainties associated with renewable power generation and load as well as to improve the process of tracking Pareto front in each time sequence, a predictive double-layer optimal power flow based on support vector regression and one-step prediction is presented in this study. The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid via EVs. Moreover, vehicle to grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation.

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Section: Optimal Power Flow Problemmentioning

confidence: 99%

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

et al. 2019

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“…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]. End-users can also benefit from EESSs through reduction of the cost for the energy purchased as well as for the improvement of power quality (PQ) and reliability [7].…”

Section: Introductionmentioning

confidence: 99%

“…In the relevant literature, great effort has been paid to the optimal planning of EESSs in µGs with the objective of the cost reduction. Optimal planning of EESSs in the distribution grids is proposed in [6], based on the minimization of the investment and operation costs. In [14], a planning procedure is proposed, which takes into account the minimization of the cost of energy imported from the external grid while considering voltage support and minimization of network losses.…”

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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“…A recent review of forecasting methods at the smart-meter level is proposed by Yildiz et al [11]. This anticipation of the future electricity demand of a household is then required by other applications, such as to optimize the operation of a microgrid [12,13], or to manage smart homes through an aggregator [14]. The required forecasting horizons range from a few hours to few days ahead depending on the application.…”

Section: Introductionmentioning

confidence: 99%

Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges

et al. 2018

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In the recent years, several short-term forecasting models of household electricity demand have been proposed in the literature. This is partly due to emerging smart-grid applications, which require these kinds of forecasts to manage systems such as smart homes, prosumer aggregations, etc., and partly thanks to the availability of data from smart meters, which enable the development of such models. Since most models are academically developed, they often do not address challenges related to their implementation in a real-world environment. In the latter case, several issues arise, related to data quality and availability, which affect the operational performance and robustness of a forecasting system. In this paper, we design a hierarchical forecasting framework based on a total of 5 probabilistic models of varying complexity, after analyzing the respective performance and advantages of the models with an offline dataset. This multi-layered framework is necessary to address the various problematic situations occurring in practice and abide by the requirements for a real-world deployment. The forecasting system is deployed in a real-world case and evaluated here on data from 20 households. Field data, comprising forecasts and measurements, are analyzed for each household. A detailed comparison is drawn between the online and offline performances. Since a notable degradation is observed in the operational environment, we discuss at length the reasons for such an effect. We determine that the exact settings of the training and test periods are marginally responsible, but that the main cause is the intrinsic evolution of the demand time series, which hinders the forecasting performance. This evolution is due to unknown household characteristics that need to be monitored to provide more adaptable models.

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Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm

Cited by 89 publications

References 32 publications

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

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

Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges

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