Optimal placement of energy storage devices for loss reduction in distribution networks

Farrokhifar, Meisam; Grillo, Samuele; Tironi, E.

doi:10.1109/isgteurope.2013.6695368

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…The presented outcomes by the authors in [42] confirm that placing the storage device at bus R18 has the best effect on loss reduction in the grid. In this structure the energy storage device saves energy during the off-peak period and injects saved energy to the grid during the peak period.…”

Section: Efficiency Considerationsupporting

confidence: 55%

“…The authors have incorporated GA and PSO respectively in [41,42] to tackle the problem. In this paper, DP is selected considering high process speed and the capability of finding global optimal solutions.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…Energy storage systems are widely used in various applications such as, transportation [26][27][28][29], micro-generation systems in building [30], smart building [31], energy management [32], load management [33], voltage control [34], power dispatch [35], optimal power flow [36], power quality evaluations [37], network expansion [38], demand balancing [39], transient stability [40], loss reduction [41][42][43], and frequency control [44].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal operation of energy storage devices with RESs to improve efficiency of distribution grids; technical and economical assessment

Farrokhifar

2016

International Journal of Electrical Power & Energy Systems

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Section: Efficiency Considerationsupporting

confidence: 55%

Section: Optimization Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal operation of energy storage devices with RESs to improve efficiency of distribution grids; technical and economical assessment

Farrokhifar

2016

International Journal of Electrical Power & Energy Systems

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“…e coordinate scheduling of deployed BESSs and MT units in the uncertain renewable energy source (RES) batteries and motor system can shift the peak load, improve the absorption level of intermittent renewable energy, enhance the reliability of power system, delay the investment of power equipment, and so on [1,2]. However, the escalating presence of BESSs requires scheduling and optimizing their coordination and management [3,4]. is is because such coordinated management can absorb intermittency of renewable power generation besides significant arbitrage benefits under dynamic electricity pricing owing to their flexibility of dispatching energy at an appropriate time.…”

Section: Introductionmentioning

confidence: 99%

Optimal Coordinated Energy Management in Active Distribution System with Battery Energy Storage and Price-Responsive Demand

Xing

Zhao

Shen

et al. 2021

Mathematical Problems in Engineering

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Contemporary distribution networks can be seen with diverse dispatchable and nondispatchable energy resources. The coordinated scheduling of these dispatchable resources, together with nondispatchable resources, can provide several technoeconomic and social benefits. Since battery energy storage systems (BESSs) and microturbine units (MT units) are capital-intensive, a thorough investigation of their coordinated scheduling under the economic criterion will be a challenging task while considering dynamic electricity prices and uncertainties of renewable power generation and load demand. This paper proposes a comprehensive methodological framework for optimal coordinated scheduling of BESSs with MT unit considering existing renewable energy resources and dynamic electricity prices to maximize the daily profit function of the utility by employing a recently explored modified African buffalo optimization algorithm. The key attributes of the proposed methodology are comprised of mean price-based adaptive scheduling embedded within a decision mechanism system (DMS) to maximize arbitrage benefits. DMS keeps track of system states as a priori, thus resulting in an artificial intelligence-based solution technique for sequential optimization. Further, a novel concept of fictitious charges is also proposed to restrict the counterproductive operational management of BESSs. The proposed model and method are demonstrated on the 33-bus distribution system, and the obtained results verify the effectiveness of the proposed methodology.

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“…T HE increasing presence of electrical storage systems in power networks requires to optimize their management [1], [2]. Network losses and the price of energy are two key S. Grillo and E. Tironi are with the Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, I-20133 Milan, Italy (e-mail: samuele.grillo@polimi.it; enrico.tironi@polimi.it).…”

mentioning

confidence: 99%

Optimal Storage Scheduling Using Markov Decision Processes

Grillo

Pievatolo

Tironi

2016

IEEE Trans. Sustain. Energy

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The paper presents a method based on Markov decision processes to optimally schedule energy storage devices in power distribution networks with renewable generation. The time series of renewable generation is modeled as a Markov chain which allows for the implementation of a stochastic dynamic programming algorithm. The output of this algorithm is an optimal scheduling policy for the storage device achieving the minimization of an objective function including cost of energy and network losses. Besides this, other properties, such as energy storage placement and size, can be assessed and compared in optimized systems with different layouts

show abstract

Optimal placement of energy storage devices for loss reduction in distribution networks

Cited by 13 publications

References 15 publications

Optimal operation of energy storage devices with RESs to improve efficiency of distribution grids; technical and economical assessment

Optimal operation of energy storage devices with RESs to improve efficiency of distribution grids; technical and economical assessment

Optimal Coordinated Energy Management in Active Distribution System with Battery Energy Storage and Price-Responsive Demand

Optimal Storage Scheduling Using Markov Decision Processes

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