Sizing energy storage systems in DC networks: A general methodology based upon power losses minimization

Fantauzzi, M.; Lauria, D.; Mottola, Fabio; Scalfati, Andrea

doi:10.1016/j.apenergy.2016.11.044

Cited by 27 publications

(15 citation statements)

References 28 publications

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“…In this sub-section, the formulation of the problem concerning the minimization of power losses in DC networks and its analytical solution in presence of EESSs are repeated briefly based on reference [40]. The solution of this minimization problem provides the optimal energy capacity to be requested of the EESSs connected to the grid.…”

Section: Analytical Evaluation Of the Optimal Sizing Of The Eesssmentioning

confidence: 99%

“…The balance Equation 5, allows the inclusion of an isoperimetric constraint that can be managed analytically in the proposed minimization problem. In particular, based on a generalized matrix model of the DC network and on a linear approximation of the load flow Equation 4, a generalized methodology for the analytical solution of the minimization problem, Equations (3)-(5) was proposed in [40]. Thus, a suitable network's resistance matrix can be built with the aim of providing the calculus of the network's power losses as a function of the nodal injected currents.…”

Section: Analytical Evaluation Of the Optimal Sizing Of The Eesssmentioning

confidence: 99%

“…where 1 is the vector of all ones, and R = G −1 EE . Thus, a closed form expression for the network power losses can be derived as [40]:…”

Section: Analytical Evaluation Of the Optimal Sizing Of The Eesssmentioning

confidence: 99%

“…The probabilistic procedure proposed in this paper uses a Monte Carlo-based method that applies an analytical optimization of different EESSs iteratively in DC networks in the presence of renewable-based, distributed generation (DG) units and PEVs. The analytical formulation was based on the general methodology presented in [40], which allows determining the sizes of the EESSs that would be required to minimize the grid power losses. Among different scenarios characterized by different penetrations of PEVs and DG units, the sizes of EESSs have been derived from the results of the Monte Carlo simulation procedure which is widely adopted for statistical studies [41].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks

et al. 2018

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The growing diffusion of electric vehicles connected to distribution networks for charging purposes is an ongoing problem that utilities must deal with. Direct current networks and storage devices have emerged as a feasible means of satisfying the expected increases in the numbers of vehicles while preserving the effective operation of the network. In this paper, an innovative probabilistic methodology is proposed for the optimal sizing of electrical storage devices with the aim of maximizing the penetration of plug-in electric vehicles while preserving efficient and effective operation of the network. The proposed methodology is based on an analytical solution of the problem concerning the power losses minimization in distribution networks equipped with storage devices. The closed-form expression that was obtained is included in a Monte Carlo simulation procedure aimed at handling the uncertainties in loads and renewable generation units. The results of several numerical applications are reported and discussed to demonstrate the validity of the proposed solution. Also, different penetration levels of generation units were analyzed in order to focus on the importance of renewable generation.

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Section: Analytical Evaluation Of the Optimal Sizing Of The Eesssmentioning

confidence: 99%

Section: Analytical Evaluation Of the Optimal Sizing Of The Eesssmentioning

confidence: 99%

“…where 1 is the vector of all ones, and R = G −1 EE . Thus, a closed form expression for the network power losses can be derived as [40]:…”

Section: Analytical Evaluation Of the Optimal Sizing Of The Eesssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks

et al. 2018

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“…Minimization of investment and operation costs in distribution networks is considered in the planning tool proposed in [16], which accounts for constraints on bus voltages. Costs of both power losses and EESS' operation are included in the analytical planning tool presented in [17]. In [18], the optimal allocation of EESSs in distribution networks is performed on the basis of a tradeoff among investment and operation costs, technical constraints and 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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Robust PSO ‐based framework for concurrent active/reactive and reserve management in microgrids in the presence of energy storage systems

2021

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Summary Nowadays, concurrent participation of distributed energy resources in providing electrical energy and ancillary services brings many benefits. Also, energy storage systems (ESSs) along with renewable generations have a growing penetration rate in modern power systems aimed at declining environmental issues. Due to the uncertainties of intermittent nonpredictable power resources in microgrids (MGs), high integration of these products leads to an increase in ancillary services requirements and it necessitates the coordinated management of these technologies with the ESSs. In this paper, for the first time, a robust model based on particle swarm optimization (PSO) metaheuristic is developed to cope with the uncertainty of renewables in concurrent active/reactive and reserve management problem in the MG with ESS. The robust framework provides a medium priority in comparison with deterministic and stochastic techniques. The objective function of the robust concurrent active/reactive and reserve scheduling problem in MGs is expressed as maximizing social welfare (SW). The proposed model is carried out using a max‐min optimization scheme. The robust design will be attained in such a way that the maximizer at the outer level seeks an optimal solution against the worst‐case objective function achieved through the minimizer at the inner level taking into account the uncertainty neighborhood. The performance of the presented approach has been evaluated on a typical MG. Simulation results verify that the suggested robust‐PSO technique will aid MG operators to decrease daily operational costs and to yield a higher SW.

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Sizing energy storage systems in DC networks: A general methodology based upon power losses minimization

Cited by 27 publications

References 28 publications

A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks

A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks

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

Robust PSO ‐based framework for concurrent active/reactive and reserve management in microgrids in the presence of energy storage systems

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