Abstract
In recent years, many studies have proposed the use of energy storage systems (ESSs) for the mitigation of renewable energy source (RES) intermittent power output. However, the correct estimation of the ESS degradation costs is still an open issue, due to the difficult estimation of their aging in the presence of intermittent power inputs. This is particularly true for battery ESSs (BESSs), which have been proven to exhibit complex aging functions. Unfortunately, this collides with considering aging costs when performing ESS planning and management procedures, which are crucial for the exploitation of this technology. In order to overcome this issue, this paper presents the genetic algorithm-based multi-period optimal power flow (GA-MPOPF) procedure, which aims to economically optimize the management of ESSs by taking into account their degradation costs. The proposed methodology has been tested in two different applications: the planning of the correct positioning of a Li-ion BESS in the PG& E 69 bus network in the presence of high RES penetration, and the definition of its management strategy. Simulation results show that GA-MPOPF is able to optimize the ESS usage for time scales of up to one month, even for complex operative costs functions, showing at the same time excellent convergence properties.
Highlights
The wide adoption of distributed generation (DG) is changing the paradigms on which power transmission and distribution systems are based [1]
This paper introduces a novel multi-period optimal power flow (OPF) procedure based on genetic algorithms (GAs) [15,16], called GA-multi-period optimal power flow (MPOPF)
This paper presents a multi-period optimal power flow methodology based on genetic algorithms
Summary
The wide adoption of distributed generation (DG) is changing the paradigms on which power transmission and distribution systems are based [1]. The ability of ESSs to provide services to the grid is strongly time-dependent, and cannot be decoupled from their past and future usage For this reason, canonical single-period optimization approaches like optimal power flow (OPF) do not properly fit the time-dependence characteristics needed for the management and control of ESSs in power systems. Canonical single-period optimization approaches like optimal power flow (OPF) do not properly fit the time-dependence characteristics needed for the management and control of ESSs in power systems To overcome this limitation, different studies addressed this issue by proposing multi-period optimal power flow (MPOPF) techniques [8,9,10,11,12,13]
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