Aging Cost Optimization for Planning and Management of Energy Storage Systems

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.

Optimal positioning of storage systems in microgrids based on complex networks centrality measures

“…For each configuration we then compute the optimized impact of the ESS on the voltage stability of the system by means of Genetic Algorithm-based Multi-period Optimal Power Flow (GA-MPOPF) recently published 9 , and extended with a reactive power optimization step presented in the methods section. The effect of the ESS position has been performed by placing it in all the system nodes.…”

“…To compute the voltage distributions we used a MPOPF method based on Genetic Algorithms optimization (GA-MPOPF), first proposed in 9 , and now improved with a further reactive power optimization step, described in detail in the following. This method has been used for the estimation of the varying impact of ESS on voltage regulation, when positioned on different nodes of the system.…”

“…In particular, ESS are considered crucial for the full adoption of RES, because of their ability to limit network unbalances caused by fluctuating RES, by storing the surplus of energy produced for a future use. Furthermore, ESS are important under the economical point of view thanks for the increase system stability that they ensure, even considering limits like degradation due to calendar and cycle aging 7 , 8 and the installation costs, that according to the type of use may hamper the lifetime of ESS reducing the Return of Investments 9 . On the other hand, increasing attention has been also paid in the definition of grid planning features oriented to the enhancement in terms of passive resilience, and, particularly in last years, the complex networks methods has been proven to be a solid framework for capturing and describing complex phenomena in power grids 4 , 10 , 11 .…”

“…Here we propose a general approach able to put in evidence how the centrality of the power network nodes can be used as a criterion for the optimal placement of ESS. We compute the impact of ESS on the network by means of a recent method based on genetic algorithms and optimal power flow 9 , further improved with a step for the reactive power optimization. Results show that there is a statistically significant correlation between the node eigenvector centrality and the optimal position of ESS, with a positive impacts on their voltage regulation abilities, and a overall reduction of the voltage fluctuations by a value up to 50%, significantly increasing the power quality.…”

An online energy management tool for sizing integrated PV-BESS systems for residential prosumers

“…Afterwards, different BESS sizes are considered by introducing a sizing coefficient (K m ) that amplifies both BESS energy and power base capability (E 0 and P 0 , respectively). Consequently, for each (S n ,K m ) pairs of values, the BESS is driven to maximize the user's selfsufficiency through a Genetic Algorithm Multi Period Power Flow approach (GA-MPOPF) [49,50], by complying with all the system constraints. The GA-MPOPF ends when both n and m reach the corresponding maximum values (N and M , respectively, which should be set in accordance with typical maximum sizes for residential users), delivering the user's self-sufficiency map to the Clustering module.…”