CSS/Italy

Italian Chapter of Complex Systems Society

Stochastic systems

research

A Statistical Approach for Modeling the Aging Effects in Li-Ion Energy Storage Systems

By Mario Mureddu, Angelo Facchini, Alfonso Damiano

Published in IEEE Access, vol 6, 2018, 42196

 

 This paper presents a novel approach for the technical and economic assessment of Li-ion battery energy storage systems (BESS) in smart grids supported by renewable energy sources. The approachis based on the denition of a statistical battery degradation cost model (SBDCM), able to estimate the expected costs related to BESS aging, according to the statistical properties of its expected cycling patterns.

This new approach can improve the assessment of the economical sustainability of BESSs in this kind of applications, helping in this way the planning processes in electricity infrastructures in presence of high penetration of intermittent renewable energy sources. The SBDCM proposed in this paper is a statistical generalisation of a battery degradation model presented in the literature. The proposed approach has been validated numerically comparing the results with those of the deterministic model considering for the BESS a stochastic dataset of input signals. In order to test the usefulness of the proposed model in a real world application, the proposed SBDCM has been applied to the evaluation of the economic benefit associated to the development of distributed energy storage system scenarios in the Italian power system, aimed to provide ancillary services for supporting electricity market.

research

Portfolio analysis and geographical allocation of renewable sources: A stochastic approach

Antonio Scala, Angelo Facchini, Umberto Perna, Riccardo Basosi

Published in Energy Policy, 125 (2019), 154-159

We take inspiration from the Modern Portfolio Theory introduced by Markowitz to propose a simplified strategy for the portfolio management of renewable energy sources based on Gaussian fluctuations with tunable correlations.

By analysing the impact of production fluctuations, we show how – depending on the sources’ temporal correlation patterns – a careful geographical allocation of different types of renewal energy sources can reduce both the energy needs for balancing the power system and its uncertainty. The proposed strategy can be easilyintegrated in a decision support system for the planning of renewable energy sources. Therefore, providing  policy/decision makers with an additional tool. We test our strategy on a set of case studies including a real-case based on literature data for solar and wind sources, and discuss how to extend the computation to non-Gaussian sources.

The paper shows that in the Markowitz framework an efficient trade-off between production and  fluctuations can be easily achieved, and that such framework also leads to important considerations on energy security. In perspective, analysis of time series together with such enriched frameworks would allow for the analysis of multiple realistic renewable generation scenarios helping decisions on the optimal size and spatial allocation of future energy storage facilities.