By Saman Korjani, Angelo Facchini, Mario Mureddu, Guido Caldarelli, Alfonso Damiano
Published in Scientific Reports (2018) 8:16658
We propose a criterion based on complex networks centrality metrics to identify the optimal position of Energy Storage Systems in power networks. To this aim we study the relation between centrality metrics and voltage fluctuations in power grids in presence of high penetration of renewable energy sources and storage systems. For testing purposes we consider two prototypical IEEE networks and we compute the correlation between node centrality (namely Eigenvector, Closeness, Pagerank, Betweenness) and voltage fluctuations in presence of intermittent renewable energy generators and intermittent loads measured from domestic users. We show that the topological characteristics of the power networks are able to identify the optimal positioning of active and reactive power compensators (such as energy storage systems) used to reduce voltage fluctuations according to the common quality of service standards. Results show that, among the different metrics, eigenvector centrality shows a statistically significant exponential correlation with the reduction of voltage fluctuations. This finding confirms the technical know-how for which storage systems are heuristically positioned far from supply reactive nodes. This also represents an advantage both in terms of computational time, and in terms of planning of wide resilient networks, where a careful positioning of storage systems is needed, especially in a scenario of interconnected microgrids where intermittent distributed energy sources (such as wind or solar) are fully deployed.
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.
Using a combination of Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) we find that, after the the change in the GC time, short term price volatility significantly decreased between 2001 and 2008 while long term price volatility is not affected by CG change. Similar results are obtained by means of spectral analysis on the price series, showing a significant reduction in its variability. The results of our analysis suggest that a dynamical regime shift of the price occurred, and such shift is linked to the GC change whereby shorter GC intervals facilitate short-term forecasting on electricity demand and better reliability on the supply side. Therefore, GC closer to real time is associated to reduced price fluctuations in the wholesale market.
