Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities

Abstract

In this work, a strategy for scheduling a battery energy storage system (BESS) in a renewable energy community (REC) is proposed. RECs have been defined at EU level by the 2018/2001 Directive; some Member States transposition into national legislation defined RECs as virtual microgrids since they still use the existing low voltage local feeder and share the same low-medium voltage transformer. This work analyzes a REC which assets include PV generators, BESS and non-controllable loads, operating under the Italian legislative framework. A methodology is defined to optimize REC economic revenues and minimize the operation costs during the year. The proposed BESS control strategy is composed by three different modules: (i) a machine learning-based forecast algorithm that provides a 1-day-ahead projection for microgrid loads and PV generation, using historical dataset and weather forecasts; (ii) a mixed integer linear programming (MILP) algorithm that optimizes the BESS scheduling for minimal REC operating costs, taking into account electricity price, variable feed-in tariffs for PV generators, BESS costs and maximization of the self-consumption; (iii) a decision tree algorithm that works at the intra-hour level, with 1 min timestep and with real load and PV generation measurements adjusting the BESS scheduling in real time. Validation of the proposed strategy is performed on data acquired from a real small-scale REC set up with an Italian energy provider. A 10% average revenue increase could be obtained for the prosumer alone when compared to the non-optimized BESS usage scenario; such revenue increase is obtained by reducing the BESS usage by around 30% when compared to the unmanaged baseline scenario.

Optimal Scheduling of Controllable Resources in Energy Communities: An Overview of the Optimization Approaches

“…Linear Programming [13,27,28,34] Mixed-integer linear programming (MILP) [9][10][11][12]14,16,[18][19][20][21][22][23][24][25]29,32,33,[35][36][37][38] Nonlinear programming (NLP) [15] 4.2.1. Linear Programming (LP)…”

“…MILP optimization techniques have been widely used for the management of EC resources. Authors [12] maximized the revenue of an REC, optimizing the scheduling of a BESS using an MILP formulation. Day-ahead forecasting with a time window of 24 h and a real-time prediction with an intrahour level with a 1 min time step were conducted.…”

Scenarios for the energy renovation of a residential building

“…Additionally, various articles explore the optimization of renewable energy production and self-consumption systems for air conditioning. These analyses encompass both thermal [8,9] and electrical [10] storage systems. Furthermore, there are studies assessing the integration of renewable electricity in Renewable Energy Communities, investigating the sharing dynamics within such communities [11,12].…”

Carbon-Free Electricity Generation in Spain with PV–Storage Hybrid Systems

“…Most analyses found in literature show that off-grid systems [23,24], isolated grids [25], and micro-grids [26,27] powered by renewable energy plus EES are technically and economically feasible. In the same way, different works focused on the study of the integration of renewable energy sources (RESs) in Europe also conclude that including energy storage systems as support energy systems will allow us to maintain the flexibility of the electrical grid.…”