Risk-Constrained Community Battery Utilisation Optimisation for Electric Vehicle Charging with Photovoltaic Resources

TL;DR

This paper presents a long-term planning model for community batteries that optimally store renewable energy and charge EVs, enhancing grid stability and supporting net-zero emissions, using a novel stochastic approach and mixed-integer programming.

Contribution

It introduces a novel stochastic mathematical model for battery investment decisions considering uncertainties in renewable generation and EV demands, optimized via mixed-integer linear programming.

Findings

Optimal battery sizing and type selection for PV surplus utilization.

Enhanced grid stability through strategic battery deployment.

Facilitation of EV integration towards net-zero emissions.

Abstract

High penetration of renewable generation in the electricity grid presents power system operators with challenges including voltage instability mainly due to fluctuating power generation. To cope with intermittent generation, community batteries introduce an elegant solution for storing excess generation of renewable resources and reverting to the grid in peak demand periods. The question of the right battery type and size coupled with the right investment is challenging. Furthermore, the growth in adapting EVs imposes additional demand challenges on the power system compared to traditional industrial and household demand. This paper introduces long-term planning for community batteries to capture the surplus generation of PV resources for a given area and redirect these resources to charge EVs, without direct injection to the grid. For long-term investment planning on batteries, we consider 15 years' worth of historical data associated with solar irradiance, temperature, EV demands, and household demands. A novel stochastic mathematical model is proposed for decision-making on battery specifications (the type and capacity per year) based on the four standard battery types provided by the CSIRO in Australia. Uncertainties related to the EVs and RESs are captured by a non-parametric robust technique, named information gap decision theory, from optimistic and pessimistic perspectives. The investment decision-making part is formulated as mixed-integer linear programming taking advantage of the powerful commercial solver -- GUROBI -- which leads to finding feasible global solutions with low computational burden. The outcomes of this investigation not only detect optimal battery installation strategies to improve the stability profile of the grid by capturing the excess generation of PV resources but also facilitate EV integration in the community toward reaching net-zero emissions targets.