This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction.

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

This paper presents both the techno-economic planning and a comprehensive sensitivity analysis of an off-grid fully renewable energy-based microgrid (MG) intended to be used as an electric vehicle (EV) charging station. Different possible plans are compared using technical, economic, and techno-economic characteristics for different numbers of wind turbines and solar panels, and both single and hybrid energy storage systems (ESSs) composed of new Li-ion, second-life Li-ion, and new lead–acid batteries. A modified cost of energy (MCOE) index including EVs’ unmet energy penalties and present values of ESSs is proposed, which can combine both important technical and economic criteria together to enable a techno-economic decision to be made. Bi-objective and multi-objective decision-making are provided using the MCOE, total met load, and total costs in which different plans are introduced as the best plans from different aspects. The number of wind turbines and solar panels required for the case study is obtained with respect to the ESS capacity using weather data and assuming EV demand according to the EV population data, which can be generalized to other case studies according to the presented modelling. Through studies on hybrid-ESS-supported MGs, the impact of two different global energy management systems (EMSs) on techno-economic characteristics is investigated, including a power-sharing-based and a priority-based EMS. Single Li-ion battery ESSs in both forms, new and second-life, show the best plans according to the MCOE and total met load; however, the second-life Li-ion shows lower total costs. The hybrid ESSs of both the new and second-life Li-ion battery ESSs show the advantages of both the new and second-life types, i.e., deeper depths of discharge and cheaper plans.

Responsive loads and energy storage units can play an important role to provide an economic and reliable operation of future energy systems. In this article, a stochastic energy and reserve market clearing scheme is presented considering demand response programs and energy storage devices. The approach is proposed to deal with stochastic and non-dispatchable renewable sources with high levels of penetration in the energy system. A two-stage stochastic programming scheme is formulated where in the first stage the energy market is cleared according to the forecasted amount of wind generation and demands and in the second stage the real time market is solved according to the assumed scenarios. Both stages are formulated as mixed integer linear programming problems. The introduced stochastic day-ahead scheduling scheme also regards load forecasting errors and unit/line outages. In order to generate sufficient scenarios for wind generation and demand uncertainties, the scenario generation procedure is defined as an optimization problem which is solved by particle swarm optimization algorithm.