Charging station Stochastic Programming for Hydrogen/Battery Electric Buses using Multi-Criteria Crow Search Algorithm

Abstract

Today, the zero-emission transport system is a public will in megacities. A promising alternative among the countermeasures is to employ Battery Electric Buses (BEB) and Fuel-Cell Buses (FCB) at reasonable prices. This paper addresses Multi-product Charging Stations (MCS) in the selected bus terminals for refilling hydrogen and electricity. The core of MCS is constituted of a pair of Electrolyzer/Fuel Cell working back to back by Proton Exchange Membrane (PEM) technology. The innate flexibility in the operation of such a configuration enables for providing regulating services when the market price is soaring. The multi-criteria programming for the hourly schedule in different weather conditions is investigated using the Crow Search Algorithm. The objective is to enhance the daily profit of charging stations through the optimized multi-products/services selection. Uncertainty in wind speed, market price, and electric vehicles is considered in scenarios. The weightings are decided through the Analytic Hierarchy Process (AHP) and Criteria Importance through Inter-criteria Correlation (CRITIC) methods. The superiority of MC-CSA Stochastic Programming is showcased by simulations on a modified 33-bus IEEE distribution system using DK2 (east Denmark) data under subsidized and liberal markets. Regarding the daily profit outcomes, CRITIC was advantageous on windy days when the market prices vary with wind speed whilst AHP was preferred in low wind hours. The most lucrative operations are achievable when the products are fully accepted by regulating markets, BEBs, and FCBs on windy days. The results indicate that the additional remuneration on green hydrogen leaves a deeper impact rather than subsidies on electricity prices for EVs.