Integrated Energy System for Highway Service Areas Based on PV/Fuel Cell

This paper presents a comprehensive energy system for highway service areas, incorporating electricity, heating, cooling, and hydrogen utilization. A stochastic scenario generation method considering the uncertainties in energy supply and demand is proposed. Probability density functions, Latin hypercube sampling, and k-means clustering are employed to generate a set of scenarios for typifying renewable energy and load data on typical days in each quarter. An optimization algorithm is applied to determine the optimal system configuration and energy dispatch strategy. Economic comparisons are conducted, highlighting the advantages of incorporating hydrogen flow in the integrated energy system. The results demonstrate that the stochastic scenario generation method, considering uncertainty in energy supply and demand, can transform uncertain data into a series of random scenarios with corresponding probability distributions, effectively capturing the spatiotemporal uncertainties in the data and reducing computational complexity and model dimensions. Compared to conventional grid power supply, the proposed system reduces overall operating costs by 37.6%. The addition of hydrogen flow in the integrated energy system enables combined heat and power generation, facilitates effective storage of renewable energy output, reduces the investment in heat pumps and energy storage systems, and increases the proportion of "green electricity" within the system.