Simulation and analysis of renewable energy resource integration for electric vehicle charging stations in Thailand

This paper presents simulation results and analysis of renewable energy system integration to supplying EV charging stations using Provincial Electricity Authority (PEA) head office located in Bangkok, the capital territory of Thailand. This study has incorporated three types of renewable energy resources, i.e. solar, wind and energy storage. The MERIT™ program is being used in this study to simulate the system performance. However, relevant system data and other parameters, i.e. energy matching (%) between power demand and power supplied from renewable energy resources, capital cost (£) incurred in building renewable energy system, the amount of surplus and deficit (kWh), are also brought into consideration. This work also targeted to devise the annual proportion of three size cases of EVs available in market today - small, medium and large, respectively. On comparing the simulation results with real electricity generating situations, it is envisaged that the obtained solutions being employed to improve performance of a completely installed renewable energy system integrated into EV charging stations, located at PEA head office, are expected to alleviate the electricity use of the grid and meet the charging demand of EV's in the long term

Optimizing Generation Maintenance Scheduling Considering Emission Factors

Conventional generation maintenance scheduling (GMS) is a solution to increase the reliability of power systems and minimize the operation and maintenance costs paid by generation companies (GenCos). Nonetheless, environmental aspects, such as zero carbon emissions, have attracted global attention, leading to emission costs being paid by electricity generators. Therefore, to obtain GMS plans that consider these factors, this paper proposes multi-objective GMS models to minimize operation, maintenance, and emission costs by using lexicographic optimization as a mathematical tool. A demand response program (DRP) is also adapted to decrease emission generation and operational expenditures. The probability that no generation unit (GU) fails unexpectedly and the average net reserve value, comprising the system reliability with and without considering the GU failure rate, are demonstrated. Numerical examples are implemented for the IEEE 24-bus reliability test system. A GMS algorithm presented in a published work is run and compared to verify the robustness of the proposed GMS models. Our results indicate that this paper provides comprehensive approaches to the multi-objective GMS problem focusing on operation, maintenance, carbon, and DRP costs in consideration of technical and environmental aspects. The use of lexicographic optimization allows for the systematic and hierarchical consideration of these objectives, leading to significant benefits for GenCos