Thanet Chitsuphaphan

EDUCATION

PhD in Operational Research, University of Essex

RESEARCH INTERESTS

Spatial forecasting model for photovoltaic system
Stochastics programming model for home energy management system
Optimization and applied statistics in various fields

Scopus Publications

EVB-Supportive Energy Management for Residential Systems with Renewable Energy Supply
Xinan Yang, Thanet Chitsuphaphan, Hongsheng Dai, and Fanlin Meng
MDPI AG
This study examines the potential role that an Electric Vehicle Battery (EVB) can play in Home Energy Management System (HEMS) based on a future development on the performance and costs of batteries. The value of EVB in an HEMS with different home connection settings and energy consumption/storage/generation capacities are investigated to advise the optimal future HEMS setups. Solar PV are considered as the residential renewal energy supply, which is the main resource of uncertainty of the system. A novel forecasting model is deployed which incorporates geographical information, solar radiation forecast and weather-related conditions into an exponential-based method to simulate day-ahead solar PV output. Optimal flows of energy and usage of storage (batteries) are then captured by a Stochastic Programming (SP) model and solved by CPLEX. Managerial insights and optimal designs of the HEMS are drawn based on the results obtained.


Stochastic Programming for Residential Energy Management with Electric Vehicle under Photovoltaic Power Generation Uncertainty
Thanet Chitsuphaphan, Xinan Yang, and Hongsheng Dai
IEEE
Battery has been used as a tool to smooth the latency of electricity supply and demand since its created. Electric vehicle (EV), which becomes more and more popular in recent years, can be seen as a consumer with its own electricity storage/battery. This article explores the potential of connecting EV battery to standard home battery and their optimal usage for energy storage in a small household system with photovoltaic (PV) power supplies. A two-stage stochastic programming model is developed with the day-ahead PV generation forecasting via spatial exponential smoothing, to optimise the storage level in home and EV batteries throughout the day so as to match self-supply and consumption to the maximum extend and save cost. Real data are used in the tests according to the UK household electricity survey and EV database, so as to inform the optimal battery size for household usage under different size of the PV supplies.