A mixed conditional value-at-risk/information gap decision theory framework for optimal participation of a multi-energy distribution system in multiple energy markets

This study provides a unique risk-based hybrid two-stage operational model for a coordinated power and gas distribution system that enables it to engage optimally in the electricity, gas, and thermal markets in order to serve electricity, gas, and heat demands. The suggested hybrid model enables the integrated energy distribution system (IEDS) operator to apply several risk-averse methods concurrently, based on the nature of the uncertainties and the availability of data for unknown parameters. The conditional value-at-risk-based stochastic programming (CVaR-SP) technique is utilized to cover the uncertainty in wind speed, vehicle behavior in a parking lot, day-ahead energy prices, and multi-energy demands. Furthermore, the infromation gap decision theory (IGDT) model is used to control uncertainty in real-time energy prices without requiring the use of a probability distribution function or the creation of scenarios. To provide a high-flexibility structure to meet a variety of demands, the IEDS is outfitted with some local energy resources, including a gas-fired power-only unit, a gas-fired combined heat and power plant, a wind turbine, a gas boiler, an electric vehicle parking lot (EVPL), multi-energy storages (MESs), and integrated demand response (IDR). According to the numerical data, the IEDS operator can lower the risk level by 2% while raising the operating cost by 1.3%. Furthermore, the simultaneous use of MESs, EVPL in vehicle-to-grid mode, and IDR reduce operating costs by up to 17.5%.