Risk-Constrained Scheduling of a Solar Ice Harvesting System Using Information Gap Decision Theory

Abstract

In summer, air conditioning systems with high electricity requirement are large consumers, which may lead to load-generation mismatch, cascaded outages, and wide-area blackouts. To avoid them, on-peak electrical demand of air conditioning units can be supplied via renewable energy resources such as solar. By increasing rate of solar radiations and ambient temperature, total cooling demand of residential buildings increases causing a rise in electricity consumption. Hence, use of solar energy for making ice and building space cooling not only reduces CO₂ footprints of fossil fuel-based power generation facilities and electricity usage in residential sector but also increases the coefficient of performance of the ice harvesting cycle. Meanwhile, hourly fluctuations of solar irradiance lead to uncertainty of cooling demand. Therefore, this chapter presents an information gap decision theory (IGDT)-based framework for robust scheduling of an ice storage system, which consists of air source heat pump (ASHP). In ASHP’s cooling cycle, R134a absorbs heat from inside air at evaporator coil and extracts it to ambience, while producing a cooled air with temperature of −8 °C entering a water tank for making ice crystals. Uncertain nature of building cooling load affects optimum operating point of this refrigeration process. Hence, IGDT is implemented on cooling demand to minimize total energy cost of ice storage system and assess both robustness and opportunistic aspects of optimal operating strategies for making two risk-averse and risk-seeker decisions under uncertain operating conditions, respectively.