Information Gap Decision Theory-Based Risk-Averse Scheduling of a Combined Heat and Power Hybrid Energy System

• Published in: Sustainability Vol. 15; no. 6; p. 4825
• Main Authors: Shi, Lumin, Tian, Man-Wen, Alizadeh, As’ad, Mohammadzadeh, Ardashir, Nojavan, Sayyad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Alternative energy sources; Cogeneration; Cogeneration power plants; Decision making; Decision theory; Demand side management; Electric power demand; Electric power generation; Electric power grids; Electric power production; Electricity pricing; Energy; Energy industry; Energy management; Energy prices; Energy storage; Equipment and supplies; Heat; Heating; Integer programming; Linear programming; Load; Mathematical programming; Operating costs; Optimization; Optimization techniques; Photovoltaics; Power lines; Power plants; Risk; Risk aversion; Risk taking; Scheduling; Supply and demand; Sustainability; Uncertainty; Wind power
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su15064825

This research investigates the optimal management of electric and heat energies in a hybrid energy system (HES). In the studied HES, a pair of photovoltaic and battery storage devices is used to supply the electricity demand, and a boiler system to supply the heat demand directly. In addition, a modified cycle power plant acted as a combined heat and power (CHP) unit to increase the generation capacity and supply reliability. The HES is also able to connect to the electric grid to exchange power according to real-time energy prices. The uncertainty of renewable generation, demand levels, and energy prices challenge the decision-making process. To deal with the uncertainty of these overlapping parameters, a comprehensive information-gap decision theory (IGDT) approach is proposed in this paper that, despite other works, considers the uncertainties in an integrated framework and derives risk-averse and risk seeker strategies in different steps. The problem is modeled as mixed-integer linear programming and solved using the GAMS optimization package. Concerning simulation results, from the viewpoint of a risk-seeking decision maker, the increment of the uncertainty degree by 10.906% results in a reduced operating cost of 8.6%. From the viewpoint of a risk-averse decision maker, the increment of the uncertainty degree by 10.208% results in 8.6% more operating cost.