Multi-Objective Optimization and Performance Assessments of an Integrated Energy System Based on Fuel, Wind and Solar Energies

• Published in: Entropy (Basel, Switzerland) Vol. 23; no. 4; p. 431
• Main Authors: Li, Jingyun, Zhao, Hong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06.04.2021; MDPI
• Subjects: Alternative energy sources; Carbon dioxide; Emissions control; Energy consumption; Fuels; Gas turbines; gas-wind-photovoltaic system; Genetic algorithms; Heat; Heat pumps; Impact analysis; Indicators; integrated energy system; Integrated energy systems; multi-objective optimization; Multiple objective analysis; Natural gas; nondominated sorting genetic algorithm II (NSGA-II); Optimization; Renewable energy; sensitivity analysis; Sorting algorithms; Storage batteries; Thermal storage; Wind turbines; gas-wind-photovoltaic system; multi-objective optimization; sensitivity analysis; nondominated sorting genetic algorithm II (NSGA-II); integrated energy system
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e23040431

The integrated energy system (IES) is an efficient method for improving the utilization of renewable energy. This paper proposes an IES based on fuel, wind and solar energies, following an optimization study focused on determining optimal device capacities. The study included gas turbines, wind turbines, solar photovoltaic panels, ground source heat pumps, absorption chillers/heaters, batteries, and thermal storage. Objectives were incorporated into the optimization model for the overall performance of the IES; these included the primary energy saving rate, annual cost-saving rate, and carbon dioxide emission reduction. Then, the nondominated sorting genetic algorithm II was employed to solve the optimization problem for multiple objectives. Ultimately, the verification and sensitivity analyses of the optimization method were achieved by a case study of hospital buildings in Harbin. The optimization results indicated a primary energy saving rate, annual cost saving rate, and carbon dioxide emission reduction rate of 17.3%, 39.8%, and 53.8%, respectively. The total installed capacity for renewable energy generation accounted for 64.5% of performance optimization. Moreover, the price of natural gas affected the economic indicators of the IES–but failed to impact energy consumption indicators.