MODELING AND OPTIMIZATION OF DISTRICT COOLING SYSTEM

Abstract

This research focuses on modeling and optimization of the design and operation of a district cooling system (DCS). The research investigates two solution paths: (1) the system transformation leading to re-organizing the conventional system (powered by the grid electricity) to achieve better economic and energy performance, and (2) the system integration of solar cooling technologies to enhance sustainable development for decreasing CO2 emissions. The multi-chillers district cooling plant is modeled by using algebraic modeling language (AML). The mathematical model is developed as a mixed-integer linear programming (MILP) problem. Frameworks are proposed to ease the DCS understanding and bridge the system modeling and optimization gap. A Model-Based System Engineering (MBSE) driven by the stakeholders and system requirements is developed. The DCS model includes the design, operation, and control by considering the interconnectivity of its components. The chiller short cycling requirement is modeled as system sequencing control. The optimization leads to reducing the total cost, including the design and operation. Consequently, it becomes cost-effective and reduces energy consumption; hence, decreasing carbon emission. Results show that the change of requirements improves the system performance. Renewable energy is considered an add-on to cooling technologies. Multiple configurations of solar-cooling systems are analyzed through a generalized model by considering the energy prices and the available installation area. The results show that the electricity tariff and the available installation area have an impact on the cost competitiveness of the solar energy integration. The results show that when the electricity tariff is subsided (low), the conventional Grid-based DCS is the most competitive. However, the photovoltaic-DCS configuration is more competitive than the thermal and hybrid systems and reduces the pollution up to 58.3%. The hybrid-DCS configuration has the lowest operation cost and the highest environmental performance as it decreases pollution up to 89.5%. The thermal-DCS configuration becomes economically attractive only at high electricity tariff and medium to high available installation area, and decreases the pollution up to 43.76%. The two developed models are useful for the conceptual design phase of the DCS, where the front end engineering design (FEED) phase requires such information (optimal design and operation) for further detail development.