Thermal energy demand fulfillment of Kolhapur through modeling and optimization of integrated renewable energy systems. (June 2019)

• Record Type: Journal Article
• Authors: Wagh, M.M.; Kulkarni, V.V.
• Is Part Of: Renewable energy focus. Volume 29(2019)
• Page Start: 114
• Page End: 122
• Publication Date: 2019-06
• Subjects: Renewable energy sources -- Periodicals; Solar energy -- Periodicals; 333.79405
• DOI: 10.1016/j.ref.2019.03.004
• Languages: English
• ISSNs: 1755-0084

Abstract

Highlights: We used Microsoft XL optimizer and Particle Swarm Optimization to find optimized integrated combinations of RES. Thermal energy demand data is generated for a midtownand found highest requirement of hot water and cooking heat. Solar water heaters, biogas and biomass gasifier are the most promising RES technologies to satisfy the thermal energy demand of a midtown. Abstract : To reduce the dependency on fossil fuels, the renewable energy systems must be utilized in an interactive manner and need to be optimized. Currently, the thermal energy demand of any city is mostly satisfied by the conventional resources. It produces considerable strain on fossile fuel reserves and lead to increase green house gas emissions also. The thermal demand of city can be satisfied by integrative use of renewable energy resources. This integrative use needs modelling and optimization. In this paper, optimised combinations of three renewable energy systems, such as solar water heating systems, biomass gasifiers and biogas energy systems are presented to satisfy the thermal demand of a region. A Microsoft excel optimizer software is used to solve the multiobjective optimization problem to find the optimum combinations. Minimization of lifecycle cost and CO2 emission are considered as main two multiobjectives. Particle Swarm Optimization (PSO) algorithm is utilized to solve the constraint optimization problem. The thermal energy demand satisfaction of Kolhapur city, a midtown city, in western Maharashtra, India, is taken for case study. The daily average thermal energy demand of one day for each month is satisfied in this case study for the whole year.