Two-stage capacity optimization approach of multi-energy system considering its optimal operation

Luo, X. J.; Oyedele, Lukumon O.; Akinade, Olugbenga O.; Ajayi, Anuoluwapo O.

Abstract

With the depletion of fossil fuel and climate change, multi-energy systems have attracted widespread attention in buildings. Multi-energy systems, fuelled by renewable energy, including solar and biomass energy, are gaining increasing adoption in commercial buildings. Most of previous capacity design approaches are formulated based upon conventional operating schedules, which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system. Therefore, a two-stage capacity optimization approach is proposed for the multi-energy system with its optimal operating schedule taken into consideration. To demonstrate the effectiveness of the proposed capacity optimization approach, it is tested on a renewable energy fuelled multi-energy system in a commercial building. The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit, heat recovery unit, heat exchanger, absorption chiller, electric chiller, biomass boiler, building integrated photovoltaic and photovoltaic thermal hybrid solar collector. The variable efficiency owing to weather condition and part-load operation is also considered. Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization, respectively. The two optimization stages are interrelated; thus, the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively. With the adoption of the proposed novel capacity optimization approach, there is a 14% reduction of year-round biomass consumption compared to one with the conventional capacity design approach.

• Journal Article Type: Article
• Acceptance Date: Apr 1, 2020
• Online Publication Date: Apr 24, 2020
• Publication Date: Aug 1, 2020
• Deposit Date: May 8, 2020
• Publicly Available Date: May 11, 2020
• Journal: Energy and AI
• Print ISSN: 2666-5468
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Article Number: 100005
• DOI: https://doi.org/10.1016/j.egyai.2020.100005
• Keywords: Multi-energy system, Renewable energy, Biomass, Genetic algorithm, Capacity design, Optimization
• Public URL: https://uwe-repository.worktribe.com/output/5964726