气候变化领域集成服务门户

Solar and wind energy are the significant renewable energy sources that can be used to tackle the climate change issue. The aim of the study is to design and compare different architectures of community-level energy systems, in order to find a positive energy community in cold climate. The design proposed is a centralized solar district heating network, which is integrated with renewable-based electricity network to meet the heating and electrical demand of a community of 100 houses. The renewable-based energy system consists of photovoltaic panels, wind turbines and stationary electrical storage. In present study the demand of the building appliances, district heating network auxiliaries and electric vehicles are included. TRNSYS is used to simulate these systems. Lastly, multi-objective optimization is done using MOBO (Multi-objective optimization tool). The objective of the optimization problem is to minimize two objective functions-the imported electricity and the life cycle costs. The onsite energy fraction, matching and exported electricity are also evaluated for comparison. The optimization results illustrate that in terms of imported energy, the cases with 600 kW (200 wind turbines) and storages are better compared to the cases without the turbines and storage. For the high performing system (200 turbines with storages and 75 electric vehicles), the corresponding onsite energy fraction (OEF) varied from 1% to 97% and the onsite energy matching (OEM) varied from 76% to 62%, respectively, while the imported electricity can be reduced to 2 kWh/m(2)/yr. However without storage, the onsite energy fraction (OEF) varied from 1% to 58% and the onsite energy matching (OEM) varied from 90% to 27% respectively. In all the systems, initially investments are made in the wind turbines, storages and lastly in the photovoltaic panels to improve the performance of the optimized solutions. It is found that storages can improve the onsite fraction and matching. Moreover, photovoltaic becomes more important in the cases with higher number of electric vehicles.