(340o) Multi-Objective Design Optimisation of a Distributed Energy System through 3E (economic, environmental and exergy) Analysis

To facilitate the commitments of reducing greenhouse gas emissions will require the utilisation of renewable energy resources, as well as shifting away from a centralised generation. Distributed energy systems (DESs) are a promising alternative to conventional centralised layouts. Thus, there is a need for the development of models able to optimally design DES which show savings in cost as well as having a low carbon impact. Current literature focuses on the design optimisation of a DES through economical and environmental cost minimisation [1-4]. However, these two criteria alone do not show the complete picture and do not satisfy the long-term sustainability priorities. The inclusion of exergy analysis allows for the satisfaction of this criteria through the rational use of energy resources. The use of exergy analysis within DESs was first studied by [5], with a multiobjective approach whereby cost and exergy efficiency are considered. The novelty of this paper is twofold. The first is the investigation of exergy DES design optimisation through a multiobjective approach whilst considering the economic and environmental cost, thus making this work the first to simultaneously minimise three objective functions in the context of DES. Multiple energy generation technologies are considered (e.g. photovoltaic panels, natural gas boilers, CHP units), as well as the possibility to satisfy the energy demand from the national grid. The multiobjective problem is developed with the aim to optimally select the type and number of technologies within the DES, as well as the layout of the heating pipeline network and the operation of the technologies. The second is the use and comparison of the two most commonly used solution methodologies for solving multiobjective optimisation problems, namely the weighted sum method and the epsilon-constraint method. The approach is applied to a case study of a neighbourhood of 5 houses in Bristol (UK), considering the calendar year split into 24 different periods (6 periods per day for 4 representative seasons). Out of the set of Pareto optimal solutions, a best compromised solution is chosen using the fuzzy-based method. Additionally, the Pareto frontier provides decision makers a number of solutions available to them based on economic, environmental and sustainability priorities. Numerical results reveal reduction of 58-92% in the environmental cost and of 89-91% in the primary exergy input.

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2. Wouters, et al., 2015, Renewable Energy and Environmental Sustain. 2(5), pp. 1-6
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4. Yang, et al., 2017, Applied Thermal Engineering 110, pp. 1358-1370
5. Di Somma, et al., 2015, Energy Conversion and Management 103, pp. 739-751