Thermal and Electrical Energy Mix Optimization(EMO) Method for Real Large-scaled Residential Town Plan

Abstract

Since Paris Climate Change Conference in 2015, many policies to reduce the emission of greenhouse gas have been accelerating, which are mainly related to renewable energy resources and micro-grid. Presently, the technology development and demonstration projects are mostly focused on diversifying the power resources by adding wind turbine, photo-voltaic and battery storage system in the island-type small micro-grid. It is expected that the large-scaled micro-grid projects based on the regional district and town/complex city, e.g. the block type micro-grid project in Daegu national industrial complex will proceed in the near future. In this case, the economic cost or the carbon emission can be optimized by the efficient operation of energy mix and the appropriate construction of electric and heat supplying facilities such as cogeneration, renewable energy resources, BESS, thermal storage and the existing heat and electricity supplying networks. However, when planning a large residential town or city, the concrete plan of the energy infrastructure has not been established until the construction plan stage and provided by the individual energy suppliers of water, heat, electricity and gas. So, it is difficult to build the efficient energy portfolio considering the characteristics of town or city. This paper introduces an energy mix optimization(EMO) method to determine the optimal capacity of thermal and electric resources which can be applied in the design stage of the real large-scaled residential town or city, and examines the feasibility of the proposed method by applying the real heat and electricity demand data of large-scale residential towns with thousands of households and by comparing the result of HOMER simulation developed by National Renewable Energy Laboratory(NREL).

Fig. 1. Real residential town for the energy simulation

Fig. 2. Daily pattern of heat load in winter

Fig. 3. Daily pattern of electric load in winter

Fig. 4. Electricity-heat hybrid system for HOMERsimulation

Fig. 5. Hourly status of energy facilities from HOMERsimulation by electricity load following mode

Fig. 6. Hourly status of energy facilities from EMO modelby electricity load following mode

Fig. 7. Hourly status of energy facilities from EMO modelby heat load following mode including TES

Table 1. Parameters for economic analysis

Table 2. HOMER simulation results on hourly scheduling and total energy cost

Table 3. HOMER simulation results on Optimal capacity of energy resources

Table 4. EMO model results on hourly scheduling and total energy cost

Table 5. EMO model results on optimal capacity of energy resources

Table 6. EMO model results on hourly scheduling and total energy cost for the heat load following mode with TES

Table 7. EMO model results on Optimal capacity of energy resources for the heat load following mode with TES