Intelligent operation of thermal storage systems based heat pump pool for cost efficiency

The customers in the rural areas experience high heating costs than in the cities. The flexibility offered by the heat pumps (HP) and increasing the renewable energy sources (RES) share can play a key role in providing cheap heat without affecting the customer comfort preferences. This paper proposed a genetic algorithm based optimal heat consumption of individual households as a pool for minimizing the energy cost. The smart models of heat pump (HP) and phase change material (PCM) based heat storage systems are developed. These models are simulated for a cluster of 20 households having solar installations with different heat consumption behaviors that are located in a selected rural area i.e., Skive municipality in North Jutland, Denmark. The thermal units are intelligently coordinated by utilizing the electricity during periods of low price and excess production from local renewable resources including solar-photovoltaic (PV), thereby estimating the optimal heat consumption profiles. This study is simulated in DIgSILENT powerfactory and optimization routine from Matlab is utilised, which demonstrates the control of flexible HP units for balancing the local generation.