Microgrid energy management strategies assessment through coupled thermal-electric considerations

Battery Energy Storage Systems (BESSs) are considered as one of the most promising technological options towards high self-sufficiency of building facilities and higher independency from the main grid through autonomous operation. At the same time, BESS are an essential component for a cost-optimized operation of grid-connected microgrids. In addition, the ever-increasing trend to replace conventional heating systems with electrically driven heat pumps, reveals a continuously emerging trend for electrification supported by electrical storage systems. In this study, a grid-connected microgrid of a sport centre facility located in Barcelona (Spain), which includes i) a solar PV (Photovoltaic) installation, ii) a BESS and iii) a heat pump (HP), is investigated through dynamic simulations, considering both thermal and electrical loads. Targeting the best compromise between self-sufficiency and reduction in the cost of electricity, two different energy management strategies are examined for the supervision and control of the grid-connected microgrid operation, taking into account the impact of the already operating HVAC (Heating Ventilation and Air Conditioning) systems along with the HP operation. In particular, the investigated energy management strategies are considered to be two of the most prominent ones and involve 1) peak shaving of the microgrid consumption with off-peak grid power and 2) pricing-based operation of the BESS (arbitrage), according to the main grid electricity price. Under this scope, an integrated thermal and electrical dynamic model of the sport centre energy system is developed in APROS software. The results are benchmarked against the corresponding ones from the standard model of TRNSYS. The impact of each management strategy is assessed in terms of the resulting power flows and the associated cost of electricity, for each operational mode. Based on the simulation results, the most advantageous energy management algorithm is determined for each case study, revealing that the integrated thermal-electrical dynamic modelling can be a useful tool for designing adaptive energy management algorithms.