Optimization and Performance Analysis of a Distributed Energy System Considering the Coordination of the Operational Strategy and the Fluctuation of Annual Hourly Load

Abstract

The operation strategies of a distributed energy system (DES) are usually proposed according to the electrical load (FEL) and the thermal load (FTL), which take the cooling/heating load or electric load as unique constraint conditions that result in a too high or too low equipment load rate. This paper proposes a new hybrid operation strategy (HOS) that takes the full utilization of natural gas and the minimization of power consumption from the power grid as constraints and coordinates the cooling/electricity ratio and heating/electricity ratio of buildings and equipment. In the optimization phase of a DES, an optimization method based on the discretization of the load is proposed to investigate the influence of the uncertainty of the load on the DES, which helps to avoid repeated load simulations and provides stronger adjustability by quoting the normal distribution function to obtain multiple sets of load data with different fluctuations. Further, a multi-objective optimization model combining the genetic algorithm (GA) and mixed integer linear programming algorithm (MILP) was established to find the optimal configuration of equipment capacities by comprehensively considering the annual total cost, carbon emissions, and energy efficiency of the DES. Finally, an office building example was used to validate the feasibility of the above theories and methods. Compared with the FEL and FTL, the HOS reduced the energy waste of the DES by 19.7% and 15.5%, respectively. Compared with using a typical daily load, using an annual hourly load to optimize the DES-HOS produced a better comprehensive performance and lower adverse impacts derived from load fluctuations.