Chance-constrained optimization of distributed power and heat storage in integrated energy networks

Abstract

Energy storage is widely recognized as an effective alternative to match the instantaneous imbalance between distributed renewable energy sources and loads. However, suitable installation sites and capacities are necessarily determined in integrated energy networks. This paper constructs a two-layer optimization model of integrated power and heat networks to obtain the optimal installation nodes and capacities of electric energy storage (EES) and thermal energy storage (TES) units. In the optimization model, the interchange of power to heat with storage units is collaborated to unitize distributed renewable energy sources and decrease comprehensive costs, including investment cost of storage units and operation cost. A chance-constrained optimization model is proposed to transform the uncertain variables of electric and heat loads into a deterministic optimization problem. The impacts of confidence levels involved in load probability distributions on the optimal nodes and capacities are analyzed. The results demonstrate that the optimal installation nodes are determined by the distributions of distributed power sources and load ratios in networks. The increased confidence level globally raises the optimal installation capacities of EES and TES. When the confidence level increases from 0.50 to 0.99, the utilization rate of wind power increases by 3.91 %, whereas the comprehensive cost increases by 22.34 %. The hybrid integrations of EES and TES consume more uncertain renewable power and improve the economic performances by the interchange of power to heat.