4.7 Article

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

期刊

JOURNAL OF ENERGY STORAGE
卷 55, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.est.2022.105662

关键词

Integrated energy network; Power networks; Heating networks; Energy storage; Uncertainty; Chance -constrained programming

资金

  1. National Natural Science Foundation of China
  2. Bureau of Shihezi Science Technology
  3. [51876064]
  4. [2021ZD02]

向作者/读者索取更多资源

This paper presents a two-layer optimization model for determining the optimal installation nodes and capacities of electric energy storage (EES) and thermal energy storage (TES) in integrated energy networks. By utilizing storage units to convert electric power into heat, the economic performance and utilization rate of renewable energy sources are improved.
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 neces-sarily determined in integrated energy networks. This paper constructs a two-layer optimization model of in-tegrated 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 compre-hensive 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 optimi-zation 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.

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