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This study introduces an Improved Gray Wolf Optimizer (IGWO) to enhance integrated energy system scheduling by incorporating comprehensive demand response. The new method optimizes electric and heat loads, improving new energy accommodation and reducing costs.

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Area of Science:

  • Energy Systems Engineering
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Integrated energy systems (IES) face high-dimensional nonlinear optimal scheduling challenges.
  • Traditional Grey Wolf Optimizer (GWO) often gets trapped in local optima, leading to suboptimal scheduling schemes.
  • Effective dispatchability of electric and heat loads is crucial for IES efficiency.

Purpose of the Study:

  • To propose an improved optimization algorithm for IES scheduling.
  • To develop a comprehensive demand response model for electric and heat loads.
  • To enhance the accommodation of new energy sources and reduce system operating costs.

Main Methods:

  • Developed a comprehensive demand response model including electric and heat load response.
  • Constructed an Improved Gray Wolf Optimizer (IGWO) by integrating Fuzzy C-means (FCM), Harris Hawk Optimizer (HHO), Particle Swarm Optimizer (PSO), and Bat Optimizer (BO).
  • Applied IGWO to solve the nonlinear optimal scheduling problem of IES with demand response.

Main Results:

  • The proposed IGWO algorithm demonstrated improved population diversity and enhanced ability to escape local optima compared to traditional methods.
  • The comprehensive demand response model effectively balanced electric and heat load dispatchability.
  • IGWO achieved better scheduling results, improving new energy accommodation and reducing system operating costs.

Conclusions:

  • The IGWO algorithm offers a more efficient and robust solution for IES optimal scheduling.
  • Integrating demand response with dispatchable signals enhances IES performance and promotes source-load interaction.
  • This approach contributes to a more sustainable and cost-effective energy system operation.