Optimized Operation of Integrated Energy Microgrid with Energy Storage Based on Short-Term Load Forecasting

Abstract

This research proposes an optimization technique for an integrated energy system that includes an accurate prediction model and various energy storage forms to increase load forecast accuracy and coordinated control of various energies in the current integrated energy system. An artificial neural network is utilized to create an accurate short-term load forecasting model to effectively predict user demand. The 0–1 mixed integer linear programming approach is used to analyze the optimal control strategy for multiple energy systems with storage, cold energy, heat energy, and electricity to solve the problem of optimal coordination. Simultaneously, a precise load forecasting method and an optimal scheduling strategy for multienergy systems are proposed. The equipment scheduling plan of the integrated energy system of gas, heat, cold, and electricity is proposed after researching the operation characteristics and energy use process of the equipment in the combined power supply system. A system economic operation model is created with profit maximization in mind, while also taking into account energy coordination between energy and the power grid. The rationality of the algorithm and model is verified by analyzing the real data of a distributed energy station in Wuhan for two years.

Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles

“…Furthermore, the study concluded that microgrids are experiencing growing global interest and can derive advantages from the effective implementation of converter control techniques, energy management systems, power quality enhancement strategies, and the optimal integration of electric vehicles and energy storage systems. Additionally, a day-ahead optimal scheduling approach for integrated energy systems was introduced, which incorporates precise load prediction models and multiple energy storage technologies to enhance economic optimization [27]. The integrated energy system was represented by a mixed-integer linear programming (MILP) model, where the goal function aimed at optimizing comprehensive income.…”

Vulnerable Area Identification of Islanded Combined Electrical and Heat Networks Based on Static Sensitivity Analysis

“…Traditional fossil fuels are facing the problems of depletion and environmental pollution, and to alleviate this problem, the integrated energy system (IES) can improve the utilization of energy by unified planning, design, and operation optimization of multiple energy sources such as electricity, gas, and heat [1][2][3]. Therefore, IESs have been rapidly developed in various countries and regions in recent years and are one of the popular research areas at the intersection of electrical engineering, energy, thermal, and low carbon economy [4][5][6][7]. However, due to the integration of multiple energy systems in the IES, disturbances in one subsystem have the potential to be transmitted to other subsystems via the coupling elements, thereby resulting in an impact on this system.…”

Distributionally Robust Optimal Dispatching of CHP Microgrid considering Concentrating Solar Power and Uncertainty

“…Li et al [21] adopt the multicycle prediction method of renewable energy generation and load based on automatic reinforcement learning and propose an optimal dispatching model of the isolated microgrid. Dong H et al [22] propose an integrated energy system optimization technique, including accurate prediction models and multiple forms of energy storage, to improve the load forecasting accuracy and coordinated control of various energy sources in the current integrated energy system. In addition, demand response is also an important factor to be considered in the operation of the microgrid.…”