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Industrial parks cover a variety of production capacities and energy-consuming entities, with large load demand and complex energy-using structure, and common problems such as low energy utilization efficiency and unreasonable energy structure. The construction of an integrated energy system (IES) with a combined cooling, heating and power system as the core unit in the industrial park is of great significance for achieving reliable, efficient and clean energy use in the park. Therefore, this article is based on the integrated energy system of the industrial park, aims at the lowest total cost of park operators, and considers the constraints of grid node balance, equipment output and energy storage equipment, and constructs source-grid-load-storage linkage operation optimization model, and build a chaotic particle swarm algorithm (CPSO) to solve the model. Finally, a typical industrial park in my country is taken as an example to analyze the scientificity of the model.

The economy of the Regional Integrated Energy System (RIES) scheduling scheme is affected by the parameter accuracy of different energy conversion equipment models. The traditional static energy hub (EH) model regards the equipment efficiency as a constant and ignores the variable condition characteristics of the equipment efficiency changing with the load rate. This paper presents an optimal scheduling method of RIES considering the characteristics of equipment under variable operating conditions. Firstly, the architecture of the integrated energy system is analyzed. Secondly, based on the DEH model, an optimal scheduling method of RIES was proposed considering the characteristics of equipment under variable operating conditions. Finally, an optimal scheduling analysis is performed for a typical RIs. The results show that the proposed method can improve the accuracy of the equipment model and reduce the cost prediction error by considering the variable operating conditions of the equipment, thus providing a more economical scheduling scheme for Ries.

This paper studies the design of clean electric energy alternatives based on electric energy alternative equipment. First, sort out the current research status of energy substitution at home and abroad, and determine the research focus of electric energy substitution; list the electric energy substitution equipment that is suitable for construction, industrial, commercial and other fields, analyze its technical characteristics and specific application scenarios; list each device To analyze the performance of the equipment, establish a multi-factor output model for energy storage and production equipment according to the key influencing factors of the equipment performance coefficient; finally, based the above research, consider the substitutability of each equipment With complementary characteristics, the design is suitable for high-clean energy electric energy to replace typical structures.

Traditional demand response technologies optimize the load profile by cutting or shifting the electrical load, but they affect the customer’s energy experience, and thus the customer’s motivation to participate in demand response and load controllability are limited. For community integrated energy operators, the study of integrated customer demand response can explore their dispatch potential. Based on consumer psychology, we study the alternative energy use behavior of residential customers, establish a customer optimization model and a community integrated energy operator optimization model, and set up a Stackelberg master-slave game model with the operator as the leader and the customer as the follower. The simulation case verifies that the utilization of load-side multi-energy complementary characteristics and integrated demand response behavior can effectively improve the utilization of renewable energy and the efficiency of integrated energy operators.

The planning of integrated energy system is a very complex multi-target, multi-constraint, nonlinear, random uncertainty mixed integrated combination optimization problem, its planning and design process should not only consider the interdependence between the system capacity, energy conversion, energy storage, energy use and other links, but also consider the interaction and integration of cold, hot, electricity and other multi-energy flows, which is essentially a non-deterministic polynomial difficult problem. China’s energy continues to develop rapidly, all kinds of sensors and intelligent equipment data is increasing, the data obtained in the equipment and all kinds of sensors collected energy load prediction related factors such as temperature, weather, wind speed and other data volume increased dramatically, the data dimension is also increasing, the scale of data has also increased from GB to TB or even higher, based on the traditional prediction methods and intelligent prediction methods, has been far below the load forecast desired to achieve accuracy and speed requirements, Therefore, the use of big data technology to predict energy demand is an important future direction.

At present, regional integrated energy system is considered as a novel form to improve the comprehensive energy efficiency and economy of the system by integrating multiple energy subsystems. The key link of integrated energy system planning and optimization is to determine the kind and installed capacity of source-side equipment, and how to make the equipment of the system cooperate and couple with each other. A model of equipment capacity allocation at the source side of integrated energy system is proposed, which can get the best equipment kind and installed capacity in the planning and optimization area. The objectives of the model are the optimal economy and the minimum carbon emission. In order to verify the effectiveness of the model proposed in this paper, three typical scenarios are set for comparative analysis. In the simulation, by comparing the economy and carbon emission of planning results in different scenarios, it can be found that there is a potential connection between the installed capacity of source-side equipment, and at the same time, the carbon emission will also affect the installed capacity of equipment. Renewable energy equipment has made great contribution to carbon emission, but its investment cost has greatly increased the system planning cost. Therefore, system economy and carbon emission are mutually exclusive to a certain extent.

In response to the global energy crisis, the integrated energy system has become an important part of the energy revolution. Aiming at the economic operation of the Integrated Energy System (IES), this paper proposes a system optimization planning model that takes the economic and environmental optimization as the goal and establishes a coupled capacity planning system, and jointly optimizes the capacity configuration and configuration of a single energy supply module. Power output to pursue maximum benefit. In order to illustrate the advantages of this method, on the basis of simulation, a dynamic programming method is used to solve the model. Finally, the simulation results show the effectiveness of the planning optimization model proposed in this paper, and provide a new path for solving the optimization problem of integrated energy system planning.