A demand side based source network load storage integrated grid-connected scheduling optimization method

Abstract

This invention relates to a demand-side integrated grid-connected scheduling optimization method for power generation, grid, load, and storage. Firstly, by refining the modeling and accurately quantifying the response capabilities of diverse demand-side loads, it fully explores the adjustable potential of the demand side, improving the system's flexibility and economy. Secondly, by establishing a multi-objective optimization model encompassing user satisfaction, operating costs, renewable energy consumption, and grid losses, it overcomes the limitations of traditional single-objective optimization, achieving synergistic optimization among multiple stakeholders. Thirdly, this invention employs an improved particle swarm optimization algorithm and designs a collaborative solution strategy across long and short time scales, ensuring both long-term economic balance and rapid response to renewable energy fluctuations, significantly improving the global optimality of the scheduling scheme. Finally, the scheduling scheme generated by this invention covers all aspects of power generation, grid, load, and storage, providing a complete and executable instruction set for integrated grid-connected coordinated scheduling. This invention has the advantages of precision, efficiency, convenience, and applicability.

Description

Technical Field

[0001] This invention belongs to the field of power grid dispatching technology, specifically relating to an integrated grid-connected dispatching optimization method based on the demand side, which integrates power generation, grid, load, and storage. Background Technology

[0002] With the large-scale grid connection of renewable energy, the power system faces severe challenges brought about by intermittency and volatility. The traditional "source-following-load" dispatch mode is no longer sufficient to meet the grid stability and economic operation requirements under the high proportion of renewable energy access. Currently, integrated source-grid-load-storage dispatch technology, by coordinating resources of power sources, grids, loads, and energy storage, has become a key path to improve renewable energy absorption capacity and ensure the safe operation of the system. However, existing integrated source-grid-load-storage dispatch methods focus more on the active management of the power source and grid sides, and the depth of demand-side resource exploration is insufficient. On the one hand, there is a lack of accurate quantification of the response characteristics of different types of flexible loads such as industrial, commercial, and residential loads, making it difficult to effectively utilize the demand-side response potential. On the other hand, the optimization model fails to simultaneously consider multiple objectives such as user electricity satisfaction, system operating costs, renewable energy absorption rate, and grid losses, and the solution algorithm is prone to getting trapped in local optima, making it difficult to achieve global optima. Therefore, to solve the above problems, it is necessary to develop an integrated source-grid-load-storage grid-connected dispatch optimization method that can accurately quantify the demand-side adjustability, integrate multi-objective optimization, and achieve multi-timescale collaborative solution. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for accurately quantifying demand-side adjustability, integrating multi-objective optimization, and achieving multi-timescale collaborative solution of integrated grid-connected scheduling optimization of source-grid-load-storage.

[0004] The objective of this invention is achieved as follows: a demand-side integrated grid-grid-load-storage scheduling optimization method, comprising the following steps: Step 1: Collect demand-side multi-load data and user demand response parameters, power output data of new energy and conventional power sources, grid operation parameters and energy storage equipment operation parameters within the scheduling cycle. Then, perform noise reduction and normalization on the collected data, remove abnormal data, and build a basic database for integrated scheduling of source, grid, load and storage. Step 2: Based on the demand-side data within the database, analyze the demand response characteristics of different types of flexible loads, establish a quantitative model of demand-side load response, and calculate the load adjustable capacity and response probability under different electricity price incentives and dispatch instructions, so as to achieve accurate quantification of the adjustable capacity of demand-side resources. Step 3: Taking maximizing demand-side electricity satisfaction, minimizing system operating costs, maximizing renewable energy consumption, and minimizing grid operating losses as multiple optimization objectives, and combining the quantitative results of demand-side load response in Step 2, establish a multi-objective source-grid-load-storage integrated scheduling optimization objective function, and set constraints; thereby constructing a multi-objective scheduling optimization model. Step 4: An improved particle swarm optimization algorithm that integrates chaotic initialization and adaptive weights is adopted. The optimization scheduling is decomposed into two stages through a multi-objective scheduling optimization model: long-term power balance scheduling and short-term real-time response scheduling. The global optimal scheduling scheme is obtained, which includes the power unit output plan, the energy storage system charging and discharging plan, the demand-side load adjustment plan, and the power grid power flow distribution scheme. Step 5: Distribute the globally optimal scheduling scheme to the execution units of power sources, power grids, loads, and energy storage to achieve integrated grid-connected coordinated scheduling of power sources, grids, loads, and energy storage.

[0005] Furthermore, the demand-side multi-load data in step 1 includes the power consumption, time of day, response threshold, electricity price preference, and peak-valley characteristics of industrial interruptible load, commercial adjustable load, and residential flexible load.

[0006] Furthermore, in step 4, during the long-term power balance scheduling phase, a day-ahead scheduling scheme is formulated with hourly intervals and the optimization objective of minimizing the overall system operating cost. This scheme includes start-up and shutdown plans for conventional power units, charging and discharging plans for energy storage systems, and demand-side load adjustment plans. In the short-term real-time response scheduling phase, with minute intervals and the optimization objectives of maximizing renewable energy absorption and minimizing grid operating losses, demand-side resources are continuously optimized and adjusted based on the day-ahead scheduling scheme, and real-time scheduling instructions are generated. Finally, by synergistically integrating the day-ahead scheduling scheme and the real-time scheduling instructions, the globally optimal scheduling scheme can be obtained.

[0007] Furthermore, the constraints in step 3 include system power balance constraints, equipment operation constraints, demand-side resource scheduling constraints, and line transmission and node voltage security constraints.

[0008] The beneficial effects of this invention are as follows: First, by refining the modeling of diverse loads on the demand side and accurately quantifying their response capabilities, this invention fully taps the adjustable potential of the demand side, enhancing the system's flexibility and economy. Second, by establishing a multi-objective optimization model that includes user satisfaction, operating costs, renewable energy consumption, and network losses, this invention overcomes the limitations of traditional single-objective optimization and achieves synergistic optimization among multiple stakeholders. Third, by employing an improved particle swarm optimization algorithm and designing a collaborative solution strategy across long and short time scales, this invention ensures both the long-term economic balance of the system and rapid tracking and response to renewable energy fluctuations, significantly improving the global optimality of the scheduling scheme. Finally, the scheduling scheme generated by this invention covers all aspects of the source, grid, load, and storage, providing a complete and executable instruction set for integrated grid-connected coordinated scheduling. In summary, this invention has the advantages of precision, efficiency, convenience, and applicability. Detailed Implementation

[0009] The present invention will now be further described.

[0010] Example: A demand-side integrated grid-grid-load-storage scheduling optimization method, comprising the following steps: Step 1: Collect demand-side multi-variable load data and user demand response parameters, power output data of new energy and conventional power sources, grid operation parameters, and energy storage device operation parameters within the scheduling cycle. Then, perform noise reduction and normalization processing on the collected data, remove abnormal data, and construct a basic database for integrated scheduling of power generation, grid, load, and storage. Among them, the demand-side multi-variable load data includes the power consumption, electricity consumption period, response threshold, electricity price preference, and load peak-valley characteristics of industrial interruptible load, commercial adjustable load, and residential flexible load. Step 2: Based on the demand-side data within the database, analyze the demand response characteristics of different types of flexible loads, establish a quantitative model of demand-side load response, and calculate the load adjustable capacity and response probability under different electricity price incentives and dispatch instructions, so as to achieve accurate quantification of the adjustable capacity of demand-side resources. Step 3: Taking the maximization of demand-side electricity satisfaction, the minimum system operating cost, the maximum renewable energy consumption, and the minimum grid operating loss as multiple optimization objectives, and combining the quantitative results of demand-side load response in Step 2, establish a multi-objective source-grid-load-storage integrated scheduling optimization objective function, and set constraints such as system power balance constraints, equipment operation constraints, demand-side resource scheduling constraints, and line transmission and node voltage security constraints, thereby constructing a multi-objective scheduling optimization model; Step 4: An improved particle swarm optimization algorithm integrating chaotic initialization and adaptive weights is adopted. Through a multi-objective scheduling optimization model, the optimal scheduling is decomposed into two stages: long-term power balance scheduling and short-term real-time response scheduling. These stages are solved collaboratively to obtain the globally optimal scheduling scheme. The globally optimal scheduling scheme includes power unit output plans, energy storage system charging and discharging plans, demand-side load adjustment plans, and grid power flow distribution schemes. In the long-term power balance scheduling stage, with hourly intervals and minimizing the overall system operating cost as the optimization objective, a day-ahead scheduling scheme is formulated, including conventional power unit start-up and shutdown plans, energy storage system charging and discharging plans, and demand-side load adjustment plans. In the short-term real-time response scheduling stage, with minute intervals and maximizing the renewable energy absorption rate and minimizing grid operating losses as the optimization objectives, demand-side resources are continuously optimized and adjusted based on the day-ahead scheduling scheme, and real-time scheduling instructions are generated. Finally, the day-ahead scheduling scheme and real-time scheduling instructions are collaboratively integrated to obtain the globally optimal scheduling scheme.

[0011] Step 5: Distribute the globally optimal scheduling scheme to the execution units of power sources, power grids, loads, and energy storage to achieve integrated grid-connected coordinated scheduling of power sources, grids, loads, and energy storage.

[0012] In its use, this invention first collects and preprocesses diverse demand-side load data, user demand response parameters, power output data from renewable and conventional power sources, grid operating parameters, and energy storage device operating parameters within the scheduling cycle to establish a basic database for integrated source-grid-load-storage scheduling. Then, based on the demand-side data within the database, it analyzes the demand response characteristics of different types of flexible loads, thereby establishing a quantitative model for demand-side load response. This achieves precise quantification of the adjustability of demand-side resources, fully tapping the adjustability potential of the demand side and improving the system's flexibility and economy. Finally, it aims to maximize demand-side electricity satisfaction, minimize system operating costs, maximize renewable energy absorption, and minimize grid operating losses as multiple optimization objectives, and combines... Based on the quantification results of demand-side load response, constraints are set to establish a multi-objective optimization model that includes user satisfaction, operating costs, renewable energy consumption, and grid losses. This overcomes the one-sidedness of traditional single-objective optimization while achieving synergistic optimization among multiple stakeholders. Finally, an improved particle swarm optimization algorithm integrating chaotic initialization and adaptive weights is adopted. Through the multi-objective scheduling optimization model, the optimal scheduling is decomposed into two stages: long-term power balance scheduling and short-term real-time response scheduling, which are solved collaboratively to obtain the globally optimal scheduling scheme. After completing the above operations, the globally optimal scheduling scheme is distributed to the execution units of power sources, grid, load, and energy storage, thereby realizing integrated grid-connected coordinated scheduling of power sources, grid, load, and energy storage. In summary, this invention has the advantages of accuracy, efficiency, convenience, and applicability.

[0013] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A demand-side integrated grid-grid-load-storage scheduling optimization method, characterized in that, Includes the following steps: Step 1: Collect demand-side multi-load data and user demand response parameters, power output data of new energy and conventional power sources, grid operation parameters and energy storage equipment operation parameters within the scheduling cycle. Then, perform noise reduction and normalization on the collected data, remove abnormal data, and build a basic database for integrated scheduling of source, grid, load and storage. Step 2: Based on the demand-side data within the database, analyze the demand response characteristics of different types of flexible loads, establish a quantitative model of demand-side load response, and calculate the load adjustable capacity and response probability under different electricity price incentives and dispatch instructions, so as to achieve accurate quantification of the adjustable capacity of demand-side resources. Step 3: With the goal of maximizing demand-side electricity satisfaction, minimizing system operating costs, maximizing renewable energy consumption, and minimizing grid operating losses, and combining the quantitative results of demand-side load response in Step 2, establish a multi-objective source-grid-load-storage integrated scheduling optimization objective function, and set constraints. Thus, a multi-objective scheduling optimization model is constructed; Step 4: An improved particle swarm optimization algorithm that integrates chaotic initialization and adaptive weights is adopted. The optimization scheduling is decomposed into two stages through a multi-objective scheduling optimization model: long-term power balance scheduling and short-term real-time response scheduling. The global optimal scheduling scheme is obtained, which includes the power unit output plan, the energy storage system charging and discharging plan, the demand-side load adjustment plan, and the power grid power flow distribution scheme. Step 5: Distribute the globally optimal scheduling scheme to the execution units of power sources, power grids, loads, and energy storage to achieve integrated grid-connected coordinated scheduling of power sources, grids, loads, and energy storage.

2. The demand-side integrated grid-grid-load-storage scheduling optimization method as described in claim 1, characterized in that: The demand-side multi-load data in step 1 includes the power consumption, time of day, response threshold, electricity price preference, and peak-valley characteristics of industrial interruptible load, commercial adjustable load, and residential flexible load.

3. The demand-side integrated grid-grid-load-storage scheduling optimization method as described in claim 1, characterized in that: In the long-term power balance scheduling stage of step 4, a day-ahead scheduling scheme is formulated with hourly intervals and minimizing the overall system operating cost as the optimization objective. This scheme includes the start-up and shutdown plans of conventional power units, the charging and discharging plans of energy storage systems, and the demand-side load adjustment plans. In the short-timescale real-time response scheduling phase, with a time interval of minutes, the optimization objectives are to maximize the renewable energy absorption rate and minimize the power grid operation loss. Based on the day-ahead scheduling scheme, the demand-side resources are continuously optimized and adjusted, and real-time scheduling instructions are generated. Finally, the day-ahead scheduling scheme and real-time scheduling instructions are synergistically integrated to obtain the globally optimal scheduling scheme.

4. The demand-side integrated grid-grid-load-storage scheduling optimization method as described in claim 1, characterized in that: The constraints in step 3 include system power balance constraints, equipment operation constraints, demand-side resource scheduling constraints, and line transmission and node voltage security constraints.