Auxiliary service optimization scheduling method applied to electricity market environment

Abstract

The invention discloses an auxiliary service optimization scheduling method applied to an electric power market environment, and relates to the technical field of electric power markets, and the main points of the technical scheme are that the method comprises the steps: constructing a unified transaction rule framework, and integrating the transaction process and the settlement mode of an electric power auxiliary service market; collecting real-time operation data of the power system and auxiliary service transaction information in the power market; market clearing is carried out based on a unified transaction rule framework; constructing an optimal scheduling model with multi-factor comprehensive consideration and solving the optimal scheduling model; and selecting an optimal scheduling scheme from the non-inferior solutions, and converting the optimal scheduling scheme into a specific scheduling instruction to be issued. According to the method, a unified transaction rule framework is constructed, the transaction process of the electric power auxiliary service market is simplified, the dynamic characteristics of the electric power system, the uncertainty of new energy power generation and the random change factors of the load are integrated into the optimal scheduling model, the actual operation condition of the electric power system can be accurately reflected, and the operation efficiency of the electric power system is improved. And the rationality and adaptability of the scheduling scheme are improved.

Description

Technical Field

[0001] This invention relates to the field of electricity market technology, and more specifically, to an ancillary service optimization scheduling method applied in an electricity market environment. Background Technology

[0002] With the accelerated global energy transition, the power system is undergoing profound changes. The proportion of renewable energy generation (including wind power and photovoltaics) in the power structure continues to rise, traditional thermal power is gradually transforming into flexible power sources, and distributed energy resources are constantly emerging. At the same time, the power market mechanism is constantly improving, and the power ancillary services market, as a key link in ensuring the stable and reliable operation of the power system, is receiving increasing attention. Ancillary services cover various types such as frequency regulation, reserve power, reactive power support, and peak shaving, and can be used to address uncertainties and real-time balance needs on the generation and load sides. They are of great practical significance for improving the operating efficiency of the power system, reducing costs, promoting the consumption of renewable energy, and ensuring system reliability and power quality.

[0003] At present, the power ancillary services market has a certain trading mechanism. These mechanisms have played a positive role in regulating market operation in the early stage. However, with the diversification of market players and the increase in the complexity of the power system, the existing trading rules have gradually become cumbersome. At the same time, there are the following problems: (1) Due to the cumbersome trading rules of the power ancillary services market in some existing technologies, the trading process and settlement method of different types of ancillary services are very different, which increases the participation cost and trading difficulty of market players; (2) When constructing the optimization scheduling model, some existing technologies do not fully consider the dynamic characteristics of the power system, the uncertainty of new energy power generation and the random changes of load; (3) Most existing scheduling methods focus on single objective optimization, such as reducing the cost of ancillary services as the main objective, while ignoring other important objectives such as the reliability and environmental protection of the power system.

[0004] Therefore, the present invention aims to provide an ancillary service optimization scheduling method applicable to the power market environment to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized scheduling method for ancillary services in the power market environment. This invention simplifies the transaction process of the power ancillary services market by constructing a unified trading rule framework. At the same time, it integrates the dynamic characteristics of the power system, the uncertainty of new energy power generation, and the random variation factors of load into the optimized scheduling model, which can accurately reflect the actual operation of the power system, improve the rationality and adaptability of the scheduling scheme, and realize the comprehensive optimized operation of the power system.

[0006] By adopting a multi-objective optimization method, which takes into account multiple important objectives such as ancillary service costs, system reliability, and environmental protection, the power system has achieved comprehensive optimized operation, thereby improving the overall efficiency and social benefits of the power system.

[0007] The above-mentioned technical objective of the present invention is achieved through the following technical solution: a method for optimized scheduling of ancillary services in an electricity market environment, comprising the following steps:

[0008] S1. Construct a unified trading rules framework, integrate the trading processes and settlement methods of different types of ancillary services in the power ancillary services market, and build a centralized information platform to display and manage trading information;

[0009] S2. Collect real-time operation data of the power system and ancillary service transaction information in the power market, and preprocess the data;

[0010] S3. Conduct market clearing based on a unified transaction rules framework to determine the suppliers, purchase volumes, and transaction prices of various ancillary services;

[0011] S4. Based on the dynamic characteristics of the power system, the uncertainty of new energy power generation, and the random changes in load, construct an optimized scheduling model that considers multiple factors to achieve comprehensive optimization of power system scheduling.

[0012] S5. Solve the optimization scheduling model to obtain multiple non-dominated solutions that minimize auxiliary service costs, maximize system reliability, and maximize environmental benefits;

[0013] S6. Based on the power system's operational needs and market policy orientation, select the optimal dispatching scheme from multiple non-dominated solutions and convert it into specific dispatching instructions for issuance.

[0014] The present invention is further configured such that the process of constructing a unified transaction rule framework in step S1 includes the following steps:

[0015] S11. Sorting out and integrating the transaction rules for various auxiliary services, formulating unified transaction processes and settlement methods, and reducing the participation costs and transaction difficulties for market participants;

[0016] S12. Establish a centralized information platform to centrally display and manage transaction information, market rules, and market entity information for auxiliary services, thereby improving transaction transparency and efficiency.

[0017] The present invention is further configured such that the construction process of the optimized scheduling model in step S4 includes the following steps:

[0018] S41. Establish a dynamic model of the power system to reflect its dynamic behavior and obtain its dynamic characteristics.

[0019] S42. The probability distribution function is used to describe the fluctuation of new energy power generation, a probability model is introduced to quantify the uncertainty of new energy power generation, and it is used as the input parameter of the optimization scheduling model.

[0020] S43. Establish a stochastic model of load based on historical data and statistical analysis methods to obtain the impact of random load fluctuations on power system dispatch.

[0021] S44. Integrate the dynamic characteristics of the power system, the uncertainty of new energy power generation, and the random fluctuations of load into the optimization dispatch model to accurately reflect the actual operation of the power system.

[0022] The present invention is further configured such that: in step S5, solving the optimized scheduling model is performed by using intelligent optimization algorithms or mathematical programming methods to solve the model, generating different combinations of scheduling schemes, and obtaining multiple non-dominated solutions.

[0023] The present invention is further configured such that the process of selecting the optimal scheduling scheme in step S6 is as follows: non-inferior solutions are sorted and screened according to the reliability index, environmental protection index and ancillary service cost of the power system, and the scheduling scheme that meets the operation requirements of the power system and has the best comprehensive benefits is selected.

[0024] The present invention also provides an ancillary service optimization scheduling system applied in the power market environment, including a unified trading rule framework construction module, a data collection and preprocessing module, a market clearing module, an optimization scheduling model construction module, a solution algorithm execution module, and a decision and instruction issuance module;

[0025] The unified transaction rules framework construction module is used to integrate transaction processes and settlement methods, and to build an information platform;

[0026] The data collection and preprocessing module is used to collect power system operation data and market transaction information, and to perform preprocessing.

[0027] The market clearing module is used to clear the market according to unified trading rules and determine the suppliers, purchase quantities and transaction prices of ancillary services.

[0028] The optimized scheduling model construction module is used to construct an optimized scheduling model that considers multiple factors to reflect the actual operation of the power system;

[0029] The solution algorithm execution module is used to solve the optimization scheduling model, generate different combinations of scheduling schemes, and obtain multiple non-dominated solutions;

[0030] The decision-making and instruction-issuing module is used to select the optimal scheduling scheme from multiple non-dominated solutions and convert it into scheduling instructions for issuance.

[0031] The present invention is further configured such that: when constructing the model, the optimization scheduling model construction module takes into account the dynamic characteristics of the power system, the uncertainty of new energy power generation, and the random changes in load, and uses the minimization of ancillary service costs, the maximization of system reliability, and the maximization of environmental benefits as multi-objective optimization functions.

[0032] The present invention also provides an ancillary service optimization scheduling device for use in an electricity market environment, comprising at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to implement an ancillary service optimization scheduling method for use in an electricity market environment.

[0033] The present invention also provides a computer-readable storage medium storing computer instructions for execution by a computer to implement an ancillary service optimization scheduling method applicable in a power market environment.

[0034] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements an ancillary service optimization scheduling method applied in a power market environment.

[0035] In summary, the present invention has the following beneficial effects:

[0036] 1. This invention, by constructing a unified trading rule framework, integrates the trading processes and settlement methods of different types of ancillary services in the power ancillary services market, greatly simplifying the participation process for market participants. Market participants no longer need to spend a lot of time and energy adapting to the complex rules of different regions and different types of ancillary services, thereby significantly reducing the cost for market participants to participate in the power ancillary services market, increasing the market's attractiveness and participation. At the same time, the unified trading rules and information platform enable market participants to obtain and publish trading information more quickly, reducing information asymmetry, accelerating the speed of transaction matching, and the optimized scheduling methods and systems can more accurately match the needs of supply and demand, improving the success rate and efficiency of transactions, and helping market participants achieve better economic benefits.

[0037] 2. This invention improves the scientific nature and adaptability of dispatching. The optimized dispatching model comprehensively considers factors such as the dynamic characteristics of the power system, the uncertainty of new energy power generation, and random changes in load, so that the dispatching scheme can more accurately reflect the actual operating status of the power system. This helps to predict and respond to various uncertain events in the system in advance, such as fluctuations in new energy power generation and sudden changes in load, thereby improving the stability and reliability of the power system and reducing the risk of system operation.

[0038] 3. This invention achieves multi-objective comprehensive optimization, breaking through the limitations of single-objective optimization in existing scheduling methods. It incorporates multiple important objectives such as minimizing ancillary service costs, maximizing system reliability, and maximizing environmental benefits into the optimized scheduling model. Through reasonable weight allocation and optimization algorithms, it can achieve effective balance and coordination among various objectives, enabling the power system to achieve a better operating state in multiple dimensions such as economy, reliability, and environmental protection, thus meeting society's comprehensive demand for clean, efficient, and reliable power supply.

[0039] 4. This invention enhances the ability to accommodate new energy sources. As the proportion of new energy power generation in the power system continues to increase, its uncertainty poses a huge challenge to the scheduling and operation of the power system. This method optimizes scheduling and makes full use of various ancillary service resources to cope with the volatility of new energy sources, including frequency regulation and reserve services. It can better balance the real-time changes between new energy power generation and load, improve the power system's ability to accommodate new energy sources, and promote the realization of energy transition and sustainable development goals.

[0040] 5. This invention can promote energy transition and sustainable development. By optimizing scheduling and rationally allocating various ancillary service resources, it encourages more flexible resources (including energy storage, interruptible loads, etc.) and new energy power generation to participate in electricity market transactions. This helps to increase the proportion of new energy in the power system and reduce dependence on traditional fossil energy. It has important strategic significance for promoting the optimization and upgrading of the energy structure and achieving the carbon peak and carbon neutrality goals, and provides clean and low-carbon energy support for the sustainable development of society.

[0041] 6. This invention optimizes the operation of the power system, reduces ancillary service costs, improves system operating efficiency, and reduces power outage losses and equipment wear caused by power system instability, thereby lowering the overall electricity cost for society and contributing to improved economic efficiency. Simultaneously, it prioritizes environmental benefits during dispatching, giving priority to generators and ancillary service resources with high environmental benefits and reducing the operating time of high-polluting units. This effectively reduces pollutant emissions during power production, playing a positive role in improving environmental quality and addressing climate change, and achieving a virtuous cycle between economic development and environmental protection. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating an ancillary service optimization scheduling method applied in a power market environment according to Embodiment 1 of the present invention.

[0043] Figure 2 This is a flowchart illustrating the process of constructing a unified transaction rule framework in Embodiment 1 of the present invention;

[0044] Figure 3This is a schematic diagram of the construction process of the optimized scheduling model in Embodiment 1 of the present invention;

[0045] Figure 4 This is a schematic diagram of the structure of an auxiliary service optimization scheduling system applied in the power market environment according to Embodiment 2 of the present invention;

[0046] Figure 5 This is a schematic diagram of the framework of an auxiliary service optimization scheduling device applied in the power market environment in Embodiment 3 of the present invention. Detailed Implementation

[0047] The following is in conjunction with the appendix Figures 1-5 The present invention will be described in further detail below.

[0048] Example 1: An ancillary service optimization scheduling method applied in an electricity market environment, comprising the following steps:

[0049] S1. Construct a unified trading rules framework, integrate the trading processes and settlement methods of different types of ancillary services in the power ancillary services market, and build a centralized information platform to display and manage trading information.

[0050] This embodiment streamlines and integrates the transaction rules for various ancillary services in the power ancillary services market, establishing a unified transaction process and settlement method. Specifically, it standardizes the transaction processes for different types of ancillary services, such as frequency regulation, reserve, and reactive power, enabling market participants to participate in transactions of different types of ancillary services with similar processes, thereby reducing their participation costs and transaction difficulties.

[0051] Establish a centralized information platform for the power ancillary services market to centrally display and manage transaction information, market rules, and market participant information for various ancillary services. Through this platform, market participants can easily access and publish transaction information, improving transaction transparency and efficiency.

[0052] S2. Collect real-time operating data of the power system and ancillary service transaction information in the power market, and preprocess the data.

[0053] This embodiment obtains the hourly load forecast results for the next day, the output forecast results of new energy power plants (wind farms and photovoltaic power plants), and the power receiving plan from the power system's energy management system (EMS). Simultaneously, it obtains transaction data such as market participant bidding information from the power market trading system. The data acquisition frequency is once daily, completed the day before the scheduling day.

[0054] After data collection is completed, the collected data undergoes cleaning, verification, and preprocessing. This includes removing outliers and errors, and using linear interpolation to interpolate missing data, ensuring data accuracy and completeness. Data completeness is required to reach over 95%, and the accuracy of cleaning and verification is controlled within ±1%.

[0055] S3. Conduct market clearing based on a unified transaction rules framework to determine the suppliers, purchase volumes, and transaction prices of various ancillary services.

[0056] Based on system load forecasts and new energy power generation forecasts, and considering changes in grid operation modes, the total demand for various ancillary services (frequency regulation, reserve, etc.) is dynamically assessed and calculated. In this embodiment, the initial reserve demand during the evening peak period in the power reserve ancillary service market can be calculated using the following formula:

[0057] R peak =max(P emax ,R min )+αP genus

[0058] Among them, R peak For initial backup demand during the evening peak hours; P emax It boasts the largest single power supply capacity across the entire network; R min The minimum reserve capacity specified by the higher-level power dispatching agency; P genus The maximum predicted output of new energy sources during the evening peak hours is α; α is the reserve ratio coefficient of new energy sources, initially set at 20%.

[0059] Market participants declare the capacity and price of ancillary services according to the rules. The clearing algorithm considers various constraints (including unit ramp-up rate, maximum output, etc.) to clear the market and determine the suppliers, purchase quantities, and transaction prices for various ancillary services. In the standby market, the objective is economic optimization, i.e., minimizing the total standby cost while meeting the system's standby needs. The objective function is:

[0060]

[0061] in, Provide backup capacity R to backup supplier i during time period t. i,t The cost function; Provide backup capacity R to backup supplier i during time period t. i,t Marginal cost function; N x The number of backup suppliers; T is the number of time periods within the scheduling cycle.

[0062] The constraints that the clearing algorithm must satisfy include the availability of backups, the total amount of winning bids, and the supplier's own constraints, as shown in the following formula:

[0063] Alternative callability constraints:

[0064]

[0065] in, Indicates the unit's maximum climb rate. This represents the maximum output limit of unit i during time period t.

[0066] Total spare bid quantity constraint:

[0067]

[0068] in, This represents the system's backup capacity requirements.

[0069] Backup service provider constraints:

[0070]

[0071] in, and These represent the standby declaration quantities for suppliers i and d during time period t, respectively. The clearing algorithm employs a branch and bound algorithm, with calculation accuracy controlled within ±0.1% and calculation time not exceeding 30 minutes.

[0072] S4. Based on the dynamic characteristics of the power system, the uncertainty of new energy power generation, and the random changes in load, construct an optimized scheduling model that considers multiple factors to achieve comprehensive optimization of power system scheduling.

[0073] This embodiment uses time-series simulation to simulate the operating state of a power system at different times, establishing transient stability models for generators and dynamic variation characteristics models for loads. The time-series simulation step size is 1 second, the simulation duration is 24 hours, and the accuracy of the model is required to reach over 90%.

[0074] A probabilistic model is introduced to quantify the uncertainty of new energy power generation. Probabilistic distribution functions such as normal distribution and Weibull distribution are used to describe the fluctuation of power generation of new energy sources such as wind power and photovoltaic power, and these are used as input parameters for the optimization scheduling model. The goodness of fit of the probabilistic model is required to reach more than 95%.

[0075] Based on historical data and statistical analysis methods, a stochastic load model is established to consider the impact of random load fluctuations on power system dispatch. The historical data covers the past year, and the model's prediction accuracy is required to reach over 85%.

[0076] This model integrates the dynamic characteristics of the power system, the uncertainty of new energy generation, and the randomness of load into an optimized scheduling model to achieve comprehensive optimization of power system dispatch. The model's objective function includes minimizing ancillary service costs, maximizing system reliability, and maximizing environmental benefits. A multi-objective optimized scheduling model is constructed by comprehensively considering multiple objectives using a weighted coefficient method. The weighted coefficients are dynamically adjusted monthly based on the power system's operational needs and market policy guidance, with an adjustment range of 0.1-1.0 and an adjustment accuracy of 0.01. The model is solved using intelligent optimization algorithms, such as genetic algorithms and particle swarm optimization, with 100 iterations, a population size of 50, and a convergence accuracy of ±0.01.

[0077] S5. Solve the optimization scheduling model to obtain multiple non-dominated solutions that minimize auxiliary service costs, maximize system reliability, and maximize environmental benefits.

[0078] This embodiment employs an intelligent optimization algorithm to solve the constructed multi-objective optimization scheduling model, obtaining multiple non-dominated solutions, i.e., different combinations of scheduling schemes. During the solution process, the algorithm parameters are set as follows: the crossover probability of the genetic algorithm is 0.8, and the mutation probability is 0.1; the learning factor of the particle swarm optimization algorithm is 1.5, and the inertia weight is 0.9. The solution time does not exceed one hour, and the number of non-dominated solutions obtained is no less than 10.

[0079] S6. Based on the power system's operational needs and market policy orientation, select the optimal dispatching scheme from multiple non-dominated solutions and convert it into specific dispatching instructions for issuance.

[0080] This embodiment selects the optimal dispatch scheme from multiple non-dominated solutions based on the power system's operational needs and market policy guidance. Specifically, non-dominated solutions are ranked and screened according to system reliability indicators, environmental indicators, and ancillary service costs. The weight of reliability indicators is 0.4, environmental indicators are 0.3, and ancillary service costs are 0.3. The dispatch scheme that meets the system's operational requirements and has the best overall benefits is selected and transformed into specific dispatch instructions, which are then issued to each generating unit and ancillary service provider to guide their operation. The dispatch instructions are issued before 24:00 on the day before the dispatch day, and the accuracy of the instructions is required to reach over 99%.

[0081] Example 2: An ancillary service optimization scheduling system applied in the power market environment, including a unified trading rule framework construction module, a data collection and preprocessing module, a market clearing module, an optimization scheduling model construction module, a solution algorithm execution module, and a decision and instruction issuance module.

[0082] The unified transaction rules framework module integrates transaction processes and settlement methods to build an information platform. This module includes streamlining and consolidating transaction rules for various ancillary services, establishing unified transaction processes and settlement methods, and reducing participation costs and transaction difficulties for market participants. Simultaneously, a centralized information platform is established to centrally display and manage transaction information, market rules, and market participant information for ancillary services, improving transaction transparency and efficiency. The program is developed using Python, with the Django framework used to build the web application, and the Vue.js framework used for the front-end user interface. The program's algorithms include a rule-organizing algorithm and an information display algorithm. The rule-organizing algorithm uses a decision tree algorithm to classify and organize transaction rules for various ancillary services; the information display algorithm uses a data visualization algorithm to present transaction information and market rules to users in intuitive charts and tables. In terms of data structure, a relational database, MySQL, is used to store transaction rules, market participant information, and other data. The database table structure includes a transaction rules table, a market participant table, and a transaction information table.

[0083] The data collection and preprocessing module collects and preprocesses power system operation data and market transaction information. This module gathers real-time operational data such as system load forecasts and renewable energy generation forecasts from the power system's EMS system, and transaction data such as market participant quotations from the power market trading system. The collected data is then cleaned, verified, and preprocessed, including removing outliers and errors, and interpolating missing data to ensure accuracy and completeness. The program is developed in Java, using the Spring Boot framework for the backend service, and HTML5, CSS3, and JavaScript for the frontend data display. The program's algorithms include data acquisition, data cleaning, and data interpolation algorithms. The data acquisition algorithm uses the Socket communication protocol to acquire data in real-time from the EMS and market trading systems. The data cleaning algorithm uses a rule-based approach to verify and clean the data according to predefined data quality rules. The data interpolation algorithm uses linear interpolation to interpolate missing data. For data structure, the relational database Oracle is used to store raw and preprocessed data. The database table structure includes load forecast tables, renewable energy generation forecast tables, and market quotation tables.

[0084] The market clearing module is used to clear the market according to unified trading rules, determining the suppliers, purchase quantities, and transaction prices of ancillary services. This module dynamically assesses and calculates the total demand for various ancillary services by combining system load forecasting and renewable energy power generation forecasting with changes in grid operation modes. Market participants then declare the capacity and price of ancillary services according to the rules. The clearing algorithm considers various constraints (such as unit ramp-up rate, maximum output, etc.) to determine the suppliers, purchase quantities, and transaction prices for various ancillary services. The program is developed in C++, leveraging its high computational performance to implement complex clearing algorithms. The program's algorithms include a demand calculation algorithm and a clearing algorithm. The demand calculation algorithm calculates the total demand for various ancillary services according to the above formulas; the clearing algorithm uses a branch and bound algorithm to optimize the declared data of market participants and determine the optimal market clearing result. In terms of data structure, the in-memory database Redis is used to cache the declared data and clearing results of market participants to improve data read and write speed; at the same time, the relational database PostgreSQL is used to store historical data and result data during the clearing process. The database table structure includes a declared data table, a clearing result table, etc.

[0085] The optimized scheduling model construction module is used to build an optimized scheduling model that considers multiple factors to reflect the actual operation of the power system. This module establishes a dynamic model of the power system, a new energy generation uncertainty quantification model, and a load stochasticity model, and integrates these models into the optimized scheduling model to achieve comprehensive optimization of power system scheduling. The program is developed using MATLAB, utilizing its rich mathematical toolboxes to build and solve the model. The program's algorithms include a model building algorithm and an optimization algorithm. The model building algorithm establishes a corresponding mathematical model based on the system's physical characteristics and operating laws; the optimization algorithm uses intelligent optimization algorithms, such as genetic algorithms and particle swarm optimization, to solve the model and obtain the optimal scheduling scheme. In terms of data structure, MATLAB's workspace is used to store the variables and parameters in the model, and the model data and solution results are saved in .mat file format.

[0086] The algorithm execution module is used to solve the optimal scheduling model, generating different combinations of scheduling schemes to obtain multiple non-dominated solutions. This module receives the model constructed by the optimal scheduling model construction module, and then selects an appropriate intelligent optimization algorithm to solve it based on the model's characteristics and solution requirements, obtaining multiple non-dominated solutions, i.e., different combinations of scheduling schemes. The program's algorithms include intelligent optimization algorithms such as genetic algorithms and particle swarm optimization (PSO). The parameters for genetic algorithms include population size, crossover probability, and mutation probability; the parameters for PSO include learning factors and inertia weights. In terms of data structures, arrays and matrices are used to store the position and velocity information of individuals and particles in the population, and files are used to store the intermediate and final results of the algorithm.

[0087] The decision-making and instruction issuance module selects the optimal scheduling scheme from multiple non-dominated solutions and converts it into scheduling instructions for issuance. This module selects the optimal scheduling scheme from multiple non-dominated solutions based on the power system's operational needs and market policy guidance. Specifically, it sorts and filters non-dominated solutions based on system reliability indicators, environmental indicators, and ancillary service costs, selecting the scheduling scheme that meets system operational requirements and has the best overall benefits. This is then converted into specific scheduling instructions and issued to each generating unit and ancillary service provider to guide their operation. The program is developed in Python and uses the Flask framework to build a lightweight web service to achieve instruction interaction with each generating unit and ancillary service provider. The program's algorithms include a decision-making algorithm and an instruction generation algorithm. The decision-making algorithm comprehensively evaluates and sorts non-dominated solutions based on predefined decision rules and weight coefficients; the instruction generation algorithm generates corresponding scheduling instructions based on the selected scheduling scheme, including generator output adjustment instructions and ancillary service equipment start / stop instructions. In terms of data structures, dictionaries and lists are used to store relevant information and decision results for non-dominated solutions, and a database is used to store instruction issuance records and feedback information.

[0088] Example 3: An ancillary service optimization scheduling device applied in a power market environment includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the processor to implement an ancillary service optimization scheduling method applied in a power market environment. This method includes: constructing a unified trading rule framework, integrating the trading processes and settlement methods of different types of ancillary services in the power ancillary service market, and building a centralized information platform to display and manage trading information; collecting real-time operating data of the power system and ancillary service trading information in the power market, and preprocessing the data; conducting market clearing based on the unified trading rule framework to determine the suppliers, procurement quantities, and trading prices of various ancillary services; constructing a multi-factor comprehensive optimization scheduling model based on the dynamic characteristics of the power system, the uncertainty of new energy generation, and the random changes in load, to achieve comprehensive optimization of power system scheduling; solving the optimization scheduling model to obtain multiple non-dominated solutions that minimize ancillary service costs, maximize system reliability, and maximize environmental benefits; selecting the optimal scheduling scheme from multiple non-dominated solutions based on the power system's operational needs and market policy guidance, and converting it into specific scheduling instructions for issuance.

[0089] Example 4: A computer-readable storage medium storing computer instructions for execution by a computer to implement an ancillary service optimization scheduling method applied in a power market environment. The method includes: constructing a unified trading rule framework, integrating the trading processes and settlement methods of different types of ancillary services in the power ancillary service market, and building a centralized information platform to display and manage trading information; collecting real-time operating data of the power system and ancillary service trading information in the power market, and preprocessing the data; conducting market clearing based on the unified trading rule framework to determine the suppliers, procurement quantities, and trading prices of various ancillary services; constructing a multi-factor comprehensive optimization scheduling model based on the dynamic characteristics of the power system, the uncertainty of new energy generation, and the random changes in load, to achieve comprehensive optimization of power system scheduling; solving the optimization scheduling model to obtain multiple non-dominated solutions that minimize ancillary service costs, maximize system reliability, and maximize environmental benefits; selecting the optimal scheduling scheme from the multiple non-dominated solutions based on the power system's operational needs and market policy guidance, and converting it into specific scheduling instructions for issuance.

[0090] Example 5: A computer program product, comprising a computer program, which, when executed by a processor, implements an ancillary service optimization scheduling method applied in a power market environment. This method includes: constructing a unified trading rule framework, integrating the trading processes and settlement methods of different types of ancillary services in the power ancillary service market, and building a centralized information platform to display and manage trading information; collecting real-time operating data of the power system and ancillary service trading information in the power market, and preprocessing the data; conducting market clearing based on the unified trading rule framework to determine the suppliers, procurement quantities, and trading prices of various ancillary services; constructing a multi-factor comprehensive optimization scheduling model based on the dynamic characteristics of the power system, the uncertainty of new energy generation, and the random changes in load, to achieve comprehensive optimization of power system scheduling; solving the optimization scheduling model to obtain multiple non-dominated solutions that minimize ancillary service costs, maximize system reliability, and maximize environmental benefits; and selecting the optimal scheduling scheme from multiple non-dominated solutions based on the power system's operational needs and market policy guidance, and converting it into specific scheduling instructions for issuance.

[0091] This specific embodiment is merely an explanation of the present invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A method for optimized scheduling of ancillary services applied in a power market environment, characterized in that: Includes the following steps: S1. Construct a unified trading rules framework, integrate the trading processes and settlement methods of different types of ancillary services in the power ancillary services market, and build a centralized information platform to display and manage trading information; S2. Collect real-time operation data of the power system and ancillary service transaction information in the power market, and preprocess the data; S3. Conduct market clearing based on a unified transaction rules framework to determine the suppliers, purchase volumes, and transaction prices of various ancillary services; S4. Based on the dynamic characteristics of the power system, the uncertainty of new energy power generation, and the random changes in load, construct an optimized scheduling model that considers multiple factors to achieve comprehensive optimization of power system scheduling. S5. Solve the optimization scheduling model to obtain multiple non-dominated solutions that minimize auxiliary service costs, maximize system reliability, and maximize environmental benefits; S6. Based on the power system's operational needs and market policy orientation, select the optimal dispatching scheme from multiple non-dominated solutions and convert it into specific dispatching instructions for issuance.

2. The ancillary service optimization scheduling method applied in a power market environment according to claim 1, characterized in that: The process of constructing a unified trading rule framework in step S1 includes the following steps: S11. Sorting out and integrating the transaction rules for various auxiliary services, formulating unified transaction processes and settlement methods, and reducing the participation costs and transaction difficulties for market participants; S12. Establish a centralized information platform to centrally display and manage transaction information, market rules, and market entity information for auxiliary services, thereby improving transaction transparency and efficiency.

3. The ancillary service optimization scheduling method applied in a power market environment according to claim 1, characterized in that: The construction process of optimizing the scheduling model in step S4 includes the following steps: S41. Establish a dynamic model of the power system to reflect its dynamic behavior and obtain its dynamic characteristics. S42. The probability distribution function is used to describe the fluctuation of new energy power generation, a probability model is introduced to quantify the uncertainty of new energy power generation, and it is used as the input parameter of the optimization scheduling model. S43. Establish a stochastic model of load based on historical data and statistical analysis methods to obtain the impact of random load fluctuations on power system dispatch. S44. Integrate the dynamic characteristics of the power system, the uncertainty of new energy power generation, and the random fluctuations of load into the optimization dispatch model to accurately reflect the actual operation of the power system.

4. The ancillary service optimization scheduling method applied in a power market environment according to claim 1, characterized in that: In step S5, solving the optimal scheduling model involves using intelligent optimization algorithms or mathematical programming methods to solve the model, generating different combinations of scheduling schemes, and obtaining multiple non-dominated solutions.

5. The ancillary service optimization scheduling method applied in a power market environment according to claim 1, characterized in that: The process for selecting the optimal scheduling scheme in step S6 is as follows: sort and screen non-inferior solutions according to the reliability index, environmental protection index and ancillary service cost of the power system, and select the scheduling scheme that meets the operation requirements of the power system and has the best overall benefits.

6. An ancillary service optimization scheduling system applied in a power market environment, which is applied to the ancillary service optimization scheduling method applied in a power market environment as described in any one of claims 1-5, characterized in that: It includes a unified trading rules framework construction module, a data collection and preprocessing module, a market clearing module, an optimized scheduling model construction module, a solution algorithm execution module, and a decision-making and instruction issuance module; The unified transaction rules framework construction module is used to integrate transaction processes and settlement methods, and to build an information platform; The data collection and preprocessing module is used to collect power system operation data and market transaction information, and to perform preprocessing. The market clearing module is used to clear the market according to unified trading rules and determine the suppliers, purchase quantities and transaction prices of ancillary services. The optimized scheduling model construction module is used to construct an optimized scheduling model that considers multiple factors to reflect the actual operation of the power system; The solution algorithm execution module is used to construct an optimized scheduling model that considers multiple factors to reflect the actual operation of the power system; The decision-making and instruction-issuing module is used to select the optimal scheduling scheme from multiple non-dominated solutions and convert it into scheduling instructions for issuance.

7. The ancillary service optimization dispatching system applied in the power market environment according to claim 6, characterized in that: The optimization scheduling model construction module constructs the model by taking into account the dynamic characteristics of the power system, the uncertainty of new energy power generation, and the random changes in load, and uses the minimization of ancillary service costs, the maximization of system reliability, and the maximization of environmental benefits as multi-objective optimization functions.

8. An ancillary service optimization dispatching device applied in a power market environment, characterized in that: It includes at least one processor; and a memory communicatively connected to at least one of the processors; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to implement the ancillary service optimization scheduling method for use in an electricity market environment as described in any one of claims 1-5.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions that are executed by the computer to implement the ancillary service optimization scheduling method applied in a power market environment according to any one of claims 1-5.

10. A computer program product, characterized in that: The method includes a computer program that, when executed by a processor, implements the ancillary service optimization scheduling method for an electricity market environment as described in any one of claims 1-5.

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