Load resource economy evaluation and management decision-making method

Abstract

The invention discloses a load resource economy evaluation and management decision method. The method belongs to the technical field of power system or energy management, and comprises the following operation steps: firstly, collecting load resource related data through multiple channels according to differential frequency, and carrying out cleaning, standardization and integrated preprocessing; secondly, constructing a comprehensive evaluation index system covering three indexes of cost, benefit and reliability; determining an index weight by using an analytic hierarchy process, establishing a model in combination with a fuzzy comprehensive evaluation method, and obtaining an economic evaluation result; and finally, making differentiated management decisions such as investment, operation and maintenance, load distribution and the like for different levels of load resources according to an evaluation result. The load resource economy can be comprehensively and accurately evaluated, load optimal configuration is realized, the operation cost of a power system is reduced, the stability and reliability of the system are improved, and the method is suitable for various load resource management scenes in a complex power market environment.

Description

Technical Field

[0001] This invention belongs to the field of power system or energy management technology, and relates to a method for evaluating the economic efficiency of load resources and making management decisions. Background Technology

[0002] In power systems, load resource management has always been a crucial aspect of ensuring reliable power supply and optimizing power resource allocation. Currently, conventional load resource management methods mainly include direct load control, demand response, and load forecasting. Direct load control refers to directly limiting user electricity load through administrative or technical measures when power supply is tight, such as implementing power rationing for high-energy-consuming enterprises during peak summer electricity consumption periods. Demand response, on the other hand, guides users to change their electricity consumption behavior through price signals or incentive mechanisms, such as encouraging users to consume electricity during off-peak hours through peak-valley pricing policies, thereby achieving peak shaving and valley filling. Load forecasting utilizes historical data and relevant algorithms to predict future power load, providing a basis for power system dispatching and planning.

[0003] However, these conventional methods face numerous problems in practical applications. With the rapid development of new loads such as distributed energy and electric vehicles, load characteristics have become more complex and variable, making it difficult for traditional load forecasting methods to accurately capture load change trends, leading to decreased forecast accuracy. Regarding demand response, user participation is influenced by various factors, such as the attractiveness of incentive policies and user requirements for electricity comfort, resulting in unsatisfactory implementation effects. Furthermore, current load resource management often lacks refined classification and management of different types of load resources, making it difficult to fully tap the potential of various load resources.

[0004] Existing economic evaluation methods have significant shortcomings in load resource assessment. Many methods focus only on a single indicator, such as cost or benefit, while neglecting the comprehensive benefits of load resources. They only consider the implementation cost of load reduction projects without assessing their benefits to grid stability and reduced outage losses, resulting in an incomplete valuation of load resources. Furthermore, existing methods often fail to adequately consider the impact of multiple factors on the economics of load resources. The costs and benefits of load resources are affected by various factors such as market price fluctuations, policy changes, and technological advancements, but these factors are either simplified or excluded from consideration in traditional economic evaluations. For example, when assessing the economics of energy storage devices as load resources, the potential cost reductions from future battery technology advancements and the impact of electricity price fluctuations on energy storage revenue are not considered. This single-indicator economic evaluation method, which fails to adequately consider the influence of multiple factors, cannot provide accurate and comprehensive basis for load resource management decisions and is ill-suited to the increasingly complex and volatile electricity market environment and load resource management needs. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a load resource economic evaluation and management decision-making method that can comprehensively and accurately assess the economics of load resources, fully consider the comprehensive benefits and multi-factor influence of load resources, thereby improving the utilization efficiency of load resources, reducing the operating costs of the power system, enhancing the stability and reliability of the power system, and providing strong support for the scientific management and optimal allocation of load resources, so as to adapt to the increasingly complex and ever-changing power market environment and load resource management needs.

[0006] Technical solution: The present invention provides a method for evaluating the economic efficiency of load resources and making management decisions, comprising the following steps: (1) Data collection and processing; (2) Construction of an economic evaluation index system; (3) Evaluation model establishment; (4) Management decision-making methods.

[0007] Furthermore, the data collection and processing described in step (1) specifically involves: collecting load resource-related data through multiple channels, determining the data collection frequency, and cleaning, standardizing, and integrating the collected data.

[0008] Furthermore, the various channels mentioned include smart meters of power companies, power dispatch centers, and market trading platforms. For industrial loads with large fluctuations, a data collection frequency of minutes or seconds is used, while for relatively stable residential loads, a data collection frequency of hours or days is used. Data missing values ​​are filled using the mean-filling method or a prediction method based on time series models.

[0009] Furthermore, the construction of the economic evaluation index system mentioned in step (2) specifically involves: constructing an economic evaluation index system that covers cost indicators, benefit indicators, and reliability indicators; The cost indicators include the investment cost of load resources, operation and maintenance costs, and electricity procurement costs; The benefit indicators include load reduction benefits, demand response benefits, and electricity market transaction benefits; The reliability indicators include outage duration, outage frequency, and load supply guarantee rate.

[0010] Furthermore, in the construction of the economic evaluation index system, the load reduction benefit is evaluated by calculating the power generation cost saved by reducing electricity demand and the grid expansion investment avoided. The demand response benefit is calculated based on the incentive income obtained by users participating in demand response projects. The electricity market transaction benefit is determined by analyzing the profits generated by load resources participating in the buying and selling of electricity in the electricity market.

[0011] Furthermore, the evaluation model establishment in step (3) specifically involves: establishing a load resource economic evaluation model using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method, determining the weights of each evaluation indicator through the AHP, and using the fuzzy comprehensive evaluation method to conduct a comprehensive evaluation of the load resources, thereby obtaining the economic evaluation results of the load resources.

[0012] Furthermore, in establishing the evaluation model, when determining the weights of indicators using the analytic hierarchy process (AHP), the economic evaluation problem of load resources is decomposed into a target layer, a criterion layer, and an indicator layer. Pairwise comparisons are made between the indicators at each level to construct a judgment matrix. The weights of each indicator relative to the target layer are obtained by calculating the eigenvectors and eigenvalues ​​of the judgment matrix. When applying the fuzzy comprehensive evaluation method, the membership degree of each evaluation indicator at the corresponding evaluation level is determined based on its actual value. A fuzzy relation matrix is ​​constructed, and the fuzzy relation matrix and the indicator weight vector are fuzzy synthesized to obtain the comprehensive membership degree of load resources at different evaluation levels.

[0013] Furthermore, the management decision-making method described in step (4) is as follows: management decisions are generated based on the evaluation results, different strategies are adopted for load resources with different economic evaluation results, and the load allocation scheme is optimized based on the economic evaluation results of load resources and the real-time demand of the power system, and the investment direction and focus are determined with reference to the evaluation results.

[0014] Furthermore, in the management decision-making methodology, for load resources with excellent economic evaluation results, investment and promotion efforts should be increased, investment in the load resource project should be increased, and its construction scale should be expanded; for load resources with good economic evaluation results, the existing management strategy should be maintained and its performance changes should be continuously monitored; for load resources with average economic evaluation results, the key factors affecting their economic efficiency should be analyzed and targeted improvement measures should be proposed; for load resources with poor economic evaluation results, optimization and transformation or gradual elimination should be considered.

[0015] Furthermore, in the management decision-making method, the load allocation strategy is to prioritize the allocation of loads to load resources with good economics and high reliability; when making investment decisions, increase investment in load resource projects with good development prospects and high economics, and be cautious or stop investing in projects with poor economics and limited development potential.

[0016] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: The present invention can comprehensively and accurately evaluate the economic efficiency of load resources, realize the optimal allocation of loads, reduce the operating cost of the power system, improve the stability and reliability of the system, and is applicable to various load resource management scenarios in complex power market environments. Attached Figure Description

[0017] Figure 1 This is a flowchart of the operation of the present invention. Detailed Implementation

[0018] The specific technical solution of the present invention will be further described in detail below with reference to specific examples.

[0019] As shown in the figure, the method for evaluating the economic efficiency of load resources and making management decisions according to the present invention includes the following steps: 1. Data collection and processing: Load resource-related data is collected through multiple channels, including but not limited to smart meter data from power companies, which provides real-time electricity load information for users; operational data from power dispatch centers, covering the load status of various nodes in the power grid; and market transaction data, such as electricity price fluctuation information. The data collection frequency is determined based on load characteristics and analysis needs. For industrial loads with large fluctuations, a data collection frequency of minutes or even seconds is used to capture load changes in a timely manner; for relatively stable residential loads, an hourly or daily data collection frequency can be used. The collected data undergoes preprocessing, including data cleaning to remove outliers and missing values; data standardization to transform data from different sources and with different dimensions into a comparable standard form; and data integration to consolidate scattered data into a unified database for convenient subsequent analysis. 2. Construction of an economic evaluation index system: A comprehensive economic evaluation index system is constructed, covering multiple aspects such as cost indicators, benefit indicators, and reliability indicators. Cost indicators include investment costs of load resources, such as the cost of building energy storage facilities and installing smart meters; operation and maintenance costs, including daily maintenance and repair costs of equipment and personnel management costs; and electricity procurement costs, i.e., the cost of purchasing electricity from the external grid. Benefit indicators include load reduction benefits, which assess the economic benefits brought about by reducing electricity demand through load management measures, such as reduced generation costs and avoided grid expansion investment; demand response benefits, which calculate the incentive income obtained by users participating in demand response projects; and electricity market transaction benefits, which analyze the profits generated by load resources participating in the buying and selling of electricity in the electricity market. Reliability indicators include outage duration, outage frequency, and load supply guarantee rate, which are used to measure the impact of load resources on the reliability of the power system. Outage duration reflects the average duration of outages for users, outage frequency reflects the number of outage events occurring within a certain period, and load supply guarantee rate indicates the proportion of load that can be reliably supplied within a specific period to the total demand load. 3. Evaluation Model Establishment: A load resource economic evaluation model was established using the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation. First, the weights of each evaluation indicator were determined using AHP. The load resource economic evaluation problem was decomposed into a target layer (load resource economic evaluation), a criterion layer (cost indicators, benefit indicators, reliability indicators, etc.), and an indicator layer (specific evaluation indicators). Power industry experts were invited to conduct pairwise comparisons of the indicators at each level, constructing a judgment matrix. By calculating the eigenvectors and eigenvalues ​​of the judgment matrix, the weights of each indicator relative to the target layer were obtained, thus clarifying the relative importance of different indicators in the evaluation system. Then, fuzzy comprehensive evaluation was applied to comprehensively evaluate the load resources. Based on the actual values ​​of each evaluation indicator, its membership degree at the corresponding evaluation level was determined, and a fuzzy relation matrix was constructed. The fuzzy relation matrix was then fuzzily synthesized with the indicator weight vectors obtained through AHP to obtain the comprehensive membership degree of the load resources at different evaluation levels, thereby deriving the economic evaluation results of the load resources. The evaluation levels can be divided into excellent, good, average, and poor, to intuitively display the economic level of the load resources. 4. Management Decision-Making Methods Based on the evaluation results, generate scientific and reasonable management decisions; for load resources with excellent economic evaluation results, increase investment and promotion efforts; if distributed energy in a certain region demonstrates good economic performance as a load resource, increase investment in distributed energy projects in that region, expand their construction scale, and increase their proportion in the power system; for load resources with good economic evaluation results, maintain the existing management strategy and continuously monitor their performance changes; for load resources with average economic evaluation results, analyze the key factors affecting their economic performance and propose targeted improvement measures; if the high operating and maintenance costs are due to equipment aging, develop an equipment upgrade plan to improve equipment operating efficiency and reduce costs; for load resources with poor economic evaluation results, consider optimization and renovation or gradual elimination; if an old industrial load facility has high energy consumption and low efficiency... For loads that are inefficient and have excessively high upgrade costs, a phase-out plan can be developed to guide enterprises to adopt more advanced and efficient load equipment. Regarding load allocation strategies, the load allocation scheme should be optimized based on the economic evaluation results of load resources and the real-time demand of the power system. Loads should be prioritized for allocation to economically efficient and reliable load resources to achieve economical and efficient operation of the power system. For example, during peak summer electricity consumption, priority should be given to calling upon economically efficient energy storage equipment and user loads with high demand response participation to ensure power supply while reducing costs. In terms of investment decisions, the economic evaluation results of load resources should be referenced to determine investment directions and priorities. For load resource projects with good development prospects and high economic efficiency, such as new energy storage technologies and smart grid construction, investment should be increased. For projects with poor economic efficiency and limited development potential, investment should be cautious or stopped. Example

[0020] Taking an industrial park as an example, this park houses multiple industrial enterprises of different types with significantly varying load characteristics, and their electricity demand fluctuates markedly with production activities. During the data collection and processing phase, hourly electricity consumption data for each enterprise in the park over the past year, along with real-time electricity market price data, are collected through smart meters and a power monitoring system. The data is then cleaned to remove outliers caused by equipment malfunctions, communication interruptions, etc., and a small number of missing values ​​are filled using mean imputation or time series model-based forecasting methods. Finally, the data is standardized to ensure comparability.

[0021] An economic evaluation index system was constructed. Regarding cost indicators, the electricity procurement costs of each enterprise were calculated, including monthly electricity expenses based on electricity consumption and real-time electricity prices, as well as annual investment and operation / maintenance costs for equipment. For efficiency indicators, the load reduction benefits were assessed, calculating the savings in power generation costs and avoided grid expansion investments resulting from reduced electricity demand achieved through load management measures such as peak-shifting production and energy-saving equipment upgrades. Relevant cost data was obtained through communication with power generation companies and grid companies for estimation. For reliability indicators, the number and duration of power outages in the industrial park over the past year were statistically analyzed to calculate the load supply guarantee rate.

[0022] The weights of each indicator were determined using the Analytic Hierarchy Process (AHP). An expert panel composed of power experts, park managers, and energy analysts was invited to conduct pairwise comparisons of cost, benefit, and reliability indicators, constructing a judgment matrix. Calculations showed that the weight of the cost indicator was 0.3, the benefit indicator was 0.4, and the reliability indicator was 0.3. Fuzzy comprehensive evaluation was then applied, determining the membership degree based on the actual values ​​of each indicator and constructing a fuzzy relation matrix. It was assumed that a certain enterprise performed well in terms of cost indicators (membership degrees of 0.6, 0.3, and 0). 1, 0, 0 correspond to excellent, good, average, poor, and very poor, respectively. The efficiency index is excellent (membership degree is 1, 0, 0, 0, 0), and the reliability index is good (membership degree is 0, 0.7, 0.2, 0.1, 0). The fuzzy relation matrix and the index weight vector are fuzzy synthesized to obtain the comprehensive membership degree of the enterprise at different evaluation levels. The calculated comprehensive membership degree is (0.4, 0.37, 0.19, 0.04, 0), indicating that the enterprise's load resource economic evaluation result is good.

[0023] Management decisions are made based on the evaluation results. For enterprises with good evaluation results, the existing cooperation model is maintained and they are encouraged to continue to optimize their electricity consumption behavior. For some enterprises with high cost indicators, it is recommended that they upgrade their equipment to reduce operation and maintenance costs. For a certain high-energy-consuming enterprise, it is recommended that it replace its transformer and motor with energy-saving ones. It is expected that the annual operation and maintenance costs will be reduced and the economic efficiency of load resources will be improved after the upgrade.

[0024] After implementing this method, the industrial park's electricity costs decreased by 15%, the load supply guarantee rate increased from 90% to 95%, and the number of power outages decreased by 30%, effectively improving the efficiency of load resource utilization and the stability of the power system.

Claims

1. A method for evaluating the economic efficiency of load resources and making management decisions, characterized in that, Includes the following steps, (1) Data collection and processing; (2) Construction of an economic evaluation index system; (3) Evaluation model establishment; (4) Management decision-making methods.

2. The method for evaluating the economic efficiency of load resources and making management decisions according to claim 1, characterized in that, The data collection and processing described in step (1) specifically involves: collecting load resource-related data through multiple channels, determining the data collection frequency, and cleaning, standardizing, and integrating the collected data.

3. The method for evaluating the economic efficiency of load resources and making management decisions according to claim 2, characterized in that, The various channels mentioned include smart meters of power companies, power dispatch centers and market trading platforms. For industrial loads with large fluctuations, a data collection frequency of minutes or seconds is used, while for relatively stable residential loads, a data collection frequency of hours or days is used. Data missing values ​​are filled using the mean-filling method or a prediction method based on time series models.

4. The method for evaluating the economic efficiency of load resources and making management decisions according to claim 2, characterized in that, The construction of the economic evaluation index system mentioned in step (2) specifically involves: constructing an economic evaluation index system that covers cost indicators, benefit indicators, and reliability indicators; The cost indicators include the investment cost of load resources, operation and maintenance costs, and electricity procurement costs; The benefit indicators include load reduction benefits, demand response benefits, and electricity market transaction benefits; The reliability indicators include outage duration, outage frequency, and load supply guarantee rate.

5. The method for evaluating the economic efficiency of load resources and making management decisions according to claim 4, characterized in that, In the construction of the economic evaluation index system, the load reduction benefit is evaluated by calculating the power generation cost saved by reducing electricity demand and the grid expansion investment avoided. The demand response benefit is calculated based on the incentive income obtained by users participating in demand response projects. The electricity market transaction benefit is determined by analyzing the profits generated by load resources participating in the buying and selling of electricity in the electricity market.

6. The method for evaluating the economic efficiency of load resources and making management decisions according to claim 1, characterized in that, The evaluation model establishment in step (3) specifically involves: establishing a load resource economic evaluation model using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method; determining the weights of each evaluation index using the AHP; and using the fuzzy comprehensive evaluation method to conduct a comprehensive evaluation of the load resources, thereby obtaining the economic evaluation results of the load resources.

7. The method for evaluating the economic efficiency of load resources and making management decisions according to claim 1, characterized in that, In the evaluation model establishment, when using the analytic hierarchy process to determine the weight of indicators, the load resource economic evaluation problem is decomposed into the target layer, the criterion layer and the indicator layer. The indicators at each level are compared pairwise to construct a judgment matrix. The weight of each indicator relative to the target layer is obtained by calculating the eigenvector and eigenvalue of the judgment matrix. When using the fuzzy comprehensive evaluation method, the membership degree of each evaluation indicator at the corresponding evaluation level is determined based on the actual value of each indicator. A fuzzy relation matrix is ​​constructed, and the fuzzy relation matrix and the indicator weight vector are subjected to fuzzy synthesis operation to obtain the comprehensive membership degree of the load resource at different evaluation levels.

8. The method for evaluating the economic efficiency of load resources and making management decisions according to claim 1, characterized in that, The management decision-making method described in step (4) is as follows: management decisions are generated based on the evaluation results, different strategies are adopted for load resources with different economic evaluation results, and the load allocation scheme is optimized based on the economic evaluation results of load resources and the real-time demand of the power system. The investment direction and focus are determined with reference to the evaluation results.

9. The method for evaluating the economic efficiency of load resources and making management decisions according to claim 8, characterized in that, In the management decision-making methodology, for load resources with excellent economic evaluation results, investment and promotion efforts should be increased, investment in the load resource project should be increased, and its construction scale should be expanded; for load resources with good economic evaluation results, the existing management strategy should be maintained and its performance changes should be continuously monitored; for load resources with average economic evaluation results, the key factors affecting its economy should be analyzed and targeted improvement measures should be proposed; for load resources with poor economic evaluation results, optimization and transformation or gradual elimination should be considered.

10. The method for evaluating the economic efficiency of load resources and making management decisions according to claim 8, characterized in that, In management decision-making methods, the load allocation strategy is to prioritize the allocation of loads to load resources with good economics and high reliability. When making investment decisions, investment should be increased for load resource projects with good development prospects and high economics, while investment should be cautious or stopped for projects with poor economics and limited development potential.

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