Low-carbon economic planning method for improving elasticity of power distribution network
By establishing an uncertain model for the output of new energy and the elastic recovery strength and carbon emission intensity index system of the distribution network, combined with the most preferred address and capacity configuration model of distributed power supply, the problem of improving the elasticity and low-carbon economy of the distribution network under the conditions of high proportion of new energy access is solved, and the self-healing ability and power supply reliability of the distribution network when facing risks is achieved.
Patent Information
- Application Number
- CN202510031429.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-09
- Publication Date
- 2025-05-13
AI Technical Summary
The existing technology is difficult to effectively improve the elasticity and low-carbon economy of the distribution network, especially in the case of high proportion of new energy access, which leads to insufficient self-healing capabilities when facing risks and low power supply reliability.
By establishing an uncertainty model for the output of new energy, multiple scenarios are generated, typical scenarios are selected in combination with clustering and optimization algorithms, and the elastic recovery strength and carbon emission intensity index system of the distribution network is established, the most preferred address and capacity configuration model of distributed power supply in the distributed power grid is proposed, and a new plan for distribution network planning is formulated, and a distribution network failure risk model is built into the predicted typical scenarios.
It improves the self-healing ability of the distribution network when facing risks, enhances the reliability of power supply, improves the elasticity and low-carbon economy of the distribution network, and achieves a better balance of supply and demand.
Smart Images
Figure CN119990596A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power grid control, and in particular to a low-carbon economic planning method for improving the elasticity of a distribution network. Background Art
[0002] Energy is the pillar of national economic development. How to use energy efficiently and greenly is a difficult problem that my country and the world need to face. The transformation of energy structure is an inevitable trend. In recent years, my country has gradually developed new energy and started to connect it to the distribution network in the form of distributed power sources at a high or even ultra-high ratio.
[0003] With the gradual development of the national economy, people's demand for electricity continues to increase, which leads to the continuous expansion of the scale of modern power grid interconnection. As a result, the operation status of the power system has become more and more complex. As conventional energy on the earth gradually depletes and the demand for environmental protection and energy conservation increases, the development of new energy has gradually become the main direction to solve these problems. A large number of environmentally friendly, efficient and flexible clean energy sources have gradually been connected to the main network of the power system. However, the emergence of new energy also brings many uncertainties to the safe and stable operation of the power system. When the penetration rate of new energy in the power grid continues to increase, the source-load boundary of the power grid will gradually become blurred, resulting in many uncertain factors that are more likely to affect the operation of the system. In this context, if local system failures caused by human or natural factors are not properly handled, the scope of the accident may be further expanded, and even cause large-scale power outages.
[0004] At present, a lot of technical research and development work has been carried out on the resilience and low-carbon research of distribution networks, but some studies have not separated economic and carbon emission targets, and have not modeled and analyzed the uncertainty of renewable energy output. Summary of the invention
[0005] The technical problem to be solved by the present invention is to overcome the existing defects and provide a low-carbon economic planning method for improving the flexibility of distribution networks, thereby improving the self-healing ability of distribution networks when facing risks, thereby enhancing the reliability of power supply, improving the flexibility of distribution networks and achieving better low-carbon economy, better achieving supply and demand balance, and effectively solving the problems in the background technology.
[0006] To achieve the above object, the present invention provides the following technical solution: a low-carbon economic planning method for improving the flexibility of a distribution network, comprising the following steps: S1. Modeling the uncertainty of renewable energy output; The output of renewable energy is related to uncertain factors such as environment, climate, temperature and human factors. In the modeling of renewable energy power generation, the forecast error of renewable energy power generation is represented by the normal distribution model, and the forecast error of renewable energy power generation is used as a random variable. , which follows a normal distribution , whose probability distribution function As shown below: (1) The actual output of new energy is also a random variable: (2) Where: To contribute to the actual generation of new energy. It is the predicted value of renewable energy power generation; S2. Based on the uncertainty model established above, multiple scenarios are generated, and then the renewable energy scenario reduction is performed based on the combination of clustering and optimization algorithms to screen out typical scenarios; S3. Establish an indicator system for the resilience of the distribution network in the face of accidents and the carbon emission intensity of the distribution network; S4. Propose the optimal location and capacity configuration model of distributed power sources in distributed power grids, and formulate a new distribution network planning plan with the lowest total system cost, the greatest carbon emission reduction, and the highest system power supply reliability as the objective function. Bring it into the typical prediction scenario, and construct a distribution network failure risk model by analyzing the correlation between the disaster intensity distribution characteristics and the operating risks of distribution network components.
[0007] Preferably, step S2 comprises the following steps: a. Eliminate missing values and outliers in the data, retain complete daily time series data, perform standardization after data noise reduction, and complete data preprocessing; b. Conduct statistical analysis on wind power and photovoltaic data on an annual and monthly basis to discover patterns; c. Use Elbow Method and Silhouette Coefficient to comprehensively select the k value, and then use k-means to cluster the wind power and photovoltaic data according to their respective k values; d. Use different algorithms to extract typical output curves from the clustering result curves, compare the results, and select the typical scenario corresponding to the appropriate optimization algorithm.
[0008] Preferably, step S3 comprises the following steps: 1) Collect basic data on the resilience of the distribution network to be evaluated and the carbon emission intensity; 2) Calculate the specific values of each evaluation index one by one ; 3) In order to achieve the quantitative comparability of evaluation indicators, it is necessary to improve the observability, including the determination of indicator weights and dimensionless processing; 4) Carry out comprehensive evaluation calculations and analyze the evaluation results of the elasticity of the distribution network.
[0009] Preferably, the objective function in step S4 is: 1) Lowest total cost The total cost mainly includes the investment cost of distribution network energy storage devices within one operation cycle. , Operation and maintenance costs , Network loss cost And the cost of purchasing electricity from the upper grid ; The objective function is as follows: (3) (4) Where: is the total cost of the distribution network in one operation cycle; 2) The largest carbon emission reduction Carbon emission reduction represents the difference between the carbon emissions of the original distribution network and the improved distribution network with distributed generation and ESS; (5) In the formula, is the total carbon emission reduction of the distribution network during the planning period, is the carbon emission coefficient of thermal power, For the The load power of node j at time t in year; 3) Highest reliability For a typical distribution network, meeting load demand is crucial, so the power reliability objective function is: (6) in, Indicates the power supply reliability of the distribution network. , and They represent the load power, photovoltaic power and purchased electricity of node j at time t in year i respectively.
[0010] Compared with the prior art, the beneficial effects of the present invention are: modeling the uncertainty problem of renewable energy power generation, and at the same time, in the distribution network with a high proportion of renewable energy access, combining low-carbon economy and elasticity to establish a comprehensive indicator, and using elasticity improvement methods and strategies to improve the elasticity strength of the distribution network, thereby improving the self-healing ability of the distribution network in the face of risks, thereby enhancing the reliability of power supply, improving the elasticity of the distribution network and achieving better low-carbon economy, and better achieving supply and demand balance. BRIEF DESCRIPTION OF THE DRAWINGS
[0011] Figure 1 Schematic diagram of the steps of the present invention; Figure 2 A schematic diagram of typical scene generation of the present invention; Figure 3 A schematic diagram of a comprehensive indicator evaluation process for a distribution network according to the present invention; Figure 4 A schematic diagram of the flexible distribution network planning process of the present invention. DETAILED DESCRIPTION
[0012] The present invention can be explained in detail through the following embodiments. The purpose of disclosing the present invention is to protect all technical improvements within the scope of the present invention. In the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "front", "back", "left", "right", etc. indicating directions or positional relationships, they only correspond to the drawings of the present application for the convenience of describing the present invention, and do not indicate or imply that the device or element referred to must have a specific direction.
[0013] See also Figure 1-4 The present invention provides a technical solution: a low-carbon economic planning method for improving the flexibility of a distribution network, comprising the following steps: S1. Modeling the uncertainty of renewable energy output; The output of renewable energy is related to uncertain factors such as environment, climate, temperature and human factors. In the modeling of renewable energy power generation, the forecast error of renewable energy power generation is represented by the normal distribution model, and the forecast error of renewable energy power generation is used as a random variable. , which follows a normal distribution , whose probability distribution function As shown below: (1) The actual output of new energy is also a random variable: (2) Where: To contribute to the actual generation of new energy. It is the predicted value of renewable energy power generation; As the proportion of renewable energy power generation increases, the volatility and uncontrollability of renewable energy also increase accordingly, bringing challenges to the dispatch of power systems. At the same time, if the volatility of renewable energy power generation cannot be accurately estimated, it may lead to insufficient or excessive power supply. It is necessary to solve the uncertainty of renewable energy power generation. Establishing an uncertainty model can more accurately predict the output of renewable energy and provide relevant information such as probability distribution to help formulate reasonable dispatch strategies to improve the flexibility of distribution networks and achieve better low-carbon economy, and better achieve supply and demand balance.
[0014] S2. Based on the uncertainty model established above, multiple scenarios are generated, and then the renewable energy scenario reduction is performed based on the combination of clustering and optimization algorithms to screen out typical scenarios; The following steps are involved: a. Eliminate missing values and outliers in the data, retain complete daily time series data, perform standardization after data noise reduction, and complete data preprocessing; b. Conduct statistical analysis on wind power and photovoltaic data on an annual and monthly basis to discover patterns; c. Use Elbow Method and Silhouette Coefficient to comprehensively select the k value, and then use k-means to cluster the wind power and photovoltaic data according to their respective k values; d. Use different algorithms to extract typical output curves from the clustering result curves, compare the results, and select the typical scenario corresponding to the appropriate optimization algorithm.
[0015] Uncertainty is a common challenge in power systems, especially for source-side factors such as renewable energy. The generation of source-side uncertainty scenarios can provide a basis for optimizing the dispatch and operation strategies of power systems. By fully considering uncertainty factors such as the volatility of renewable energy and the uncertainty of fuel supply, strategies such as power generation dispatch, energy storage dispatch and standby unit configuration can be optimized to achieve more efficient, reliable and economical power system operation. By generating typical scenarios, uncertainties under different scenarios can be simulated, enabling decision makers to more accurately evaluate various possibilities and formulate corresponding decision strategies, which helps reduce the risk of decision-making and improve the quality of decision-making in the power generation dispatch of power systems, helping dispatchers evaluate decisions such as supply and demand balance, external network transactions and standby unit dispatch under various circumstances, and improving the economy and reliability of power systems.
[0016] S3. Establish an indicator system for the resilience of the distribution network in the face of accidents and the carbon emission intensity of the distribution network; The following steps are involved: 1) Collect basic data on the resilience of the distribution network to be evaluated and the carbon emission intensity; 2) Calculate the specific values of each evaluation index one by one ; 3) In order to achieve the quantitative comparability of evaluation indicators, it is necessary to improve the observability, including the determination of indicator weights and dimensionless processing; 4) Carry out comprehensive evaluation calculations and analyze the evaluation results of the elasticity of the distribution network.
[0017] Reasonably allocate elastic resources (such as distributed energy, energy storage systems and flexible loads), through comprehensive weighted processing of multiple indicators, and establish multi-objective functions with multiple indicators to obtain the optimal planning scheme, maximize the utilization efficiency of resources and the elasticity of the distribution network, and consider comprehensive elasticity and low-carbon economy in the planning research;
[0018] Since the measurement units of various indicators are not unified and the importance of indicators also needs to be clarified, how to unify the multi-type indicators and then transform them into observable forms of expression is an effective way to rationally use the indicator system and analyze the resilience of the distribution network, as well as the carbon emission intensity and the overall development level. On the basis of low-carbon economy, the response ability of the system in different states during the elastic recovery process is used to construct indicators for the distribution network from the perspectives of reliability, economy, and low carbon. For the part of improving the observability of indicators, the normalization processing method of indicators in the evaluation system and the method of determining the importance are studied to ensure the accurate evaluation of the resilience of the distribution network. For the response ability of the system in different states during the elastic recovery process, evaluation indicators reflecting the resilience of the system are proposed, and a comprehensive evaluation of the resilience of the observable distribution network resilience evaluation framework is proposed.
[0019] S4. Propose the optimal location and capacity configuration model of distributed power sources in distributed power grids, and formulate a new distribution network planning plan with the lowest total system cost, the greatest carbon emission reduction, and the highest system power supply reliability as the objective function. Bring it into the typical prediction scenario, and construct a distribution network failure risk model by analyzing the correlation between the disaster intensity distribution characteristics and the operating risks of distribution network components.
[0020] The objective function is: 1) Lowest total cost The total cost mainly includes the investment cost of distribution network energy storage devices within one operation cycle. , Operation and maintenance costs , Network loss cost And the cost of purchasing electricity from the upper grid ; The objective function is as follows: (3) (4) Where: is the total cost of the distribution network in one operation cycle; 2) The largest carbon emission reduction Carbon emission reduction represents the difference between the carbon emissions of the original distribution network and the improved distribution network with distributed generation and ESS; (5) In the formula, is the total carbon emission reduction of the distribution network during the planning period, is the carbon emission coefficient of thermal power, For the The load power of node j at time t in year; 3) Highest reliability For a typical distribution network, meeting load demand is crucial, so the power reliability objective function is: (6) in, Indicates the power supply reliability of the distribution network. , and They represent the load power, photovoltaic power and purchased electricity of node j at time t in year i respectively.
[0021] By adopting the above steps and methods, we can maximize the use of a high proportion of new energy, improve the utilization rate of new energy, reduce dependence on traditional energy, and reduce carbon emissions. Through flexible planning and optimization algorithms, we can make energy supply and demand more balanced and flexible, reduce the occurrence of surplus and shortage, help reduce energy procurement costs, improve energy utilization efficiency, and thus reduce energy prices. Flexible planning can reasonably allocate a variety of flexible resource energies, improve grid stability and reliability, reduce the risk of power outages and failures, ensure the continuity of power supply, and meet user needs.
[0022] The parts of the present invention that are not described in detail are prior art. It is obvious to those skilled in the art that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and it is intended that all changes that fall within the meaning and scope of equivalent elements are included in the content of the present invention.
Claims
1. A low-carbon economic planning method for improving the flexibility of distribution networks, characterized in that: The following steps are involved: S1. Modeling the uncertainty of renewable energy output; The output of renewable energy is related to uncertain factors such as environment, climate, temperature and human factors. In the modeling of renewable energy power generation, the forecast error of renewable energy power generation is represented by the normal distribution model, and the forecast error of renewable energy power generation is used as a random variable. , which follows a normal distribution , whose probability distribution function As shown below: （1） The actual output of new energy is also a random variable: （2） Where: To contribute to the actual generation of new energy. It is the predicted value of renewable energy power generation; S2. Based on the uncertainty model established above, multiple scenarios are generated, and then the renewable energy scenario reduction is performed based on the combination of clustering and optimization algorithms to screen out typical scenarios; S3. Establish an indicator system for the resilience of the distribution network in the face of accidents and the carbon emission intensity of the distribution network; S4. Propose the optimal location and capacity configuration model of distributed power sources in distributed power grids, and formulate a new distribution network planning plan with the lowest total system cost, the greatest carbon emission reduction, and the highest system power supply reliability as the objective function. Bring it into the typical prediction scenario, and construct a distribution network failure risk model by analyzing the correlation between the disaster intensity distribution characteristics and the operating risks of distribution network components.
2. A low-carbon economic planning method for improving distribution network flexibility according to claim 1, characterized in that: The step S2 comprises the following steps: a. Eliminate missing values and outliers in the data, retain complete daily time series data, perform standardization after data noise reduction, and complete data preprocessing; b. Conduct statistical analysis on wind power and photovoltaic data on an annual and monthly basis to discover patterns; c. Use Elbow Method and Silhouette Coefficient to comprehensively select the k value, and then use k-means to cluster the wind power and photovoltaic data according to their respective k values; d. Use different algorithms to extract typical output curves from the clustering result curves, compare the results, and select the typical scenario corresponding to the appropriate optimization algorithm.
3. A low-carbon economic planning method for improving distribution network flexibility according to claim 1, characterized in that: The step S3 comprises the following steps: 1) Collect basic data on the resilience of the distribution network to be evaluated and the carbon emission intensity; 2) Calculate the specific values of each evaluation index one by one ; 3) In order to achieve the quantitative comparability of evaluation indicators, it is necessary to improve the observability, including the determination of indicator weights and dimensionless processing; 4) Carry out comprehensive evaluation calculations and analyze the evaluation results of the elasticity of the distribution network.
4. A low-carbon economic planning method for improving distribution network flexibility according to claim 1, characterized in that: The objective function in step S4 is: 1) Lowest total cost The total cost mainly includes the investment cost of distribution network energy storage devices within one operation cycle. , Operation and maintenance costs , Network loss cost And the cost of purchasing electricity from the upper grid ; The objective function is as follows: （3） （4） Where: is the total cost of the distribution network in one operation cycle; 2) The largest carbon emission reduction Carbon emission reduction represents the difference between the carbon emissions of the original distribution network and the improved distribution network with distributed generation and ESS; （5） In the formula, is the total carbon emission reduction of the distribution network during the planning period, is the carbon emission coefficient of thermal power, For the The load power of node j at time t in year; 3) Highest reliability For a typical distribution network, meeting load demand is crucial, so the power reliability objective function is: （6） in, Indicates the power supply reliability of the distribution network. , and They represent the load power, photovoltaic power and purchased electricity of node j at time t in year i respectively.