Optimized dispatching method for power distribution network

Abstract

The invention discloses an optimal scheduling method for a power distribution network. The method comprises the following steps: acquiring real-time operation data of a multi-type distributed resource cluster; evaluating the active power regulation capability of the multi-type distributed resource cluster by adopting a cloud edge fusion architecture; acquiring a real-time operation demand of the power distribution network, and determining an operation risk level of the power distribution network; a main and distribution network collaborative optimization strategy is adopted to formulate an optimization scheduling scheme of the power distribution network; the multi-type distributed resource cluster is adjusted, and the operation efficiency and economical efficiency of the power distribution network can be remarkably improved by implementing source, load and storage collaborative optimization scheduling, considering demand response, adopting advanced algorithms and technologies and other measures.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power distribution network dispatching, in particular to a power distribution network optimal dispatching method. BACKGROUND

[0002] Power distribution network optimal dispatching is a complex and critical process that involves multiple aspects, including the coordinated optimization of sources, loads, and storage, the consideration of demand response, and the use of advanced algorithms and technologies.

[0003] In active power distribution networks, the coordinated optimization and dispatching of sources (distributed energy), loads (loads), and storage (energy storage systems) are the key to achieving optimal operation of the power system. In traditional power distribution networks, the supply-demand balance of the power system is mainly controlled by central dispatching. However, in active power distribution networks, due to the connection of emerging power equipment such as distributed energy, electric vehicles, and energy storage systems, the power system has become more decentralized and complex. Therefore, a monitoring system that can monitor and control each node in real time is needed, and these data are transmitted to the central control system through the communication system, and then optimized and dispatched. SUMMARY

[0004] The purpose of the present application is to solve the above problems and provide a power distribution network optimal dispatching method.

[0005] The technical solution adopted by the present application to solve its technical problems is: A power distribution network optimal dispatching method, characterized by comprising: Obtaining real-time operation data of a multi-type distributed resource cluster, the multi-type distributed resource cluster including controllable units such as distributed power generation, load, and energy storage; Using a cloud-edge fusion architecture to evaluate the active regulation capability of the multi-type distributed resource cluster, obtaining an active regulation capability evaluation result; Obtaining real-time operation requirements of the power distribution network, and determining the operation risk level of the power distribution network according to the real-time operation requirements; According to the active regulation capability evaluation result and the operation risk level, using a main and distribution network coordinated optimization strategy to develop an optimal dispatching scheme for the power distribution network; Downloading the optimal dispatching scheme to the control system of the power distribution network, and adjusting the multi-type distributed resource cluster to make the power distribution network operate according to the optimal dispatching scheme.

[0006] Further, the cloud-edge fusion architecture is used to evaluate the active regulation capability of the multi-type distributed resource cluster, comprising: In the cloud, an equivalent model of the multi-type distributed resource cluster is established according to the real-time operation data; At the edge, real-time state information of the multi-type distributed resource cluster is acquired, and according to the equivalent model and the real-time state information, active regulation capability of the multi-type distributed resource cluster is evaluated.

[0007] Further, the operation risk level of the power distribution network is determined according to the real-time operation demand, and the operation risk level of the power distribution network is determined according to the real-time operation demand. Real-time load data and power supply output data of the power distribution network are acquired. According to the real-time load data and power supply output data, a real-time power balance state of the power distribution network is calculated. According to the real-time power balance state, the operation risk level of the power distribution network is determined.

[0008] Further, according to the active regulation capability evaluation result and the operation risk level, a main-distribution network collaborative optimization strategy is adopted to formulate an optimal dispatching scheme of the power distribution network, and the optimal dispatching scheme of the power distribution network is formulated according to the active regulation capability evaluation result and the operation risk level. According to the active regulation capability evaluation result, distributed resource clusters that can participate in optimal dispatching in the power distribution network are determined. According to the operation risk level, a target function and constraint conditions of the optimal dispatching of the power distribution network are determined. A main-distribution network collaborative optimization algorithm is adopted to solve the target function, and an optimal dispatching scheme of the power distribution network is generated.

[0009] Further, the optimal dispatching scheme takes into account the safety and economy of power grid operation, and the optimal dispatching scheme of the power distribution network is formulated under the premise of meeting the safe operation constraints of the power distribution network and taking the minimum operation cost of the power distribution network as the target.

[0010] Further, in the operation process of the power distribution network, real-time operation data of the multi-type distributed resource cluster and real-time operation demand of the power distribution network are continuously acquired, and the active regulation capability evaluation result and the operation risk level are dynamically updated.

[0011] Further, according to the updated active regulation capability evaluation result and operation risk level, the optimal dispatching scheme is optimized in real time, the operation state of the power distribution network is dynamically adjusted, and the safe and economic operation of the power distribution network is ensured.

[0012] The beneficial effects of the present application are as follows: 1. The present application can significantly improve the operation efficiency and economy of the power distribution network by realizing collaborative optimal dispatching of source, load and storage, considering demand response, and adopting advanced algorithms and technologies, and contributes to the sustainable development of the power system. DETAILED DESCRIPTION

[0013] ​In order for those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0014] A power distribution network optimization scheduling method, characterized in that it comprises: Obtaining real-time operation data of a multi-type distributed resource cluster, the multi-type distributed resource cluster including controllable units such as distributed power generation, load and energy storage, etc. Using a cloud-edge fusion architecture, the active regulation capability of the multi-type distributed resource cluster is evaluated to obtain an active regulation capability evaluation result. Obtaining real-time operation requirements of the power distribution network, and determining the operation risk level of the power distribution network according to the real-time operation requirements. According to the active regulation capability evaluation result and the operation risk level, a main-distribution network collaborative optimization strategy is used to formulate an optimization scheduling scheme for the power distribution network. The optimization scheduling scheme is sent to the control system of the power distribution network, and the multi-type distributed resource cluster is adjusted so that the power distribution network operates according to the optimization scheduling scheme.

[0015] Further, the evaluation of the active regulation capability of the multi-type distributed resource cluster using the cloud-edge fusion architecture comprises: in the cloud, an equivalent model of the multi-type distributed resource cluster is established according to the real-time operation data; in the edge, real-time state information of the multi-type distributed resource cluster is obtained, and the active regulation capability of the multi-type distributed resource cluster is evaluated according to the equivalent model and the real-time state information. Using the cloud-edge fusion architecture to evaluate the active regulation capability of the multi-type distributed resource cluster can fully utilize the powerful computing capability of the cloud and the advantage of real-time data acquisition of the edge.

[0016] Cloud processing; 1. Data collection and preprocessing: The cloud first collects real-time operation data from various distributed resources (such as renewable energy power generation, energy storage systems, electric vehicle charging stations, etc.).

[0017] The data is preprocessed, including data cleaning, denoising, missing value processing, etc., to ensure the accuracy and integrity of the data.

[0018] 2. Establishing an equivalent model: Based on the pre-processed data, the cloud uses advanced modeling techniques (such as machine learning, deep learning, etc.) to establish an equivalent model of the multi-type distributed resource cluster.

[0019] This model can reflect the overall behavior characteristics of the distributed resource cluster, including active output, response speed, adjustment range, etc.

[0020] Edge processing; 1. Real-time state information acquisition: The edge end obtains the state information of the multi-type distributed resource cluster in real time through sensors, intelligent meters and other devices, such as current active output, remaining capacity, fault state, etc.

[0021] These information are transmitted to the edge computing node in real time through high-speed communication network.

[0022] 2. Active regulation capacity evaluation: The edge end uses the received real-time state information and the equivalent model established by the cloud to evaluate the active regulation capacity of the distributed resource cluster.

[0023] The evaluation process may involve complex calculations and optimization algorithms, but thanks to the distributed processing capability and low latency characteristics of edge computing, these calculations can be completed in a short time.

[0024] The evaluation results include the maximum active regulation amount, regulation speed and possible regulation strategy that the distributed resource cluster can provide at the current time.

[0025] Cloud-edge collaboration; The cloud and the edge maintain real-time communication and data synchronization to ensure the accuracy and real-time performance of the equivalent model.

[0026] When the edge end detects significant changes in the state of the distributed resource cluster, it can send an update request to the cloud in time to retrain or update the equivalent model.

[0027] The cloud can also send scheduling instructions or optimization suggestions to the edge end according to global optimization requirements to achieve precise control and optimized scheduling of the distributed resource cluster.

[0028] By adopting the cloud-edge fusion architecture, real-time and accurate evaluation of the active regulation capacity of the multi-type distributed resource cluster can be achieved, providing strong support for stable operation and efficient scheduling of the power system.

[0029] Furthermore, determining the operational risk level of the distribution network based on the real-time operational requirements includes: acquiring real-time load data and power output data of the distribution network; calculating the real-time power balance state of the distribution network based on the real-time load data and power output data; and determining the operational risk level of the distribution network based on the real-time power balance state.

[0030] Determining the operational risk level of a distribution network based on real-time operational needs is a complex but crucial process involving multiple stages and factors. The following is a detailed explanation of this process: I. Obtain real-time load data and power output data of the distribution network. Real-time load data: Load data is a crucial parameter for the operation of a power distribution network, reflecting users' electricity demand. Load data, including active power, reactive power, voltage, and current, can be acquired in real time through smart meters or other measuring devices installed at the user's end.

[0031] Power output data: Power output data refers to the real-time output power of various power sources (such as power plants and distributed energy resources) in the distribution network. This data can be obtained through measuring equipment or monitoring systems installed at the power source end.

[0032] II. Calculating the real-time power balance state of the distribution network The concept of power balance: Power balance in a power system refers to the balance between active and reactive power, requiring that the power transmitted by the power system and the system load be kept in balance at all times. At any given moment, the production, transmission, distribution, and consumption of power in the power system must be strictly balanced in terms of power; otherwise, power quality cannot be guaranteed.

[0033] Calculation process: Based on real-time load and power output data, the real-time power demand of the distribution network can be calculated. Simultaneously, by monitoring power losses in transmission lines and transformers within the distribution network, the real-time power supply can be calculated. Comparing the real-time power demand with the real-time power supply yields the real-time power balance status of the distribution network.

[0034] III. Determining the operational risk level of the distribution network Risk level assessment: The operational risk level of a distribution network can be assessed based on its real-time power balance. If real-time power demand exceeds real-time power supply by a significant margin, the operational risk level of the distribution network is high, potentially leading to power quality issues such as voltage drops and frequency fluctuations, and even power outages. Conversely, if real-time power demand and supply are roughly balanced or slightly in surplus, the operational risk level of the distribution network is low.

[0035] Risk level classification: To more intuitively represent the operational risk level of a power distribution network, it can be divided into different risk levels. For example, based on factors such as the degree of power imbalance, duration, and potential consequences, risk levels can be classified into five levels: extremely high risk, significant risk, relatively high risk, general Class A risk, and general Class B risk. Different response measures and management strategies can be adopted for different risk levels.

[0036] IV. Countermeasures and Management Strategies Strengthen monitoring and early warning: Strengthen real-time monitoring and early warning of the power distribution network to promptly identify and address potential power imbalance issues. Utilize advanced sensors and monitoring technologies to collect data on various indicators in real time and perform efficient and accurate data processing.

[0037] Optimize scheduling and control: Based on real-time power balance status, optimize the dispatching and control strategies of the distribution network. By adjusting power output and load distribution, achieve power balance and stable operation of the distribution network.

[0038] Strengthen equipment maintenance and management: Regularly maintain and inspect equipment in the distribution network to ensure its normal operation and reliability. Replace aging or damaged equipment in a timely manner to improve the overall performance and security of the distribution network.

[0039] Develop emergency response plans: Detailed emergency plans and procedures have been developed to address potential severe power imbalances. In emergency situations, these plans should be activated quickly to implement effective measures for handling and restoration.

[0040] By acquiring real-time load and power output data, calculating real-time power balance status, and determining operational risk levels, real-time monitoring and early warning of the distribution network's operational status can be achieved, providing strong support for the stable operation and efficient dispatch of the power system.

[0041] Furthermore, the step of formulating an optimized dispatching scheme for the distribution network based on the active power regulation capability assessment results and the operational risk level, using a main-distribution network collaborative optimization strategy, includes: determining the distributed resource clusters within the distribution network that can participate in optimized dispatching based on the active power regulation capability assessment results; determining the objective function and constraints for optimized dispatching of the distribution network based on the operational risk level; and solving the objective function using a main-distribution network collaborative optimization algorithm to generate an optimized dispatching scheme for the distribution network.

[0042] Based on the active power regulation capacity assessment results and operational risk levels, formulating an optimized dispatch scheme for the distribution network using a coordinated optimization strategy for the main and distribution networks is a systematic and meticulous process. The following is a detailed explanation of this process: I. Identify distributed resource clusters within the distribution network that can participate in optimized scheduling. Application of active power regulation capacity assessment results: The active power regulation capacity assessment results reveal the regulation potential and response speed of various distributed resources (such as distributed power sources, energy storage systems, and demand response resources) within the distribution network. Based on these assessment results, distributed resources with sufficient regulation and rapid response capabilities can be selected to form a distributed resource cluster that can participate in optimized scheduling.

[0043] Resource cluster partitioning: Resource clusters can be partitioned based on factors such as geographical location, resource type, and adjustment characteristics. By partitioning resources appropriately, the adjustment capabilities of distributed resources can be utilized more effectively, improving the efficiency of optimized scheduling.

[0044] II. Determining the objective function and constraints for optimal dispatching of the distribution network Setting the objective function: Different optimization objectives can be set depending on the level of operational risk. For example, when the risk is high, the objective may be to minimize power imbalance, voltage fluctuations, or frequency deviations; when the risk is low, the objective may be to maximize economic benefits, energy conservation and emission reduction, or user satisfaction.

[0045] Definition of constraints: The constraints include physical constraints of the distribution network (such as line transmission capacity, transformer capacity, etc.), security constraints (such as voltage range, frequency range, etc.), and operational constraints of distributed resources (such as output range, response time, etc.). These constraints ensure the feasibility and safety of the optimized scheduling scheme in actual operation.

[0046] III. Solving the objective function using a main-distribution network collaborative optimization algorithm. Algorithm selection: The main grid and distribution network collaborative optimization algorithm needs to comprehensively consider the operating status and constraints of both the main grid and the distribution network to achieve global optimum. Commonly used algorithms include genetic algorithms, particle swarm optimization, and mixed-integer linear programming.

[0047] The algorithm's solution process: The objective function and constraints are transformed into a mathematical model. Using a selected optimization algorithm, the model is iteratively solved until a solution is found that satisfies the constraints and optimizes the objective function.

[0048] IV. Generating an optimized dispatching scheme for the power distribution network The content of the plan: Optimized scheduling schemes include output plans for each distributed resource, load allocation schemes, and reactive power compensation measures. These schemes aim to achieve the safe, economical, and efficient operation of the distribution network.

[0049] Implementation of the plan: The optimized scheduling plan is distributed to the control systems of each distributed resource and the dispatch center of the distribution network. Each control system executes corresponding adjustment operations according to the plan to achieve optimized scheduling of the distribution network.

[0050] By rationally partitioning resource clusters, setting objective functions and constraints, and solving optimization algorithms, an optimized scheduling scheme that meets actual needs can be generated, thereby improving the operational efficiency and security of the distribution network. Furthermore, the optimized scheduling scheme takes into account both the safety and economy of power grid operation, including: under the premise of meeting the safety operation constraints of the distribution network, and with the goal of minimizing the operating cost of the distribution network, an optimized scheduling scheme for the distribution network is formulated.

[0051] When developing optimal dispatching schemes for distribution networks, it is crucial to balance the safety and economy of network operation. The following is a detailed explanation of this process, particularly how to develop optimal dispatching schemes with the goal of minimizing distribution network operating costs while meeting the constraints of safe network operation: I. Balancing Safety and Economy Safety first: Safety is the primary objective of power grid operation. When developing optimized dispatching schemes, it is essential to ensure that the distribution network meets safety constraints under various operating conditions, such as voltage range, frequency range, and line transmission capacity. These safety constraints are ensured through real-time monitoring and early warning systems, safety and stability analysis tools, and other means.

[0052] Economic considerations: While ensuring safety, economic efficiency is another important objective for optimizing dispatching schemes. Economic efficiency is usually measured by minimizing the operating costs of the distribution network, including electricity purchase costs, equipment maintenance costs, and reactive power compensation costs.

[0053] II. Develop an optimized scheduling plan Setting the objective function: To minimize the operating cost of the distribution network, an objective function is constructed. This objective function may include electricity purchase cost, equipment loss cost, and the operating cost of reactive power compensation equipment. An optimization algorithm is used to solve the objective function, finding the scheduling scheme that minimizes the operating cost.

[0054] Considerations for constraints: When solving the objective function, various safety constraints must be considered. These constraints include voltage range, frequency range, line transmission capacity, and equipment output range. By transforming these constraints into mathematical inequalities or equations and embedding them into the optimization algorithm, it is ensured that the solved scheduling scheme meets safety requirements.

[0055] Optimized utilization of distributed resources: When formulating optimized dispatching schemes, the regulation capacity and economic characteristics of distributed resources should be fully considered. Through reasonable dispatching strategies, the output of distributed resources can be fully utilized to reduce electricity purchase costs. Simultaneously, measures such as reactive power compensation and voltage regulation can improve the power factor and voltage quality of the power grid, thereby reducing equipment loss costs.

[0056] Real-time adjustments and scrolling optimizations: Due to the real-time changes in the power grid's operating status, the optimized dispatching scheme needs to be adjusted in real time. By monitoring the power grid status in real time and based on the latest load forecasts and distributed resource output forecasts, the dispatching scheme is continuously optimized. Continuous optimization ensures that the dispatching scheme always adapts to the actual operating status of the power grid, improving the accuracy and flexibility of dispatching.

[0057] III. Implementation and Optimization Implementation of the plan: The optimized scheduling plan is distributed to the control systems of each distributed resource and the dispatch center of the distribution network. Each control system executes corresponding adjustment operations according to the plan to achieve optimized scheduling of the distribution network.

[0058] Effect evaluation and feedback: The effectiveness of the optimized scheduling scheme is evaluated, including indicators such as safety and economy. Based on the evaluation results, feedback and optimization are provided to improve the scheduling effectiveness and accuracy.

[0059] By setting reasonable objective functions, considering constraints, optimizing the use of distributed resources, and implementing real-time adjustments and rolling optimizations, a safe and economical optimized scheduling scheme can be developed to improve the operating efficiency and economic benefits of the distribution network.

[0060] Furthermore, during the operation of the distribution network, real-time operating data of the various types of distributed resource clusters and the real-time operating requirements of the distribution network are continuously acquired, and the active power regulation capability assessment results and the operating risk level are dynamically updated.

[0061] Furthermore, based on the updated active power regulation capacity assessment results and operational risk level, the optimized scheduling scheme is optimized in real time to dynamically adjust the operating status of the distribution network and ensure the safe and economical operation of the distribution network.

Claims

1. A method for optimizing the scheduling of a power distribution network, characterized in that, include: Acquire real-time operational data of multiple types of distributed resource clusters, including controllable units such as distributed generation, load, and energy storage; The active power regulation capability of the multi-type distributed resource clusters is evaluated using a cloud-edge converged architecture, and the active power regulation capability evaluation result is obtained. Obtain the real-time operational requirements of the distribution network, and determine the operational risk level of the distribution network based on the real-time operational requirements; Based on the active power regulation capacity assessment results and the operational risk level, an optimized dispatching scheme for the distribution network is formulated using a main and distribution network coordinated optimization strategy. The optimized scheduling scheme is sent to the control system of the distribution network to adjust the multi-type distributed resource clusters so that the distribution network operates according to the optimized scheduling scheme.

2. The distribution network optimization scheduling method as described in claim 1, characterized in that, include: In the cloud, based on the real-time operational data, an equivalent model of the multi-type distributed resource cluster is established; At the edge, real-time status information of the multi-type distributed resource clusters is obtained, and the active power regulation capability of the multi-type distributed resource clusters is evaluated based on the equivalent model and the real-time status information.

3. The distribution network optimization scheduling method as described in claim 1, characterized in that, include: Acquire real-time load data and power output data of the power distribution network; Based on the real-time load data and power output data, calculate the real-time power balance status of the distribution network; The operational risk level of the distribution network is determined based on the real-time power balance status.

4. The distribution network optimization scheduling method as described in claim 1, characterized in that, include: Based on the active power regulation capability assessment results, the distributed resource clusters within the distribution network that can participate in optimized scheduling are identified; Based on the aforementioned operational risk level, the objective function and constraints for optimal power distribution network scheduling are determined. The objective function is solved by employing a main-distribution network collaborative optimization algorithm to generate an optimized scheduling scheme for the distribution network.

5. The distribution network optimization scheduling method as described in claim 1, characterized in that, This includes developing optimized dispatching schemes for the distribution network while meeting the constraints of safe operation and minimizing the operating costs of the distribution network.

6. The distribution network optimization scheduling method as described in claim 1, characterized in that, During the operation of the distribution network, the real-time operation data of the multi-type distributed resource clusters and the real-time operation requirements of the distribution network are continuously acquired, and the active power regulation capability assessment results and the operation risk level are dynamically updated.

7. The distribution network optimization scheduling method as described in claim 6, characterized in that, Based on the updated active power regulation capacity assessment results and operational risk level, the optimized scheduling scheme is optimized in real time to dynamically adjust the operating status of the distribution network and ensure the safe and economical operation of the distribution network.