Coordinated optimization method and system for community micro-grid

Abstract

The invention provides a community micro-grid coordinated optimization method and system, and relates to the technical field of power systems. The method comprises the steps that according to community classification and local wind energy and solar energy resources, wind power and photovoltaic positions and capacities are determined, energy storage equipment is selected and configured, and a community micro-grid framework is constructed; the power monitoring equipment is used for analyzing residential electricity consumption data, identifying electricity consumption peak, valley and average quantity, and classifying residential electricity consumption behaviors; a Bi-Level theory is adopted to construct a double-layer optimization model: an upper layer optimizes micro-grid planning and configuration, and a lower layer optimizes operation scheduling and maintenance; a model constraint condition is set in combination with residential electricity consumption behaviors, new energy intermittency and energy storage characteristics; and during solving, the upper layer adopts a target optimization algorithm, and the lower layer adopts a linear programming algorithm, so that a micro-grid configuration and scheduling optimal scheme meeting constraints is obtained. According to the technical scheme, community characteristics, residential electricity consumption behaviors, new energy and energy storage characteristics can be comprehensively considered to carry out coordinated optimization of the community microgrid.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and more specifically, to a method and system for coordinated optimization of community microgrids. Background Art

[0002] A community microgrid is a micro power system that integrates distributed power sources (such as wind power, photovoltaics), energy storage, power electronics, loads and monitoring. It has self-control, protection and energy management capabilities, can be connected to the grid or operate in an isolated manner, and provide stable electricity for community residents.

[0003] However, the current microgrid system is mainly oriented to island operation and adopts hierarchical control. Although it attempts to cope with the uncertainty of wind and solar power by optimizing the dispatching model, there are still many shortcomings: (1) The traditional design does not fully consider the diversity of community residents and the integration of a large number of heterogeneous energy sources, resulting in inefficient energy utilization. (2) The connectivity between microgrids and the main grid is insufficient. As the scale and complexity of microgrids increase, the incorporation of a large number of random and volatile new energy sources can easily cause an impact on the main grid and affect stability. Hierarchical control coordination is difficult and can easily lead to control delays or conflicts. (3) In terms of dispatching models, although the uncertainty of wind and solar power is taken into account, there is a lack of strategies to deal with complex situations such as extreme weather. It is difficult to achieve flexible and efficient coordinated control between microgrids and loads, and it is impossible to ensure the stable operation of the power grid under complex conditions.

[0004] In summary, existing technologies have obvious defects in the planning and operation of community microgrids. There is an urgent need for a coordinated optimization method that can comprehensively consider community characteristics, residents' electricity consumption behavior, new energy and energy storage characteristics to improve grid connectivity, stability and the ability to cope with complex situations. Summary of the invention

[0005] The purpose of this application is to provide a community microgrid coordination optimization method and system, which can comprehensively consider community characteristics, residents' electricity consumption behavior, new energy and energy storage characteristics to coordinate and optimize community microgrids, so as to improve grid connectivity, stability and the ability to cope with complex situations.

[0006] This application is implemented as follows:

[0007] In a first aspect, the present application provides a method for coordinated optimization of a community microgrid, comprising the following steps: determining the location and capacity of wind turbines and solar photovoltaic panels based on community classification results and the distribution of local wind and solar energy resources, and considering the selection and configuration of energy storage equipment to establish a community microgrid system architecture; analyzing the residential electricity consumption data collected in real time by power monitoring equipment, and classifying the residents' electricity consumption behavior based on the key indicators obtained by the analysis, including peak and trough time periods and average electricity consumption, to obtain classification information of residents' electricity consumption behavior; applying Bi-Level theory to establish a two-layer optimization model of the community microgrid system architecture, wherein the upper layer of the two-layer optimization model focuses on the planning and configuration optimization of the microgrid, and the lower layer focuses on the operation and scheduling optimization of the microgrid; establishing constraints for optimizing the two-layer optimization model based on the classification information of residents' electricity consumption behavior, the intermittent and random nature of new energy, and the key characteristics of energy storage equipment; using the target optimization algorithm in the upper layer of the two-layer optimization model and the linear programming algorithm in the lower layer to solve the two-layer optimization model to obtain the optimal solution of the microgrid configuration parameters and scheduling scheme that meets the constraints.

[0008] In a second aspect, the present application provides a community microgrid coordination optimization system, which includes:

[0009] The first module is used to determine the location and capacity of wind turbines and solar photovoltaic panels according to the community classification results and the distribution of local wind and solar energy resources, and consider the selection and configuration of energy storage equipment to establish the community microgrid system architecture; the second module is used to analyze the residential electricity consumption data collected in real time by power monitoring equipment, and classify the residents' electricity consumption behavior according to the key indicators obtained by the analysis, including peak and valley time periods and average electricity consumption, to obtain the classification information of residents' electricity consumption behavior; the third module is used to apply Bi-Level theory to establish a two-layer optimization model of the community microgrid system architecture, wherein the upper layer of the two-layer optimization model focuses on the planning and configuration optimization of the microgrid, and the lower layer focuses on the operation and scheduling optimization of the microgrid; the fourth module is used to establish the constraint conditions for optimizing the two-layer optimization model according to the classification information of residents' electricity consumption behavior, the intermittent and random nature of new energy, and the key characteristics of energy storage equipment; the fifth module is used to solve the two-layer optimization model by using the target optimization algorithm in the upper layer of the two-layer optimization model and the linear programming algorithm in the lower layer, and obtain the optimal solution of the microgrid configuration parameters and scheduling scheme that meet the constraints.

[0010] In a third aspect, the present application provides an electronic device, comprising a memory for storing one or more programs; a processor; when the one or more programs are executed by the processor, a method as described in any one of the first aspects is implemented.

[0011] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method as described in any one of the above-mentioned first aspects.

[0012] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0013] This application proposes a method for coordinated optimization of community microgrids. By comprehensively considering multiple factors, it realizes the coordinated optimization of community microgrids, and provides strong technical support and reference for the future construction and operation of microgrids. Among them, through reasonable planning and configuration of new energy equipment and energy storage equipment, local wind and solar energy resources can be maximized, dependence on traditional energy can be reduced, and energy utilization efficiency can be improved. At the same time, through in-depth understanding and analysis of residents' electricity consumption behavior, more accurate power dispatching strategies can be formulated to achieve accurate matching of power supply and demand and reduce power waste. In addition, by establishing a two-layer optimization model and solving the optimal microgrid configuration parameters and dispatching scheme, the stability and reliability of the system can be enhanced, and the ability to cope with the intermittent and random nature of new energy can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A flowchart of an embodiment of a community microgrid coordination optimization method of the present application;

[0016] Figure 2 A schematic diagram of a community microgrid system architecture in one embodiment of the present application;

[0017] Figure 3 The theoretical framework of double-layer optimization for an embodiment of the present application;

[0018] Figure 4 A structural block diagram of an electronic device provided in an embodiment of the present application.

[0019] Icon: 201, processor; 202, memory; 203, communication interface. DETAILED DESCRIPTION

[0020] To make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, rather than all of the embodiments. The components of the embodiment of the present application described and shown in the accompanying drawings here can be arranged and designed in various configurations. In this article, relational terms such as first and second, etc. are only used to distinguish an entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations.

[0021] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0022] Example 1

[0023] After an in-depth analysis of existing community microgrid technologies, this application found that the current microgrid system is mainly oriented towards island operation, and the design does not fully consider the diversity of community residents and the integration of a large number of heterogeneous energy sources, resulting in inefficient energy utilization. At the same time, there is insufficient connectivity between microgrids and with large power grids, the incorporation of new energy poses a threat to the stability of the main grid, and hierarchical control and coordination are difficult. In addition, although the dispatch model takes into account the uncertainty of wind and solar power, it lacks strategies to deal with complex situations such as extreme weather, and cannot achieve flexible and efficient collaborative control.

[0024] In view of these defects in the prior art, the embodiment of the present application provides a method for coordinated optimization of community microgrids, which first determines the location and capacity of wind turbines and solar photovoltaic panels according to the community classification and distribution of new energy resources, and configures energy storage equipment to establish a system architecture. Secondly, the power monitoring equipment is used to collect residents' electricity consumption data in real time, analyze electricity consumption behavior, and classify it. Then, the Bi-Level theory is applied to construct a two-layer optimization model, the upper layer optimizes microgrid planning and configuration, and the lower layer optimizes operation scheduling. Next, constraints are established based on residents' electricity consumption behavior, new energy intermittency and energy storage characteristics. Finally, the upper layer uses the target optimization algorithm, and the lower layer uses the linear programming algorithm to solve the two-layer optimization model to obtain the optimal microgrid configuration parameters and scheduling scheme. Through these processes, the connectivity, stability and ability to cope with complex situations of the community microgrid will be improved, and efficient use of energy and high-quality electricity consumption experience for residents will be achieved.

[0025] See also Figure 1 The community microgrid coordination optimization method specifically includes the following steps:

[0026] Step S101: According to the community classification results and the distribution of local wind and solar energy resources, the location and capacity of wind turbines and solar photovoltaic panels are determined, and the selection and configuration of energy storage equipment are considered to establish a community microgrid system architecture.

[0027] It should be noted that in the process of establishing the community microgrid system architecture, by accurately determining the location and capacity of new energy equipment and rationally selecting and configuring energy storage equipment, it will be possible to maximize the use of local wind and solar energy resources, reduce energy waste, and improve energy efficiency.

[0028] For example, when considering the selection and configuration of energy storage equipment (such as batteries) and establishing a wind-solar-storage community microgrid system architecture based on EIoT, the established microgrid system architecture can include a power sensing layer, a power network layer, a power data layer, a power application layer, and a power security layer. This architecture involves the intelligent control and energy management system of the microgrid, which can ensure effective coordination between various energy components. Among them, the Energy Management System (EMS) plays a vital role in the energy storage system. Through real-time monitoring, intelligent control and optimized scheduling, it can improve energy utilization efficiency, reduce operating costs, and ensure the safety and reliability of the system.

[0029] Step S102: Analyze the residential electricity consumption data collected in real time by the power monitoring equipment, and classify the residents' electricity consumption behavior according to the key indicators including peak and valley time periods and average electricity consumption, so as to obtain the classification information of residents' electricity consumption behavior.

[0030] In the above step S102, an in-depth understanding of residents' electricity consumption behavior will help understand residents' electricity consumption habits and needs, and can provide strong support for subsequent power dispatch and energy storage strategies, achieve accurate matching of power supply and demand, and improve energy utilization efficiency.

[0031] For example, smart meters or other power monitoring equipment with high-precision data collection capabilities can be installed at community users, so that residents' electricity consumption data can be collected in real time through power monitoring equipment, including parameters such as electricity consumption, electricity consumption time, and electricity power. The collected data is then transmitted to the data center through wireless (such as ZigBee, Wi-Fi, 4G / 5G, etc.) or wired (such as optical fiber, cable, etc.) communication methods in the EIoT energy network. During data transmission, the security layer in the system architecture is used to ensure stable and secure signal transmission to prevent data loss or tampering. Sufficient storage devices and data processing software are also equipped at the data layer to receive and store large amounts of electricity consumption data, and perform preliminary sorting and verification.

[0032] Next, we can analyze the collected residential electricity consumption data to obtain various patterns and characteristics of residential electricity consumption, including specific time periods of peak and trough electricity consumption, average electricity consumption and other key indicators. Then, we can classify the residents' electricity consumption behaviors based on the various patterns and characteristics of residential electricity consumption, and obtain the classification information of residents' electricity consumption behaviors.

[0033] Furthermore, these analysis results can also serve as a basis for power purchase and sale decisions between community microgrids and large power grids. By identifying power consumption patterns, community microgrids can more accurately predict power demand and make more reasonable purchase and sale decisions in the power market. During peak power consumption periods, community microgrids can consider purchasing more power from the large power grid to meet residents' needs; during low power consumption periods, they can sell excess power to the large power grid to achieve optimal energy allocation and maximize cost-effectiveness. Such a strategy can not only improve energy efficiency, but also bring economic benefits to the community and promote sustainable development.

[0034] Please continue reading Figure 1 , Step S103: Apply Bi-Level theory to establish a two-level optimization model of the community microgrid system architecture, where the upper level of the two-level optimization model focuses on the planning and configuration optimization of the microgrid, and the lower level focuses on the operation and scheduling optimization of the microgrid. The theoretical framework of the two-level optimization is as follows Figure 3 shown.

[0035] In the above step S103, by constructing a two-layer optimization model, the coordinated optimization of the microgrid at the planning and operation levels can be achieved, thereby improving the overall performance and flexibility of the system. This helps to reduce the impact of the integration of new energy on the main grid and improve the stability of the overall power system.

[0036] Step S104: Establish constraint conditions for optimizing the two-layer optimization model based on the classified information of residents' electricity consumption behavior, the intermittent and random nature of new energy sources, and key features of energy storage equipment.

[0037] Step S105: The upper layer of the two-layer optimization model uses a target optimization algorithm (such as a genetic algorithm, a particle swarm algorithm, etc.), and the lower layer uses a linear programming algorithm to solve the two-layer optimization model to obtain the optimal solution of the microgrid configuration parameters and the scheduling scheme that meets the constraints.

[0038] In the above step S104, the established constraints include power supply and demand balance, energy storage equipment charging and discharging restrictions, new energy equipment output restrictions, etc., which can ensure the feasibility and effectiveness of the optimization scheme in practical applications. Then, by solving the double-layer optimization model in step S105, the optimal microgrid configuration parameters and scheduling scheme can be obtained to achieve efficient energy utilization and stable operation of the system. At the same time, it can also provide strong support for subsequent microgrid operation and maintenance.

[0039] In summary, the above embodiments can maximize the use of local wind and solar energy resources, reduce energy waste, and improve energy efficiency by accurately determining the location and capacity of new energy equipment and reasonably selecting and configuring energy storage equipment. In addition, by constructing and solving a two-layer optimization model, the coordinated optimization of the microgrid at both the planning and operation levels can be achieved, thereby improving the connectivity and stability of the power grid. This helps to reduce the impact of the integration of new energy on the main grid and improve the stability of the overall power system. In addition, by considering the diversity of residents' electricity consumption behavior, the intermittent and random nature of new energy, and the key characteristics of energy storage equipment, more comprehensive and accurate constraints can be established, thereby formulating more flexible and efficient power dispatching and energy storage strategies. This helps the microgrid to maintain stable operation in complex situations such as extreme weather and provide reliable power supply for community residents.

[0040] Based on the above scheme, in some implementations of the present application, the method further includes the following steps: construct, transform or adjust the operation of the community microgrid system according to the optimal solution of the microgrid configuration parameters and the scheduling scheme obtained by solving, and use the solution results to predict the future power consumption trend of the community, providing a basis for short-term operation and maintenance and long-term upgrades and renovations. Exemplarily, the solution results can be used to predict the future power consumption trend of the community, such as predicting the changes in power load in different seasons and time periods, and providing a basis for the short-term operation and maintenance of the community microgrid (such as regular inspection and maintenance schedule of energy storage equipment) and long-term upgrades and renovations of the old power grid (such as determining when it is necessary to increase the capacity of new energy generation or upgrade energy storage equipment).

[0041] In the above implementation, according to the solution results, necessary construction, transformation or operation adjustments are made to the community microgrid system. This includes but is not limited to the installation and commissioning of new energy equipment, the configuration and access of energy storage equipment, and the optimization and upgrading of the power dispatching system. Through these measures, it can be ensured that the community microgrid system can operate according to the optimal configuration parameters and dispatching scheme, and achieve efficient use of energy and stable operation of the system. At the same time, the future electricity consumption trend of the community is predicted by using the solved microgrid configuration parameters and dispatching scheme, combined with historical electricity consumption data and classification information of residents' electricity consumption behavior. The prediction results will help community managers and power operators better understand future changes in electricity demand and provide data support for the formulation of short-term operation and maintenance plans and long-term upgrade and transformation plans.

[0042] In short, by applying the optimal solution obtained in the actual construction, transformation or operation adjustment of the community microgrid system, the performance and stability of the system can be significantly improved. This helps to reduce power waste, improve energy efficiency and reduce operating costs. In addition, based on the power consumption trend forecast results, community managers and power operators can configure and dispatch power resources more accurately. Therefore, during peak hours, the power provided by new energy and energy storage equipment can be used first to reduce dependence on traditional power grids; during off-peak hours, the charging and discharging of energy storage equipment can be reasonably arranged to improve energy efficiency. In addition, the power consumption trend forecast results can also provide a scientific basis for the short-term operation and maintenance and long-term upgrade and transformation of community microgrids.

[0043] Based on the above scheme, Figure 2 As shown, in some implementations of the present application, the community microgrid system architecture includes: a power sensing layer, which is configured with a variety of sensors for collecting real-time information on wind energy, light energy, energy storage and load; a power network layer, which uses a gateway to convert and store the data collected by the power sensing layer, and exchange data between layers; a power data layer, which is responsible for data collection, processing, storage and analysis; a power application layer, which includes an application server and a cloud server, which is used to provide a user interaction interface through a web page and application on a personal computer or mobile device, allowing authorized users to access and operate the application server and the cloud server; and a power security layer, which is used to collect and analyze key factors affecting the safe operation of the microgrid in real time, so as to discover potential safety risks in the microgrid system and evaluate the safety status of community electricity use in real time.

[0044] Specifically, the power sensing layer is equipped with a variety of sensors to collect various information about wind energy, light energy (i.e. solar energy), energy storage devices (such as battery packs), and loads (i.e. user electrical devices) in real time. This information includes but is not limited to wind speed, light intensity, charge and discharge status of energy storage devices, power consumption, and user power consumption and power consumption patterns. Through these sensors, the system can grasp various dynamics within the microgrid in real time, providing a basis for subsequent data processing and analysis.

[0045] The power network layer uses gateway devices to convert and store the raw data collected by the power sensor layer so that it can be used by higher-level systems. The gateway device is also responsible for data exchange between layers to ensure the smooth flow of information. Through the power network layer, the system can achieve centralized management and distributed processing of data, improving data processing efficiency and accuracy.

[0046] The power data layer is responsible for data collection, processing, storage and analysis. It can use advanced data processing and analysis technology to clean, organize, classify and mine the data obtained from the power sensor layer to extract valuable information and rules. Through the power data layer, the system can achieve real-time monitoring and early warning of the microgrid operating status, providing data support for subsequent decision-making and optimization.

[0047] The power application layer includes application servers and cloud servers, which provide user interaction interfaces through web pages and applications on personal computers or mobile devices. Authorized users can access and operate application servers and cloud servers through these interfaces to achieve remote monitoring, scheduling and management of microgrid systems. The power application layer also provides a variety of functions and services, such as power consumption data analysis, fault alarm, remote control, etc., so that users can understand the operating status of the microgrid at any time and perform corresponding operations.

[0048] The power safety layer is responsible for real-time collection and analysis of key factors that affect the safe operation of microgrids, such as voltage fluctuations, current overloads, equipment failures, etc. By analyzing these factors, the system can promptly detect potential safety risks in the microgrid system and evaluate the safety status of community electricity use in real time. The power safety layer also provides a safety warning and emergency response mechanism, which can automatically trigger corresponding measures to ensure the safe operation of the system when safety risks are detected.

[0049] In summary, in the above implementation, through the collaborative work of the power sensing layer and the power network layer, the system can collect and process various information inside the microgrid in real time, and promptly discover and handle potential faults and abnormal situations, thereby improving the reliability and stability of the system. At the same time, the power data layer can conduct in-depth analysis of the operation data of the microgrid, extract valuable information and laws, and provide a scientific basis for subsequent decision-making and optimization. This helps to achieve the rational use of new energy and the optimal configuration of energy storage equipment, improve energy utilization efficiency and reduce operating costs. The power application layer provides a wealth of functions and services, which makes it convenient for users to understand the operating status of the microgrid at any time and perform corresponding operations. This not only improves user participation and satisfaction, but also helps to cultivate users' energy-saving awareness and green lifestyle. In addition, the power safety layer can collect and analyze the key factors affecting the safe operation of the microgrid in real time, and promptly discover and handle potential safety risks. This helps to ensure the safe operation of the system and the safety of users' electricity use, and reduce losses and impacts caused by faults or accidents.

[0050] Based on the aforementioned scheme, in some implementations of the present application, the upper layer of the two-layer optimization model is further used to evaluate and select the optimal configuration of key components including wind turbines, solar photovoltaic panels, and energy storage equipment, based on the initial investment cost, operation and maintenance cost, expected life span of the components, and their impact on the environment, while taking into account changes in components over time and external environmental factors; the lower layer of the two-layer optimization model is further used to adjust the output of energy components based on real-time monitored microgrid operation data to achieve supply and demand balance, and predict and respond to potential supply and demand fluctuations, and start backup power supplies or energy storage equipment when necessary.

[0051] In the above implementation, the upper layer of the two-layer optimization model focuses on the planning and configuration optimization of the microgrid, which involves determining the optimal capacity and layout of various energy components. Specifically, the upper layer model will evaluate and select the optimal configuration of key components such as wind turbines, solar photovoltaic panels, and energy storage devices to ensure that the microgrid can meet the power demand while maximizing cost-effectiveness and minimizing environmental impact. This process will take into account the initial investment cost, operation and maintenance cost, expected life of the components, and their impact on the environment, as well as the changes in component performance over time and external environmental factors such as weather conditions and changes in market demand. The lower layer of the two-layer optimization model focuses on the operation and scheduling optimization of the microgrid. The goal of this level is to effectively allocate the output power of each energy component in different time periods to respond to real-time power demand and grid operation status. The lower layer model will monitor the operation data of the microgrid in real time, including changes in energy supply and demand, and adjust the output of energy components accordingly to achieve supply and demand balance. This involves not only the immediate distribution of energy, but also predicting and responding to potential supply and demand fluctuations, and starting backup power or energy storage equipment when necessary. Through this dynamic operation scheduling, microgrids can achieve higher energy utilization efficiency and lower operating costs while ensuring the reliability and stability of power supply.

[0052] That is, through the above implementation method, the economic, environmental and technical factors of the microgrid system are comprehensively considered, the optimal configuration and real-time energy management of key components are achieved, and strong support is provided for the efficient, stable and sustainable development of the community microgrid. Among them, the upper model can select the most economical component configuration scheme by comprehensively considering the initial investment cost, operation and maintenance cost and expected life of the components, as well as external environmental factors, and reduce the construction and operation costs of the microgrid. The upper model also considers the impact of the components on the environment, which helps to select more environmentally friendly energy components, reduce energy consumption and emissions during the operation of the microgrid, and promote sustainable development. The lower model can adjust the output of energy components in real time through real-time monitoring and intelligent scheduling, achieve supply and demand balance, effectively respond to potential supply and demand fluctuations, and improve the stability and reliability of the microgrid. The lower model also realizes the storage and release of energy by intelligently scheduling the charging and discharging of energy storage equipment, improves energy utilization efficiency, and reduces energy waste.

[0053] Based on the aforementioned scheme, in some implementations of the present application, the constraints include minimum power supply guarantee constraints for residential users, constraints on fluctuations in renewable energy power generation, and constraints on capacity limitations, charging and discharging efficiency, and cycle life of energy storage equipment.

[0054] In the above implementation, the minimum power supply guarantee constraint for residential users ensures that the microgrid can provide the residential users with a minimum power supply under any circumstances to meet their basic power needs, thereby improving the power satisfaction. For example, this can be achieved by setting a minimum power supply threshold. When the power supply capacity of the microgrid is lower than the threshold, the system will trigger corresponding emergency measures, such as starting the backup power supply or adjusting the energy distribution, to ensure that the power consumption of the residential users is not affected.

[0055] Since the power generation of renewable energy sources such as wind power and solar power has significant volatility and uncertainty, by constraining the fluctuation of power generation of new energy sources, it can ensure that the microgrid can flexibly respond to such fluctuations, maintain the stable operation of the system, and reduce power outages or power quality degradation caused by imbalance between supply and demand. For example, the power generation of new energy sources can be monitored in real time, and the output of other energy components (such as energy storage devices) can be adjusted, and energy distribution can be optimized through intelligent scheduling algorithms to achieve supply and demand balance.

[0056] Energy devices play a vital role in microgrids, storing excess energy and releasing it when needed. However, the capacity, charging and discharging efficiency, and cycle life of energy storage devices are limited. Therefore, the constraints of capacity limitation, charging and discharging efficiency, and cycle life of energy storage devices require that microgrids must consider these characteristics of energy storage devices during the optimization process to avoid overcharging and discharging or frequent charging and discharging, so as to extend their service life and improve energy utilization efficiency.

[0057] Based on the aforementioned scheme, in some implementations of the present application, the constraint conditions for optimizing the two-layer optimization model are established according to the classification information of residents' electricity consumption behavior, the intermittent and random nature of new energy sources, and the key characteristics of energy storage equipment, including: establishing corresponding electricity consumption-side constraint conditions for each type of residential user according to the classification information of residents' electricity consumption behavior; establishing constraint conditions for fluctuations in new energy power generation power according to the intermittent and random nature of new energy sources and utilizing analysis of local historical weather and meteorological data and new energy power generation data; establishing constraint conditions for energy storage equipment according to the key characteristics of energy storage equipment including capacity limitation, charging and discharging efficiency, and cycle life.

[0058] It should be noted that the electricity consumption behavior of residents is diverse and different, and different types of residential users (such as households, businesses, industries, etc.) may have significant differences in electricity demand, electricity consumption mode and electricity consumption time. Therefore, in order to more accurately reflect the electricity demand of residents, the above implementation method establishes corresponding electricity consumption side constraints for each type of residential user based on the classification information of residents' electricity consumption behavior. These conditions may include the minimum and maximum values ​​of electricity demand, electricity consumption time period, peak and valley time period, etc., to ensure that the microgrid can fully consider the actual situation of residents' electricity consumption during the optimization process.

[0059] At the same time, the power generation of renewable energy (such as wind power and solar energy) is intermittent and random, and is affected by many factors such as weather, season, and geographical location. In order to accurately reflect the fluctuation of renewable energy power generation, the above implementation method uses the analysis of local historical weather and meteorological data and renewable energy power generation data to establish constraints on the fluctuation of renewable energy power generation. These conditions can include the predicted value of renewable energy power generation, the prediction error range, the fluctuation range of power generation, etc., to ensure that the microgrid can fully consider the uncertainty of renewable energy power generation during the optimization process.

[0060] In addition, energy storage devices play a vital role in microgrids, storing excess energy and releasing it when needed. However, the capacity, charge and discharge efficiency, and cycle life of energy storage devices are limited. In order to optimize the use of energy storage devices, the above implementation method establishes corresponding constraints based on these key characteristics of energy storage devices. These conditions may include the capacity limit of energy storage devices, the maximum and minimum values ​​of charge and discharge power, charge and discharge efficiency, cycle life, etc., to ensure that the microgrid can fully consider the characteristics and limitations of energy storage devices during the optimization process.

[0061] That is, by establishing corresponding power consumption constraints for each type of residential user, the microgrid can more accurately reflect the residential power demand and ensure that the actual situation of residential power consumption is fully considered during the optimization process, thereby improving the reliability of the microgrid. At the same time, by using historical weather and meteorological data and new energy power generation data to establish constraints on the fluctuation of new energy power generation, the microgrid can more accurately predict the fluctuation of new energy power generation, thereby optimizing energy distribution and scheduling, reducing energy waste, improving energy utilization efficiency, and enhancing the economy of the microgrid. In addition, by establishing constraints based on the key characteristics of energy storage equipment, the microgrid can optimize the use of energy storage equipment, avoid excessive charging and discharging or frequent charging and discharging, thereby extending the service life of energy storage equipment and reducing operation and maintenance costs.

[0062] In summary, the above implementation method comprehensively considers multiple factors such as residents' electricity consumption behavior, power fluctuations of renewable energy power generation, and characteristics of energy storage equipment by refining the process of establishing constraint conditions, providing strong support for the optimization and sustainable development of microgrids, and improving the reliability, economy, and intelligence level of microgrids.

[0063] Based on the aforementioned scheme, in some implementations of the present application, the target optimization algorithm is adopted in the upper layer of the two-layer optimization model, and the linear programming algorithm is adopted in the lower layer, including: the non-dominated sorting genetic algorithm NSGA-II is adopted in the upper layer of the two-layer optimization model for multi-objective optimization, and the simplex method is adopted in the lower layer for linear programming solution.

[0064] In the proposed community microgrid two-layer optimization model, in order to more effectively solve the multi-objective optimization problem of the upper layer and the linear programming problem of the lower layer, a specific algorithm combination is used in the above implementation method: the non-dominated sorting genetic algorithm NSGA-II is used for multi-objective optimization in the upper layer, and the simplex method is used for linear programming in the lower layer. This algorithm combination aims to improve the efficiency and accuracy of the optimization process and ensure that the microgrid system can achieve economic, environmentally friendly and reliable operation under the premise of meeting various constraints.

[0065] Among them, the non-dominated sorting genetic algorithm NSGA-II is a multi-objective optimization algorithm based on genetic algorithms. It balances the conflicts between multiple objectives through non-dominated sorting and elite retention strategies to find a set of Pareto optimal solutions. The algorithm can simultaneously consider multiple optimization objectives (such as minimizing costs, minimizing environmental impacts, maximizing power supply reliability, etc.) and find the best balance between these objectives. In the upper layer of the two-layer optimization model, since multiple conflicting objectives (such as economy and environmental protection) need to be optimized simultaneously, the multi-objective optimization capability of the non-dominated sorting genetic algorithm NSGA-II makes it an ideal choice.

[0066] The simplex method is a classic linear programming algorithm that searches for the optimal solution in the solution space through an iterative process. The algorithm starts from an initial vertex and gradually moves toward the optimal solution by comparing the objective function values ​​of adjacent vertices until an optimal solution that satisfies all constraints is found or no solution is determined. In the lower layer of the two-layer optimization model, since linear programming problems need to be solved (such as output adjustment of energy components, charging and discharging strategies of energy storage devices, etc.), the efficiency and accuracy of the simplex method make it a suitable algorithm choice.

[0067] In summary, using the non-dominated sorting genetic algorithm NSGA-II and the simplex method to optimize multi-objective optimization and linear programming problems respectively can significantly improve the efficiency of the optimization process. This means that in the same amount of time, the system can explore more solution spaces and find better solutions.

[0068] Based on the aforementioned scheme, in some implementations of the present application, the optimal solution of the microgrid configuration parameters and scheduling scheme that meets the constraints includes: determining the optimal capacity of wind turbines, solar photovoltaic panels and energy storage equipment, as well as the output power of each energy component and the charging and discharging strategy of the energy storage equipment in different time periods.

[0069] In the above implementation, based on the local wind and solar energy resources and historical power generation data, combined with the power demand of the microgrid and the characteristics of the energy storage equipment, a multi-objective optimization algorithm (such as NSGA-II) is used for iterative calculation to determine the optimal capacity of wind turbines and solar photovoltaic panels. This step aims to maximize the utilization of renewable energy while ensuring the reliability and economy of the system's power supply.

[0070] At the same time, it uses linear programming algorithms (such as the simplex method) to calculate the optimal capacity of energy storage equipment based on the determined capacity of wind turbines and solar photovoltaic panels, as well as the power demand and scheduling strategy of the microgrid. This step aims to balance the investment cost and operating benefits of energy storage equipment and ensure that the system can flexibly respond to changes in energy supply and demand in different time periods.

[0071] Furthermore, it uses an optimization algorithm to calculate the output power of each energy component (including wind turbines, solar photovoltaic panels and traditional energy equipment) in different time periods based on the power demand of the microgrid, the power generation forecast of renewable energy and the status of energy storage equipment. This step is to ensure that the system can meet the power demand in different time periods while maximizing the utilization of renewable energy.

[0072] In addition, it combines the output power of energy components and the power demand of microgrids to formulate a charging and discharging strategy for energy storage equipment. This step aims to balance the charging and discharging status of energy storage equipment, avoid excessive charging and discharging or frequent charging and discharging, thereby extending the service life of energy storage equipment and reducing operation and maintenance costs.

[0073] Example 2

[0074] The embodiment of the present application provides a community microgrid coordination optimization system, which includes:

[0075] The first module is used to determine the location and capacity of wind turbines and solar photovoltaic panels according to the community classification results and the distribution of local wind and solar energy resources, and consider the selection and configuration of energy storage equipment to establish the community microgrid system architecture; the second module is used to analyze the residential electricity consumption data collected in real time by power monitoring equipment, and classify the residents' electricity consumption behavior according to the key indicators obtained by the analysis, including peak and valley time periods and average electricity consumption, to obtain the classification information of residents' electricity consumption behavior; the third module is used to apply Bi-Level theory to establish a two-layer optimization model of the community microgrid system architecture, wherein the upper layer of the two-layer optimization model focuses on the planning and configuration optimization of the microgrid, and the lower layer focuses on the operation and scheduling optimization of the microgrid; the fourth module is used to establish the constraint conditions for optimizing the two-layer optimization model according to the classification information of residents' electricity consumption behavior, the intermittent and random nature of new energy, and the key characteristics of energy storage equipment; the fifth module is used to solve the two-layer optimization model by using the target optimization algorithm in the upper layer of the two-layer optimization model and the linear programming algorithm in the lower layer, and obtain the optimal solution of the microgrid configuration parameters and scheduling scheme that meet the constraints.

[0076] For the specific implementation process of the above system, please refer to the community microgrid coordination optimization method provided in Example 1, which will not be repeated here.

[0077] Example 3

[0078] See also Figure 4 , the embodiment of the present application provides an electronic device, which includes at least one processor 201 and at least one memory 202; wherein the processor 201 and the memory 202 are directly connected to each other, or communicate with each other through a communication interface 203, or are electrically connected through one or more communication buses or signal lines to achieve data transmission or interaction; the memory 202 stores program instructions that can be executed by the processor 201, and the processor 201 calls the program instructions to execute a community microgrid coordination optimization method. For example, the following is realized:

[0079] According to the community classification results and the distribution of local wind and solar energy resources, the location and capacity of wind turbines and solar photovoltaic panels are determined, and the selection and configuration of energy storage equipment are considered to establish the community microgrid system architecture; the residential electricity consumption data collected in real time by power monitoring equipment is analyzed to classify the residents' electricity consumption behavior based on the key indicators obtained from the analysis, including peak and trough time periods and average electricity consumption, to obtain the classification information of residents' electricity consumption behavior; the Bi-Level theory is applied to establish a two-layer optimization model of the community microgrid system architecture, wherein the upper layer of the two-layer optimization model focuses on the planning and configuration optimization of the microgrid, and the lower layer focuses on the operation and scheduling optimization of the microgrid; according to the classification information of residents' electricity consumption behavior, the intermittent and random nature of new energy, and the key characteristics of energy storage equipment, the constraint conditions for optimizing the two-layer optimization model are established; the upper layer of the two-layer optimization model adopts the target optimization algorithm, and the lower layer adopts the linear programming algorithm to solve the two-layer optimization model to obtain the optimal solution of the microgrid configuration parameters and scheduling scheme that meets the constraint conditions.

[0080] Among them, the memory 202 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc.

[0081] The processor 201 may be an integrated circuit chip with signal processing capability. The processor 201 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0082] Understandably, Figure 4 The structure shown is for illustration only. The electronic device may also include Figure 4 More or fewer components as shown, or with Figure 4 Different configurations shown. Figure 4 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0083] Example 4

[0084] The present application provides a computer-readable storage medium having a computer program stored thereon, which implements a community microgrid coordination optimization method when executed by a processor 201. For example, the following is implemented:

[0085] According to the community classification results and the distribution of local wind and solar energy resources, the location and capacity of wind turbines and solar photovoltaic panels are determined, and the selection and configuration of energy storage equipment are considered to establish the community microgrid system architecture; the residential electricity consumption data collected in real time by power monitoring equipment is analyzed to classify the residents' electricity consumption behavior based on the key indicators obtained from the analysis, including peak and trough time periods and average electricity consumption, to obtain the classification information of residents' electricity consumption behavior; the Bi-Level theory is applied to establish a two-layer optimization model of the community microgrid system architecture, wherein the upper layer of the two-layer optimization model focuses on the planning and configuration optimization of the microgrid, and the lower layer focuses on the operation and scheduling optimization of the microgrid; according to the classification information of residents' electricity consumption behavior, the intermittent and random nature of new energy, and the key characteristics of energy storage equipment, the constraint conditions for optimizing the two-layer optimization model are established; the upper layer of the two-layer optimization model adopts the target optimization algorithm, and the lower layer adopts the linear programming algorithm to solve the two-layer optimization model to obtain the optimal solution of the microgrid configuration parameters and scheduling scheme that meets the constraint conditions.

[0086] If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0087] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A community microgrid coordination optimization method, characterized in that: The following steps are involved: According to the community classification results and the distribution of local wind and solar energy resources, determine the location and capacity of wind turbines and solar photovoltaic panels, and consider the selection and configuration of energy storage equipment to establish the community microgrid system architecture; Analyze the residential electricity consumption data collected in real time by power monitoring equipment, and classify the residents' electricity consumption behavior based on the key indicators obtained by the analysis, including peak and valley time periods and average electricity consumption, to obtain the classification information of residents' electricity consumption behavior; Applying Bi-Level theory, a two-level optimization model of the community microgrid system architecture is established. The upper level of the two-level optimization model focuses on the planning and configuration optimization of the microgrid, while the lower level focuses on the operation and dispatch optimization of the microgrid. According to the classified information of residents' electricity consumption behavior, the intermittent and random nature of new energy, and the key characteristics of energy storage equipment, the constraint conditions for optimizing the two-layer optimization model are established; The double-layer optimization model is solved by adopting a target optimization algorithm in the upper layer and a linear programming algorithm in the lower layer to obtain the optimal solution of the microgrid configuration parameters and the scheduling scheme that meets the constraints.

2. The method according to claim 1, characterized in that The method further comprises: Based on the optimal solutions of the microgrid configuration parameters and scheduling schemes obtained, the community microgrid system is constructed, renovated or adjusted in operation, and the solution results are used to predict the future electricity consumption trend of the community, providing a basis for short-term operation and maintenance and long-term upgrades and renovations.

3. The method according to claim 2, characterized in that The using the solution results to predict the future electricity consumption trend of the community includes: using the solution results to predict the future changes in the electricity load of the community in different seasons and different time periods.

4. The method according to claim 1, characterized in that The community microgrid system architecture includes: The power sensing layer is equipped with a variety of sensors to collect real-time information on wind energy, solar energy, energy storage, and load; The power network layer is used to convert and store the data collected by the power sensor layer using the gateway, and to exchange data between the layers; The power data layer is responsible for data collection, processing, storage and analysis; The power application layer includes application servers and cloud servers, which are used to provide a user interaction interface through web pages and applications on personal computers or mobile devices, allowing authorized users to access and operate application servers and cloud servers; And the power safety layer is used to collect and analyze the key factors affecting the safe operation of the microgrid in real time, so as to discover the potential safety risks in the microgrid system and evaluate the safety status of community electricity use in real time.

5. The method according to claim 1, characterized in that The upper layer of the two-layer optimization model is further used to evaluate and select the optimal configuration of key components including wind turbines, solar photovoltaic panels, and energy storage devices, taking into account the initial investment cost, operation and maintenance cost, expected life span of the components, and their impact on the environment, while also considering the changes of the components over time and external environmental factors; The lower layer of the two-layer optimization model is further used to adjust the output of energy components according to the real-time monitored microgrid operation data to achieve supply and demand balance, predict and respond to potential supply and demand fluctuations, and activate backup power or energy storage equipment when necessary.

6. The method according to claim 1, characterized in that The constraints include minimum power supply guarantee constraints for residential users, constraints on fluctuations in power generation from renewable energy sources, and constraints on capacity limitations, charging and discharging efficiency, and cycle life of energy storage equipment.

7. The method according to claim 1 or 6, characterized in that: According to the classified information of residents' electricity consumption behavior, the intermittent and random nature of new energy, and the key characteristics of energy storage equipment, the constraint conditions for optimizing the two-layer optimization model are established, including: According to the classification information of residents' electricity consumption behavior, corresponding electricity consumption constraints are established for each type of residential users; According to the intermittent and random nature of renewable energy, the constraints on the fluctuation of renewable energy power generation are established by analyzing the local historical weather and meteorological data and renewable energy power generation data. Constraints for energy storage devices are established based on key characteristics of the energy storage device, including capacity limitation, charge and discharge efficiency, and cycle life.

8. The method according to claim 1, characterized in that The upper layer of the two-layer optimization model adopts a target optimization algorithm, and the lower layer adopts a linear programming algorithm, including: the upper layer of the two-layer optimization model adopts a non-dominated sorting genetic algorithm NSGA-II for multi-objective optimization, and the lower layer adopts a simplex method for linear programming solution.

9. The method according to claim 1, characterized in that: The optimal solution of the microgrid configuration parameters and scheduling scheme that meets the constraints includes: determining the optimal capacity of wind turbines, solar photovoltaic panels and energy storage devices, as well as the output power of each energy component and the charging and discharging strategy of the energy storage device in different time periods.

10. A community microgrid coordination optimization system, characterized in that: include: The first module is used to determine the location and capacity of wind turbines and solar photovoltaic panels based on the community classification results and the distribution of local wind and solar energy resources, and consider the selection and configuration of energy storage equipment to establish the community microgrid system architecture; The second module is used to analyze the residential electricity consumption data collected in real time by power monitoring equipment, and classify the residents' electricity consumption behavior according to the key indicators including peak and valley time periods and average electricity consumption obtained by analysis, so as to obtain the classification information of residents' electricity consumption behavior; The third module is used to apply Bi-Level theory to establish a two-level optimization model of the community microgrid system architecture, in which the upper level of the two-level optimization model focuses on the planning and configuration optimization of the microgrid, and the lower level focuses on the operation and scheduling optimization of the microgrid; The fourth module is used to establish the constraint conditions for optimizing the two-layer optimization model according to the classified information of residents' electricity consumption behavior, the intermittent and random nature of new energy, and the key characteristics of energy storage equipment; The fifth module is used to solve the double-layer optimization model by using the target optimization algorithm in the upper layer and the linear programming algorithm in the lower layer to obtain the optimal solution of the microgrid configuration parameters and the scheduling scheme that meets the constraints.

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