Comprehensive energy microgrid group energy sharing strategy optimization method

Abstract

The invention relates to the technical field of energy sharing optimization, in particular to a comprehensive energy microgrid group energy sharing strategy optimization method, which comprises the following steps of collecting real-time energy demand and supply data of an energy microgrid group, calculating energy demand and supply difference, analyzing supply and demand gaps of each microgrid, and calculating the energy sharing strategy of the microgrid group. And executing an energy demand identification operation according to a supply and demand gap analysis result, performing supply and demand matching analysis, determining an energy sharing range, and obtaining a microgrid sharing demand comprehensive analysis record. According to the invention, through collecting the energy demand and supply data of the microgrid group in real time, calculating the supply and demand difference of each microgrid, optimizing the sharing range and implementing the supply and demand matching analysis, the flexibility and pertinence of resource scheduling are improved, a refined sharing rule is formulated to ensure the reasonable allocation of resources, and the energy flow is dynamically adjusted according to the real-time feedback. According to the method, load fluctuation and supply change are handled, through load matching degree analysis and scheduling optimization, the energy demand of each micro-grid is balanced, and the economical efficiency and environmental friendliness of the comprehensive energy system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy sharing optimization, and particularly to an optimization method for energy sharing strategies of an integrated energy microgrid group. Background Art

[0002] Energy sharing optimization is an important branch in the field of energy management, focusing on efficient resource sharing and optimal scheduling among multiple energy systems (such as electricity, heat, cooling, gas, etc.). The core objective is to achieve the collaborative utilization of energy among multiple energy systems through optimization algorithms and models, so as to achieve energy conservation and consumption reduction, and improve the stability and economy of the system. The technologies involved include multi - energy system optimization, demand response management, distributed energy management, load balancing, electricity market trading and planning, etc. With the wide application of renewable energy and the development of smart grid technology, energy sharing optimization has gradually become a key technology for improving energy utilization efficiency and promoting the development of low - carbon economy.

[0003] Among them, the optimization method for energy sharing strategies of an integrated energy microgrid group is to achieve collaborative work and optimal resource allocation among multiple microgrid groups by optimizing energy sharing strategies. The purpose is to optimize the flow and distribution of energy based on the supply - demand relationship of different energy forms in a multi - microgrid environment, using intelligent algorithms and decision models, so as to improve the overall efficiency of the system, reduce operating costs, and enhance the flexibility and reliability of the energy system. It is particularly suitable for the coordinated management of energy sharing in a distributed energy environment, especially for improving the economy and environmental friendliness of the integrated energy system.

[0004] In the prior art, the scheduling strategies of energy sharing often rely on fixed optimization models and lack sufficient flexibility to cope with real - time and dynamic changes. In practical applications, traditional methods cannot reflect the load fluctuations or the immediate changes in energy supply in real time, resulting in uneven energy distribution and unable to achieve the best sharing effect. For example, when the load of some microgrids suddenly increases or the supply is insufficient, the existing methods are difficult to effectively regulate the resource flow, resulting in some microgrids being overly dependent on external supply while the resources of some other microgrids are not fully utilized. In addition, the sharing rules and resource scheduling of the prior art mostly rely on static preset parameters and do not fully consider the personalized needs of different microgrids, resulting in low system efficiency. Due to the lack of a dynamic adjustment mechanism, existing solutions often cannot optimize the scheduling in real time when facing uncertainties or complex environments, and are prone to unnecessary energy waste or configuration errors. Summary of the Invention

[0005] The object of the present invention is to solve the deficiencies existing in the prior art and propose an optimization method for energy sharing strategies of an integrated energy microgrid group.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An optimization method for the energy sharing strategy of an integrated energy microgrid group, comprising the following steps:

[0007] S1: Collect the real-time energy demand and supply data of the energy microgrid group, calculate the energy demand and supply difference, analyze the supply-demand gap of each microgrid, perform energy demand identification operations according to the analysis results of the supply-demand gap, conduct supply-demand matching analysis, determine the energy sharing scope, and obtain a comprehensive analysis record of the microgrid sharing demand;

[0008] S2: Through the comprehensive analysis record of the microgrid sharing demand, refine the microgrid resource allocation situation, formulate energy sharing rules based on the load demand, power generation capacity, and energy storage status factors of each microgrid, perform resource scheduling among the energy microgrid groups according to the results of the energy sharing rule formulation, mark the shared energy quantity and time point, and obtain a microgrid collaborative sharing strategy;

[0009] S3: Based on the microgrid collaborative sharing strategy, monitor the energy supply-demand and power generation status of the microgrid in real time, perform real-time analysis on the power generation capacity and load fluctuation of the microgrid, adjust the energy flow among the energy microgrid groups, and obtain a global scheduling optimization plan;

[0010] S4: According to the global scheduling optimization plan, perform load balancing adjustment, calculate the matching degree between the energy demand of each microgrid and the real-time supply, perform scheduling adjustment on the energy flow according to the calculation results of the matching degree, optimize the energy sharing of the energy microgrid group, meet the load balancing constraint, and output a comprehensive implementation plan for the energy sharing of the energy microgrid group.

[0011] As a further solution of the present invention, the analysis steps for the supply-demand gap of each microgrid are as follows:

[0012] S111: Monitor the microgrid group and collect real-time data, count the energy demand and supply, synchronize information using the data interface, and perform timestamp marking to generate a synchronized energy demand and supply dataset;

[0013] S112: Based on the synchronized energy demand and supply dataset, use the formula:

[0014] ΔE(t) = R(t) - S(t)

[0015] Calculate the energy difference value ΔE(t) of each microgrid at each time point to generate an energy difference dataset, where t represents the time point, R(t) represents the demand quantity, and S(t) represents the supply quantity;

[0016] S113: Based on the energy difference dataset, extract key information, analyze and identify the energy gap of each microgrid at the difference time point, count the shortage and surplus of energy, and generate the analysis results of the supply-demand gap of each microgrid.

[0017] As a further solution of the present invention, the steps for obtaining the comprehensive analysis record of the microgrid sharing demand are as follows:

[0018] S121: Based on the analysis results of the supply-demand gap of each microgrid, conduct demand identification, classify the data, identify emergency demands, regular demands, and supply surplus situations, and generate an energy demand identification report for each microgrid;

[0019] S122: Utilize the energy demand identification report of each microgrid to conduct energy supply-demand matching analysis for each microgrid. Through data comparison and demand assessment, allocate resources, confirm the supply-demand balance of each microgrid, and form a comprehensive energy matching report;

[0020] S123: Based on the comprehensive energy matching report, formulate an energy sharing plan among microgrids, determine the sharing scope, role positioning of participating microgrids, and price policies, and obtain the comprehensive analysis record of the microgrid sharing demand.

[0021] As a further solution of the present invention, the steps for formulating the energy sharing rules are as follows:

[0022] S211: According to the comprehensive analysis record of the microgrid sharing demand, collect and record the load demand, power generation capacity, and energy storage status of each microgrid, and generate an overview of energy demand and supply;

[0023] S212: Based on the overview of energy demand and supply, use the formula:

[0024]

[0025] Calculate the energy distribution efficiency R to obtain the preliminary energy sharing rules, where d i represents the load demand of the i-th microgrid, p i represents the influence degree of the power generation capacity of the corresponding microgrid on the total energy distribution, c i represents the influence degree of the energy storage status of the corresponding microgrid on the total energy distribution, s i represents the current energy storage capacity of the corresponding microgrid, and n represents the number of microgrids;

[0026] S213: Apply the preliminary energy sharing rules, adjust and optimize according to the actual operation conditions of the differential microgrids, and verify the matching of each rule with the actual demand and operation feasibility to obtain the optimized energy sharing rules.

[0027] As a further solution of the present invention, the steps for obtaining the microgrid collaborative sharing strategy are as follows:

[0028] S221: Analyze the optimized energy sharing rules, determine the current energy demand and supply status of each microgrid according to the actual monitoring data, determine the energy quantity and scheduling priority, and generate a preliminary resource scheduling plan;

[0029] S222: Based on the preliminary resource scheduling plan, use the formula:

[0030]

[0031] Calculate the average energy scheduling quantity S, mark the shared energy quantity and time points, and obtain the adjusted resource scheduling strategy. Among them, E i represents the predetermined shared energy quantity of the i-th microgrid, and T i represents the corresponding time point, and L i represents the load demand value of the corresponding microgrid;

[0032] S223: Execute the adjusted resource scheduling strategy, perform real-time monitoring and feedback, dynamically adjust the energy flow and overall consumption among the microgrid group, and establish a microgrid collaborative sharing strategy.

[0033] As a further solution of the present invention, the obtaining steps of the global scheduling optimization plan are:

[0034] S311: Based on the microgrid collaborative sharing strategy, collect the real-time energy production and consumption data of each microgrid node, and generate a preliminary energy supply and demand report by analyzing the data fluctuations between nodes;

[0035] S312: According to the preliminary energy supply and demand report, use the formula:

[0036]

[0037] Calculate the energy imbalance degree Z of each node to obtain an energy imbalance report. Among them, P i is the energy production quantity of the i-th node, and C i is the energy consumption quantity of the i-th node;

[0038] S313: According to the energy imbalance report, dynamically adjust the energy flow direction, and balance the energy demand of the entire microgrid group by adjusting the energy quotas between nodes to obtain a global scheduling optimization plan.

[0039] As a further solution of the present invention, the calculation steps of the matching degree are:

[0040] S411: Based on the global scheduling optimization plan, integrate the current energy demand and real-time supply information of each microgrid to establish a comprehensive energy data model;

[0041] S412: Use the comprehensive energy data model to perform data verification and standardization processing, and verify the consistency and accuracy of the input data to obtain standardized energy data;

[0042] S413: Based on the standardized energy data, use the formula:

[0043]

[0044] Calculate the matching degree of the i-th microgrid, and generate the matching degree analysis results of each microgrid, where D ti and S ti are the demand and supply at time t respectively, and N is the total number of time points.

[0045] As a further solution of the present invention, the obtaining steps of the energy sharing comprehensive implementation plan for the energy microgrid group are as follows:

[0046] S421: Based on the matching degree analysis results of each microgrid, analyze the energy supply and demand differences between microgrids, identify the microgrids with energy surplus and energy shortage, and generate an energy difference analysis record;

[0047] S422: According to the energy difference analysis record, design an energy flow scheduling strategy, redistribute the energy of the surplus microgrid to the microgrid with energy shortage, and obtain an adjusted energy flow strategy;

[0048] S423: Apply the adjusted energy flow strategy, confirm the effectiveness of energy sharing through real-time monitoring, comprehensively evaluate the strategy execution effect, match the load balance requirements of all microgrids, and form and output an energy sharing implementation plan for the energy microgrid group.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0050] In the present invention, by collecting the energy demand and supply data of the microgrid group in real time, calculating the supply and demand differences of each microgrid, optimizing the sharing range and implementing supply and demand matching analysis, the flexibility and pertinence of resource scheduling are improved, refined sharing rules are formulated to ensure the reasonable allocation of resources, the energy flow is dynamically adjusted according to real-time feedback, load fluctuations and supply changes are responded to, and the energy demands of each microgrid are balanced through load matching degree analysis and scheduling optimization, so as to improve the economy and environmental friendliness of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is the main process flow chart of the present invention;

[0052] Figure 2 is the analysis flow chart of the supply and demand gap of each microgrid of the present invention;

[0053] Figure 3 is the acquisition flow chart of the comprehensive analysis record of the microgrid sharing demand of the present invention;

[0054] Figure 4 is the flow chart for formulating the energy sharing rules of the present invention;

[0055] Figure 5 It is the flowchart for obtaining the microgrid collaborative sharing strategy of the present invention;

[0056] Figure 6 It is the flowchart for obtaining the global scheduling optimization scheme of the present invention;

[0057] Figure 7 It is the flowchart for calculating the matching degree of the present invention;

[0058] Figure 8 It is the flowchart for obtaining the comprehensive implementation plan for energy sharing of the energy microgrid group of the present invention. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0061] Please refer to Figure 1 , a method for optimizing the energy sharing strategy of an integrated energy microgrid group, including the following steps:

[0062] S1: Collect the real-time energy demand and supply data of the energy microgrid group, calculate the energy demand and supply difference, analyze the supply-demand gap of each microgrid, perform an energy demand identification operation according to the analysis result of the supply-demand gap, conduct a supply-demand matching analysis, determine the energy sharing range, and obtain a comprehensive analysis record of the microgrid sharing demand;

[0063] S2: Refine the microgrid resource allocation situation through the comprehensive analysis record of the microgrid sharing demand, formulate energy sharing rules based on the load demand, power generation capacity and energy storage state factors of each microgrid, perform resource scheduling among the energy microgrid groups according to the result of formulating the energy sharing rules, mark the shared energy quantity and time point, and obtain the microgrid collaborative sharing strategy;

[0064] S3: Based on the microgrid collaborative sharing strategy, monitor the energy supply-demand and power generation status of the microgrid in real time, conduct real-time analysis on the power generation capacity and load fluctuations of the microgrid, adjust the energy flow among the energy microgrid clusters, and obtain a global scheduling optimization plan.

[0065] S4: According to the global scheduling optimization plan, conduct load balancing adjustment, calculate the matching degree between the energy demand and real-time supply of each microgrid, and perform scheduling adjustment on the energy flow according to the calculation result of the matching degree to optimize the energy sharing of the energy microgrid clusters, meet the load balancing constraint, and output the comprehensive implementation plan for the energy sharing of the energy microgrid clusters.

[0066] The comprehensive analysis record of the microgrid sharing demand includes the analysis results of energy demand differences, the analysis results of energy supply differences, and the record of the distribution of supply-demand gaps; the microgrid collaborative sharing strategy includes the resource scheduling plan, the shared energy quantity, and the shared time point; the global scheduling optimization plan includes the record of power generation capacity adjustment, the analysis results of load fluctuations, and the optimized energy flow path; the comprehensive implementation plan for the energy sharing of the energy microgrid clusters includes the record of load matching degree, the results of energy flow scheduling adjustment, and the shared optimization plan.

[0067] Please refer to Figure 2 , the analysis steps for the supply-demand gap of each microgrid are as follows:

[0068] S111: Monitor the microgrid cluster and collect real-time data, count the energy demand and supply, synchronize the information using the data interface, and perform timestamp marking to generate the synchronized energy demand and supply datasets.

[0069] Obtain real-time data from the monitoring systems of each microgrid, including the energy demand and supply. The data is extracted through the data interface of the microgrid management system to ensure the real-time and accuracy of the data. The data interface is usually designed with a timestamp function that can automatically record the specific time of data collection, ensuring the accuracy of data synchronization. Use the synchronized data for subsequent processing, such as time series analysis, which can accurately track the changing trends of energy demand and supply, is crucial for adjusting the microgrid operation strategy and ensuring energy supply-demand balance. In this way, it is possible to effectively predict and respond to possible energy shortages or surpluses, thus ensuring the efficient and reliable operation of the microgrid.

[0070] S112: Based on the synchronized energy demand and supply datasets, use the formula:

[0071] ΔE(t) = R(t) - S(t)

[0072] Calculate the energy difference value ΔE(t) at each time point of each microgrid to generate an energy difference dataset, where t represents the time point, R(t) represents the demand, and S(t) represents the supply.

[0073] At a specific moment \(t = 10\), the energy demand \(R(10)\) of a specific microgrid is 500 units, while the supply \(S(10)\) is 450 units. Substituting into the formula gives \(\Delta E(10)=500 - 450 = 50\) units. This indicates that at time point 10, there is an energy shortage of 50 units in this microgrid. This result is directly related to the subsequent demand analysis steps, namely identifying and evaluating the energy supply - demand gap in order to take measures to adjust the supply or further analyze the energy use efficiency, thus ensuring the balance between energy supply and demand.

[0074] S113: Based on the energy difference data set, extract key information, analyze and identify the energy gap of each microgrid at different time points, count the shortage and surplus of energy, and generate the analysis results of the supply - demand gap of each microgrid;

[0075] Analyze the energy difference data set to identify the energy gap of each microgrid at different time points, which includes the shortage and surplus of energy. The analysis process uses clustering analysis or time - series analysis, which can help identify patterns and trends in the data. For example, through time - series analysis, future energy demand and supply situations can be predicted, which is very important for optimizing energy management strategies. In addition, by comparing the energy gaps at different time points, the performance and stability of the microgrid can be evaluated, and the energy supply strategy can be adjusted when necessary to ensure the balance of energy supply and demand. Ultimately, this analysis result will help formulate more effective energy management measures to ensure the high - efficiency and sustainability of microgrid operation.

[0076] Please refer to Figure 3 , the steps to obtain the comprehensive analysis record of microgrid sharing demand are as follows:

[0077] S121: Based on the analysis results of the supply - demand gap of each microgrid, conduct demand identification, classify the data, identify emergency demands, regular demands, and supply surplus situations, and generate an energy demand identification report for each microgrid;

[0078] Based on the analysis results of the supply - demand gap of each microgrid, specifically perform the energy demand identification of each microgrid. Through in - depth analysis of past data, determine the demand status of different categories, such as emergency demands, regular demands, and supply surplus. The key to this identification process lies in understanding and predicting the energy demands of each microgrid under different circumstances to achieve more effective resource allocation and optimization. By analyzing historical consumption patterns, peak and trough consumption periods can be identified, and then excess energy can be stored when the supply is sufficient and released when the demand increases, ensuring the stability and economy of energy supply, thereby effectively reducing energy waste and increasing economic returns.

[0079] S122: Use each microgrid energy demand identification report to conduct energy supply-demand matching analysis for each microgrid. Through data comparison and demand assessment, allocate resources, confirm the supply-demand balance of each microgrid, and form a comprehensive energy matching report.

[0080] After energy demand identification, further conduct supply-demand matching analysis, which involves matching the specific demands of each microgrid with available resources to ensure the maximization of resource utilization and the optimization of economic benefits. In this process, by comparing the actual demands of each microgrid with existing resources, evaluate the rationality and efficiency of resource allocation. At the same time, it also involves the exploration and utilization of potential resources, such as supplementing traditional energy supply through the integration and storage solutions of renewable energy. In addition, by achieving efficient resource matching, not only can the environmental impact of energy production be reduced, but also the economic sustainability of the microgrid can be improved. This strategic resource allocation and optimization is a key step to ensure the stable operation of the energy system and the maximization of economic benefits.

[0081] S123: Based on the comprehensive energy matching report, formulate an energy sharing plan among microgrids, determine the sharing scope, role positioning of participating microgrids, and price policies, and obtain a comprehensive analysis record of microgrid sharing demands.

[0082] After completing the supply-demand matching analysis, formulate a detailed energy sharing plan, which defines in detail the sharing scope, roles of participating parties, and corresponding responsibilities. In this process, considering the operating costs and demand elasticity of different microgrids, design a sharing model aimed at optimizing resource use and cost-effectiveness. In addition, the formulation of the strategy also needs to consider market dynamics, including energy price fluctuations and policy changes, to ensure the feasibility and fairness of the sharing plan. Through this comprehensive strategy, each microgrid can support the sustainable operation of the entire energy network while ensuring its own economic interests, not only improving the efficiency of energy use, but also promoting environmental protection and the realization of social responsibilities, which is an important part of modern energy management.

[0083] Please refer to Figure 4 , the steps for formulating energy sharing rules are as follows:

[0084] S211: According to the comprehensive analysis record of microgrid sharing demands, collect and record the load demands, power generation capabilities, and energy storage status of each microgrid, and generate an overview of energy demand and supply.

[0085] Comprehensively analyze the comprehensive analysis record of the shared needs of the microgrid, collect the load demand, power generation capacity, and energy storage status of each microgrid. By regularly monitoring the energy consumption pattern and peak load time of each microgrid, collect this data, and combine it with historical energy usage records to analyze and obtain the average power demand and power consumption peak of each microgrid during a specific period. In addition, the evaluation of the power generation capacity is carried out through the actual power generation efficiency and historical power generation data to support the performance evaluation of the power generation equipment. The determination of the energy storage status is based on the charging level and discharge rate of the energy storage equipment monitored in real time. Integrate this information to construct the current energy supply and demand situation of each microgrid.

[0086] S212: Based on the energy demand and supply profile, use the formula:

[0087]

[0088] Calculate the energy distribution efficiency R to obtain the preliminary energy sharing rule. Among them, d i represents the load demand of the i-th microgrid, p i represents the influence degree of the power generation capacity of the corresponding microgrid on the total energy distribution, c i represents the influence degree of the energy storage status of the corresponding microgrid on the total energy distribution, s i represents the current energy storage of the corresponding microgrid, and n represents the number of microgrids;

[0089] d i represents the load demand of the i-th microgrid. Assume d 1 = 50kW, d 2 = 30kW, that is, the current load demands of these two microgrids.

[0090] p i represents the influence degree of the power generation capacity of this microgrid on the total energy distribution. Assume p 1 = 0.5, p 2 = 0.3, indicating the power generation capacity influence factors of these two microgrids.

[0091] c i represents the influence degree of the energy storage status of this microgrid on the total energy distribution. Assume c 1 = 0.7, c 2 = 0.4, representing the influence factor of the energy storage status.

[0092] s i represents the current energy storage of this microgrid. Assume s 1 = 100kWh, s 2 = 150kWh, indicating the amount of energy currently stored.

[0093] Use these values for calculation:

[0094]

[0095] The results show that based on the load demand, power generation capacity, and energy storage status of the current microgrid, the calculated energy distribution efficiency index is approximately 0.36, which means that considering the specific parameters of each microgrid, energy is relatively evenly distributed, ensuring the balance between energy demand and supply in each microgrid. This value can be further used to adjust and optimize the energy distribution strategy between microgrids to improve the overall energy utilization efficiency.

[0096] S213: Apply the preliminary energy sharing rules, adjust and optimize them according to the actual operation of the differential microgrid, verify the matching of each rule with the actual demand and operation feasibility, and obtain the optimized energy sharing rules;

[0097] Adjust and optimize the preliminary energy sharing rules according to the actual operation of each microgrid to ensure that each rule meets the actual demand and operation feasibility. By on-site inspection of the operation of each microgrid, evaluate the adaptability and efficiency of the rules in actual application. At the same time, adjust the parameters in the rules according to the actual situation, such as peak-hour scheduling strategy and load response strategy, and conduct simulation tests on the adjusted rules to verify their performance and stability under different operating conditions. Finally, obtain refined energy sharing rules, which are specified as operation manuals and implemented in each microgrid to ensure fair and efficient energy distribution.

[0098] Please refer to Figure 5 , the steps to obtain the microgrid collaborative sharing strategy are as follows:

[0099] S221: Analyze the optimized energy sharing rules, determine the current energy demand and supply status of each microgrid according to the actual monitoring data, determine the energy quantity and scheduling priority, and generate a preliminary resource scheduling plan;

[0100] Analyze and evaluate the customized energy sharing rules based on the real-time monitoring data and historical energy consumption patterns of each microgrid, so as to accurately determine the current and expected energy demand and supply status of each microgrid. By automatically collecting and processing the energy usage data from each microgrid, including load fluctuations, peak load times, and energy consumption during low-demand periods. After the data is processed by the algorithm, a specific plan for scheduling energy for each microgrid is generated, including the necessary energy quantity and scheduling priority. This process also includes predicting possible energy supply interruptions and demand peaks for each microgrid in order to formulate more accurate response strategies to ensure that the implementation of the scheduling plan can meet the actual demand and adapt to future changes, and finally form a dynamically adjustable and sustainable resource management plan.

[0101] S222: Based on the preliminary resource scheduling plan, use the formula:

[0102]

[0103] Calculate the average energy dispatch volume S, mark the shared energy volume and time points, and obtain the adjusted resource dispatch strategy, where E i represents the predetermined shared energy volume of the i-th microgrid, and T i represents the corresponding time point, and L i represents the load demand value of the corresponding microgrid;

[0104] E i : The predetermined shared energy volume of the i-th microgrid, with a value range of [100, 200] kWh.

[0105] T i : The corresponding time point, with a value range of [1, 2] hours.

[0106] L i : The load demand of this microgrid, with a value range of [50, 100] kW.

[0107]

[0108] The calculation result S≈40.82 indicates that, considering the predetermined shared energy volumes and predetermined times of the two microgrids, the adjusted resource dispatch strategy can provide approximately 40.82 kWh of energy per hour on average. This figure is the average energy dispatch efficiency calculated based on the load demand of the microgrids and the energy supply time points, reflecting the average amount of energy that can be supplied to each microgrid per hour within the given time.

[0109] S223: Execute the adjusted resource dispatch strategy, conduct real-time monitoring and feedback, dynamically adjust the energy flow and overall consumption among the microgrid group, and establish a microgrid collaborative sharing strategy;

[0110] Execute the previously generated resource dispatch strategy, and use the real-time data transmitted from each microgrid to monitor the energy flow and consumption efficiency. This system can not only automatically adjust the energy distribution to adapt to changes in actual operating conditions, but also provide immediate feedback on the effectiveness of the strategy execution. In this way, any problems related to energy distribution and usage efficiency can be identified and solved, such as responding to sudden high load demands by adjusting the energy flow direction. In addition, the dispatch strategy will be continuously optimized. By learning historical data and operating trends, it can predict and address possible system risks, such as insufficient energy supply or excessive demand. Through highly automated and intelligent operations, the implementation of the strategy not only improves the energy utilization efficiency, but also enhances the reliability and response ability of the entire microgrid system.

[0111] Please refer to Figure 6 , and the steps to obtain the global dispatch optimization plan are as follows:

[0112] S311: Based on the microgrid collaborative sharing strategy, collect the real-time energy production and consumption data of each microgrid node. By analyzing the data fluctuations among the nodes, generate a preliminary energy supply and demand report.

[0113] Based on the microgrid collaborative sharing strategy, first configure smart meters installed at each node. The meters can monitor and record the energy production and consumption data of each node in real time. At the same time, the energy management system equipped at each node can automatically send the collected data to the central data processing center through a wireless network. At the central processing center, it is responsible for receiving and storing the data from each node, has a data cleaning function, and can automatically identify and eliminate outliers in the data, such as unexpected energy consumption peaks or production drops. Through further analysis of the normal data, the average energy production and consumption of each node in different time periods can be calculated, and these values can be compared to identify the imbalance between energy supply and demand. In addition, through historical data analysis, the future energy demand trend can be predicted, providing a scientific basis for operation decisions.

[0114] S312: According to the preliminary energy supply and demand report, use the formula:

[0115]

[0116] Calculate the energy imbalance degree Z of each node to obtain an energy imbalance report, where P i is the energy production of the i-th node, and C i is the energy consumption of the i-th node;

[0117] During a certain time period, the production P of node A A = 120 units, and the consumption C A = 100 units. The production P of node B B = 200 units, and the consumption C B = 180 units.

[0118] According to the formula, the calculation process of the energy imbalance degrees of nodes A and B is as follows:

[0119]

[0120] Z = 0.091 + 0.053 = 0.144

[0121] The calculation result 0.144 indicates that there is a certain degree of imbalance in the energy supply and demand relationship of the entire microgrid during the current evaluation period. This metric provides a quantitative basis for the system administrator to decide whether to adjust the energy flow to achieve more efficient energy utilization and a more balanced supply and demand state.

[0122] S313: According to the energy imbalance report, dynamically adjust the energy flow direction. By regulating the energy quotas between nodes, balance the energy demands of the entire microgrid cluster to obtain a global scheduling optimization plan.

[0123] After receiving the energy imbalance report, the central control system takes actions to adjust the energy flow between each microgrid node. First, it evaluates which nodes have the most serious energy imbalance and sorts all nodes based on the degree of energy imbalance of each node. After sorting, for nodes with excessive energy supply, automatically reduce their energy output to the power grid and at the same time increase the supply to nodes with energy shortages. The automated control system involved in the adjustment process can monitor the energy production and consumption data of each node in real time, dynamically adjust the energy distribution strategy according to the real-time data, and can simulate the effects of various scheduling strategies before actual adjustment to predict the impact of each strategy on the balance of the entire microgrid, evaluate the potential effects of different strategies, and thus select the optimal energy allocation plan. During the simulation process, calculate the expected energy production and consumption of each node after adjustment, and compare the data differences before and after adjustment to ensure that the selected strategy can effectively reduce the degree of energy imbalance and improve the energy efficiency and stability of the entire microgrid.

[0124] Please refer to Figure 7 , and the calculation steps of the matching degree are as follows:

[0125] S411: Based on the global scheduling optimization plan, integrate the current energy demands and real-time supply information of each microgrid to establish a comprehensive energy data model.

[0126] Based on the global scheduling optimization plan, integrate the current energy demands and real-time supply information of each microgrid for data integration. Through an advanced data management system, collect data including but not limited to the real-time power output of the microgrid, energy storage status, external power grid supply status, and prediction data to ensure that the collected data covers the comprehensive energy scenario of each microgrid. After performing data integration, apply database cleaning techniques and verification algorithms to process the data, exclude outliers and duplicate records, and ensure the accuracy and consistency of the data. Through this coherent data processing process, establish a comprehensive energy data model containing all necessary information. The model can be used for subsequent analysis and energy scheduling decision support to ensure that the operation data of each microgrid is updated and monitored in real time, and finally generate a comprehensive energy data model that comprehensively reflects the current energy demand and supply status.

[0127] S412: Use the comprehensive energy data model to perform data verification and standardization processing, check the consistency and accuracy of the input data, and obtain standardized energy data.

[0128] Using the integrated energy data model generated in the foregoing steps, perform data verification and standardization processing. By using advanced data processing and standardization algorithms, perform precise verification on each piece of data in the data model, including standardization of data formats, normalization of numerical values, and logical consistency checks, to ensure that the data is not only accurate but also meets the input requirements of the energy dispatch system. During the processing, perform in-depth analysis on the energy data model, identify patterns and trends in the data, and utilize the information to optimize the microgrid energy management, thereby obtaining a verified and standardized energy data. The data will be directly used to calculate the energy demand and supply matching degree of each microgrid, ensuring high reliability of the obtained data, and obtaining a standardized and reliable energy data model.

[0129] S413: Based on the standardized energy data, use the formula:

[0130]

[0131] Calculate the matching degree M of the i-th microgrid i , and generate the matching degree analysis results of each microgrid. Among them, D ti and S ti are the demand and supply quantities at time t respectively, and N is the total number of time points;

[0132] The demand and supply data of a certain microgrid per hour within a day (N = 24 hours) are known. Randomly select D ti and S ti with values of [50, 45, 60, 55, 50, 45, 60, 55, 50, 45, 60, 55, 50, 45, 60, 55, 50, 45, 60, 55, 50, 45, 60, 55] and [45, 50, 55, 60, 45, 50, 55, 60, 45, 50, 55, 60, 45, 50, 55, 60, 45, 50, 55, 60, 45, 50, 55, 60] respectively. The calculation process is as follows:

[0133]

[0134] The results show that within a day, the energy matching degree of this microgrid is 0.0484, which indicates that the average relative deviation between demand and supply is small, the energy supply and demand are relatively balanced, meaning that the energy supply and demand matching degree of the microgrid is good within this day, indicating that the implementation effect of the energy dispatch plan is good, the difference between demand and supply is small, and the microgrid operates stably.

[0135] Please refer to Figure 8 , and the steps to obtain the comprehensive implementation plan for energy sharing in the energy microgrid group are as follows:

[0136] S421: Based on the matching degree analysis results of each microgrid, analyze the energy supply and demand differences between microgrids, identify the microgrids with energy surplus and energy shortage, and generate an energy difference analysis record.

[0137] Based on the energy matching degree of each microgrid calculated above, analyze the energy supply and demand differences between microgrids. Through data analysis, deeply explore the microgrids with energy surplus or shortage, collect and process the real-time supply and demand data of microgrids, including energy output and consumption. Through time series analysis of the data, identify the patterns and fluctuations of the energy flow. The analysis results will directly affect the strategic decision-making of energy dispatching, ensure that the dispatching strategy can effectively solve the problem of energy imbalance, and finally generate a detailed energy difference analysis record. The record will list the energy status of each microgrid and recommend specific adjustment measures, providing a scientific basis for the dispatching strategy.

[0138] S422: According to the energy difference analysis record, design an energy flow dispatching strategy to redistribute the energy of the surplus microgrid to the microgrid with energy shortage, and obtain an adjusted energy flow strategy.

[0139] According to the energy difference analysis record, elaborate on the specific strategy for adjusting the energy flow. By optimizing the redistribution process of energy from the surplus microgrid to the shortage microgrid and reconfiguring the existing energy flow pattern, ensure that each energy transfer is based on the latest demand and supply data. The optimization model will calculate the most effective energy distribution plan and verify its effect through simulation tests, ensuring that the strategy can not only meet the current energy demand but also optimize the long-term energy use efficiency, and establish an adjusted and efficient energy flow strategy. This strategy will directly affect the overall energy efficiency and sustainability of the microgrid group.

[0140] S423: Apply the adjusted energy flow strategy, confirm the effectiveness of energy sharing through real-time monitoring, comprehensively evaluate the implementation effect of the strategy, match the load balance requirements of all microgrids, and form and output an energy sharing implementation plan for the microgrid group.

[0141] Integrate the newly developed energy flow strategy into the management system of the microgrid group, update the energy management module of the system, adjust the response parameters of the automatic control system, and optimize the user interface to reflect the new energy flow information. The system ensures the stability and efficiency of energy supply under various operating conditions through updates. At the same time, through real-time monitoring and data analysis, it can continuously learn and adapt to new energy usage patterns. The optimization process also includes periodic effect evaluation to ensure that each measure achieves the expected goal. Finally, output a detailed energy sharing implementation plan for the microgrid group. This plan details the background, implementation process, and expected effects of each step of the operation, providing valuable practical experience and data support for future energy management.

[0142] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the relevant art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing energy sharing strategy of integrated energy microgrid group, characterized in that: The following steps are involved: Collect real-time energy demand and supply data of energy microgrids, calculate the difference between energy demand and supply, analyze the supply and demand gap of each microgrid, perform energy demand identification operations based on the supply and demand gap analysis results, conduct supply and demand matching analysis, determine the energy sharing scope, and obtain a comprehensive analysis record of microgrid shared demand; Through the comprehensive analysis and record of the microgrid sharing demand, the microgrid resource allocation is refined, and energy sharing rules are formulated according to the load demand, power generation capacity and energy storage status factors of each microgrid. According to the results of the energy sharing rule formulation, resource scheduling is performed between energy microgrid groups, and the shared energy amount and time point are marked to obtain the microgrid collaborative sharing strategy; Based on the microgrid collaborative sharing strategy, the energy supply and demand and power generation status of the microgrid are monitored in real time, the power generation capacity and load fluctuation of the microgrid are analyzed in real time, the energy flow between the energy microgrid groups is adjusted, and a global scheduling optimization plan is obtained; According to the global scheduling optimization plan, load balancing adjustments are made, the matching degree between the energy demand and real-time supply of each microgrid is calculated, and scheduling adjustments are made to the energy flow according to the matching degree calculation results to optimize the energy sharing of the energy microgrid group, meet the load balancing constraints, and output the comprehensive implementation plan for energy sharing of the energy microgrid group.

2. The energy sharing strategy optimization method for integrated energy microgrid group according to claim 1 is characterized in that: The analysis steps of the supply and demand gap of each microgrid are as follows: Monitor the microgrid group and collect real-time data, count energy demand and supply, synchronize information using data interfaces, and timestamp to generate synchronized energy demand and supply data sets; Based on the synchronized energy demand and supply data sets, the formula is used: ΔE(t)=R(t)-S(t) Calculate the energy difference value ΔE(t) of each microgrid at each time point to generate an energy difference data set, where t represents the time point, R(t) represents the demand, and S(t) represents the supply; Based on the energy difference data set, key information is extracted, the energy gap of each microgrid at the difference time point is analyzed and identified, the energy shortage and surplus are counted, and the supply and demand gap analysis results of each microgrid are generated.

3. The energy sharing strategy optimization method for integrated energy microgrid group according to claim 2 is characterized in that: The steps for obtaining the comprehensive analysis record of the microgrid shared demand are as follows: Based on the supply and demand gap analysis results of each microgrid, identify the demand, classify the data, identify emergency demand, regular demand and oversupply, and generate an energy demand identification report for each microgrid; Using the energy demand identification report of each microgrid, perform energy supply and demand matching analysis of each microgrid, perform resource allocation through data comparison and demand assessment, confirm the supply and demand balance of each microgrid, and form a comprehensive energy matching report; Based on the comprehensive energy matching report, an energy sharing plan between microgrids is formulated, the sharing scope, the role positioning of participating microgrids and the price policy are determined, and a comprehensive analysis record of microgrid sharing demand is obtained.

4. The method for optimizing energy sharing strategy of integrated energy microgrid group according to claim 3 is characterized in that: The steps for formulating the energy sharing rules are as follows: According to the comprehensive analysis record of the microgrid shared demand, the load demand, power generation capacity and energy storage status of each microgrid are collected and recorded to generate an energy demand and supply profile; Based on the energy demand and supply profile, the formula is used: Calculate the energy allocation efficiency R and obtain the preliminary energy sharing rule, where d i represents the load demand of the i-th microgrid, p i represents the impact of the corresponding microgrid power generation capacity on the total energy distribution, c i Represents the impact of the energy storage state of the corresponding microgrid on the total energy distribution, s i represents the current energy storage capacity of the corresponding microgrid, and n represents the number of microgrids; The preliminary energy sharing rules are applied, adjusted and optimized according to the actual operation conditions of the microgrids, and each rule is verified to match the actual needs and operational feasibility, so as to obtain the optimized energy sharing rules.

5. The method for optimizing energy sharing strategy of integrated energy microgrid group according to claim 4 is characterized in that: The steps for obtaining the microgrid collaborative sharing strategy are as follows: Analyze the optimized energy sharing rules, determine the current energy demand and supply status of each microgrid based on actual monitoring data, determine the energy quantity and scheduling priority, and generate a preliminary resource scheduling plan; Based on the preliminary resource scheduling plan, the formula is adopted: Calculate the average energy scheduling amount S, mark the shared energy amount and time point, and get the adjusted resource scheduling strategy, where E i represents the scheduled shared energy amount of the i-th microgrid, T i represents the corresponding time point, L i Represents the load demand value of the corresponding microgrid; The adjusted resource scheduling strategy is executed, real-time monitoring feedback is performed, energy flow and overall consumption between microgrid groups are dynamically adjusted, and a microgrid collaborative sharing strategy is established.

6. The method for optimizing energy sharing strategy of integrated energy microgrid group according to claim 5 is characterized in that: The steps for obtaining the global scheduling optimization solution are: Based on the microgrid collaborative sharing strategy, real-time energy production and consumption data of each microgrid node is collected, and a preliminary energy supply and demand report is generated by analyzing data fluctuations between nodes; Based on the preliminary energy supply and demand report, the formula is used: Calculate the energy imbalance degree Z of each node and get the energy imbalance report, where P i is the energy production of the ith node, C i is the energy consumption of the i-th node; According to the energy imbalance report, the energy flow is dynamically adjusted, and the energy demand of the entire microgrid group is balanced by adjusting the energy quota between nodes to obtain a global scheduling optimization solution.

7. The method for optimizing energy sharing strategy of integrated energy microgrid group according to claim 6 is characterized in that: The steps for calculating the matching degree are: Based on the global dispatch optimization scheme, the current energy demand and real-time supply information of each microgrid are integrated to establish a comprehensive energy data model; Using the comprehensive energy data model, data verification and standardization are performed to check the consistency and accuracy of input data to obtain standardized energy data; Based on the standardized energy data, the formula is adopted: Calculate the matching degree M of the i-th microgrid i , generate the matching analysis results of each microgrid, where D ti and S ti are the demand and supply at time t respectively, and N is the total number of time points.

8. The method for optimizing energy sharing strategy of integrated energy microgrid group according to claim 7 is characterized in that: The steps for obtaining the comprehensive implementation plan for energy sharing of the energy microgrid group are as follows: Based on the matching degree analysis results of each microgrid, analyzing the energy supply and demand differences between the microgrids, identifying microgrids with excess energy and insufficient energy, and generating energy difference analysis records; According to the energy difference analysis record, an energy flow scheduling strategy is designed to reallocate the energy of the excess microgrid to the microgrid with insufficient energy, thereby obtaining an adjusted energy flow strategy; Apply the adjusted energy flow strategy, confirm the effectiveness of energy sharing through real-time monitoring, comprehensively evaluate the strategy execution effect, match the load balancing requirements of all microgrids, and form and output the energy sharing implementation plan for the energy microgrid group.

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