A Community Microgrid Energy Storage Scheduling Method

Abstract

The present invention discloses a community microgrid energy storage dispatching method, which relates to the field of microgrid energy storage technology. Through demand survey, data collection, energy storage system and power system docking and equipment remote control, and full process management of microgrid dispatching, the effective management and control of different power demands within the community are effectively realized. Through peak-shifting dispatching, emergency dispatching, peak-cutting and valley-flattening dispatching, and other intelligent dispatching methods, not only can different power demands be met in a targeted manner, but also the overall power fluctuation of the community power grid can be reduced, the risks of the community power grid can be resolved, and certain economic benefits can be obtained by using the peak-valley electricity price difference. Reasonable power dispatching and energy storage dispatching can improve the stability and reliability of the power grid, reduce the occurrence of voltage fluctuations and power grid failures, and through the management and dispatching of energy storage facilities, renewable energy can be better integrated, the development and application of renewable energy can be promoted, and energy transformation and sustainable development can be promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid energy storage, and specifically relates to a community microgrid energy storage scheduling method. Background Art

[0002] With the diversification of community residents' lives, the diversification of electricity consumption demands has emerged. In addition to basic residential electricity consumption, public areas have emerged in the community, such as electricity consumption for greening, street lights, etc., public lighting electricity consumption, such as parking lot lighting, etc., new energy vehicle charging electricity consumption, community commercial electricity consumption, important facilities, such as elevator, power supply facilities, water supply facilities, fire protection facilities, etc. for guaranteed electricity consumption, outdoor temporary electricity consumption and other various electricity consumption demands. Therefore, a community microgrid energy storage scheduling method is needed.

[0003] The prior art, such as a hydrogen-based community microgrid energy storage configuration and cooperative scheduling method disclosed in the invention patent application with publication number CN117996797A, belongs to the field of integrated energy. This method is used to solve the scheduling problems of the mutual conversion of electricity and hydrogen inside the hydrogen-based community microgrid including renewable energy, the storage of electric energy and hydrogen energy, and the cooperative sharing of electric energy and hydrogen energy, as well as the optimal configuration problems of the electric energy storage system and the hydrogen energy storage system. It can minimize the social cost including infrastructure cost and operation cost, promote the local consumption of renewable energy, and at the same time meet the daily hydrogen demand of users. The heterogeneous energy storage and sharing cooperation framework and internal payment strategy based on optimal configuration obtained by using the method provided by the present invention can ensure that all participants make a profit, reduce the total social cost, and improve the user-friendliness of hydrogen filling.

[0004] In view of the above solution, the applicant of the present invention found that the above technology has at least the following technical problems: 1. The above solution lacks scheduling according to different electricity consumption demands. Since the requirements for electricity load are different and the requirements for electricity guarantee capabilities are also different, most of the construction of community power supply and distribution facilities is built by developers on behalf, only providing basic power supply and distribution facilities, without any intelligent regulation and emergency guarantee capabilities. The scheduling ability of community power supply and distribution settings is indeed lacking, forming a relatively serious supply-demand problem with the increasing variety of electricity consumption demands in the current community, and it is also unable to further meet more community electricity consumption demands.

[0005] 2. Since large and medium-sized industrial projects will consider the problems of electricity load fluctuation and reliability at the beginning of their construction, they will build relevant intelligent power supply and distribution equipment and facilities by themselves, adopt an intelligent power grid scheduling system, and even cooperate with new energy supporting facilities such as photovoltaic and energy storage. However, when building a community power grid, more complex application scenarios are usually not considered, and they are not willing to invest in an intelligent power grid scheduling system. However, with the increasing diversification of current community electricity consumption demands, and residential electricity consumption should be the electricity consumption demand guaranteed at the first priority at any time. Summary of the Invention

[0006] Aiming at the above existing technical deficiencies, the purpose of the present invention is to provide a method for energy storage scheduling of a community microgrid.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for energy storage scheduling of a community microgrid, including: Step 1, analysis of electricity demand pattern types: Through research, on-site visits and surveys, confirm the electricity demand patterns of the target community, and obtain the electricity load and urgency and stability levels corresponding to each electricity demand pattern, and then analyze the electricity demand pattern types corresponding to each electricity demand pattern.

[0008] Step 2, judgment of the increase in energy storage facilities: Obtain the peak load values, peak load durations and load fluctuation amplitudes corresponding to each electricity demand pattern type in each time period in the historical period, analyze and obtain the electricity load evaluation coefficients corresponding to each electricity demand pattern type in each time period in the historical period, and then judge whether it is necessary to increase energy storage facilities in the future period.

[0009] Step 3, analysis of electricity dispatch: Use Internet of Things intelligent acquisition to connect with the community's main transformer, and uniformly connect the management systems of each electricity-consuming facility to the community microgrid platform. If no energy storage facilities are added, when the electricity load of the target community is insufficient at a certain time period, then analyze the scheduled values of the electricity demand parameters of each electricity-consuming facility in the target community during this time period, and schedule the electricity-consuming facility during this time period according to the corresponding scheduled values of the electricity demand parameters.

[0010] Step 4, analysis of peak shaving and valley filling dispatch: If energy storage facilities are added, connect each energy storage facility to the community microgrid platform. When the electricity load of the target community is insufficient at a certain time period, then analyze the scheduled values of the electricity demand parameters of each electricity-consuming facility in the target community during this time period according to the analysis of the electricity dispatch in Step 3, and analyze the peak shaving and valley filling dispatch of the energy storage facilities during this time period.

[0011] Preferably, the process of judging whether it is necessary to increase energy storage facilities in the future period is as follows: C1. Compare the electricity load evaluation coefficients corresponding to each electricity demand pattern type in each time period in the historical period with the standard electricity load evaluation coefficients of the corresponding electricity demand pattern type in the corresponding time period set. If the electricity load evaluation coefficients corresponding to a certain electricity demand pattern type in a certain time period in the historical period are all less than or equal to the standard electricity load evaluation coefficients of the corresponding electricity demand pattern type in the corresponding time period, it is determined that no energy storage facilities need to be added in the future period.

[0012] C2. If there are electricity load evaluation coefficients corresponding to a certain electricity demand pattern type in a certain time period in the historical period that are greater than the standard electricity load evaluation coefficients of the corresponding electricity demand pattern type in the corresponding time period, it is determined that energy storage facilities need to be added in the future period.

[0013] Preferably, the analysis of the scheduling values of the power consumption demand parameters of each power consumption facility in the target community during this period is as follows: D1. When the power consumption load in a certain period of the target community is insufficient, according to the basic weights corresponding to the power consumption demands of each power consumption facility set by the system, the corresponding weight values are assigned to the power consumption demands of each power consumption facility, and according to the reduction amplitude formula when the load is insufficient: reduction amplitude = 1 - weight value^2, the reduction amplitude corresponding to the power consumption demand of each power consumption facility is calculated.

[0014] D2. And compare the reduction amplitude corresponding to the power consumption demand of each power consumption facility with the reduction amplitude corresponding to each power consumption demand parameter scheduling value in the database. If the reduction amplitude corresponding to the power consumption demand of a certain power consumption facility is the same as the reduction amplitude corresponding to a certain power consumption demand parameter scheduling value in the database, then use the power consumption demand parameter scheduling value in the database as the power consumption demand parameter scheduling value of each power consumption facility during this period.

[0015] D3. Arrange the reduction amplitudes corresponding to the power consumption demands of each power consumption facility in ascending order, and then schedule the power consumption demand parameter scheduling values of each power consumption facility during this period according to the order of the reduction amplitudes for priority.

[0016] D4. When the power consumption load in a certain period of the target community is insufficient and the charging load of new energy vehicles is large, the community microgrid docks with the new energy vehicle charging pile management system to carry out peak-shifting regulation of the charging of charging piles, and postpones most of the charging time to the night and early morning periods.

[0017] Preferably, the analysis of the peak shaving and valley filling scheduling of the energy storage facilities during this period is as follows: E1. When the power consumption load in a certain period of the target community is insufficient, then each energy storage facility needs to discharge, and each energy storage facility discharges according to the power consumption demand parameter scheduling value of each power consumption facility to carry out discharge scheduling for each power consumption facility during this period.

[0018] E2. When the power consumption load in a certain period of the target community is normal, then each energy storage facility needs to charge, obtain the charging costs corresponding to the normal power consumption loads in each period of the current cycle, and arrange the charging costs corresponding to the normal power consumption loads in each period of the current cycle in ascending order, so as to obtain each period with the lowest charging cost in the current cycle, and then charge each energy storage facility in each period with the lowest charging cost in the current cycle.

[0019] The beneficial effects of the present invention are as follows: 1. The present invention provides a method for energy storage scheduling of a community microgrid. Through the whole process management of demand research, data collection, connection between the energy storage system and the power consumption system, remote control of equipment, and microgrid scheduling, it effectively realizes the effective management and control of different power consumption demands within the community. Through methods such as peak-shifting scheduling, emergency scheduling, load shifting and valley filling scheduling, and other intelligent scheduling methods, it can not only specifically meet different power consumption demands, but also reduce the overall power consumption fluctuation of the community power grid, resolve the risks of the community power grid, and obtain certain economic benefits by taking advantage of the peak-valley electricity price difference.

[0020] 2. In the embodiments of the present invention, through the analysis of different power consumption demand pattern types, the power consumption facilities and energy storage facilities can be scheduled according to actual needs, the electric energy can be reasonably allocated, and the utilization efficiency of electric energy can be improved. At the same time, through the analysis of historical power load data, the fluctuation of future power consumption demands can be predicted, and it can be timely judged whether it is necessary to increase energy storage facilities to reduce the risks of the power system operation. Reasonable power consumption scheduling and energy storage scheduling can improve the stability and reliability of the power grid, reduce voltage fluctuations and the occurrence of power grid failures, ensure the normal operation of the power grid. Through fine scheduling and management, the power consumption experience of users can be improved, the power consumption cost can be reduced, the stability and reliability of power consumption can be guaranteed, and user satisfaction can be enhanced.

[0021] 3. In the embodiments of the present invention, through the intelligent scheduling analysis of power consumption facilities and energy storage facilities, the energy of each facility can be utilized to the greatest extent, the power consumption demands can be reasonably allocated, and the utilization efficiency of the facilities can be improved. It is conducive to load shifting and valley filling scheduling analysis, and the electric energy of the energy storage facility can be released when the power consumption load is insufficient, avoiding waste of energy and improving energy utilization efficiency. Reasonable power consumption scheduling and load shifting and valley filling scheduling can reduce the power grid load during peak hours, avoid power consumption during peak electricity price periods, thereby reducing the power consumption cost of the community. Through the management and scheduling of energy storage facilities, renewable energy can be better integrated, its utilization rate can be improved, the development and application of renewable energy can be promoted, and energy transformation and sustainable development can be promoted. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0023] Figure 1 It is a flowchart of the implementation steps of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] As shown in the embodiments of the present invention Figure 1 A method for energy storage scheduling of a community microgrid includes: Step 1: Analysis of electricity demand pattern types: Through investigation, on-site visits, and surveys, confirm the electricity demand patterns of the target community, and obtain the electricity load, urgency level, and stability level corresponding to each electricity demand pattern, and then analyze the electricity demand pattern types corresponding to each electricity demand pattern.

[0026] In a specific embodiment, the process of obtaining the electricity load, urgency level, and stability level corresponding to each electricity demand pattern is as follows: A1. Through real-time monitoring and data recording, use smart meters to monitor each electricity demand pattern, record its electricity consumption, and accumulate the electricity consumption corresponding to each electricity demand pattern to obtain the electricity load of each electricity demand pattern.

[0027] A2. Through communication with community residents, shops, and relevant government departments, and by forming a questionnaire survey, let community residents, shops, and relevant government departments fill out the questionnaire survey. The content of the questionnaire survey includes the urgency score and stability score corresponding to each electricity demand pattern. Calculate the average value of the urgency score and stability score of each electricity demand pattern corresponding to each questionnaire survey respectively to obtain the average urgency value and average stability value corresponding to each electricity demand pattern. Then, respectively compare the average urgency value and average stability value corresponding to each electricity demand pattern with the average values corresponding to each urgency level and each stability level in the database to obtain the urgency level and stability level corresponding to each electricity demand pattern.

[0028] In another specific embodiment, the process of analyzing the electricity demand pattern types corresponding to each electricity demand pattern is as follows: Respectively compare the electricity load, urgency level, and stability level corresponding to each electricity demand pattern with the electricity load, urgency level, and stability level corresponding to each electricity demand pattern type in the database. If the electricity load, urgency level, and stability level corresponding to a certain electricity demand pattern are respectively the same as those corresponding to a certain electricity demand pattern type in the database, then record the electricity demand pattern type in the database as the electricity demand pattern type corresponding to this electricity demand pattern.

[0029] It should be noted that the types of electricity demand patterns include the electricity consumption pattern with priority on stability, the high-load electricity consumption pattern, the stable electricity consumption pattern, etc.

[0030] Step 2: Judgment on the increase of energy storage facilities: Obtain the peak load values, peak load durations, and load fluctuation amplitudes corresponding to each electricity demand pattern type in each time period during the historical period, analyze to obtain the electricity load evaluation coefficients corresponding to each electricity demand pattern type in each time period during the historical period, and then judge whether it is necessary to increase energy storage facilities in the future period.

[0031] In a specific embodiment, the process of obtaining the peak load values, peak load durations, and load fluctuation amplitudes corresponding to each electricity demand pattern type in each time period during the historical period is as follows: B1. Obtain the electricity consumption data during the historical period through the power company and smart meters, and then draw the electricity load distribution and fluctuation graph of this community based on the electricity consumption data during the historical period. By displaying the electricity load values on the time axis, a curve or bar graph is formed to represent the electricity load conditions in different time periods.

[0032] B2. Through the electricity load distribution graph, observe the peak electricity load values that appear for each electricity demand pattern type in each time period during the historical period. The peak electricity load value is the peak load value, and then obtain the peak load values corresponding to each electricity demand pattern type in each time period during the historical period.

[0033] B3. By observing the duration of the time period corresponding to the peak load value in the graph of each electricity demand pattern type in each time period during the historical period, determine the duration of each peak load, and then obtain the peak load durations corresponding to each electricity demand pattern type in each time period during the historical period.

[0034] B4. According to the electricity load fluctuation graph, observe the fluctuation of the load values of each electricity demand pattern type in each time period during the historical period between different time periods, calculate the load fluctuation amplitude. The load fluctuation amplitude is the maximum difference in electricity load values within a period of time to determine the load fluctuation amplitude, and then obtain the load fluctuation amplitudes corresponding to each electricity demand pattern type in each time period during the historical period.

[0035] In another specific embodiment, the process of analyzing to obtain the electricity load evaluation coefficients corresponding to each electricity demand pattern type in each time period during the historical period is as follows: Denote the peak load values, peak load durations, and load fluctuation amplitudes corresponding to each electricity demand pattern type in each time period during the historical period as Z ig 、X ig and V ig, where \(i\) represents the number corresponding to each type of electricity demand pattern, \(i = 1, 2,\cdots,n\), \(n\) is any integer greater than 2, \(g\) represents the number corresponding to each time period, \(g = 1, 2,\cdots,u\), \(u\) is any integer greater than 2, and substituting into the calculation formula

[0036] , the electricity load evaluation coefficient \(\delta\) corresponding to each type of electricity demand pattern in each time period in the historical period is obtained ig , where \(Z'\), \(X'\), and \(V'\) are the standard peak load value, standard peak load duration, and standard load fluctuation amplitude corresponding to the set electricity demand pattern type respectively, and \(\eta_1\), \(\eta_2\), and \(\eta_3\) are the weight factors corresponding to the peak load value, peak load duration, and load fluctuation amplitude of the set electricity demand pattern type respectively.

[0037] It should be noted that \(\eta_1\), \(\eta_2\), and \(\eta_3\) are greater than 0 and less than 1.

[0038] It also should be noted that through the summary of a large amount of research data and experimental data. According to the standard peak load value, standard peak load duration, and standard load fluctuation amplitude corresponding to the electricity demand pattern type set by professional institutions and research institutions. At the same time, based on the professional knowledge and research basis of domain experts, and through discussion and confirmation with industry organizations or professional institutions. The weight factors corresponding to the peak load value, peak load duration, and load fluctuation amplitude of the electricity demand pattern type are set by experts according to their own experience and knowledge.

[0039] In another specific embodiment, the process of determining whether energy storage facilities need to be added in the future period is as follows: C1. Compare the electricity load evaluation coefficient corresponding to each type of electricity demand pattern in each time period in the historical period with the standard electricity load evaluation coefficient of the set electricity demand pattern type in the corresponding time period. If the electricity load evaluation coefficient corresponding to a certain type of electricity demand pattern in a certain time period in the historical period is less than or equal to the standard electricity load evaluation coefficient of the set electricity demand pattern type in the corresponding time period, it is determined that energy storage facilities do not need to be added in the future period.

[0040] C2. If the electricity load evaluation coefficient corresponding to a certain type of electricity demand pattern in a certain time period in the historical period is greater than the standard electricity load evaluation coefficient of the set electricity demand pattern type in the corresponding time period, it is determined that energy storage facilities need to be added in the future period.

[0041] In the embodiments of the present invention, through the analysis of different types of electricity consumption demand patterns, the electricity-consuming facilities and energy storage facilities can be scheduled according to the actual demand, the electric energy can be reasonably allocated, and the utilization efficiency of the electric energy can be improved. At the same time, through the analysis of historical electricity load data, the fluctuation of future electricity consumption demand can be predicted, and it can be judged in time whether it is necessary to increase the energy storage facilities, reducing the operation risk of the power system. Reasonable electricity scheduling and energy storage scheduling can improve the stability and reliability of the power grid, reduce voltage fluctuations and the occurrence of power grid failures, and ensure the normal operation of the power grid. Through fine scheduling and management, the electricity consumption experience of users can be improved, the electricity consumption cost can be reduced, the stability and reliability of electricity consumption can be guaranteed, and the user satisfaction can be enhanced.

[0042] Step 3: Analysis of electricity scheduling: Use Internet of Things intelligent acquisition to connect with the community's main transformer, and uniformly connect the management systems of each electricity-consuming facility to the community microgrid platform. If no energy storage facility is added, when the electricity load of the target community is insufficient during a certain period, then analyze the scheduling values of the electricity consumption demand parameters of each electricity-consuming facility in the target community during this period, and schedule the electricity-consuming facility during this period according to the corresponding scheduling values of the electricity consumption demand parameters.

[0043] In a specific embodiment, the analysis of the scheduling values of the electricity consumption demand parameters of each electricity-consuming facility in the target community during this period is as follows: D1. When the electricity load of the target community is insufficient during a certain period, according to the basic weights corresponding to the electricity consumption demands of each electricity-consuming facility set by the system, assign corresponding weight values to the electricity consumption demands of each electricity-consuming facility, and calculate the reduction amplitude corresponding to the electricity consumption demand of each electricity-consuming facility according to the reduction amplitude formula when the load is insufficient: reduction amplitude = 1 - weight value^2.

[0044] D2. Compare the reduction amplitude corresponding to the electricity consumption demand of each electricity-consuming facility with the reduction amplitude corresponding to each scheduling value of the electricity consumption demand parameters in the database. If the reduction amplitude corresponding to the electricity consumption demand of a certain electricity-consuming facility is the same as the reduction amplitude corresponding to a certain scheduling value of the electricity consumption demand parameters in the database, then use the scheduling value of the electricity consumption demand parameters in the database as the scheduling value of the electricity consumption demand parameters of each electricity-consuming facility during this period.

[0045] D3. Arrange the reduction amplitudes corresponding to the electricity consumption demands of each electricity-consuming facility in ascending order, and then schedule the scheduling values of the electricity consumption demand parameters of each electricity-consuming facility during this period in the order of the reduction amplitude.

[0046] D4. When the electricity load of the target community is insufficient during a certain period and the charging load of new energy vehicles is large, the community microgrid docks with the management system of new energy vehicle charging piles to carry out peak-shifting regulation of charging piles, and postpones most of the charging time to night and early morning hours.

[0047] It should be noted that when the load is insufficient, the reduction amplitude of the system for this electricity demand item = 1 - weight value 2. The system default basic weights are as follows: residential electricity: 0.85, important facility guarantee electricity: 0.9, new energy vehicle charging electricity: 0.7, public area electricity: 0.7, public lighting electricity: 0.65, community store electricity: 0.45, outdoor temporary electricity: 0.3.

[0048] Step 4. Analysis of peak shaving and valley filling scheduling: If energy storage facilities are added and each energy storage facility is connected to the community microgrid platform, when the electricity load of a target community is insufficient at a certain time period, then the electricity demand parameter scheduling values of each electricity facility in the target community at this time period are analyzed according to the analysis of electricity scheduling in Step 3, and the peak shaving and valley filling scheduling of the energy storage facilities at this time period is analyzed.

[0049] In a specific embodiment, the analysis of the peak shaving and valley filling scheduling of the energy storage facilities at this time period is as follows: E1. When the electricity load of a target community is insufficient at a certain time period, each energy storage facility needs to discharge, and each energy storage facility discharges according to the electricity demand parameter scheduling value of each electricity facility to perform discharge scheduling for each electricity facility at this time period.

[0050] E2. When the electricity load of a target community is normal at a certain time period, each energy storage facility needs to charge. Obtain the charging costs corresponding to each time period when the electricity load is normal in the current cycle, and arrange the charging costs corresponding to each time period when the electricity load is normal in the current cycle in ascending order, so as to obtain each time period with the lowest charging cost in the current cycle. Then, each energy storage facility is charged during each time period with the lowest charging cost in the current cycle.

[0051] It should be noted that investment: procurement or lease of energy storage batteries, procurement or lease of energy storage systems; usage: energy storage facilities use low valley electricity prices to charge during the low valley period of community electricity consumption; discharge during the peak period of community electricity consumption or during temporary important guarantee periods, such as sudden power outages of important facilities, etc.; economic benefits: the difference between peak and valley electricity prices in the community: taking a certain city as an example, it is currently about 0.6 yuan / kWh. If selling electricity to businesses around the community, it is about 1.0 - 1.5 yuan / kWh. Social benefits: For temporary emergency electricity demand, energy storage facilities can seamlessly provide emergency power supply for 2 - 8 hours; distributed energy storage: Utilize new energy vehicle charging piles in the community that support V2G technology. With the authorization and consent of the vehicle owners, part of the electric energy in the currently idle new energy vehicles is reversely output to the community microgrid to achieve distributed energy storage. The advantage of distributed energy storage is that the owners can enjoy a certain share of economic benefits during the discharge process.

[0052] In the embodiments of the present invention, through intelligent scheduling analysis of power consumption facilities and energy storage facilities, the energy of each facility can be utilized to the greatest extent, the power consumption demand can be reasonably allocated, the utilization efficiency of the facilities can be improved, and it is beneficial to peak shaving and valley filling scheduling analysis. When the power consumption load is insufficient, the electric energy of the energy storage facility can be released to avoid waste of energy and improve the energy utilization efficiency. Through reasonable power consumption scheduling and peak shaving and valley filling scheduling, the grid load can be reduced during peak hours, and power consumption during peak electricity price hours can be avoided, thereby reducing the electricity cost of the community. Through the management and scheduling of the energy storage facility, renewable energy can be better integrated, its utilization rate can be improved, the development and application of renewable energy can be promoted, and energy transformation and sustainable development can be promoted.

[0053] The present invention provides a method for energy storage scheduling of a community microgrid. Through the whole process management of demand research, data collection, docking of the energy storage system and the power consumption system, and remote control of equipment, as well as microgrid scheduling, the effective management and control of different power consumption demands within the community are effectively realized. Through methods such as peak shifting scheduling, emergency scheduling, peak shaving and valley filling scheduling, and other intelligent scheduling methods, not only can different power consumption demands be targeted, but also the overall power consumption fluctuation of the community grid can be reduced, the risks of the community grid can be resolved, and certain economic benefits can be obtained by using the peak-valley electricity price difference.

[0054] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall fall within the protection scope of the present invention.

Claims

1. A community microgrid energy storage scheduling method, characterized in that: include: Step 1: Analysis of electricity demand pattern types: Through research, field visits and surveys, confirm the electricity demand patterns of the target community, and obtain the electricity load, urgency and stability corresponding to each electricity demand pattern, and then analyze the electricity demand pattern type corresponding to each electricity demand pattern; Step 2: Determine whether to increase energy storage facilities: Obtain the peak load value, peak load duration, and load fluctuation amplitude corresponding to each power demand pattern type in each period in the historical cycle, analyze and obtain the power load assessment coefficient corresponding to each power demand pattern type in each period in the historical cycle, and then determine whether energy storage facilities need to be added in the future cycle; Step 3: Analysis of power dispatch: Use the Internet of Things to intelligently collect and connect to the community main transformer, and connect the management systems of various power facilities to the community microgrid platform. If no energy storage facilities are added, when the power load of the target community is insufficient during a certain period of time, the power demand parameter dispatch values ​​of each power facility in the target community during that period are analyzed, and the power facilities of the power facility during that period are dispatched according to the corresponding power demand parameter dispatch values; The power demand parameter dispatching values ​​of each power facility in the target community during this period are analyzed, and the specific analysis process is as follows: D1. When the target community has insufficient electricity load during a certain period of time, the power demand of each power facility is assigned a corresponding weight value according to the basic weight corresponding to the power demand of each power facility set by the system, and the reduction range corresponding to the power demand of each power facility is calculated according to the reduction range formula when the load is insufficient: reduction range = 1-weight value 2; D2, and compare the reduction range of the power demand of each power facility with the reduction range of the power demand parameter dispatch value in the database. If the reduction range of the power demand of a certain power facility is the same as the reduction range of the power demand parameter dispatch value in the database, then the power demand parameter dispatch value in the database is used as the power demand parameter dispatch value of each power facility in the period; D3. Arrange the reduction ranges corresponding to the power demand of each power-consuming facility in order from small to large, and then prioritize the power demand parameter scheduling values ​​of each power-consuming facility in the period according to the order of the reduction ranges; D4. When the target community has insufficient electricity load during a certain period of time and the charging load of new energy vehicles is large, the community microgrid connects with the new energy vehicle charging pile management system to regulate the peak power consumption of the charging piles and postpone most of the charging time to the night and early morning hours; Step 4: Analysis of peak-shaving and valley-flattening scheduling: If energy storage facilities are added and connected to the community microgrid platform, when the target community has insufficient electricity load during a certain period of time, the electricity demand parameter scheduling values ​​of each electricity facility in the target community during that period are analyzed according to the analysis of electricity scheduling in step 3, and the peak-shaving and valley-flattening scheduling of the energy storage facilities during that period is analyzed; The peak shaving and valley leveling scheduling of energy storage facilities during this period is analyzed, and the specific analysis process is as follows: E1. When the power load of the target community is insufficient during a certain period of time, each energy storage facility needs to discharge. Each energy storage facility will perform discharge scheduling for each power facility during that period according to the power demand parameter scheduling value of each power facility. E2. When the electricity load of the target community is normal during a certain period of time, each energy storage facility needs to be charged, and the charging costs corresponding to the normal electricity load in each period of the current cycle are obtained, and the charging cost cases corresponding to the normal electricity load in each period of the current cycle are arranged in order from small to large, so as to obtain the time periods with the lowest charging costs in the current cycle, and then charge each energy storage facility in the time periods with the lowest charging costs in the current cycle.

2. A community microgrid energy storage dispatching method as claimed in claim 1, characterized in that: The specific acquisition process of obtaining the power load, urgency and stability corresponding to each power demand mode is as follows: A1. Through real-time monitoring and data recording, use smart meters to monitor each power demand mode, record its power consumption, and accumulate the power consumption corresponding to each power demand mode to obtain the power load of each power demand mode; A2. Through communication with community residents, shops and relevant government departments, and through the formation of questionnaires, community residents, shops and relevant government departments are asked to fill in the questionnaires. The content of the questionnaires includes the urgency score and stability score corresponding to each electricity demand mode. The urgency score and stability score of each electricity demand mode corresponding to each questionnaire are averaged to obtain the average urgency score and stability score corresponding to each electricity demand mode. The average urgency score and stability score corresponding to each electricity demand mode are compared with the average values ​​of each urgency degree and each stability degree in the database, so as to obtain the urgency degree and stability degree corresponding to each electricity demand mode.

3. A community microgrid energy storage dispatching method as claimed in claim 2, characterized in that: The specific analysis process of analyzing the power demand pattern type corresponding to each power demand pattern is as follows: The power load, urgency and stability corresponding to each power demand pattern are compared with the power load, urgency and stability corresponding to each power demand pattern type in the database. If the power load, urgency and stability corresponding to a power demand pattern are the same as those corresponding to a power demand pattern type in the database, the power demand pattern type in the database is recorded as the power demand pattern type corresponding to the power demand pattern.

4. A community microgrid energy storage dispatching method as claimed in claim 3, characterized in that: The specific acquisition process of obtaining the peak load value, peak load duration and load fluctuation amplitude corresponding to each power demand mode type in each time period in the historical cycle is as follows: B1. Obtain electricity consumption data in the historical period through power companies and smart meters, and then draw the electricity load distribution and fluctuation diagram of the community based on the electricity consumption data in the historical period. By displaying the electricity load value on the time axis, a curve or bar chart is formed to represent the electricity load situation in different periods; B2. Through the power load distribution diagram, the peak power load value of each power demand pattern type in each time period in the historical cycle is observed. The peak power load value is the peak load value, and then the peak load value corresponding to each power demand pattern type in each time period in the historical cycle is obtained; B3. Determine the duration of each peak load by observing the duration of the time period corresponding to the peak load value of each power demand pattern type in each time period diagram in the historical cycle, and then obtain the peak load duration corresponding to each power demand pattern type in each time period in the historical cycle; B4. Based on the electricity load fluctuation chart, the fluctuation of load values ​​in different time periods for each electricity demand pattern type in the historical period is observed, and the load fluctuation amplitude is calculated. The load fluctuation amplitude is the maximum difference in electricity load values ​​within a period of time to determine the load fluctuation amplitude, and then the load fluctuation amplitude corresponding to each electricity demand pattern type in each time period in the historical period is obtained.

5. A community microgrid energy storage dispatching method as claimed in claim 4, characterized in that: The analysis obtains the power load assessment coefficient corresponding to each power demand pattern type in each time period in the historical cycle. The specific analysis process is as follows: The peak load value, peak load duration and load fluctuation amplitude corresponding to each type of electricity demand pattern in each period in the historical cycle are recorded as Z ig , X ig and V ig , where i represents the number corresponding to each power demand mode type, i=1,2...n, n is any integer greater than 2, g represents the number corresponding to each time period, g=1,2...u, u is any integer greater than 2, substitute into the calculation formula The power load evaluation coefficient δ corresponding to each power demand pattern type in each period in the historical cycle is obtained. ig , where Z′, X′, and V′ are the standard peak load value, standard peak load duration, and standard load fluctuation range corresponding to the set power demand pattern type, respectively; η1, η2, and η3 are the weight factors corresponding to the peak load value, peak load duration, and load fluctuation range of the set power demand pattern type, respectively.

6. A community microgrid energy storage dispatching method as claimed in claim 5, characterized in that: The specific process of judging whether to add energy storage facilities in the future cycle is as follows: C1. Compare the power load assessment coefficients corresponding to each power demand pattern type in each time period in the historical cycle with the set standard power load assessment coefficients of the power demand pattern type in the corresponding time period. If the power load assessment coefficients corresponding to a certain power demand pattern type in a certain time period in the historical cycle are all less than or equal to the set standard power load assessment coefficients of the power demand pattern type in the corresponding time period, it is determined that no energy storage facilities need to be added in the future cycle; C2. If the electricity load assessment coefficient corresponding to a certain electricity demand pattern type in a certain period of time in the historical cycle is greater than the set standard electricity load assessment coefficient of the electricity demand pattern type in the corresponding period of time, it is determined that energy storage facilities need to be added in the future period.

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