Regional energy system energy storage method based on flexible heat supply network technology

Abstract

The invention belongs to the field of comprehensive energy control, and particularly relates to a regional energy system energy storage method based on a flexible heat supply network technology, and the method comprises the steps: integrating the working state of a historical energy storage device, the energy demand and environment data, combining a seasonal prediction model, and precisely predicting the power generation amount, the energy demand and the redundancy amount of each region in different time periods; intelligent optimization of regional energy storage-discharge quantity is realized by utilizing an energy configuration model; a distributed enhanced control model constructed based on a federal algorithm ensures efficient storage-discharge and energy scheduling of the energy storage device; evaluating the performance and energy supply satisfaction rate of the energy storage device in real time, and dynamically adjusting an energy storage-release strategy through a comprehensive evaluation score feedback mechanism; according to the invention, through different types of energy storage modes, the adaptability and response capability of the regional energy system to different environments are improved.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy control, and in particular relates to a regional energy system energy storage method based on flexible thermal network technology. Background Art

[0002] At present, with the large-scale grid connection of renewable energy such as wind power, the intermittent and uncertain output of wind power has brought great challenges to the power system. At the same time, traditional thermal power equipment has limitations in peak load regulation and flexibility, and it is difficult to meet the balance between the growing demand for heating and power supply. In the existing technology, although there have been attempts to jointly operate wind power and thermal power, the flexibility of regional thermal network energy storage and the diversity of user load characteristics are often ignored, resulting in the failure to maximize the overall benefits of the system.

[0003] For example, the patent with authorization announcement number CN107910894B discloses a control method, device and system for the joint operation of wind power equipment and thermal power equipment, including: determining the constraints of thermal power and electric power of thermal power equipment, the constraints of electric power of wind power equipment and the constraints of the sum of electric power of wind power equipment and thermal power equipment based on equipment parameters; determining the energy storage constraints of the regional heating network based on energy storage parameters; on the premise of meeting the above constraints, on the premise of meeting the heat load demand of thermal power equipment and with the goal of maximizing the benefits of the joint operation system of wind power equipment and thermal power equipment, determining the electric power of wind power equipment, the thermal power and electric power of thermal power equipment; sending wind power output consumption instructions to wind power equipment and sending scheduling instructions to thermal power equipment.

[0004] For example, the patent with authorization announcement number CN113762643B discloses a method for optimizing the configuration of energy storage capacity of a regional integrated energy system, including obtaining the power grid operation parameters of the area to be analyzed; constructing an objective function and constraints for optimizing the configuration of energy storage capacity of the regional integrated energy system; solving the objective function under the constraints; and completing the optimal configuration of energy storage capacity of the regional integrated energy system based on the solution results.

[0005] The above existing technologies have the following problems: the existing technologies only consider energy storage from the source end or the comprehensive demand response from the load end or meet the requirements of coordinated scheduling of electricity and thermal energy and wind power consumption, but cannot accurately store and store different types of energy and perform inter-regional scheduling according to the actual needs of the actual area. For this reason, the present invention provides a regional energy system energy storage method based on flexible thermal network technology. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a regional energy system energy storage method based on flexible thermal network technology. The method integrates the historical working status of energy storage devices, energy demand and environmental data, and combines seasonal prediction models to accurately predict the power generation, energy demand and redundancy in different time periods in each region. Secondly, the energy configuration model is used to realize the intelligent optimization of regional energy storage and release. The distributed enhanced control model constructed based on the federal algorithm ensures the efficient storage and release and energy scheduling of the energy storage device. The performance of the energy storage device and the energy supply satisfaction rate are evaluated in real time, and the energy storage and release strategy is dynamically adjusted through a comprehensive evaluation score feedback mechanism. The present invention improves the adaptability and responsiveness of the regional energy system to different environments through different types of energy storage methods.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The energy storage method of the regional energy system based on flexible thermal network technology includes:

[0009] S1. Obtain the historical working status data, energy demand data and environmental data of energy storage devices in different regions, and obtain the total power generation, energy demand and energy shortage in different time periods of each region through the configured prediction model with seasonal characteristics;

[0010] S2. According to the total power generation, energy demand and energy shortage in each region at different time periods, the energy configuration model is used to obtain the storage and release of different types of energy in each region at the corresponding time period;

[0011] S3. Construct a distributed enhanced control model based on the federated algorithm, and configure the enhanced control model into the regional energy system. According to the storage and release amount of different types of energy, the regional energy system is used to control the energy storage devices in different regions to store and release different types of energy and schedule them.

[0012] S4. Set a comprehensive score threshold, obtain the storage-discharge and scheduling process parameters of each regional energy storage device and the energy supply satisfaction rate data of the same region per unit time in real time, and obtain the regional storage comprehensive evaluation score through the evaluation algorithm. When the regional storage comprehensive evaluation score is less than the comprehensive score threshold, the regional storage comprehensive evaluation score is fed back to the energy configuration model to optimize the energy storage-discharge and scheduling process of each region in the corresponding time period, otherwise no adjustment is made.

[0013] Specifically, the steps of obtaining the energy shortage amount in each area at different time periods include:

[0014] S101, obtaining and preprocessing the electricity-heat energy consumption and temperature data of different regions for one year;

[0015] S102, inputting the preprocessed data into a clustering algorithm, clustering the regions, and obtaining regions with the same energy demand data and temperature change trends;

[0016] S103, aligning the electric energy consumption and the heat energy consumption corresponding to the same change trend area in the time dimension, and averaging the aligned electric energy consumption and the heat energy consumption respectively, to obtain the average electric energy consumption and the average heat energy consumption time series corresponding to the same change trend area;

[0017] S104, obtaining a sequence of average electric energy consumption change rate and average thermal energy consumption change rate according to the time series of average electric energy consumption and average thermal energy consumption;

[0018] S105, input the average electric energy consumption change rate and the average thermal energy consumption change rate sequence into the clustering algorithm, and obtain K time period length intervals with the same electric energy consumption and thermal energy consumption change rates and corresponding change rate values ​​in sequence.

[0019] Specifically, the steps of obtaining the energy shortage amount in each area at different time periods also include:

[0020] S106. Use the time period length intervals with the same change rate as the segmentation window to segment the corresponding regional electric energy consumption, heat energy consumption and temperature sequences in the time dimension, and use the corresponding change rate values ​​as seasonal factors and the segmented electric energy consumption and heat energy consumption sequences to form the segmented input sequence pairs (Ti, Xi, λi) and (Tj, Yj, γj), where Xi represents the i-th electric energy consumption segmentation sequence, Ti represents the temperature segmentation sequence corresponding to the i-th electric energy consumption segmentation sequence; λi represents the seasonal factor corresponding to the i-th electric energy consumption segmentation sequence; where Yj represents the j-th heat energy consumption segmentation sequence, Tj represents the temperature segmentation sequence corresponding to the j-th heat energy consumption segmentation sequence, and γj represents the seasonal factor corresponding to the j-th heat energy consumption segmentation sequence;

[0021] S107, construct an integrated prediction model based on the integrated algorithm and the BiLSTM algorithm, input (Ti, Xi, λi) into BiLSTM1 in the prediction model, and input (Tj, Yj, γj) into BiLSTM2 in the prediction model for training, to obtain a trained prediction model;

[0022] S108, obtaining the electricity-heat energy consumption and temperature of each region in the previous three days of the current time period, and predicting the electricity-heat demand of each region in the corresponding time period through the trained prediction model.

[0023] Specifically, the steps of obtaining the energy shortage amount in each area at different time periods include:

[0024] S109, obtaining historical total power generation and total heat supply data under different environmental conditions in different regions, and using the obtained data to fine-tune the pre-trained integrated prediction model, and at the same time predicting the total power generation and total heat supply in the corresponding time period of S108;

[0025] S110, based on the total power generation and total heat supply obtained and the forecast, the electricity-heat demand in each area in the corresponding time period is obtained, and the energy shortage corresponding to the power supply and heat supply in different areas in the corresponding time period is obtained. and It indicates the amount of power shortage corresponding to the length of the kth time period in the mth area. It indicates the heat energy shortage corresponding to the length interval of the kth time period in the mth area.

[0026] Specifically, the steps of storage, release and dispatch of different types of energy include:

[0027] When the current time period When , all regional energy storage devices enter the energy storage mode. The specific steps of the energy storage process include:

[0028] S2011, obtaining the remaining capacity of the corresponding electricity and heat storage devices in each area in the current time period, the corresponding costs of electricity and heat storage per unit time, and the electricity-to-heat cost per unit time Q P→H or heat-to-electricity cost Q H→P ;

[0029] S2012, when satisfied When and Then directly and Stored in the corresponding regional electricity and heat storage devices, where and represents the remaining space of the electric storage device and the remaining space of the heat storage device corresponding to the mth area in the kth time period in sequence; represents the operating cost of the power storage device corresponding to the mth area in the kth time period, represents the operating cost of the heat storage device corresponding to the mth area in the kth time period;

[0030] S2013, if and When the current time period is reached, the electrical energy is converted into thermal energy and stored in the thermal storage device until the thermal storage device is full;

[0031] S2014: If there is power remaining and the mth area satisfies Then, through the heat network model, the dispatching cost of dispatching heat energy from the mth area to the mth area is obtained: like and Then the remaining thermal energy is dispatched to the m!th area for storage through the enhanced control model, otherwise the remaining electricity is stored in the power storage device corresponding to the m!th area; m! represents any area among the M areas except the m!th area;

[0032] S2015, if Then repeat S2012 to S2014 to perform heat-to-electricity conversion judgment, and perform corresponding type of energy storage or scheduling storage.

[0033] Specifically, the steps of storage, release and dispatch of different types of energy also include:

[0034] S2016, when If satisfied and Then Stored in the power storage device corresponding to the mth area and then dispatched through the heat network model, if the and Then the remaining heat energy is dispatched to the mth area for storage through the enhanced control model, otherwise it is abandoned;

[0035] S2017, when satisfied and When After the storage is completed, the storage device corresponding to the mth area satisfies Then, a heat-to-electricity conversion operation is performed, and the converted electric energy is stored in the electric storage device corresponding to the mth region;

[0036] S2018. When the power storage device corresponding to the mth region cannot meet the power required for the heat-to-electricity conversion process, the remaining converted power is stored in the power storage device corresponding to the mth region until all remaining power storage devices meet the requirements. The remaining heat is discarded;

[0037] S2019, when satisfied When Then repeat S2017 and S2018 processes to convert heat into electricity for storage, otherwise repeat S2016 process to perform thermal energy scheduling and storage;

[0038] S2020, when satisfied Then The energy is stored in the power storage device in the corresponding area, and the thermal energy scheduling is judged through S2016. If the energy is satisfied, and Then the remaining heat energy is dispatched to the mth area for storage through the enhanced control model, and the excess heat energy is abandoned. If it is not satisfied, when the state of the heat storage device in the mth area is When the residual heat energy is discarded,

[0039] S2021, when satisfied and When , S2020 is repeated to perform thermal energy scheduling and storage;

[0040] S2022, when When the storage and dispatch of electric energy and thermal energy are determined, the process of S2016-S2021 is repeated;

[0041] S2023, when When the storage and dispatching of electric energy and thermal energy are determined, the process of S2016-S2022 is repeated.

[0042] Specifically, the steps of storage, release and dispatch of different types of energy also include:

[0043] S2024, when the current time period or When , the discriminant model is used to obtain the energy deficiency area, type and amount of energy deficiency;

[0044] S2025: If at least one area in the current time period lacks power and all areas meet The power shortage in the corresponding area is compared with the remaining power in the power storage device in the corresponding area. If the remaining power in the power storage device is greater than the power shortage, the power dispatching cost is compared with the power storage device operating cost. If the power dispatching cost in the current time period is greater than the power storage device operating cost and the heat-to-electricity cost is greater than the power storage device operating cost, the power storage device is operated to discharge and supplement the missing power.

[0045] S2026. If the current time period electricity dispatch cost is greater than the storage device operating cost and the heat-to-electricity cost is less than or equal to the storage device operating cost, the corresponding area heat energy is converted into electricity to supplement the electricity shortage. If the corresponding electric energy after conversion is less than the missing electric energy, the electric storage device is operated to discharge and supplement;

[0046] S2027. When the current time period electric energy dispatching cost is less than the storage device operation cost and the heat-to-electricity cost is greater than the storage device operation cost, if there is an mth area corresponding to The built-in distributed enhanced control model is used to dispatch and supplement the power. When the sum of the regional dispatched electric energy is less than the amount of electric energy missing, the corresponding power storage device is operated to supplement the missing electric energy after the dispatch replenishment;

[0047] S2028. When the electric energy dispatching cost in the current time period is less than the operating cost of the power storage device and the heat-to-electricity cost is less than or equal to the operating cost of the power storage device, the electric energy dispatching cost is compared with the heat-to-electricity cost. If the electric energy dispatching cost is greater than the heat-to-electricity cost, the thermal energy in the corresponding area is converted into electric energy to supplement the power shortage. If the corresponding power shortage cannot be met after supplementation, the electric energy dispatching supplement is performed through the S2027 process until the missing power is met.

[0048] S2029: If the electricity dispatch cost is less than or equal to the heat-to-electricity cost, then the electricity dispatch is supplemented directly through the S2027 process. If the supplement cannot meet the corresponding electricity shortage, the excess heat in the same area is dispatched through the S2026 process. If the power shortage cannot be met by converting heat into electricity, the sum of the costs of heat-to-electricity conversion and electricity dispatching is compared with the operating cost of the energy storage device. If it is less than that, the missing electricity is supplemented by combining the heat-to-electricity conversion and electricity dispatching process. If it is greater than that, the energy storage device in the power-deficient area is operated to supplement the missing electricity after the heat-to-electricity conversion until the missing electricity is met.

[0049] Specifically, the steps of storage, release and dispatch of different types of energy also include:

[0050] S2030: If the remaining electric energy in the power storage device is less than or equal to the amount of power loss, repeat the heat-to-electricity and power scheduling process of S2026-S2029 to supplement the electric energy. If the sum of the electric energy supplemented by heat-to-electricity and power scheduling and the electric energy in the power storage devices of all regions in the current time period is less than the amount of power loss in the current time period, the supplemented power loss will be fed back to the regional energy system, a power shortage warning will be issued, and the regional energy system will be used to dispatch the power generation device for secondary power generation supplement;

[0051] S2031, if at least one area in the current time period stores missing heat energy and all areas meet Then repeat the process of S2024-S2030 to schedule and supplement the thermal energy;

[0052] S2032. If at least one area in the current time period lacks thermal energy and electrical energy, the process of S2024-S2031 is repeated to perform dual scheduling of electrical energy and thermal energy.

[0053] S2033: If the corresponding electric energy or thermal energy in any area still meets the requirements after scheduling and replenishment in the process of S2024-S2032 or If conditions are met, the corresponding type of energy storage or discarding process in S2012-S2023 is used to store or discard the electrical energy or thermal energy in the corresponding area.

[0054] A computer-readable storage medium stores computer instructions, which, when executed, execute a regional energy system energy storage method based on flexible thermal network technology.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] In view of the deficiencies of the prior art, the present invention can accurately estimate the energy supply and demand situation of each region in a specific time period through the application of historical data and a prediction model with seasonal characteristics, thereby providing solid data support for optimal configuration; secondly, by precisely calculating the energy storage and release amount of each region, efficient management of energy storage and release is achieved; in addition, with the help of a distributed enhanced control model, the present invention enhances the precise control and scheduling of energy storage devices, and improves the response speed and coordination of the system; by setting a comprehensive score threshold and evaluating the operating effect of the energy storage device in real time, the present invention can quickly feedback optimization needs and ensure that the energy configuration is in the best state. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a regional energy system energy storage method based on flexible heating network technology according to Example 1 of the present invention;

[0058] Figure 2 This is a flow chart of the prediction model structure of Example 1 of the present invention. DETAILED DESCRIPTION

[0059] Example 1

[0060] See also Figure 1 The present invention provides an embodiment: a regional energy system energy storage method based on flexible thermal network technology, the steps comprising:

[0061] S1. Obtain the historical working status data, energy demand data and environmental data of energy storage devices in different regions, and obtain the total power generation, energy demand and energy shortage in different time periods of each region through the configured prediction model with seasonal characteristics;

[0062] For further information, see Figure 2 In this embodiment, the steps of obtaining the energy shortage amount in each area in different time periods include:

[0063] S101, obtaining and preprocessing the electricity-heat energy consumption and temperature data of different regions for one year;

[0064] S102, inputting the preprocessed data into a clustering algorithm, clustering the regions, and obtaining regions with the same energy demand data and temperature change trends;

[0065] S103, aligning the electric energy consumption and the heat energy consumption corresponding to the same change trend area in the time dimension, and averaging the aligned electric energy consumption and the heat energy consumption respectively, to obtain the average electric energy consumption and the average heat energy consumption time series corresponding to the same change trend area;

[0066] S104, obtaining a sequence of average electric energy consumption change rate and average thermal energy consumption change rate according to the time series of average electric energy consumption and average thermal energy consumption;

[0067] S105, inputting the average electric energy consumption change rate and the average thermal energy consumption change rate sequence into a clustering algorithm, and sequentially obtaining the length intervals of each time period having the same electric energy consumption and thermal energy consumption change rate and the corresponding change rate values.

[0068] S106. Use the time period length intervals with the same change rate as the segmentation window to segment the corresponding regional electric energy consumption, heat energy consumption and temperature sequences in the time dimension, and use the corresponding change rate values ​​as seasonal factors and the segmented electric energy consumption and heat energy consumption sequences to form the segmented input sequence pairs (Ti, Xi, λi) and (Tj, Yj, γj), where Xi represents the i-th electric energy consumption segmentation sequence, Ti represents the temperature segmentation sequence corresponding to the i-th electric energy consumption segmentation sequence; λi represents the seasonal factor corresponding to the i-th electric energy consumption segmentation sequence; where Yj represents the j-th heat energy consumption segmentation sequence, Tj represents the temperature segmentation sequence corresponding to the j-th heat energy consumption segmentation sequence, and γj represents the seasonal factor corresponding to the j-th heat energy consumption segmentation sequence;

[0069] S107, construct an integrated prediction model based on the integrated algorithm and the BiLSTM algorithm, input (Ti, Xi, λi) into BiLSTM1 in the prediction model, and input (Tj, Yj, γj) into BiLSTM2 in the prediction model for training, to obtain a trained prediction model;

[0070] S108, obtaining the electricity-heat energy consumption and temperature of each region in the previous three days of the current time period, and predicting the electricity-heat demand of each region in the corresponding time period through the trained prediction model.

[0071] S109, obtaining historical total power generation and total heat supply data under different environmental conditions in different regions, and using the obtained data to fine-tune the pre-trained integrated prediction model, and at the same time predicting the total power generation and total heat supply in the corresponding time period of S108;

[0072] S110, based on the total power generation and total heat supply obtained and the forecast, the electricity-heat demand in each area in the corresponding time period is obtained, and the energy redundancy corresponding to the power supply and heat supply in different areas in the corresponding time period is obtained. and It indicates the amount of power shortage corresponding to the length of the kth time period in the mth area. It indicates the heat energy shortage corresponding to the length interval of the kth time period in the mth area.

[0073] This process identifies regions with similar energy consumption patterns through preprocessing and cluster analysis of historical data, improving the comparability of data and the accuracy of the model. Subsequently, the dynamic characteristics of energy demand are refined by calculating the average consumption change rate and seasonal factors; BiLSTM is used for deep learning training to enable the model to process sequence data and enhance the fit of nonlinear time series, thereby improving the prediction accuracy; by fine-tuning the pre-trained model, the model parameters are further optimized to better adapt to actual environmental changes; finally, based on the model prediction results, the energy redundancy and shortage in each region can be accurately known, guiding energy scheduling, avoiding excessive power generation or insufficient heat supply, and realizing refined management and efficient use of energy.

[0074] S2. According to the total power generation, energy demand and energy shortage in each region at different time periods, the energy configuration model is used to obtain the storage and release of different types of energy in each region at the corresponding time period;

[0075] Furthermore, the energy configuration model in this embodiment is constructed by a random forest algorithm.

[0076] S3. Construct a distributed enhanced control model based on the federated algorithm, and configure the enhanced control model into the regional energy system. According to the storage and release amount of different types of energy, the regional energy system is used to control the energy storage devices in different regions to store and release different types of energy and schedule them.

[0077] Furthermore, the enhanced control model in this embodiment is constructed by the SAC algorithm;

[0078] Furthermore, the steps of storing, releasing and dispatching different types of energy in this embodiment include:

[0079] When the current time period When , all regional energy storage devices enter the energy storage mode. The specific steps of the energy storage process include:

[0080] S2011, obtaining the remaining capacity of the corresponding electricity and heat storage devices in each area in the current time period, the corresponding costs of electricity and heat storage per unit time, and the electricity-to-heat cost per unit time Q P→H or heat-to-electricity cost Q H→P ;

[0081] S2012, when satisfied When and Then directly and Stored in the corresponding regional electricity and heat storage devices, where and represents the remaining space of the electric storage device and the remaining space of the heat storage device corresponding to the mth area in the kth time period in sequence; represents the operating cost of the power storage device corresponding to the mth area in the kth time period, represents the operating cost of the heat storage device corresponding to the mth area in the kth time period;

[0082] S2013, if and When the current time period is reached, the electrical energy is converted into thermal energy and stored in the thermal storage device until the thermal storage device is full;

[0083] S2014: If there is power remaining and the mth area satisfies Then, through the heat network model, the dispatching cost of dispatching heat energy from the mth area to the mth area is obtained: like and Then the remaining thermal energy is dispatched to the m!th area for storage through the enhanced control model, otherwise the remaining electricity is stored in the power storage device corresponding to the m!th area; m! represents any area among the M areas except the m!th area;

[0084] S2015, if Then repeat S2012 to S2014 to judge the heat-to-electricity conversion, and perform the corresponding type of energy storage and dispatch storage. Further, the dispatch storage here is like the S2014 process, and the remaining heat energy is dispatched from the mth area to the mth area for storage through the heat network model and the enhanced control model.

[0085] S2016, when If satisfied Then Stored in the power storage device corresponding to the mth area and then dispatched through the heat network model, if the and Then the remaining heat energy is dispatched to the mth area for storage through the enhanced control model, otherwise it is abandoned;

[0086] S2017, when satisfied and When After the storage is completed, the storage device corresponding to the mth area satisfies Then, a heat-to-electricity conversion operation is performed, and the converted electric energy is stored in the electric storage device corresponding to the mth region;

[0087] S2018. When the power storage device corresponding to the mth region cannot meet the power required for the heat-to-electricity conversion process, the remaining converted power is stored in the power storage device corresponding to the mth region until all remaining power storage devices meet the requirements. The remaining heat is discarded;

[0088] S2019, when satisfied and When Then repeat S2017 and S2018 processes to convert heat into electricity for storage, otherwise repeat S2016 process to perform thermal energy scheduling and storage;

[0089] S2020, when satisfied and Then The energy is stored in the power storage device in the corresponding area, and the thermal energy scheduling is judged through S2016. If the energy is satisfied, and Then the remaining heat energy is dispatched to the mth area for storage through the enhanced control model, and the excess heat energy is abandoned. If it is not satisfied, when the state of the heat storage device in the mth area is When the residual heat energy is discarded,

[0090] S2021, when satisfied and When , S2020 is repeated to perform thermal energy scheduling and storage;

[0091] S2022, when When the storage and dispatch of electric energy and thermal energy are determined, the process of S2016-S2021 is repeated;

[0092] S2023, when When the storage and dispatching of electric energy and thermal energy are determined, the process of S2016-S2022 is repeated.

[0093] S2024, when the current time period or When , the discriminant model is used to obtain the energy deficiency area, type and amount of energy deficiency;

[0094] S2025: If at least one area in the current time period lacks power and all areas meet The power shortage in the corresponding area is compared with the remaining power in the power storage device in the corresponding area. If the remaining power in the power storage device is greater than the power shortage, the power dispatching cost is compared with the power storage device operating cost. If the power dispatching cost in the current time period is greater than the power storage device operating cost and the heat-to-electricity cost is greater than the power storage device operating cost, the power storage device is operated to discharge and supplement the missing power.

[0095] S2026. If the current time period electricity dispatch cost is greater than the storage device operating cost and the heat-to-electricity cost is less than or equal to the storage device operating cost, the corresponding area heat energy is converted into electricity to supplement the electricity shortage. If the corresponding electric energy after conversion is less than the missing electric energy, the electric storage device is operated to discharge and supplement;

[0096] S2027. When the current time period electric energy dispatching cost is less than the storage device operation cost and the heat-to-electricity cost is greater than the storage device operation cost, if there is an mth area corresponding to The built-in distributed enhanced control model is used to dispatch and supplement the power. When the sum of the regional dispatched electric energy is less than the amount of electric energy missing, the corresponding power storage device is operated to supplement the missing electric energy after the dispatch replenishment;

[0097] S2028. When the electric energy dispatching cost in the current time period is less than the operating cost of the power storage device and the heat-to-electricity cost is less than or equal to the operating cost of the power storage device, the electric energy dispatching cost is compared with the heat-to-electricity cost. If the electric energy dispatching cost is greater than the heat-to-electricity cost, the thermal energy in the corresponding area is converted into electric energy to supplement the power shortage. If the corresponding power shortage cannot be met after supplementation, the electric energy dispatching supplement is performed through the S2027 process until the missing power is met.

[0098] S2029: If the electricity dispatch cost is less than or equal to the heat-to-electricity cost, then the electricity dispatch is supplemented directly through the S2027 process. If the supplement cannot meet the corresponding electricity shortage, the excess heat in the same area is dispatched through the S2026 process. If the power shortage cannot be met by converting heat into electricity, the sum of the costs of heat-to-electricity conversion and electricity dispatching is compared with the operating cost of the energy storage device. If it is less than that, the missing electricity is supplemented by combining the heat-to-electricity conversion and electricity dispatching process. If it is greater than that, the energy storage device in the power-deficient area is operated to supplement the missing electricity after the heat-to-electricity conversion until the missing electricity is met.

[0099] S2030: If the remaining electric energy in the power storage device is less than or equal to the amount of power loss, repeat the heat-to-electricity and power scheduling process of S2026-S2029 to supplement the electric energy. If the sum of the electric energy supplemented by heat-to-electricity and power scheduling and the electric energy in the power storage devices of all regions in the current time period is less than the amount of power loss in the current time period, the supplemented power loss will be fed back to the regional energy system, a power shortage warning will be issued, and the regional energy system will be used to dispatch the power generation device for secondary power generation supplement;

[0100] S2031, if at least one area in the current time period stores missing heat energy and all areas meet Then repeat the process of S2024-S2030 to schedule and supplement the thermal energy;

[0101] S2032. If at least one area in the current time period lacks thermal energy and electrical energy, repeat the processes S2024-S2031 to perform dual scheduling and replenishment of electrical energy and thermal energy;

[0102] S2033: If the corresponding electric energy or thermal energy in any area still meets the requirements after scheduling and replenishment in the process of S2024-S2032 or If the conditions are met, the corresponding type of energy storage or discarding process in S2012-S2023 is used to store or discard the electrical energy or thermal energy in the corresponding area;

[0103] In the above process, in the energy storage mode, the system ensures that energy is stored at the lowest cost by monitoring the remaining capacity and related costs of the energy storage device in real time, thus avoiding unnecessary energy waste. When there is excess electricity, the system will decide whether to convert electricity into heat energy for storage or directly store electricity based on the status of the heat storage device. In the case of excess thermal energy, the system will also choose the most economical way to dispatch and store thermal energy based on cost-benefit analysis. In addition, in the energy dispatch stage, the distributed reinforcement learning control model is adopted, and the system can flexibly respond to changes in energy demand between different regions. By comparing the cost-effectiveness of electric energy dispatch and heat-to-electricity conversion, the system can select the energy supplement method with the lowest cost to ensure efficient use of energy. When there is an energy shortage, the system can quickly identify the areas and types that need energy supplementation, and by comparing the costs of different supplementary plans, select the most appropriate energy conversion or dispatching method to fill the gap. For example, when the cost of electric energy dispatch is higher than the operating cost of the energy storage device, the system will choose to run the energy storage device to discharge to supplement the missing electricity; otherwise, it will be supplemented by electric energy dispatch. In the case of thermal energy shortage, the system will also adopt a similar strategy to ensure timely replenishment of thermal energy. In summary, through the above mechanisms, not only the energy utilization efficiency is significantly improved and energy waste is reduced, but also the system's adaptive adjustment ability and the allocation of different types of energy storage are enhanced, which can adapt to more environmental conditions.

[0104] S4, set a comprehensive score threshold, obtain the storage-discharge and scheduling process parameters of each regional energy storage device and the energy supply satisfaction rate data of the same region per unit time in real time, and obtain the regional storage comprehensive evaluation score through the evaluation algorithm. When the regional storage comprehensive evaluation score is less than the comprehensive score threshold, the regional storage comprehensive evaluation score is fed back to the energy configuration model to optimize the energy storage-discharge and scheduling process of each region at the corresponding time period, otherwise no adjustment is made. Further, the evaluation algorithm in this embodiment is a comprehensive fuzzy evaluation algorithm.

[0105] Example 2

[0106] A computer-readable storage medium stores computer instructions, which, when executed, execute a regional energy system energy storage method based on flexible thermal network technology.

[0107] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements a regional energy system energy storage method based on flexible thermal network technology when executing the computer program.

[0108] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are within the protection of the present invention.

Claims

1. A regional energy system energy storage method based on flexible thermal network technology, characterized in that: include: S1. Obtain the historical working status data, energy demand data and environmental data of energy storage devices in different regions, and obtain the total power generation, energy demand and energy shortage in different time periods of each region through the configured prediction model with seasonal characteristics; S2. According to the total power generation, energy demand and energy shortage in each region at different time periods, the energy configuration model is used to obtain the storage and release of different types of energy in each region at the corresponding time period; S3. Construct a distributed enhanced control model based on the federated algorithm, and configure the enhanced control model into the regional energy system. According to the storage and release amount of different types of energy, the regional energy system is used to control the energy storage devices in different regions to store and release different types of energy and schedule them. S4. Set a comprehensive score threshold, obtain the storage-discharge and scheduling process parameters of each regional energy storage device and the energy supply satisfaction rate data of the same region per unit time in real time, and obtain the regional storage comprehensive evaluation score through the evaluation algorithm. When the regional storage comprehensive evaluation score is less than the comprehensive score threshold, the regional storage comprehensive evaluation score is fed back to the energy configuration model to optimize the energy storage-discharge and scheduling process of each region in the corresponding time period, otherwise no adjustment is made.

2. The method for storing energy in a regional energy system based on flexible heating network technology according to claim 1, characterized in that: The step of obtaining the energy shortage amount in each area at different time periods includes: S101, obtaining and preprocessing the electricity-heat energy consumption and temperature data of different regions for one year; S102, inputting the preprocessed data into a clustering algorithm, clustering the regions, and obtaining regions with the same energy demand data and temperature change trends; S103, aligning the electric energy consumption and the heat energy consumption corresponding to the same change trend area in the time dimension, and averaging the aligned electric energy consumption and the heat energy consumption respectively, to obtain the average electric energy consumption and the average heat energy consumption time series corresponding to the same change trend area; S104, obtaining a sequence of average electric energy consumption change rate and average thermal energy consumption change rate according to the time series of average electric energy consumption and average thermal energy consumption; S105, input the average electric energy consumption change rate and the average thermal energy consumption change rate sequence into the clustering algorithm, and obtain K time period length intervals with the same electric energy consumption and thermal energy consumption change rates and corresponding change rate values ​​in sequence.

3. The district energy system energy storage method based on flexible heating network technology according to claim 2, characterized in that: The step of obtaining the energy redundancy amount in each area at different time periods also includes: S106. Use the time period length intervals with the same change rate as the segmentation window to segment the corresponding regional electric energy consumption, heat energy consumption and temperature sequences in the time dimension, and use the corresponding change rate values ​​as seasonal factors and the segmented electric energy consumption and heat energy consumption sequences to form the segmented input sequence pairs (Ti, Xi, λi) and (Tj, Yj, γj), where Xi represents the i-th electric energy consumption segmentation sequence, Ti represents the temperature segmentation sequence corresponding to the i-th electric energy consumption segmentation sequence; λi represents the seasonal factor corresponding to the i-th electric energy consumption segmentation sequence; where Yj represents the j-th heat energy consumption segmentation sequence, Tj represents the temperature segmentation sequence corresponding to the j-th heat energy consumption segmentation sequence, and γj represents the seasonal factor corresponding to the j-th heat energy consumption segmentation sequence; S107, construct an integrated prediction model based on the integrated algorithm and the BiLSTM algorithm, input (Ti, Xi, λi) into BiLSTM1 in the prediction model, and input (Tj, Yj, γj) into BiLSTM2 in the prediction model for training, to obtain a trained prediction model; S108, obtaining the electricity-heat energy consumption and temperature of each region in the previous three days of the current time period, and predicting the electricity-heat demand of each region in the corresponding time period through the trained prediction model.

4. The method for storing energy in a regional energy system based on flexible heating network technology according to claim 3, characterized in that: The step of obtaining the energy shortage amount in each area at different time periods includes: S109, obtaining historical total power generation and total heat supply data under different environmental conditions in different regions, and using the obtained data to fine-tune the pre-trained integrated prediction model, and at the same time predicting the total power generation and total heat supply in the corresponding time period of S108; S110, based on the total power generation and total heat supply obtained and the forecast, the electricity-heat demand in each area in the corresponding time period is obtained, and the energy redundancy corresponding to the power supply and heat supply in different areas in the corresponding time period is obtained. and Indicates the amount of power shortage corresponding to the length of the kth time period in the mth area, It indicates the heat energy shortage corresponding to the length interval of the kth time period in the mth area.

5. The district energy system energy storage method based on flexible heating network technology according to claim 4, characterized in that: The steps of storing, releasing and dispatching different types of energy include: When the current time period When , all regional energy storage devices enter the energy storage mode, where M is the total number of regions. The specific steps of the energy storage process include: S2011, obtaining the remaining capacity of the corresponding electricity and heat storage devices in each area in the current time period, the corresponding costs of electricity and heat storage per unit time, and the electricity-to-heat cost per unit time Q P→H or heat-to-electricity cost Q H→P ; S2012, when satisfied When and Then directly and Stored in the corresponding regional electricity and heat storage devices, where and represents the remaining space of the electric storage device and the remaining space of the heat storage device corresponding to the length of the kth time period in the mth area in sequence; represents the operating cost of the power storage device corresponding to the mth area in the kth time period, represents the operating cost of the heat storage device corresponding to the mth area in the kth time period; S2013, if and When the current time period is reached, the electrical energy is converted into thermal energy and stored in the thermal storage device until the thermal storage device is full; S2014: If there is power remaining and the mth area satisfies Then, through the heat network model, the dispatching cost of dispatching heat energy from the mth area to the mth area is obtained: like and Then the remaining thermal energy is dispatched to the m!th area for storage through the enhanced control model, otherwise the remaining electricity is stored in the power storage device corresponding to the m!th area; m! represents any area among the M areas except the m!th area; S2015, if Then repeat S2012 to S2014 to perform heat-to-electricity conversion judgment, and perform corresponding type of energy storage or scheduling storage.

6. The method for storing energy in a regional energy system based on flexible heating network technology according to claim 5, characterized in that: The steps of storing, releasing and dispatching different types of energy also include: S2016, when If satisfied and Then Stored in the power storage device corresponding to the mth area and then dispatched through the heat network model, if the and Then the remaining heat energy is dispatched to the mth area for storage through the enhanced control model, otherwise it is abandoned; S2017, when satisfied and When After the storage is completed, the storage device corresponding to the mth area satisfies Then, a heat-to-electricity conversion operation is performed, and the converted electric energy is stored in the electric storage device corresponding to the mth region; S2018. When the power storage device corresponding to the mth region cannot meet the power required for the heat-to-electricity conversion process, the remaining converted power is stored in the power storage device corresponding to the mth region until all remaining power storage devices meet the requirements. The remaining heat is discarded; S2019, when satisfied and When Then repeat S2017 and S2018 processes to convert heat into electricity for storage, otherwise repeat S2016 process to perform thermal energy scheduling and storage; S2020, when satisfied Then The energy is stored in the power storage device in the corresponding area, and the thermal energy scheduling is judged through S2016. If the energy is satisfied, and Then the remaining heat energy is dispatched to the mth area for storage through the enhanced control model, and the excess heat energy is abandoned. If it is not satisfied, when the state of the heat storage device in the mth area is When the residual heat energy is discarded, S2021, when satisfied and When , S2020 is repeated to perform thermal energy scheduling and storage; S2022, when When the storage and dispatch of electric energy and thermal energy are determined, the process of S2016-S2021 is repeated; S2023, when When the storage and dispatching of electric energy and thermal energy are determined, the process of S2016-S2022 is repeated.

7. The method for storing energy in a regional energy system based on flexible heating network technology according to claim 6, characterized in that: The steps of storing, releasing and dispatching different types of energy also include: S2024, when the current time period or When , the discriminant model is used to obtain the energy deficiency area, type and amount of energy deficiency; S2025: If at least one area in the current time period lacks power and all areas meet The power shortage in the corresponding area is compared with the remaining power in the power storage device in the corresponding area. If the remaining power in the power storage device is greater than the power shortage, the power dispatching cost is compared with the power storage device operating cost. If the power dispatching cost in the current time period is greater than the power storage device operating cost and the heat-to-electricity cost is greater than the power storage device operating cost, the power storage device is operated to discharge and supplement the missing power. S2026. If the current time period electricity dispatch cost is greater than the storage device operating cost and the heat-to-electricity cost is less than or equal to the storage device operating cost, the corresponding area heat energy is converted into electricity to supplement the electricity shortage. If the corresponding electric energy after conversion is less than the missing electric energy, the electric storage device is operated to discharge and supplement; S2027. When the current time period electric energy dispatch cost is less than the storage device operation cost and the heat-to-electricity cost is greater than the storage device operation cost, if there is an mth area corresponding to The built-in distributed enhanced control model is used to dispatch and supplement the power. When the sum of the regional dispatched electric energy is less than the amount of electric energy missing, the corresponding power storage device is operated to supplement the missing electric energy after the dispatch replenishment; S2028. When the electric energy dispatching cost in the current time period is less than the operating cost of the power storage device and the heat-to-electricity cost is less than or equal to the operating cost of the power storage device, the electric energy dispatching cost is compared with the heat-to-electricity cost. If the electric energy dispatching cost is greater than the heat-to-electricity cost, the thermal energy in the corresponding area is converted into electric energy to supplement the power shortage. If the corresponding power shortage cannot be met after supplementation, the electric energy dispatching supplement is performed through the S2027 process until the missing power is met. S2029: If the electricity dispatch cost is less than or equal to the heat-to-electricity cost, then the electricity dispatch is supplemented directly through the S2027 process. If the supplement cannot meet the corresponding electricity shortage, the excess heat in the same area is dispatched through the S2026 process. If the power shortage cannot be met by converting heat into electricity, the sum of the costs of heat-to-electricity conversion and electricity dispatching is compared with the operating cost of the energy storage device. If it is less than that, the missing electricity is supplemented by combining the heat-to-electricity conversion and electricity dispatching process. If it is greater than that, the energy storage device in the power-deficient area is operated to supplement the missing electricity after the heat-to-electricity conversion until the missing electricity is met.

8. The method for storing energy in a regional energy system based on flexible heating network technology according to claim 7, characterized in that: The steps of storing, releasing and dispatching different types of energy also include: S2030: If the remaining electric energy in the power storage device is less than or equal to the amount of power loss, repeat the heat-to-electricity and power scheduling process of S2026-S2029 to supplement the electric energy. If the sum of the electric energy supplemented by heat-to-electricity and power scheduling and the electric energy in the power storage devices of all regions in the current time period is less than the amount of power loss in the current time period, the supplemented power loss will be fed back to the regional energy system, a power shortage warning will be issued, and the regional energy system will be used to dispatch the power generation device for secondary power generation supplement; S2031, if at least one area in the current time period stores missing heat energy and all areas meet Then repeat the process of S2024-S2030 to schedule and supplement the thermal energy; S2032. If at least one area in the current time period lacks thermal energy and electrical energy, repeat the processes S2024-S2031 to perform dual scheduling and replenishment of electrical energy and thermal energy; S2033: If the corresponding electric energy or thermal energy in any area still meets the requirements after scheduling and replenishment in the process of S2024-S2032 or If conditions are met, the corresponding type of energy storage or discarding process in S2012-S2023 is used to store or discard the electrical energy or thermal energy in the corresponding area.

9. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the regional energy system energy storage method based on flexible thermal network technology described in any one of claims 1 to 8 is executed.

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