Optimization methods for regional energy systems

Abstract

This application provides an optimized configuration method for a regional energy system, comprising the following steps: acquiring multi-dimensional basic data of the regional energy system; constructing a dynamically flexible hierarchical optimization model; performing flexible adjustments on a time scale, energy mutual assistance in spatial sub-regions, and dynamic weighting of multi-entity demand based on the multi-dimensional basic data; determining the collaborative optimization boundaries of energy storage and energy, and energy storage and load at different levels, as well as the overall collaborative optimization objectives of energy storage, energy, and load; introducing a capacity configuration sub-model, a real-time scheduling sub-model, and a game arbitration sub-model, combining a multi-entity collaborative game mechanism to resolve disagreements and dynamically adjust parameters to achieve Pareto optimality; integrating a dynamic mapping mechanism of multiple energy storage characteristics into the hierarchical optimization model; and outputting a collaborative optimized configuration scheme for energy storage, energy, and load in the regional energy system based on the collaborative results. This application achieves dynamic adaptability of the optimized configuration scheme for the regional energy system.

Description

Technical Field

[0001] This invention belongs to the field of energy optimization and allocation technology, and particularly relates to a method for optimizing and allocating regional energy systems. Background Technology

[0002] Currently, regional energy systems are evolving from single-energy supply to multi-energy complementarity and intelligent collaboration. At the technological level, digital twin technology has been implemented in scenarios such as photovoltaic power plants, increasing power generation and reducing operation and maintenance costs through real-time interaction between physical entities and virtual models. Simultaneously, multi-objective optimization algorithms and bi-level programming models are being applied in energy system configuration, gradually achieving a preliminary balance between economic efficiency and environmental protection. However, overall, regional energy systems are in a transitional phase from decentralized development to system-level integration, and from static planning to dynamic regulation.

[0003] In related energy configuration optimization technologies, the accuracy of multi-energy network coupling modeling is insufficient, and the dynamic changes in energy conversion efficiency are not fully considered, making it difficult to reflect the energy flow interaction patterns in actual operation. Furthermore, the current optimization algorithms for energy storage systems are mainly based on static planning, which cannot respond in real time to dynamic scenarios such as load fluctuations and energy price changes. In addition, the application of single digital twin technology is limited to single energy devices and has not achieved full-element virtual mapping and pre-simulation verification of regional multi-energy systems, thus affecting scheduling efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing the configuration of regional energy systems, aiming to solve the technical problems mentioned in the background section. To achieve the above objective, this application provides the following technical solutions.

[0005] According to one embodiment of this application, a method for optimizing the configuration of a regional energy system is provided, comprising the following steps:

[0006] Acquire multi-dimensional basic data of the regional energy system, including at least energy data, load data, and multiple types of energy storage data;

[0007] A dynamic and elastic hierarchical optimization model is constructed, comprising a time layer, a spatial layer, and a subject layer. Based on the aforementioned multi-dimensional basic data, dynamic and elastic hierarchical optimization is performed, and the collaborative optimization boundaries between energy storage and energy, and between energy storage and load, are determined layer by layer. The overall collaborative optimization objectives for energy storage, energy, and load are also defined. Dynamic and elastic hierarchical optimization includes elastic adjustment at the time scale, energy mutual assistance in spatial sub-regions, and dynamic weight adaptation of multi-subject demand. A capacity configuration sub-model, a real-time scheduling sub-model, and a game arbitration sub-model are introduced, combining a multi-subject collaborative game mechanism to resolve disagreements and dynamically adjust parameters to achieve Pareto optimality. A dynamic mapping mechanism for multiple energy storage characteristics is integrated into the hierarchical optimization model. By constructing a multi-energy storage characteristic coupling matrix based on the entire lifecycle data of energy storage, the optimization process is strongly coupled with the actual operating rules and lifecycle of multiple types of energy storage.

[0008] Based on the collaborative results of dynamic elastic hierarchical optimization, multi-entity collaborative game, and multi-energy storage characteristic mapping, the output is a collaborative optimization configuration scheme for energy storage and energy, and energy storage and load in the regional energy system.

[0009] Furthermore, the step of flexibly adjusting the time scale includes:

[0010] Based on time-series data of energy and time-series data of load, the characteristics of energy output fluctuation and load peak-valley variation are extracted.

[0011] The triggering factors for time-scale adjustment are determined, including the energy output fluctuation range, the load peak-to-valley difference rate, and the matching degree, where the matching degree is the time-series matching degree between energy and load.

[0012] Among them, triggering factors Represented as:

[0013]

[0014] In the formula, , , Represents the weighting coefficient, and ; This represents the absolute value of the energy output fluctuation at time t; This indicates the average daily energy output. , These represent the peak and trough values ​​of the load at time t, respectively. This represents the average load at time t; This indicates the time-series matching degree between energy output and load demand at time t;

[0015] When any triggering factor does not match the corresponding preset threshold range, the optimization cycle of the time layer in the hierarchical optimization model is flexibly adjusted based on the triggering factor, and the adjusted time layer optimization cycle is used as the time layer constraint of the dynamic elastic hierarchical optimization.

[0016] Furthermore, the energy exchange steps of the aforementioned spatial sub-regions include:

[0017] Based on the sub-region load demand data, sub-region energy distribution data, and sub-region energy storage operation data in the multi-dimensional basic data, the energy supply and demand characteristics, energy output margin, and energy storage capacity margin of each sub-region are extracted.

[0018] Based on the complementarity of the energy supply and demand characteristics, the region is divided into at least one energy mutual aid unit, and each mutual aid unit contains at least two sub-regions with supply and demand mismatch.

[0019] Based on the energy mutual assistance trigger threshold, energy mutual assistance between sub-regions is initiated. The energy mutual assistance trigger threshold includes the sub-region energy surplus threshold, the sub-region energy shortage threshold, and the sub-region energy transmission loss threshold.

[0020] The division results of energy mutual assistance units, mutual assistance trigger thresholds, and mutual assistance scheduling strategies are used as spatial layer constraints for the dynamic elastic hierarchical optimization.

[0021] Furthermore, the step of dynamic weight adaptation for multi-subject requirements includes:

[0022] Based on the multi-dimensional basic data, demand characteristics of multiple entities are extracted. These entities include grid operators, energy storage operators, and users. The demand characteristics correspond to grid peak-shaving demand characteristics, energy storage lifetime benefit demand characteristics, and user energy cost reliability demand characteristics, respectively.

[0023] The triggering conditions for multi-subject weight adjustment are determined, including: the real-time peak-shaving gap of the power grid exceeds the preset peak-shaving threshold, the actual lifespan decay rate of energy storage exceeds the preset decay threshold, and the user's energy cost exceeds the preset cost threshold; when any triggering condition is met, the urgency of demand is calculated based on the demand characteristics of each subject, and the weight allocation scheme for multi-subject collaborative optimization is dynamically updated according to the urgency of demand of each subject.

[0024] The updated multi-subject weight allocation scheme is used as the subject layer constraint for the dynamic elastic hierarchical optimization.

[0025] Furthermore, the hierarchical determination of the synergistic optimization boundaries between energy storage and energy, and between energy storage and load, as well as the overall synergistic optimization objective of energy storage, energy, and load, includes the following steps:

[0026] Based on the aforementioned multi-dimensional basic data, features are extracted according to the time layer, spatial layer, and subject layer, respectively.

[0027] In the synergistic optimization boundary of energy storage and energy, the time layer boundary includes the energy output fluctuation curve to construct the energy storage fluctuation smoothing boundary; the spatial layer boundary includes the energy storage and energy spatial matching boundary based on the energy installed capacity density of the sub-region; and the main layer boundary includes the energy storage and energy peak shaving synergy boundary based on the grid peak shaving demand threshold.

[0028] In the collaborative optimization boundary between energy storage and load, the time-layer boundary includes the peak-valley filling boundary of energy storage for load based on the peak-valley time-series distribution of load; the spatial-layer boundary includes the priority guarantee boundary of energy storage and load based on the load priority of sub-regions; and the main-layer boundary includes the reliability boundary of energy storage and load based on the user's energy reliability requirements.

[0029] In the collaborative optimization objective, the time-layer sub-objectives combine time-layer characteristics to construct time-dimensional objectives including energy absorption rate and energy storage charge-discharge cycle count; the spatial-layer sub-objectives combine spatial-layer characteristics to construct spatial-dimensional objectives including high-priority sub-area load satisfaction rate and inter-sub-area energy transmission loss; the subject-layer sub-objectives combine subject-layer characteristics to construct subject-dimensional objectives including grid peak-shaving response compliance rate and user energy cost; and the sub-objectives are integrated into the overall objective through weighted summation, with the weights dynamically adjusted based on the urgency of each subject's needs.

[0030] The two sets of collaborative optimization boundaries and the overall collaborative optimization objective determined by the hierarchical layer are used as the constraint benchmarks for dynamic elastic hierarchical optimization.

[0031] Furthermore, in the capacity configuration sub-model, based on multi-dimensional basic data and time-layer boundary data of dynamic elastic hierarchical optimization output, the input data of the capacity configuration sub-model are extracted, including energy-side data, load-side data, energy storage-side data and constraint boundary data.

[0032] With the dual optimization objectives of minimizing the total cost of energy storage throughout its entire life cycle and maximizing the annual energy consumption rate of the region, and taking the energy storage demand gap as the initial value, the optimal configuration capacity is calculated iteratively. The optimal total capacity of the region and the capacity allocation results of sub-regions are then fed back to the multi-entity collaborative game process and the dynamic elastic hierarchical optimization model.

[0033] Furthermore, in the real-time scheduling sub-model, based on multi-dimensional basic data and spatiotemporal boundary data of dynamic elastic hierarchical optimization output, the input data of the real-time scheduling sub-model are extracted, including the daily real-time output and fluctuation characteristics of the energy side, the daily real-time load and priority list of the load side, the real-time operating status and daily loss rate of the energy storage side, and the time layer response step size and spatial layer mutual assistance threshold of the constraint boundary side.

[0034] With the dual optimization objectives of maximizing intraday load satisfaction and minimizing intraday energy storage lifespan loss, and using the initial charging and discharging strategy generated based on load peaks and valleys and energy fluctuations as the initial value, the optimal real-time scheduling strategy is iteratively calculated. The optimal scheduling strategy and intraday energy storage operation data are then fed back to the multi-entity collaborative game process and dynamic elastic hierarchical optimization model.

[0035] Furthermore, in the game arbitration sub-model, based on multi-dimensional basic data and the results of multi-party collaborative game, the input data of the game arbitration sub-model is extracted, including dual-model result data, subject demand data and judgment threshold data;

[0036] With the goal of resolving the optimization differences between the two models and achieving multi-agent Pareto optimality, arbitration and optimization are carried out.

[0037] In the disagreement determination, the deviation between the initial capacity and the minimum required capacity is calculated, and arbitration is initiated when the deviation exceeds the preset disagreement threshold;

[0038] In weight allocation, the weights of the capacity model and scheduling model are dynamically allocated based on the urgency of the main demand.

[0039] In the arbitration calculation, the results of the two models are fused according to their weights, and the output of the arbitration-post-arbitration capacity and scheduling strategy is represented as follows:

[0040]

[0041] In the formula, This represents the optimal energy storage capacity output after arbitration. This represents the weighting coefficients of the capacity configuration sub-model. This represents the weight coefficient of the real-time scheduling sub-model. This represents the initial optimal capacity output by the capacity configuration sub-model. This represents the minimum demand capacity output by the real-time scheduling sub-model; Indicates the penalty coefficient for disagreement; This represents the absolute deviation between the results of the capacity configuration sub-model and the real-time scheduling sub-model.

[0042] The arbitration results are fed back to the multi-party collaborative game process and dynamic elastic hierarchical optimization model to correct the model parameters and optimization boundaries.

[0043] Furthermore, a dynamic mapping mechanism for multiple energy storage characteristics is incorporated into the hierarchical optimization model. By constructing a coupling matrix of multiple energy storage characteristics based on the entire life cycle data of energy storage, the optimization process is strongly coupled with the actual operating rules and life cycle of multiple types of energy storage. This includes the following steps:

[0044] Based on the spatiotemporal subject boundary data output by multi-dimensional basic data and hierarchical optimization model, extract multi-energy storage full life cycle data and architecture boundary data;

[0045] A coupling matrix is ​​constructed based on three dimensions: energy storage type, operating parameters, and lifespan stage.

[0046] Based on the coupling matrix, the fit between different energy storage types and each hierarchical optimization objective is calculated, and the matching relationship between energy storage types and hierarchical objectives is determined, expressed as:

[0047]

[0048] In the formula, This represents the fit between the i-th type of energy storage and the optimization objective of the j-th layer. ; The weighting coefficients represent the operating parameters of energy storage. Indicates the energy storage life stage coefficient. The weighting coefficients representing energy storage cost parameters. This represents the overall charging and discharging efficiency of the i-th type of energy storage. This indicates the highest discharge efficiency of all energy storage types within the region. This represents the rated cycle life of the i-th type of energy storage. This represents the average cycle life of all energy storage types within the region. This represents the total lifecycle cost per unit capacity of the i-th type of energy storage;

[0049] The coupling matrix is ​​used as the constraint basis for the three-dimensional architecture optimization. In the time layer, the scheduling cycle is adapted according to the energy storage life stage in the matrix; in the spatial layer, the mutual power of sub-regions is matched according to the energy storage power adjustment range in the matrix; and in the subject layer, the weight of multiple subjects is optimized according to the energy storage cost parameters in the matrix.

[0050] The coupling matrix adaptation results are fed back to the hierarchical optimization model to correct the spatiotemporal main boundary data; at the same time, the multi-energy storage full life cycle data are updated to dynamically adjust the coupling matrix benchmark value.

[0051] Compared with the prior art, the advantages of the regional energy system optimization configuration method of the present application embodiments are as follows:

[0052] This application integrates three-dimensional data of energy, load and multiple types of energy storage, and incorporates the full life cycle characteristics of energy storage into the basic data to form multi-dimensional data. The multi-dimensional data is directly related to the optimization of the time layer, spatial layer and subject layer. For example, energy data supports flexible adjustment of time scale, load data supports mutual assistance of spatial sub-regions, and energy storage data supports characteristic mapping. This solves the technical problem that the optimization results of existing technologies do not match the actual operation requirements of energy storage due to the one-sided data foundation.

[0053] This application achieves multi-dimensional collaborative optimization in dynamic scenarios through three-dimensional coupling of time, space, and subject. The time layer adjusts the charging and discharging window to cope with intraday energy fluctuations, the space layer balances uneven energy distribution through sub-region mutual assistance, and the subject layer dynamically adapts weights to take into account grid peak shaving, energy storage revenue, and user costs, thus adapting to the dynamic changes of the regional energy system.

[0054] The boundary defined by the layering in this application is used to associate dynamic data of the three-dimensional optimization architecture, so that the boundary can be dynamically adjusted, making the collaborative boundary conform to the actual operation requirements and avoiding the technical problem of static boundary restricting the optimization flexibility; and in multi-objective fusion, the collaborative unity of multiple objectives can be achieved, avoiding system imbalance caused by a single objective.

[0055] The game arbitration sub-model of this application achieves the connection between long-term capacity planning and short-term real-time scheduling by quantifying disagreements and dynamically allocating weights, avoiding the optimization gap caused by model isolation in the prior art; while the multi-subject collaborative game mechanism can make the optimization result meet the requirements of the balance of interests of multiple subjects; the multi-energy storage characteristic coupling matrix of this application strongly couples the energy storage life cycle data with the optimization process, thereby improving the energy storage operation efficiency. Attached Figure Description

[0056] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0057] In the attached diagram:

[0058] Figure 1 A flowchart illustrating the implementation of the regional energy system optimization configuration method provided in this application embodiment;

[0059] Figure 2 A structural block diagram of the optimized configuration device for a regional energy system provided in the embodiments of this application;

[0060] Figure 3 This is a hardware structure block diagram of a computer device for an optimized configuration method of a regional energy system according to an embodiment of this application. Detailed Implementation

[0061] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0062] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0063] like Figure 1As shown, according to one embodiment of this application, an optimized configuration method for a regional energy system is provided;

[0064] Includes the following steps:

[0065] Step S101: Obtain multi-dimensional basic data of the regional energy system, wherein the multi-dimensional basic data includes at least energy data, load data and multi-type energy storage data;

[0066] In step S101 of this implementation, the multi-dimensional basic data of this embodiment includes three dimensions: energy supply, energy demand, and energy storage, which correspond to energy, load, and energy storage, respectively. The basic data includes a comprehensive dataset of historical real-time data, future forecasts, and characteristic parameters. These data are correlated and complete.

[0067] Energy data includes historical real-time power output data, future power output forecast data, and geographical location and installed capacity data of energy plants. Energy data is used to reflect the characteristics of large temporal fluctuations and uneven spatial distribution of energy.

[0068] The load data in this embodiment includes real-time electricity consumption data of different energy users, load priority data, and future load growth forecast data. The load data is used to reflect the characteristics of large differences in the spatiotemporal demand and different priorities of energy users. In the load priority data, for example, according to the priority of the energy user, the allowable interruption time for hospital load is ≤5 seconds, and for ordinary residents it is ≤15 minutes.

[0069] Furthermore, the multi-type energy storage data in this embodiment includes the rated capacity, charge and discharge efficiency, total life cycle cost, and lifespan characteristics of energy storage, which are used to reflect the characteristics of different energy storage technologies with large differences in characteristics and lifespan significantly affected by operating conditions; the multi-type energy storage data is data collected for different energy storage devices to adapt to different scenarios.

[0070] Please continue to refer to Figure 1 The optimized configuration method for the regional energy system in this embodiment further includes the following steps:

[0071] Step S102: Construct a dynamic and elastic hierarchical optimization model, which includes a time layer, a spatial layer, and a subject layer;

[0072] The time layer provided in this embodiment is used to represent the temporal characteristics of the regional energy system, such as the temporal issues of energy output fluctuations and load peak-valley changes. In the time layer, the optimization cycle is dynamically adjusted to achieve temporal coordination of energy storage, energy, and load. The spatial layer is used to handle the energy supply and demand of sub-regions within the energy region. By dynamically dividing mutual assistance units and adjusting mutual assistance measurements, energy complementarity in sub-regions is achieved, and load is guaranteed through high priority. The subject layer of this embodiment includes energy operation entities, energy storage operation entities, and user entities. The subject layer is used to handle demand conflicts among multiple entities, such as grid peak-shaving demand, energy storage lifetime benefit demand, and user energy cost reliability demand. The subject layer achieves Pareto optimality for multiple entities through dynamic weight adaptation and game arbitration. Pareto optimality means that through the multi-entity collaborative game mechanism decision model divergence and dynamic parameter adjustment, an optimal state is ultimately achieved where no entity's interests deteriorate and at least one entity's interests improve, thus ensuring the balance of interests among multiple entities.

[0073] Therefore, in this embodiment, the time layer is used to handle temporal fluctuations, the spatial layer is used to handle spatial differences, and the subject layer is used to handle conflicts of interest among various energy users, such as the contradictions between the power grid, energy storage providers, and user demands.

[0074] For further details, please refer to [link / reference]. Figure 1 In this embodiment, step S102 further includes the following steps:

[0075] Based on the aforementioned multi-dimensional basic data, dynamic and elastic hierarchical optimization is performed, and the collaborative optimization boundaries of energy storage and energy, and energy storage and load are determined layer by layer, as well as the overall collaborative optimization objectives of energy storage, energy and load. Dynamic and elastic hierarchical optimization includes elastic adjustment of time scale, energy mutual assistance in spatial sub-regions, and dynamic weight adaptation of multi-entity demand.

[0076] Furthermore, in step S102 of this embodiment, in the step of making flexible adjustments to the time scale based on the multi-dimensional basic data, the cycle is adjusted and optimized according to energy fluctuations and load peak-valley changes, such as shortening the cycle when energy fluctuations are large.

[0077] Specifically, the implementation of flexible adjustment of the time scale in this embodiment includes the following steps:

[0078] Based on time-series data of energy and time-series data of load, the characteristics of energy output fluctuation and load peak-valley variation are extracted.

[0079] In extracting the characteristics of energy output fluctuations, the fluctuation amplitude and duration at different time scales are calculated. For example, the fluctuation amplitude can be the percentage difference between the maximum and minimum daily output or the annual fluctuation difference; the fluctuation duration can be the duration of daily fluctuations exceeding 20%.

[0080] In extracting the characteristics of load peak and valley changes, the peak and valley difference rate is calculated by dividing the intraday and seasonal peak and valley periods. For example, when dividing the intraday peak and valley periods, the ratio of the intraday maximum load to the minimum load is calculated. This ratio is used as the peak and valley difference rate to measure the difference between load peaks and valleys. The larger the difference rate, the more obvious the load peaks and valleys are, and the more necessary it is to adjust the energy storage discharge window.

[0081] The implementation of flexible adjustment of the time scale in this embodiment also includes the following steps:

[0082] The triggering factors for time-scale adjustment are determined, and the triggering factors include at least the energy output fluctuation range, the load peak-valley difference rate, and the matching degree, where the matching degree is the time-series matching degree between energy and load.

[0083] Among them, triggering factors Represented as:

[0084]

[0085] In the formula, , , Represents the weighting coefficient, and ; This represents the absolute value of the energy output fluctuation at time t; This indicates the average daily energy output. , These represent the peak and trough values ​​of the load at time t, respectively. This represents the average load at time t; This indicates the time-series matching degree between energy output and load demand at time t;

[0086] Energy output fluctuation range is used to characterize the ratio of the maximum range of change in energy output within a specific time period (annual or daily) to a benchmark value. It is used to determine the stability of energy output. If the fluctuation range is larger, it is necessary to enhance the ability of energy storage to mitigate fluctuations through time scale adjustments. The threshold for energy output fluctuation range is determined by the energy consumption target.

[0087] In this embodiment of the application, the load peak-valley difference rate is used to characterize the ratio of the maximum demand to the minimum demand of the load within a specific time period, and is used to determine the degree of imbalance between load supply and demand; if the peak-valley difference rate is larger, the energy storage charging and discharging window needs to be adjusted and optimized through time scale to fill the peak-valley gap.

[0088] In this application embodiment, the matching degree is used to characterize the degree of overlap between the energy output time series curve and the load demand time series curve, and to determine the synergy between energy output and load demand. If the matching degree is lower, such as when the nighttime energy peak coincides with the residential load trough, the scheduling cycle needs to be adjusted to store energy through energy storage devices in order to improve real-time coordination efficiency.

[0089] Furthermore, this embodiment adopts a differentiated adjustment strategy for anomalies of different triggering factors. Specifically, in the flexible adjustment of the time scale, when any triggering factor does not match the corresponding preset threshold range, the optimization period of the time layer in the hierarchical optimization model is flexibly adjusted based on the triggering factor. This adjustment process specifically includes:

[0090] If the annual energy output fluctuation exceeds the preset fluctuation threshold, the energy storage capacity reservation ratio within the long-term optimization cycle will be adjusted based on historical fluctuation trend data.

[0091] If the daily load peak-valley difference rate exceeds the preset fluctuation threshold, the duration of the energy storage charging and discharging window within the medium-term optimization cycle will be adjusted.

[0092] If the timing match between energy and load is lower than the preset matching threshold, shorten the scheduling response step size of the short-term optimization cycle.

[0093] Furthermore, the implementation of flexible adjustment of the time scale in this embodiment also includes the following steps:

[0094] The adjusted time-layer optimization period is used as the time-layer constraint for the dynamic elastic hierarchical optimization and fed back to the capacity configuration sub-model and the real-time scheduling sub-model.

[0095] In one implementation of this application, the energy mutual assistance of spatial sub-regions divides the region into mutual assistance units to realize mutual assistance in energy and energy storage between sub-regions. For example, when there is an energy surplus in region A, the excess energy will be transferred to region B.

[0096] Specifically, the energy mutual assistance steps for the spatial sub-regions in this embodiment include:

[0097] Based on the sub-region load demand data, sub-region energy distribution data, and sub-region energy storage operation data from the aforementioned multi-dimensional basic data, the energy supply and demand characteristics, energy output margin, and energy storage capacity margin of each sub-region are extracted. Among them, the energy supply and demand characteristics include the total energy output and total load demand of each sub-region; the energy output margin includes the portion of the sub-region's energy output that exceeds its own load demand, and a negative margin indicates a lack of energy; the energy storage capacity margin represents the remaining capacity of the sub-region's energy storage that can currently be output externally.

[0098] Based on the complementarity of the energy supply and demand characteristics, the region is divided into at least one energy mutual aid unit, and each mutual aid unit contains at least two sub-regions with supply and demand mismatch.

[0099] This embodiment sets an energy mutual assistance trigger threshold, and based on the energy mutual assistance trigger threshold, it initiates energy mutual assistance between sub-regions. The energy mutual assistance trigger threshold includes a sub-region energy surplus threshold, a sub-region energy shortage threshold, and a sub-region energy transmission loss threshold. In this embodiment, the transmission loss threshold represents the maximum allowable proportion of energy loss during the transmission between sub-regions. If the benefit of mutual assistance is less than the cost, mutual assistance will not be initiated.

[0100] Specifically, when the energy surplus of any sub-region exceeds the energy surplus threshold, and the energy shortage of a sub-region within the corresponding mutual aid unit exceeds the energy shortage threshold, and the energy transmission loss between sub-regions is lower than the transmission loss threshold, the energy mutual aid between sub-regions is initiated.

[0101] The division results of energy mutual assistance units, mutual assistance trigger thresholds, and mutual assistance scheduling strategies are used as spatial layer constraints for the dynamic elastic hierarchical optimization and fed back to the real-time scheduling sub-model of spatial layer optimization.

[0102] In one implementation of this embodiment, the step of dynamic weight adaptation of multi-subject requirements includes:

[0103] Based on the multi-dimensional basic data, demand characteristics of multiple entities are extracted. These multiple entities include at least grid operators, energy storage operators, and users. The demand characteristics correspond to grid peak-shaving demand characteristics, energy storage lifetime revenue demand characteristics, and user energy cost reliability demand characteristics, respectively.

[0104] Determine the triggering conditions for multi-subject weight adjustment, and the triggering conditions include at least: the real-time peak-shaving gap of the power grid exceeds the preset peak-shaving threshold, the actual lifespan decay rate of energy storage exceeds the preset decay threshold, and the user's energy cost exceeds the preset cost threshold.

[0105] When any triggering condition is met, the urgency of the demand is calculated based on the demand characteristics of each subject. The urgency of the demand is used to characterize the urgency of the current demand of each subject. The calculation logic is at least related to the deviation between the subject demand and the corresponding threshold, and the weight of the impact of the subject demand on the overall operation of the system.

[0106] Based on the urgency of each entity's needs, the weight allocation scheme for multi-entity collaborative optimization is dynamically updated. The weight allocation scheme is used to represent the importance ratio of each entity in the optimization process. The greater the weight, the more priority is given to meeting the needs of entities with greater weight in the optimization process.

[0107] The updated multi-subject weight allocation scheme is used as the subject layer constraint for the dynamic elastic hierarchical optimization and fed back to the game arbitration sub-model.

[0108] In one implementation of this embodiment, the step of determining the synergistic optimization boundary of energy storage, energy, and load, and the overall synergistic optimization objective of energy storage, energy, and load, includes the following steps:

[0109] Based on the aforementioned multi-dimensional basic data, features are extracted in three layers: time layer, spatial layer, and subject layer. Among them, the time layer features include energy fluctuation curves and load peak-valley time-series distribution; the spatial layer features include sub-region energy installed capacity density and sub-region load priority; and the subject layer features include grid peak-shaving demand thresholds and user energy reliability requirements.

[0110] In the synergistic optimization boundary between energy storage and energy, the time-layer boundary includes setting the energy storage smoothing range based on the energy output fluctuation curve, and constructing the energy storage fluctuation smoothing boundary for energy; the spatial-layer boundary includes setting the energy storage configuration density based on the energy installed capacity density of the sub-region, and constructing the spatial matching boundary between energy storage and energy; the main-layer boundary includes setting the energy storage response speed based on the grid peak-shaving demand threshold, and constructing the peak-shaving synergistic boundary between energy storage and energy.

[0111] In the collaborative optimization boundary between energy storage and load, the time-layer boundary includes setting the energy storage discharge range based on the peak-valley time-series distribution of load, and constructing the peak-valley filling boundary of energy storage for load; the spatial-layer boundary includes setting the energy storage configuration ratio based on the sub-region load priority, and constructing the priority guarantee boundary between energy storage and load; the main-layer boundary includes setting the energy storage guarantee duration based on the user's energy reliability requirements, and constructing the reliability boundary between energy storage and load.

[0112] The overall synergistic optimization objectives for energy storage, energy, and load are determined in a tiered manner, including: in the time-layer sub-objectives, time-dimensional objectives such as energy absorption rate and energy storage charge-discharge cycle count are constructed based on time-layer characteristics; in the spatial-layer sub-objectives, spatial-dimensional objectives such as high-priority sub-area load satisfaction rate and inter-sub-area energy transmission loss are constructed based on spatial-layer characteristics; and in the main body-layer sub-objectives, main body-dimensional objectives such as grid peak-shaving response compliance rate and user energy cost are constructed based on main body-layer characteristics.

[0113] Furthermore, the sub-objectives are integrated into an overall objective through weighted summation, with the weights dynamically adjusted based on the urgency of each subject's needs. The two sets of collaborative optimization boundaries and the overall collaborative optimization objective determined by the hierarchical structure are used as the constraint benchmarks for dynamic and flexible hierarchical optimization, and are fed back to the time-scale flexible adjustment, spatial sub-region energy mutual assistance, and multi-subject weight adaptation process.

[0114] For further details, please refer to [link / reference]. Figure 1 Step S102 in this embodiment further includes:

[0115] We introduce a capacity configuration sub-model, a real-time scheduling sub-model, and a game arbitration sub-model, and combine a multi-agent collaborative game mechanism to resolve disagreements and dynamically adjust parameters to achieve Pareto optimality.

[0116] Specifically, this application's embodiments introduce a capacity configuration sub-model, a real-time scheduling sub-model, and a game arbitration sub-model, and combine a multi-agent collaborative game mechanism to resolve disagreements and dynamically adjust parameters to achieve Pareto optimality; the game arbitration sub-model is used to resolve disagreements between the capacity configuration sub-model and the real-time scheduling sub-model, and Pareto optimality is achieved through multi-agent game theory.

[0117] In one implementation of this embodiment, in the capacity configuration sub-model, the input data of the capacity configuration sub-model is extracted based on multi-dimensional basic data and time-layer boundary data of dynamic elastic hierarchical optimization output. The extracted input data includes energy-side data, load-side data, energy storage-side data and constraint boundary data.

[0118] Energy-side data includes average annual energy output and maximum surplus over a future time period;

[0119] Load-side data includes average annual load growth and maximum load forecasts for future time periods;

[0120] Energy storage data includes the total lifecycle cost of energy storage and its charge / discharge efficiency;

[0121] Constraint boundary data include time-level adjustment and smoothing boundaries and energy consumption rate targets;

[0122] With the dual optimization objectives of minimizing the total cost of energy storage throughout its entire life cycle and maximizing the annual energy consumption rate of the region, and taking the energy storage demand gap as the initial value, the optimal configuration capacity is iteratively calculated. The optimal total capacity of the region and the capacity allocation results of sub-regions are fed back to the multi-subject collaborative game process and the dynamic elastic hierarchical optimization model to form an optimization closed loop.

[0123] In this embodiment, the total lifecycle cost P of energy storage represents all costs throughout the entire lifecycle of energy storage, from purchase and installation to decommissioning and recycling, and is expressed as: In the formula, Indicates the initial investment cost. Indicates operation and maintenance costs. Indicates the length of time since retirement. Represents residual value;

[0124] Regional annual energy consumption rate , represented as:

[0125]

[0126] In the formula, E represents the total theoretical annual energy output of the region; This indicates the amount of energy within the region that was not consumed throughout the year;

[0127] The energy storage gap in this embodiment includes the load growth gap and the energy consumption gap;

[0128] During the iterative calculation of the optimal capacity configuration, the capacity is gradually adjusted to find the capacity that simultaneously satisfies the lowest cost and a coverage rate of more than 95%; in the sub-area capacity allocation, the total capacity is allocated based on the sub-area load ratio and priority.

[0129] Furthermore, in one implementation of this application, the real-time scheduling sub-model of this embodiment is used to determine short-term energy storage operation strategies;

[0130] In the real-time scheduling sub-model, the input data of the real-time scheduling sub-model is extracted based on multi-dimensional basic data and spatiotemporal boundary data of dynamic elastic hierarchical optimization output.

[0131] The extracted input data includes: real-time daily output and fluctuation characteristics of the energy side, real-time daily load and priority list of the load side, real-time operating status and daily loss rate of the energy storage side, time-layer response step size and spatial-layer mutual assistance threshold of the constraint boundary side; the real-time operating status of the energy storage side includes remaining capacity SOC and health status SOH.

[0132] With the dual optimization objectives of maximizing intraday load satisfaction and minimizing intraday energy storage lifespan loss, and taking the initial charging and discharging strategy generated based on load peaks and valleys and energy fluctuations as the initial value, the optimal real-time scheduling strategy is iteratively calculated. The optimal scheduling strategy and intraday energy storage operation data are then fed back to the multi-subject collaborative game process and dynamic elastic hierarchical optimization model to form an optimization closed loop.

[0133] In this embodiment, the intraday lifetime loss is the product of the number of intraday charge-discharge cycles and the loss rate per cycle. The optimization target for minimizing the intraday lifetime loss of energy storage is less than 1.5%. The optimization target for maximizing the intraday load satisfaction rate is greater than 99%, and the satisfaction rate is the ratio of the actual intraday energy supply to the intraday load demand.

[0134] The initial charging and discharging strategy is generated based on load peaks and valleys and energy fluctuations. During iterative calculations, the strategy is adjusted according to the time-level response step size and the calculations are repeated until the optimal strategy that simultaneously satisfies the dual optimization objectives is found.

[0135] Furthermore, in one implementation of this application, in the game arbitration sub-model, input data is extracted based on multi-dimensional basic data and multi-subject collaborative game results, including dual-model result data, subject demand data and judgment threshold data;

[0136] The dual-model result data includes the initial capacity of the capacity configuration sub-model and the minimum required capacity of the real-time scheduling sub-model; the subject requirement data includes the urgency of multiple subject requirements and the target threshold of each subject; the judgment threshold data includes the divergence threshold and the weight allocation threshold, wherein the divergence threshold is the maximum allowable deviation between the capacity model result and the scheduling model result, and arbitration is initiated when the deviation between the capacity and the scheduling requirement exceeds 5%.

[0137] With the goal of resolving the optimization differences between the two models and achieving multi-agent Pareto optimality, arbitration and optimization are carried out.

[0138] In the disagreement determination process, the deviation between the initial capacity and the minimum required capacity is calculated. Arbitration is initiated when the deviation exceeds a preset disagreement threshold. Represented as: , Indicates scheduling requirements, Indicates the initial capacity;

[0139] In weight allocation, the weights of the capacity model and scheduling model are dynamically allocated based on the urgency of the main demand.

[0140] In the arbitration calculation, the results of the two models are fused according to their weights, and the output of the arbitration-post-arbitration capacity and scheduling strategy is represented as follows:

[0141]

[0142] In the formula, This represents the optimal energy storage capacity output after arbitration. This represents the weighting coefficients of the capacity configuration sub-model. This represents the weight coefficient of the real-time scheduling sub-model. This represents the initial optimal capacity output by the capacity configuration sub-model. This represents the minimum demand capacity output by the real-time scheduling sub-model; Indicates the penalty coefficient for disagreement; This represents the absolute deviation between the results of the capacity configuration sub-model and the real-time scheduling sub-model.

[0143] The arbitration results are fed back to the multi-party collaborative game process and dynamic elastic hierarchical optimization model to correct the model parameters and optimization boundaries.

[0144] For further details, please refer to [link / reference]. Figure 1 Step S102 in this embodiment further includes:

[0145] In the hierarchical optimization model, a dynamic mapping mechanism for multiple energy storage characteristics is incorporated. By constructing a coupling matrix of multiple energy storage characteristics based on the entire life cycle data of energy storage, the optimization process is strongly coupled with the actual operating rules and life cycle of multiple types of energy storage.

[0146] In this embodiment, the multi-energy storage characteristic coupling matrix is ​​used to guide the optimization process and adapt energy storage characteristics by associating datasets of energy storage type, operating parameters and life stage.

[0147] In one implementation of this embodiment, a multi-energy storage characteristic dynamic mapping mechanism is incorporated into the hierarchical optimization model. This involves constructing a multi-energy storage characteristic coupling matrix based on the entire lifecycle data of energy storage, thereby strongly coupling the optimization process with the actual operating patterns and lifecycles of various types of energy storage. The steps include:

[0148] Based on the spatiotemporal subject boundary data output by multi-dimensional basic data and hierarchical optimization model, multi-energy storage full life cycle data and architecture boundary data are extracted. Among them, multi-energy storage full life cycle data includes energy storage type, operating parameters and life stage; architecture boundary data includes time layer scheduling cycle, spatial layer mutual power and subject layer cost threshold.

[0149] The embodiments of this application construct a coupling matrix based on three dimensions: energy storage type, operating parameters, and lifespan stage;

[0150] Based on the coupling matrix, the fit between different energy storage types and each hierarchical optimization objective is calculated, and the matching relationship between energy storage types and hierarchical objectives is determined, expressed as:

[0151]

[0152] In the formula, This represents the fit between the i-th type of energy storage and the optimization objective of the j-th layer. ; The weighting coefficients represent the operating parameters of energy storage. Indicates the energy storage life stage coefficient. The weighting coefficients representing energy storage cost parameters. This represents the overall charging and discharging efficiency of the i-th type of energy storage. This indicates the highest discharge efficiency of all energy storage types within the region. This represents the rated cycle life of the i-th type of energy storage. This represents the average cycle life of all energy storage types within the region. This represents the total lifecycle cost per unit capacity of the i-th type of energy storage;

[0153] The matrix row dimension represents multiple energy storage types, the column dimension represents operating parameters and lifespan stage, and the matrix value is the benchmark value of operating parameters for each energy storage type under different lifespan stages.

[0154] The coupling matrix is ​​used as the constraint basis for the three-dimensional architecture optimization. In the time layer, the scheduling cycle is adapted according to the energy storage life stage in the matrix; in the spatial layer, the mutual power of sub-regions is matched according to the energy storage power adjustment range in the matrix; and in the subject layer, the weight of multiple subjects is optimized according to the energy storage cost parameters in the matrix.

[0155] The coupling matrix adaptation results are fed back to the hierarchical optimization model to correct the spatiotemporal main boundary data; at the same time, the multi-energy storage full life cycle data are updated to dynamically adjust the coupling matrix benchmark value.

[0156] Please continue to refer to Figure 1 The optimized configuration method for the regional energy system in this embodiment further includes the following steps:

[0157] Step S103: Based on the collaborative results of dynamic elastic hierarchical optimization, multi-subject collaborative game and multi-energy storage characteristic mapping, output the collaborative optimization configuration scheme of energy storage and energy, and energy storage and load in the regional energy system.

[0158] In the synergistic optimization configuration scheme of energy storage and energy, the total regional energy storage capacity Represented as:

[0159]

[0160] in, This represents the difference between the predicted maximum load for the region and the current maximum load. This difference reflects the additional energy storage capacity required due to load growth. For example, when load growth causes a power supply gap during peak hours, this gap needs to be filled by discharging energy storage. This indicates the region's maximum annual surplus electricity, reflecting the energy storage capacity required to ensure energy consumption. For example, when there is a surplus of energy output, it is necessary to store the redundant electricity through energy storage charging to avoid wasting electricity. represents the charge / discharge efficiency, with a value ranging from 0 to 1, used to characterize the degree of energy loss in the energy storage system, since there is energy loss during energy storage charging and discharging (such as charging heat loss and discharging internal resistance loss); K represents the energy storage capacity correction coefficient, with a value ≥ 1, used to comprehensively consider multiple factors for correction, in order to adapt to scenario-specific needs (lifespan, energy storage type, extreme operating conditions, etc.), and ensure the balance between reliability and economy of the energy storage system throughout its entire life cycle and under complex operating conditions;

[0161] In the coordinated optimization configuration scheme of energy storage and load, it is used for sub-region energy storage capacity allocation and load peak-valley coordinated scheduling; wherein, in the sub-region energy storage capacity allocation, the energy storage capacity of the i-th sub-region within the region... Represented as:

[0162]

[0163] in, This represents the energy storage configuration capacity of the i-th sub-region, used to meet the peak-valley regulation and priority guarantee needs of the load in this sub-region; This represents the maximum load value of the i-th sub-region during a specified time period. The highest load scenarios throughout the year are selected for statistical analysis, such as high-temperature days in summer and severe cold days in winter, to reflect the scale demand of the sub-region's load. This represents the load priority weight of the i-th sub-region, reflecting the importance of loads in different sub-regions; n: the total number of sub-regions within the region; This represents the sum of the load size and priority weight of all sub-regions, used to proportionally allocate the total regional energy storage capacity. ;

[0164] This application embodiment achieves multi-dimensional synergy between energy storage and load by allocating capacity according to load characteristics and precisely scheduling time periods according to peak and valley periods, which not only ensures the reliability of power supply to the load, but also improves the efficiency of energy storage utilization.

[0165] In another preferred embodiment of this application, the method for optimizing the configuration of a regional energy system in another embodiment of this application includes scenario data as part of the multi-dimensional basic data. A multi-level scenario prediction model is deployed in the hierarchical optimization model. Based on the multi-dimensional basic data containing scenario data, hierarchical predictions are made for short-term equipment status, medium-term scenario changes, and long-term planning adjustments. A closed-loop mechanism including prediction and execution is established to trigger the corresponding level of optimization strategy for adjustment and execution.

[0166] like Figure 2 As shown, according to another embodiment of this application, an optimized configuration system for a regional energy system is provided;

[0167] Includes the following modules:

[0168] Data acquisition module 201 is used to acquire multi-dimensional basic data of the regional energy system, wherein the multi-dimensional basic data includes at least energy data, load data and multi-type energy storage data;

[0169] The hierarchical optimization module 202 is used to construct a dynamic and elastic hierarchical optimization model, which includes a time layer, a spatial layer, and a subject layer. Based on the multi-dimensional basic data, dynamic and elastic hierarchical optimization is performed, and the collaborative optimization boundaries between energy storage and energy, and between energy storage and load are determined layer by layer, as well as the overall collaborative optimization objectives of energy storage, energy, and load. The dynamic and elastic hierarchical optimization includes elastic adjustment of the time scale, energy mutual assistance in spatial sub-regions, and dynamic weight adaptation of multi-subject demand. A capacity configuration sub-model, a real-time scheduling sub-model, and a game arbitration sub-model are introduced, and a multi-subject collaborative game mechanism is used to decide on disagreements and dynamically adjust parameters to achieve Pareto optimality. A multi-energy storage characteristic dynamic mapping mechanism is integrated into the hierarchical optimization model. By constructing a multi-energy storage characteristic coupling matrix based on the entire life cycle data of energy storage, the optimization process is strongly coupled with the actual operating rules and life cycle of multiple types of energy storage.

[0170] The scheme configuration module 203 is used to output a coordinated optimization configuration scheme for energy storage and energy, and energy storage and load in the regional energy system based on the coordinated results of dynamic elastic hierarchical optimization, multi-subject collaborative game and multi-energy storage characteristic mapping.

[0171] This application integrates three-dimensional data of energy, load and multiple types of energy storage, and incorporates the full life cycle characteristics of energy storage into the basic data to form multi-dimensional data. The multi-dimensional data is directly related to the optimization of the time layer, spatial layer and subject layer. For example, energy data supports flexible adjustment of time scale, load data supports mutual assistance of spatial sub-regions, and energy storage data supports characteristic mapping. This solves the technical problem that the optimization results of existing technologies do not match the actual operation requirements of energy storage due to the one-sided data foundation.

[0172] This application achieves multi-dimensional collaborative optimization in dynamic scenarios through three-dimensional coupling of time, space, and subject. The time layer adjusts the charging and discharging window to cope with intraday energy fluctuations, the space layer balances uneven energy distribution through sub-region mutual assistance, and the subject layer dynamically adapts weights to take into account grid peak shaving, energy storage revenue, and user costs, thus adapting to the dynamic changes of the regional energy system.

[0173] The boundary defined by the layering in this application is used to associate dynamic data of the three-dimensional optimization architecture, so that the boundary can be dynamically adjusted, making the collaborative boundary conform to the actual operation requirements and avoiding the technical problem of static boundary restricting the optimization flexibility; and in multi-objective fusion, the collaborative unity of multiple objectives can be achieved, avoiding system imbalance caused by a single objective.

[0174] The game arbitration sub-model of this application achieves the connection between long-term capacity planning and short-term real-time scheduling by quantifying disagreements and dynamically allocating weights, avoiding the optimization gap caused by model isolation in the prior art; while the multi-subject collaborative game mechanism can make the optimization result meet the requirements of the balance of interests of multiple subjects; the multi-energy storage characteristic coupling matrix of this application strongly couples the energy storage life cycle data with the optimization process, thereby improving the energy storage operation efficiency.

[0175] An embodiment of the present invention provides a computer device, which may be a computer;

[0176] like Figure 3 As shown, the processor 302, memory, input device 303, display 304, and network interface 305 are connected via system bus 301. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium 306 and internal memory 307. The non-volatile storage medium 306 stores the operating system, computer programs, and database. The internal memory 307 provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. When the processor 302 executes the computer programs stored in the memory, it implements the optimized configuration method of the regional energy system in the above embodiment.

[0177] An embodiment of the present invention provides a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the optimized configuration method of the regional energy system described in the above embodiment.

[0178] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the regional energy system optimization configuration method in the embodiments of this application. The memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created by using the regional energy system optimization configuration method, etc. Furthermore, the memory may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the local module via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0179] In some embodiments, a processor may be a central processing unit, a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is typically used to control the overall operation of a computer device. In this embodiment, the processor is used to run program code stored in memory or to process data.

[0180] In this embodiment, the processors of multiple computer devices execute various server functions and data processing by running non-volatile software programs, instructions, and modules stored in memory, thereby implementing the steps of the optimized configuration method for the regional energy system described in the above method embodiment.

[0181] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the functionality of various illustrative components, blocks, modules, circuits, and steps has been generally described. Whether this functionality is implemented as software or as hardware depends on the specific application and the design constraints imposed on the system as a whole. Those skilled in the art can implement the functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the embodiments disclosed herein.

[0182] The various exemplary logic blocks, modules, and circuits described herein can be implemented or performed using the following components designed to perform the functions herein: general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP, and / or any other such configuration.

[0183] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0184] It should be understood that, as used herein, the singular form "a" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the associatedly listed items. The embodiment numbers disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0185] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the disclosed embodiments of the present invention (including the claims) is limited to these examples; within the framework of the embodiments of the present invention, the technical features of the above embodiments or different embodiments can also be combined, and there are many other variations of different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present invention should be included within the protection scope of the embodiments of the present invention.

Claims

1. A method for optimal configuration of a district energy system, characterized by, The method comprises the following steps: acquiring multi-dimensional basic data of a regional energy system, the multi-dimensional basic data comprising at least energy data, load data and multi-type energy storage data; constructing a dynamic elastic hierarchical optimization model, the hierarchical optimization model comprising a time layer, a space layer and a subject layer; performing dynamic elastic hierarchical optimization based on the multi-dimensional basic data, and hierarchically determining a collaborative optimization boundary of energy storage and energy, a collaborative optimization boundary of energy storage and load, and a collaborative optimization target of the whole of energy storage, energy and load; the dynamic elastic hierarchical optimization comprises elastic adjustment of a time scale, energy mutual aid of a space sub-region and dynamic weight adaptation of multi-subject demand; introducing a capacity configuration sub-model, a real-time scheduling sub-model and a game arbitration sub-model, and combining a multi-subject collaborative game mechanism to decide on a difference, dynamically adjust parameters and achieve a Pareto optimal result; in the game arbitration sub-model, based on the multi-dimensional basic data and a multi-subject collaborative game result, input data of the game arbitration sub-model is extracted, comprising double-model result data, subject demand data and judgment threshold data; for the purpose of resolving a double-model optimization difference and achieving a multi-subject Pareto optimal result, arbitration and optimization are performed; in a difference judgment, a deviation degree of an initial capacity and a minimum demand capacity is calculated, and arbitration is started when the deviation degree exceeds a preset difference threshold; in weight allocation, based on a subject demand urgency, a capacity model weight and a scheduling model weight are dynamically allocated; in arbitration calculation, the double-model result is fused according to the weight, and an arbitration-after capacity and a scheduling strategy are output; the arbitration result is fed back to a multi-subject collaborative game process and the dynamic elastic hierarchical optimization model, and model parameters and optimization boundaries are corrected; in the hierarchical optimization model, a multi-energy storage characteristic dynamic mapping mechanism is integrated, a multi-energy storage characteristic coupling matrix based on energy storage full life cycle data is constructed, and the optimization process is strongly coupled with actual operation rules and a life cycle of multi-type energy storage; based on multi-dimensional basic data and time-space-subject boundary data output by the hierarchical optimization model, multi-energy storage full life cycle data and architecture boundary data are extracted; a coupling matrix is constructed in three dimensions of energy storage type, operation parameter and life stage; based on the coupling matrix, an adaptation degree of different energy storage types to each hierarchical optimization target is calculated, and a matching relationship of energy storage type and hierarchical target is determined; the coupling matrix is used as a constraint basis for three-dimensional architecture optimization; in the time layer, a scheduling period is adapted according to the energy storage life stage in the matrix; in the space layer, a sub-region mutual aid power is matched according to the energy storage power adjustment range in the matrix; in the subject layer, multi-subject weights are optimized according to the energy storage cost parameter in the matrix; the coupling matrix adaptation result is fed back to the hierarchical optimization model, and the time-space-subject boundary data is corrected; at the same time, the multi-energy storage full life cycle data is updated, and the coupling matrix benchmark value is dynamically adjusted; according to a collaborative result of the dynamic elastic hierarchical optimization, the multi-subject collaborative game and the multi-energy storage characteristic mapping, a collaborative optimization configuration scheme of energy storage and energy, energy storage and load in a regional energy system is output.

2. The method of claim 1, wherein, The step of elastic adjustment of a time scale comprises: extracting energy output fluctuation characteristics and load peak-valley change characteristics based on time sequence data of energy and time sequence data of load; Determine the trigger factor of time scale adjustment, which includes the amplitude of energy output fluctuation, the peak-valley difference rate of load and the matching degree, and the matching degree is the time sequence matching degree of energy and load; wherein the trigger factor is represented as: In the formula, , , represents a weight coefficient, and ; represents an absolute value of energy output fluctuation at time t; represents daily average energy output; , respectively represent peak and valley values of load at time t, represents an average value of load at time t; represents a time sequence matching degree of energy output and load demand at time t; When any trigger factor does not match the corresponding preset threshold range, the optimization period of the time layer in the hierarchical optimization model is flexibly adjusted based on the trigger factor, and the adjusted time layer optimization period is taken as the time layer constraint of the dynamic flexible hierarchical optimization.

3. The method of claim 2, wherein, The step of energy mutual aid of the spatial sub-area includes: Based on the sub-area load demand data, sub-area energy distribution data and sub-area energy storage operation data in the multi-dimensional basic data, the energy supply and demand characteristics, energy output margin and energy storage capacity margin of each sub-area are extracted; According to the complementarity of the energy supply and demand characteristics, the region is divided into at least one energy mutual aid unit, and each mutual aid unit contains at least two sub-areas with supply and demand mismatch; Based on the energy mutual aid trigger threshold, the inter-sub-area energy mutual aid is started, and the energy mutual aid trigger threshold includes the sub-area energy surplus threshold, the sub-area energy shortage threshold and the inter-sub-area energy transmission loss threshold; The division result of the energy mutual aid unit, the mutual aid trigger threshold and the mutual aid dispatching strategy are taken as the spatial layer constraint of the dynamic flexible hierarchical optimization.

4. The method of claim 3, wherein, The step of dynamic weight adaptation of multi-agent demand includes: Based on the multi-dimensional basic data, the demand characteristics of multi-agent are extracted, and the multi-agent includes grid operation agent, energy storage operation agent and user agent, and the demand characteristics correspond to grid peak shaving demand characteristics, energy storage life benefit demand characteristics and user energy cost reliability demand characteristics respectively; Determine the multi-agent weight adjustment trigger condition, which includes: the real-time peak shaving gap of the grid exceeds the preset peak shaving threshold, the actual life attenuation rate of the energy storage exceeds the preset attenuation threshold, and the user energy cost exceeds the preset cost threshold; When any trigger condition is met, the demand urgency is calculated based on the demand characteristics of each agent, and the weight distribution scheme of multi-agent collaborative optimization is dynamically updated according to the demand urgency of each agent; The updated multi-agent weight distribution scheme is taken as the agent layer constraint of the dynamic flexible hierarchical optimization.

5. The method of claim 4, wherein, The hierarchical determination of the collaborative optimization boundary of energy storage and energy, energy storage and load, and the collaborative optimization target of the whole of energy storage, energy and load includes the following steps: Based on the multi-dimensional basic data, the characteristics are extracted according to the time layer, the spatial layer and the agent layer; In the collaborative optimization boundary of energy storage and energy, the time layer boundary includes constructing the fluctuation suppression boundary of energy storage to energy based on the energy output fluctuation curve; the spatial layer boundary includes constructing the spatial matching boundary of energy storage and energy based on the sub-area energy installation density; and the agent layer boundary includes constructing the peak shaving collaborative boundary of energy storage and energy based on the grid peak shaving demand threshold; In the collaborative optimization boundary of energy storage and load, the time layer boundary includes constructing the peak-valley filling boundary of energy storage to load based on the time sequence distribution of load peak-valley; the spatial layer boundary includes constructing the priority guarantee boundary of energy storage and load based on the sub-area load priority; and the agent layer boundary includes constructing the reliability boundary of energy storage and load based on the user energy reliability requirement; In the collaborative optimization boundary of energy storage and load, the time layer boundary includes constructing the peak-valley filling boundary of energy storage to load based on the time sequence distribution of load peak-valley; the spatial layer boundary includes constructing the priority guarantee boundary of energy storage and load based on the sub-area load priority; and the agent layer boundary includes constructing the reliability boundary of energy storage and load based on the user energy reliability requirement; In the collaborative optimization target, the time layer sub-target combines the time layer characteristics to build a time dimension target containing energy consumption rate, energy storage charging and discharging cycle number; the spatial layer sub-target combines the spatial layer characteristics to build a spatial dimension target containing high priority sub-area load satisfaction rate, sub-area energy transmission loss; the main body layer sub-target combines the main body layer characteristics to build a main body dimension target containing grid peak regulation response compliance rate, user energy cost; the sub-targets are fused into the overall target by weighted summation, and the weights are dynamically adjusted based on the emergency degree of each subject demand; The two groups of collaborative optimization boundaries and the overall collaborative optimization target determined by layering are used as the constraint reference of dynamic elastic layering optimization.

6. The method of claim 5, wherein, In the capacity configuration sub-model, based on multi-dimensional basic data and time layer boundary data output by dynamic elastic layering optimization, input data of the capacity configuration sub-model are extracted, including energy side data, load side data, energy storage side data and constraint boundary data; Taking the minimization of total life cycle cost of energy storage and the maximization of annual energy consumption rate in the region as double optimization targets, and taking the energy storage demand gap as the initial value, the optimal configuration capacity is iteratively calculated, and the regional optimal total capacity and sub-area capacity allocation results are fed back to the multi-subject collaborative game process and the dynamic elastic layering optimization model.

7. The method of claim 6, wherein, In the real-time scheduling sub-model, based on multi-dimensional basic data and spatio-temporal boundary data output by dynamic elastic layering optimization, input data of the real-time scheduling sub-model are extracted, including energy side daily real-time output and fluctuation characteristics, load side daily real-time load and priority list, energy storage side real-time operation state and daily loss rate, constraint boundary side time layer response step and spatial layer mutual aid threshold; Taking the maximization of daily load satisfaction rate and the minimization of daily life loss of energy storage as double optimization targets, and taking the initial charging and discharging strategy generated based on load peak and energy fluctuation as the initial value, the optimal real-time scheduling strategy is iteratively calculated, and the optimal scheduling strategy and daily operation data of energy storage are fed back to the multi-subject collaborative game process and the dynamic elastic layering optimization model.

8. The method of claim 7, wherein, In the arbitration calculation of the game arbitration sub-model, the double model results are fused according to the weights, and the arbitrated capacity and scheduling strategy are output, which are represented as: In the formula, represents the optimal energy storage capacity output after arbitration, represents the weight coefficient of the capacity configuration sub-model, represents the weight coefficient of the real-time scheduling sub-model, represents the initial optimal capacity output by the capacity configuration sub-model, represents the minimum demand capacity output by the real-time scheduling sub-model; represents the divergence penalty coefficient; represents the absolute deviation of the capacity configuration sub-model and the real-time scheduling sub-model result.

9. The method of claim 8, wherein, In the layering optimization model, based on the coupling matrix, the adaptability of different energy storage types to each layering optimization target is calculated, the matching relationship between the energy storage type and the layering target is determined, and it is represented as: In the formula, represents the degree of adaptation of the i-th type of energy storage to the j-th layer optimization target, ; represents the weight coefficient of the energy storage operation parameter, and μ represents the energy storage life stage coefficient, represents the weight coefficient of the energy storage cost parameter, represents the charge-discharge comprehensive efficiency of the i-th type of energy storage, represents the highest discharge efficiency of all energy storage types in the region, represents the rated cycle life of the i-th type of energy storage, represents the average cycle life of all energy storage types in the region; represents the unit capacity life cycle cost of the i-th type of energy storage.

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