Adaptive triggering scheduling method and system for shared energy storage of ac-dc power distribution system

Abstract

The present application relates to the technical field of power distribution network dispatching, and provides a shared energy adaptive triggering dispatching method and system for an AC-DC power distribution system, which comprises: constructing a sudden power shortage state identification and adaptive triggering discrimination model, constructing a multi-agent optimal dispatching model considering shared energy, and coordinating the operation of AC subnets, DC ring networks, converters and shared energy; based on the triggering discrimination model and the optimal dispatching model, a multi-stage dynamic dispatching strategy is formulated, which combines day-ahead benchmark dispatching, intra-day rolling correction and state triggering response; the multi-stage dynamic dispatching strategy is executed, and a compensation mechanism for shared energy participating in the rapid response to sudden power shortage is constructed according to the dynamic dispatching strategy. The present application solves the problems of response lag, single energy calling and insufficient cross-regional support for sudden power shortage, and improves the response capability to source and load mutation, the utilization efficiency of energy storage and the multi-regional power mutual aid capability.

Description

Technical Field

[0001] This invention relates to the field of power distribution network dispatching technology, and in particular to a shared energy storage adaptive triggering dispatching method and system for AC / DC power distribution systems. Background Technology

[0002] As the proportion of renewable energy integrated into AC / DC power distribution systems continues to increase, the randomness and volatility of source-side power output, as well as the uncertainty of load demand, have significantly increased. This leads to a mismatch between predicted and actual power output during actual operation of the power distribution system. In particular, under circumstances such as a sudden drop in photovoltaic power output, a sudden change in wind power output, or a rapid increase in local load, AC / DC power distribution systems may experience sudden power shortages in a short period of time, thereby affecting the system's power balance and safe and stable operation.

[0003] However, existing AC / DC power distribution systems typically rely on time-scale optimization methods such as day-ahead scheduling and intraday rolling scheduling to formulate operation plans. While these methods can correct source-load prediction deviations to some extent, their scheduling cycles are relatively fixed, making it difficult to respond promptly to sudden power shortage events.

[0004] In the process of realizing this invention, the inventors discovered that the prior art has at least the following drawbacks and shortcomings: Existing AC / DC power distribution system status perception methods are mostly based on source load prediction, node voltage, line power and equipment status monitoring. They do not consider the impact of the deviation between the day-ahead power purchase plan and the intraday rolling demand, the available capacity of shared energy storage and the remaining capacity of AC / DC interconnection channels on the identification of sudden power shortages, making it difficult to timely characterize the shortage risk of each sub-region.

[0005] Existing AC / DC power distribution system scheduling models mostly adopt day-ahead or intraday fixed-cycle rolling optimization, without considering the need for adaptive triggering of shared energy storage for rapid support based on real-time status when the power deficit exceeds the conventional adjustment capacity, resulting in a lag in response.

[0006] Existing AC / DC power distribution system mutual support strategies mostly rely on interconnection power adjustment or local energy storage output, without considering shared energy storage resources, coordinated scheduling of DC networks and converters, and dynamic determination of cross-regional support paths, making it difficult to fully leverage multi-regional mutual support capabilities. Summary of the Invention

[0007] This invention aims to at least solve one of the technical problems existing in related technologies. To this end, this invention provides a shared energy storage adaptive triggering scheduling method and system for AC / DC power distribution systems, which addresses the problems of delayed response, single energy dispatch mode, insufficient cross-regional power support capacity, and difficulty in adaptively adjusting scheduling strategies according to the system's operating status in existing AC / DC power distribution systems under sudden power shortages.

[0008] This invention provides a shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems, comprising: S1: Construct an AC / DC power distribution system topology that includes distributed new energy power generation equipment, shared energy storage devices, and multiple types of loads; S2: Construct an AC / DC power distribution system burst power shortage state identification and adaptive triggering discrimination model based on the AC / DC power distribution system topology; S3: Construct a multi-entity optimal scheduling model for AC / DC power distribution systems that considers shared energy storage based on the AC / DC power distribution system topology; S4: Construct a multi-stage dynamic scheduling strategy for shared energy storage resources that considers sudden power shortages based on the state recognition and adaptive triggering discrimination model and the optimized scheduling model; S5: Execute a multi-stage dynamic scheduling strategy, and construct a compensation mechanism for shared energy storage to participate in rapid response to sudden power shortages based on the dynamic scheduling strategy. Complete the adaptive triggering scheduling of shared energy storage in the AC / DC power distribution system based on the scheduling results and the compensation mechanism.

[0009] Furthermore, the state recognition and adaptive triggering discrimination model includes a day-ahead optimization stage, an intraday adjustment stage, and a state triggering stage; The day-ahead optimization phase involves developing an initial operation plan based on day-ahead source-load forecast data. During the intraday adjustment phase, the subnet power balance status is monitored in real time based on the rolling optimization results. When a sudden power deficit is detected and the triggering criteria of the adaptive triggering discrimination model are met, the event response mechanism is activated. During the state triggering phase, the shared energy storage dynamically adjusts its charging and discharging strategy based on the optimization objectives to provide emergency power support for power-deficient areas.

[0010] Furthermore, a sudden power deficit state is a situation where the system power deficit exceeds 20% of the planned value for the day before, and the existing regulation capacity is insufficient to eliminate the power deficit in a timely manner.

[0011] Furthermore, the triggering criterion of the adaptive trigger discrimination model is: in, For nodes Place Electricity purchased during the day within the specified time period For nodes Place Power purchased before the specified time period For nodes Place The criterion for triggering a sudden power deficit state during a given period is as follows: when the criterion value is 1, it indicates that the node... Place When a sudden power shortage occurs during a certain period, it is necessary to activate the dynamic and rapid response of shared energy storage.

[0012] Furthermore, the optimized scheduling model takes into account the dynamic response process of shared energy storage participating in the AC / DC power distribution system, and considers the operating costs and economic benefits of the AC subnetwork, DC ring network and the main body of shared energy storage in the system, so as to minimize the operating cost of the AC / DC power distribution system.

[0013] Furthermore, the objective function of the multi-agent optimal scheduling model for AC / DC power distribution systems considering shared energy storage is: in, The objective function of the multi-agent optimal scheduling model for AC / DC distribution systems considering shared energy storage is... For the objective function of the communication subnet, Let the objective function be that of the DC ring network. For energy storage operating costs, To find the minimum value of the function.

[0014] Furthermore, the operational constraints of the multi-entity optimal scheduling model of the AC / DC power distribution system considering shared energy storage include AC subgrid power flow constraints, AC subgrid power balance constraints, AC subgrid power transmission constraints between the AC subgrid and the upper-level main grid, distributed power output constraints in the AC subgrid, AC subgrid operational safety constraints, voltage source converter operational model constraints, as well as coordination constraints, state of charge constraints, and charging / discharging power constraints for shared energy storage.

[0015] Furthermore, the dynamic scheduling strategy adopts a multi-time-scale architecture, dynamically combining timing optimization with state triggering. During the day-ahead dispatch phase, based on all forecast data and with an economic focus, the day-ahead main grid power purchase plan, shared energy storage charging and discharging strategy, and tie-line interactive power plan are optimized to establish a day-ahead operation reference point. During the intraday scheduling phase, rolling optimizations are performed based on short-term forecast data and the day-ahead operating reference point. When the power deviation exceeds the preset adjustment margin and causes a sudden power shortage during the rolling optimization process, the state triggering mechanism activates the dynamic fast response strategy of shared energy storage, and restores the system power balance by allocating energy storage resources among subgrids.

[0016] Furthermore, the compensation mechanism includes response decision calculation, resource reallocation, and economic compensation; The response decision calculation determines the power deviation of each node in the system based on the intraday rolling optimization results, identifies the location and time of the sudden power shortage event based on the triggering criteria, and calculates the optimal response strategy for shared energy storage based on the minimum power shortage objective function after the sudden power shortage state is triggered. The resource reallocation is based on the decision results, adjusting the charging and discharging power allocation of shared energy storage to each subgrid, and prioritizing the supply and demand balance needs of areas that trigger sudden power shortage events; The economic compensation statistics are based on the difference between the power deficit before and after compensation for shared energy storage during all triggering periods, and the power deficit compensation for shared energy storage is calculated in combination with the power deficit compensation coefficient for shared energy storage.

[0017] The present invention also provides a shared energy storage adaptive triggering scheduling system for AC / DC power distribution systems, for executing the above-mentioned shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems, comprising: The status awareness and deficit identification module is used to acquire the operation data of each AC sub-region, DC interconnection network, AC-DC converter and shared energy storage. Based on the day-ahead power purchase plan, intraday rolling power purchase demand, source-load forecast deviation, AC-DC interconnection power and the available capacity of shared energy storage, it identifies the sudden power deficit status and outputs the power deficit risk indicators of different sub-regions. The adaptive triggering discrimination module is used to determine whether there is a sudden power shortage event in the system that requires the participation of shared energy storage based on the power shortage risk index, the remaining capacity of shared energy storage, the remaining transmission capacity of AC / DC interconnection channel and the preset triggering threshold, and to generate a shared energy storage adaptive triggering signal when the triggering conditions are met. The multi-stage collaborative scheduling module is used to execute an optimization method that combines day-ahead baseline scheduling, intraday rolling correction, and state-triggered response to coordinate the main grid power purchase, AC / DC interconnection power, and shared energy storage charging and discharging power to form a shared energy storage scheduling scheme. The cross-regional mutual assistance and coordination module is used to determine the cross-regional power support path, output the shared energy storage charging and discharging power, AC / DC interconnection power adjustment amount, cross-regional support scheme and compensation benefit results, and send them to the AC / DC power distribution system dispatch and control terminal.

[0018] The above-described one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: The method for identifying sudden power shortages in this invention comprehensively considers the day-ahead power purchase plan, intraday rolling power purchase demand, source-load forecast deviation, available capacity of shared energy storage, and remaining capacity of AC / DC interconnection channels. It can more accurately identify the power shortage risk of each AC sub-region and provide a reliable criterion for triggering the call of shared energy storage. The shared energy storage adaptive triggering scheduling method of the present invention combines day-ahead baseline scheduling, intraday rolling correction and real-time triggering response. It can generate shared energy storage rapid support instructions in a timely manner when the sudden power deficit exceeds the conventional regulation capacity, reduce scheduling response lag, and improve the system's ability to cope with sudden changes in source load and local power gaps. The shared energy storage resource pool dynamic dispatch and cross-regional mutual support method of the present invention can dynamically reconstruct the shared energy storage charging and discharging plan under the condition of satisfying the AC / DC network, voltage source converter and shared energy storage operation constraints, give priority to providing support to power deficit areas, and improve the power mutual support capability and system operation reliability among multiple AC sub-regions.

[0019] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems provided by the present invention.

[0022] Figure 2 This is a diagram showing the power balance and economic cost optimization of the AC subnet observed under multiple scenario comparisons. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. The following embodiments are used to illustrate this invention but cannot be used to limit the scope of this invention.

[0024] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0025] The following is combined Figures 1 to 2 This invention describes a shared energy storage adaptive triggering scheduling method and system for AC / DC power distribution systems.

[0026] like Figure 1 As shown, a shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems includes: S1: Construct an AC / DC power distribution system topology that includes distributed power sources, shared energy storage, and multiple types of loads; The AC / DC power distribution system topology includes several distributed new energy power generation devices such as photovoltaics and wind turbines, one shared energy storage device, and several loads of various types, including residential, industrial, and new energy industrial parks. Different AC subgrids are flexibly interconnected through DC ring networks, and AC / DC conversion and cross-regional power support are achieved between AC and DC subgrids through voltage source converters.

[0027] S2: Construct an AC / DC power distribution system burst power shortage state identification and adaptive triggering discrimination model based on the AC / DC power distribution system topology; The system identifies the risk of shortages in each sub-region and generates trigger signals by using a state recognition and adaptive triggering discrimination model.

[0028] The adaptive triggering and discrimination model for AC / DC power distribution systems triggers adjustments to scheduling decisions based on the real-time state of the system rather than preset time points. On the basis of a multi-time-scale optimization architecture, a state triggering mechanism is introduced. By continuously monitoring the power balance state of the AC / DC power distribution system, a triggering criterion for power deficit is set. When the system operation deviation exceeds the conventional scheduling margin, the dynamic optimization adjustment strategy of shared energy storage is triggered.

[0029] The state recognition and adaptive triggering discrimination model includes the day-ahead optimization stage, the intraday adjustment stage, and the state triggering stage; During the day-ahead optimization phase, an initial operation plan is formulated based on day-ahead source-load forecast data; During the intraday adjustment phase, the subnet power balance status is monitored in real time based on the rolling optimization results. When a sudden power deficit is detected and the triggering criteria of the adaptive triggering discrimination model are met, the event response mechanism is activated. During the state-triggered phase, shared energy storage can dynamically adjust its charging and discharging strategies based on optimization objectives to provide emergency power support for power deficit areas. The state-triggered mechanism only executes scheduling actions when the system state changes significantly, thereby enhancing the system's response capability to sudden power deficits while maintaining economical system operation and leveraging the flexible adjustment potential of shared energy storage.

[0030] A sudden power deficit state is defined as a system power deficit exceeding 20% ​​of the planned value, and where existing regulation capacity is insufficient to eliminate the power deficit in a timely manner. The triggering criterion for the adaptive triggering discrimination model under a sudden power deficit state is as follows: in, For nodes Place Electricity purchased during the day within the specified time period For nodes Place Power purchased before the specified time period For nodes Place The criterion for triggering a sudden power deficit state during a given period is as follows: when the criterion value is 1, it indicates that the node... Place When a sudden power shortage occurs during a certain period, it is necessary to activate the dynamic and rapid response of shared energy storage.

[0031] S3: Construct a multi-entity optimal scheduling model for AC / DC power distribution systems that considers shared energy storage based on the AC / DC power distribution system topology; The operation of the AC subgrid, DC ring network, converter, and shared energy storage is coordinated by optimizing the scheduling model.

[0032] The optimized scheduling model takes into account the dynamic response process of shared energy storage participating in the AC / DC power distribution system, and considers the operating costs and economic benefits of each entity in the system, including the AC subgrid, DC ring network, and shared energy storage, aiming to minimize the operating cost of the AC / DC power distribution system.

[0033] The total operating cost of the AC subnetwork includes the cost of purchasing electricity from the main grid and the network loss cost. The objective function of the AC subnetwork is as follows: in, For the objective function of the communication subnet, Main grid electricity purchase cost For network loss costs, For the exchange subnet set, For the set of substation nodes in AC subnet e, For the operating cycle, for Electricity price during specific time periods For nodes in subnet e Place The amount of electricity purchased from the upstream main grid during the specified time period. This is the collection of all AC transmission lines within the AC subnet e. The electricity price is for network losses. For communication branch road The resistance, For the line exist Alternating current over a period of time; The DC ring network interacts with the AC subnetwork through voltage source converters, providing energy sharing and power transfer channels. The objective function of the DC ring network operation is to maximize the benefits obtained through power interaction via voltage source converters, as shown in the following equation: in, Let the objective function be that of the DC ring network. It is a collection of DC ring network voltage source converter nodes. for Electricity price per unit of electricity during different time periods For DC-side voltage source converters at nodes Place Interaction power over a given time period.

[0034] Shared energy storage charges operating fees based on the amount of charging and discharging. The objective function for energy storage operation is to minimize the operating cost, as shown in the following formula: in, For energy storage operating costs, Let e ​​be the set of nodes in the communication subnet. This is the energy storage operating cost coefficient. Communication nodes Place Energy storage discharge power during the period and For communication nodes Place Energy storage charging power during a given time period.

[0035] The objective function of a multi-agent optimal scheduling model for AC / DC power distribution systems considering shared energy storage As described below: in, The objective function of the multi-agent optimal scheduling model for AC / DC distribution systems considering shared energy storage is... For the objective function of the communication subnet, Let the objective function be that of the DC ring network. For energy storage operating costs, To find the minimum value of the function.

[0036] The operational constraints of the multi-entity optimal scheduling model for AC / DC power distribution systems considering shared energy storage include AC subgrid power flow constraints, AC subgrid power balance constraints, AC subgrid power transmission constraints between the AC subgrid and the upper-level main grid, distributed power output constraints, AC subgrid operation safety constraints, voltage source converter operation model constraints, as well as coordination constraints, state of charge constraints, and charging / discharging power constraints for shared energy storage.

[0037] AC subnet flow constraints: in, For communication nodes Place The square of the voltage over a given period. For communication nodes Place The square of the voltage over a given period. For communication branch road The resistance, For communication branch road Reactance, For communication branch road exist Time period from node Flow to Node active power, For communication branch road exist Time period from node Flow to Node reactive power, For communication branch road exist The square of the current over a time period, For the set of lines in subnet e, For communication nodes Place Active power injected during the time period, For communication branch road exist Time period from node Flow to Node reactive power, For communication nodes Place Reactive power injected during the time period For The set of the end nodes of a branch with a first node. For The set of first nodes of branches with last nodes.

[0038] AC subnet power balance constraints: in, For communication nodes Place Contributions during a given period For communication nodes Place Distributed power output during different time periods For communication nodes Place Time-of-use energy storage discharge power, For communication nodes Place Time-of-use energy storage charging power, For communication nodes Place Active load during time period, For communication nodes Place Active power on the AC side of a time-period voltage source converter For communication nodes Place Reactive power output of time-segment communication nodes For communication nodes Place Time-of-use AC reactive load, For communication nodes Place Reactive power on the AC side of a time-period voltage source converter.

[0039] Transmission power constraints between the AC subnet and the upstream main network: in, For communication nodes Place The upper limit of active power transmitted during a given time period. For communication nodes Place The lower limit of active power transmitted during a given time period. For communication nodes Place The upper limit of reactive power during a given time period. For communication nodes Place Lower limit of reactive power during a given time period.

[0040] Output constraints of distributed generation in AC subnet: in, For communication nodes Place Predicted output of distributed power sources during different time periods.

[0041] Security constraints for the AC subnet: in, For communication branch road Upper limit of active power For communication branch road The lower limit of active power. For communication branch road Upper limit of reactive power For communication branch road Lower limit of reactive power For communication nodes Place Voltage during time period For communication nodes At the upper limit of voltage, For communication nodes At the lower voltage limit, For communication branch road Current, For communication branch road Current limit For communication branch road Lower limit of current.

[0042] Each AC subnetwork is interconnected with the DC ring network through voltage source converters to achieve bidirectional power transmission. Based on the operating model of the voltage source converter, corresponding constraints are constructed. The model transforms the non-convex optimization problem into a convex optimization problem through second-order conical convex relaxation, and applies circular constraint linearization to reduce computational complexity. The constraints of the voltage source converter operating model are as follows: in, This is the square of the equivalent current inside the voltage source converter. For communication nodes The equivalent resistance inside a voltage source converter For communication nodes The equivalent reactance inside the voltage source converter, For communication nodes Place Reactive power output of time-period voltage source converter These are the upper and lower limits of the reactive power output of the voltage source converter. This represents the lower limit of the reactive power output of a voltage source converter. For communication nodes Place Equivalent internal potential amplitude of a time-period voltage source converter For communication nodes Place DC-side voltage amplitude of a time-period voltage source converter For DC voltage utilization, For communication nodes The modulation index of the voltage source converter satisfies 0 ≤ ≤1, For communication nodes The capacity of the voltage source converter It is the set of nodes for a voltage source converter.

[0043] Based on the system operation constraints, the operation constraints of the constructed shared energy storage allow different subnets to adjust capacity allocation according to their own needs and support the sharing of stored electricity among subnets.

[0044] Based on the net power exchange between shared energy storage and the AC subgrid Establish the following coordination constraints for shared energy storage: in, For shared energy storage Charging power during the period For shared energy storage Discharge power during a given period.

[0045] The distribution of shared energy storage charging and discharging power among subgrids must satisfy the following formula: in, This is the set of shared energy storage nodes in the AC subnet e.

[0046] The state of charge change of shared energy storage needs to satisfy the following equation constraint: in, For shared energy storage The state of charge value for a given time period. For shared energy storage The state of charge value for a given time period. To improve the charging efficiency of shared energy storage, To improve the discharge efficiency of shared energy storage, For time intervals.

[0047] The state of charge of shared energy storage must satisfy the following safety operation inequality constraints: in, This is the upper limit of the shared energy storage state of charge. This is the lower limit of the state of charge for shared energy storage.

[0048] Furthermore, to ensure the continuous and stable operation of shared energy storage, the state of charge of the shared energy storage needs to be restored to its initial value at the end of the optimization cycle, as shown in the following formula: in, The initial charge value for shared energy storage.

[0049] The charging and discharging power of shared energy storage needs to meet the constraints described in the following formula: in, For communication nodes The upper limit of the charging and discharging power of shared energy storage, For communication nodes Place The time-sharing energy storage discharge state is represented by a 0-1 variable, which is 1 during discharge and 0 otherwise. For communication nodes Place The time-sharing energy storage charging status is a 0-1 variable, which is 1 when charging and 0 otherwise.

[0050] In addition, the total charging and discharging power allocated to all subgrids must not exceed the overall power capacity limit of centralized shared energy storage, as specified in the following formula: in, This is the upper limit of the total charging and discharging power of shared energy storage.

[0051] S4: Construct a multi-stage dynamic scheduling strategy for shared energy storage resources that considers sudden power shortages based on the state recognition and adaptive triggering discrimination model and the optimized scheduling model; The dynamic scheduling strategy adopts a multi-time-scale architecture, which dynamically combines timing optimization with state triggering.

[0052] During the day-ahead scheduling phase, the system operates on an economically oriented baseline plan based on all forecast data. During the intraday scheduling phase, the system performs rolling optimization of the day-ahead plan based on short-term forecast data and reserves appropriate adjustment margins to cope with possible power fluctuations. When the rolling optimization process detects that the power deviation exceeds the preset adjustment margin, which may cause a sudden power shortage, the state triggering mechanism will activate the dynamic and rapid response strategy of shared energy storage. By flexibly allocating energy storage resources among subgrids, the system can alleviate the supply and demand imbalance in the power shortage area.

[0053] Based on all-day forecast data, the day-ahead power purchase plan, shared energy storage charging and discharging strategy, and tie-line interactive power plan are optimized to establish a day-ahead operating reference point. The objective function for day-ahead scheduling is as follows: in, Let be the objective function for the day-ahead scheduling.

[0054] During the intraday phase, considering the random fluctuations in the output of distributed renewable energy generation equipment and the characteristic that prediction accuracy improves as time approaches, the proposed strategy uses ultra-short-term prediction data on a 15-minute timescale to perform rolling optimization adjustments based on the operating baseline established a day before. Since there is a certain deviation between the day-before prediction and the actual intraday operating conditions, the system will reserve some adjustment space to maintain real-time balance. However, this flexibility has limitations; the intraday power purchase plan can be adjusted upwards by 15% based on the day-before plan value.

[0055] Based on ultra-short-term forecast data, rolling optimization adjustments are made on the operating baseline established in the day-ahead phase. To reduce the risk of power deficit, a power deficit penalty is introduced into the objective function. However, this penalty term is only used as an optimization guide and is included in the objective function during optimization, but it is not included in the cost calculation. The objective function for intraday adjustment is as follows: in, The objective function for the intraday adjustment phase is... As a penalty for power deficiency, This is the power deficit penalty coefficient. For communication nodes Place The power deficit during the time period is calculated using the following expression: in, This is a key point for communication in the current stage. Place Power purchased from the upstream main grid during the time period This is a key point for intraday communication. Place The power purchased from the upstream main grid during the specified time period.

[0056] During system operation, the daily plan is updated on a rolling basis as the forecast window progresses to determine the main grid's electricity purchase, tie-line power exchange, and energy storage charging and discharging strategies for the next cycle. However, deviations between forecasts and actual operating conditions may cause system supply and demand imbalances exceeding its normal adjustment capabilities, thus triggering sudden power shortage events. When the triggering conditions are met, shared energy storage can leverage its mutual support characteristics to adjust the original charging and discharging plans in the short term. By coordinating the charging and discharging power originally belonging to other subgrids, it can provide rapid discharge support to help restore system balance and reduce power shortages within the system. During this response process, the shared energy storage operator will decide on its charging and discharging behavior based on the target triggered by the state.

[0057] S5: Execute a multi-stage dynamic scheduling strategy, and construct a compensation mechanism for shared energy storage to participate in rapid response to sudden power shortages based on the dynamic scheduling strategy. Complete the adaptive triggering scheduling of shared energy storage in the AC / DC power distribution system based on the scheduling results and the compensation mechanism. The mutual assistance characteristic of shared energy storage allows different subgrids to flexibly allocate energy storage resources according to the overall system status. Based on this characteristic, this invention constructs a compensation mechanism for shared energy storage to participate in rapid response to sudden power shortages, which is achieved through the following steps: The response decision calculation determines the power deviation of each node in the system based on the intraday rolling optimization results, identifies the location and time of sudden power shortage events based on triggering criteria, and calculates the optimal response strategy for shared energy storage based on the minimum power shortage objective function after the sudden power shortage state is triggered. The calculation expression is as follows: Resource reallocation, based on decision-making outcomes, adjusts the allocation of charging and discharging power of shared energy storage to each subgrid, prioritizing the supply and demand balance needs of areas triggering sudden power shortage events. In this scenario, shared energy storage will no longer strictly adhere to the original charging and discharging schedule, allowing for the temporary use of energy storage power and capacity originally allocated to other subgrids for rapid discharge, in order to alleviate localized supply and demand imbalances. The economic compensation statistics calculate the difference between the power deficit before and after compensation for shared energy storage during the entire triggering period. This difference, combined with the shared energy storage power deficit compensation coefficient, is used to calculate the power deficit compensation for shared energy storage. The calculation expression is as follows: in, To compensate for the power deficit of shared energy storage. To share the energy storage power deficit compensation coefficient. To compensate for the previous power deficit. To compensate for the power deficit.

[0058] Based on the AC / DC interconnected power distribution system topology constructed in this invention, which includes distributed power sources, shared energy storage, and multiple types of loads, the effectiveness of the proposed adaptive triggering scheduling method for shared energy storage considering sudden power shortages in AC / DC power distribution systems is verified.

[0059] To verify the effectiveness of this invention, analysis and calculations were performed on the constructed medium-voltage AC / DC interconnected power distribution system. The three AC subnets adopted the IEEE 33 distribution network topology, and the shared energy storage was connected to node 15 of each of the three AC subnets, with a total capacity of 12MWh, a total charging and discharging power of 3MW, a SOC limited to 10%-90%, a charging and discharging efficiency of 0.9, an operating cost coefficient of 0.028 yuan / kWh, a power deficit compensation coefficient of 0.1 yuan / kWh, a network loss cost coefficient of 0.4 yuan / kWh, and the interactive electricity price of the DC ring network was set to 80% of the time-of-use electricity price.

[0060] This invention sets up three comparison scenarios, specifically: Scenario 1: Deterministic optimization across multiple time scales. Shared energy storage is used to promote renewable energy complementarity and multi-time scale optimization is implemented, but uncertainties and state triggering mechanisms in system operation are not considered.

[0061] Scenario 2: Robust optimization across multiple time scales. Shared energy storage is used to promote renewable energy complementarity. Robust optimization is employed during the day-ahead phase to address uncertainties, but no state triggering mechanism is introduced.

[0062] Scenario 3 (Invention): Shared energy storage is used to promote the complementarity of renewable energy. In order to improve the rapid response capability to sudden power shortages, a state triggering mechanism is introduced on the basis of a multi-timescale optimization framework.

[0063] To verify the effectiveness of the state triggering mechanism of the present invention in dealing with sudden power shortages, Figure 2 The intraday power balance of subnet 1 is shown under three scenarios.

[0064] like Figure 2 As shown, in Scenario 1, during intraday operation, the system mainly refers to the deterministic day-ahead plan for scheduling. When the actual renewable energy output deviates significantly and exceeds the system's normal adjustment capacity, a certain degree of sudden power deficit occurs. In Scenario 2, due to the use of robust optimization in the day-ahead phase, the plan reserves more margin to cope with uncertainties, and its intraday supply and demand balance pressure is somewhat alleviated compared to Scenario 1. However, while robust optimization improves system robustness, it is difficult to completely cover the most extreme cases in all time periods. Therefore, in some time periods, a certain degree of sudden power deficit still exists.

[0065] Compared to scenarios 1 and 2, scenario 3 introduces a state-triggered mechanism and leverages the rapid response capability of shared energy storage. When the system detects a power deficit and the triggering conditions are met, the state-triggered mechanism activates the shared energy storage to adjust the original charging and discharging plan, providing emergency support to the power deficit area.

[0066] The comparison shows that in the other two scenarios where sudden power shortages occur, shared energy storage can respond quickly and eliminate the sudden power shortages. Specifically, when subgrid 1 detects that the triggering conditions for a sudden power shortage are met within a certain time window during the rolling optimization process, the shared energy storage will change its original plan accordingly. For example, during the period of 0-6 hours when renewable energy output fluctuates significantly, the shared energy storage limits the charging power of subgrid 2 and quickly discharges subgrid 1, which has a power shortage, to prioritize ensuring the supply and demand balance in that area. During the period of 18-24 hours, when the renewable energy output of subgrid 1 is relatively abundant and other subgrids experience power shortages, the shared energy storage can utilize the surplus output of subgrid 1 to charge and discharge to other subgrids, supporting the supply and demand balance of other subgrids while improving the overall utilization rate of renewable energy.

[0067] Figure 2 The study also compared intraday operating costs and sudden power deficits under different scenarios. In Scenario 1, the lack of specific measures and rapid response mechanisms to address uncertainties resulted in a power deficit of 4.8230 MWh. In Scenario 2, day-ahead robust optimization mitigated the impact of uncertainty to some extent, reducing the power deficit to 2.5880 MWh, but this also led to an increase in total operating costs, and some power deficit still existed. In Scenario 3, the introduction of a state-triggered mechanism prevented any power deficit, indicating that this method has certain advantages in dealing with sudden power deficits.

[0068] Regarding operating costs, after including the energy storage response cost of 482.3 yuan, the total cost of Scenario 3 is higher than that of Scenario 1, but lower than that of Scenario 2. Compared to Scenario 1, the increase in cost in Scenario 3 is mainly due to the additional charging and discharging operations performed by the shared energy storage to cope with sudden power shortages, as well as the response compensation generated by the state triggering mechanism, which increases the energy storage operating cost from 0.0798 million yuan to 0.1487 million yuan. However, considering that Scenario 3 avoids the economic losses and social impacts that may be caused by power shortages, its economic benefits still have a certain advantage. Compared to Scenario 2, the total cost of Scenario 3 is reduced by 4.57%, indicating that the state triggering mechanism not only helps to cope with sudden power shortages, but also reduces the conservatism of the optimization strategy to a certain extent.

[0069] An adaptive triggering scheduling system for shared energy storage in an AC / DC power distribution system, used to execute the aforementioned adaptive triggering scheduling method for shared energy storage in an AC / DC power distribution system, includes: The status awareness and deficit identification module is used to acquire the operation data of each AC sub-region, DC interconnection network, AC-DC converter and shared energy storage. Based on the day-ahead power purchase plan, intraday rolling power purchase demand, source-load forecast deviation, AC-DC interconnection power and the available capacity of shared energy storage, it identifies the sudden power deficit status and outputs the power deficit risk indicators of different sub-regions. The adaptive triggering discrimination module is used to determine whether there is a sudden power shortage event in the system that requires the participation of shared energy storage based on the power shortage risk index, the remaining capacity of shared energy storage, the remaining transmission capacity of AC / DC interconnection channel and the preset triggering threshold, and to generate a shared energy storage adaptive triggering signal when the triggering conditions are met. The multi-stage collaborative scheduling module is used to execute an optimization method that combines day-ahead baseline scheduling, intraday rolling correction, and state-triggered response to coordinate the main grid power purchase, AC / DC interconnection power, and shared energy storage charging and discharging power to form a shared energy storage scheduling scheme. The cross-regional mutual assistance and coordination module is used to determine the cross-regional power support path, output the shared energy storage charging and discharging power, AC / DC interconnection power adjustment amount, cross-regional support scheme and compensation benefit results, and send them to the AC / DC power distribution system dispatch and control terminal.

[0070] This invention solves the problems of delayed response, single energy dispatch mode, insufficient cross-regional power support capability, and difficulty in adaptively adjusting dispatch strategies according to the system operating status in existing AC / DC power distribution systems under sudden power shortages. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems, characterized in that, include: S1: Construct an AC / DC power distribution system topology that includes distributed new energy power generation equipment, shared energy storage devices, and multiple types of loads; S2: Construct an AC / DC power distribution system burst power shortage state identification and adaptive triggering discrimination model based on the AC / DC power distribution system topology; S3: Construct a multi-entity optimal scheduling model for AC / DC power distribution systems that considers shared energy storage based on the AC / DC power distribution system topology; S4: Construct a multi-stage dynamic scheduling strategy for shared energy storage resources that considers sudden power shortages based on the state recognition and adaptive triggering discrimination model and the optimized scheduling model; S5: Execute a multi-stage dynamic scheduling strategy, and construct a compensation mechanism for shared energy storage to participate in rapid response to sudden power shortages based on the dynamic scheduling strategy. Complete the adaptive triggering scheduling of shared energy storage in the AC / DC power distribution system based on the scheduling results and the compensation mechanism.

2. The shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems according to claim 1, characterized in that, The state recognition and adaptive triggering discrimination model includes a day-ahead optimization stage, an intraday adjustment stage, and a state triggering stage. The day-ahead optimization phase involves developing an initial operation plan based on day-ahead source-load forecast data. During the intraday adjustment phase, the subnet power balance status is monitored in real time based on the rolling optimization results. When a sudden power deficit is detected and the triggering criteria of the adaptive triggering discrimination model are met, the event response mechanism is activated. During the state triggering phase, the shared energy storage dynamically adjusts its charging and discharging strategy based on the optimization objectives to provide emergency power support for power-deficient areas.

3. The shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems according to claim 1, characterized in that, A sudden power deficit is a situation where the system power deficit exceeds 20% of the planned value for the day before, and the existing regulation capacity is insufficient to eliminate the power deficit in a timely manner.

4. The shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems according to claim 1, characterized in that, The triggering criterion for the adaptive trigger discrimination model is: in, For nodes Place Electricity purchased during the day within the specified time period For nodes Place Power purchased before the specified time period For nodes Place The criterion for triggering a sudden power deficit state during a given period is as follows: when the criterion value is 1, it indicates that the node... Place When a sudden power shortage occurs during a certain period, it is necessary to activate the dynamic and rapid response of shared energy storage.

5. The shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems according to claim 1, characterized in that, The optimized scheduling model takes into account the dynamic response process of shared energy storage participating in the AC / DC power distribution system, and considers the operating costs and economic benefits of the AC subgrid, DC ring network and shared energy storage entities in the system, so as to minimize the operating cost of the AC / DC power distribution system.

6. The shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems according to claim 5, characterized in that, The objective function of the multi-agent optimal scheduling model for AC / DC power distribution systems considering shared energy storage is: in, The objective function of the multi-agent optimal scheduling model for AC / DC distribution systems considering shared energy storage is... For the objective function of the communication subnet, Let the objective function be that of the DC ring network. For energy storage operating costs, To find the minimum value of the function.

7. The shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems according to claim 5, characterized in that, The operational constraints of the multi-agent optimization scheduling model for AC / DC power distribution systems that considers shared energy storage include AC subgrid power flow constraints, AC subgrid power balance constraints, AC subgrid power transmission constraints between the AC subgrid and the upper-level main grid, distributed power output constraints in the AC subgrid, AC subgrid operational safety constraints, voltage source converter operational model constraints, as well as coordination constraints, state of charge constraints, and charging / discharging power constraints for shared energy storage.

8. The shared energy storage adaptive triggering scheduling method for AC / DC power distribution systems according to claim 1, characterized in that, The dynamic scheduling strategy adopts a multi-time-scale architecture, dynamically combining timing optimization with state triggering. During the day-ahead dispatch phase, based on all forecast data and with an economic focus, the day-ahead main grid power purchase plan, shared energy storage charging and discharging strategy, and tie-line interactive power plan are optimized to establish a day-ahead operation reference point. During the intraday scheduling phase, rolling optimizations are performed based on short-term forecast data and the day-ahead operating reference point. When the power deviation exceeds the preset adjustment margin and causes a sudden power shortage during the rolling optimization process, the state triggering mechanism activates the dynamic fast response strategy of shared energy storage, and restores the system power balance by allocating energy storage resources among subgrids.

9. The shared energy storage adaptive triggering scheduling method for an AC / DC power distribution system according to claim 1, characterized in that, The compensation mechanism includes response decision calculation, resource reallocation, and economic compensation. The response decision calculation determines the power deviation of each node in the system based on the intraday rolling optimization results, identifies the location and time of the sudden power shortage event based on the triggering criteria, and calculates the optimal response strategy for shared energy storage based on the minimum power shortage objective function after the sudden power shortage state is triggered. The resource reallocation is based on the decision results, adjusting the charging and discharging power allocation of shared energy storage to each subgrid, and prioritizing the supply and demand balance needs of areas that trigger sudden power shortage events; The economic compensation statistics are based on the difference between the power deficit before and after compensation for shared energy storage during all triggering periods, and the power deficit compensation for shared energy storage is calculated in combination with the power deficit compensation coefficient for shared energy storage.

10. A shared energy storage adaptive triggering dispatching system for AC / DC power distribution systems, characterized in that, To execute a shared energy storage adaptive triggering scheduling method for an AC / DC power distribution system as described in any one of claims 1 to 9, comprising: The status awareness and deficit identification module is used to acquire the operation data of each AC sub-region, DC interconnection network, AC-DC converter and shared energy storage. Based on the day-ahead power purchase plan, intraday rolling power purchase demand, source-load forecast deviation, AC-DC interconnection power and the available capacity of shared energy storage, it identifies the sudden power deficit status and outputs the power deficit risk indicators of different sub-regions. The adaptive triggering discrimination module is used to determine whether there is a sudden power shortage event in the system that requires the participation of shared energy storage based on the power shortage risk index, the remaining capacity of shared energy storage, the remaining transmission capacity of AC / DC interconnection channel and the preset triggering threshold, and to generate a shared energy storage adaptive triggering signal when the triggering conditions are met. The multi-stage collaborative scheduling module is used to execute an optimization method that combines day-ahead baseline scheduling, intraday rolling correction, and state-triggered response to coordinate the main grid power purchase, AC / DC interconnection power, and shared energy storage charging and discharging power to form a shared energy storage scheduling scheme. The cross-regional mutual assistance and coordination module is used to determine the cross-regional power support path, output the shared energy storage charging and discharging power, AC / DC interconnection power adjustment amount, cross-regional support scheme and compensation benefit results, and send them to the AC / DC power distribution system dispatch and control terminal.

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