Distributed energy storage optimal configuration method and system for overload management of power distribution area
By constructing a distributed energy storage optimization configuration model and using dynamic weighting coefficients and voltage sensitivity coefficients to optimize energy storage configuration, the problem of temporary heavy overload in distribution substations was solved, achieving efficient voltage stabilization and cost optimization.
Patent Information
- Application Number
- CN202511867366.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-11
- Publication Date
- 2026-05-05
AI Technical Summary
Existing technologies are insufficient to effectively address the problem of temporary heavy overload in distribution transformer areas. Traditional governance methods are costly, slow to respond, and lack flexibility. Distributed energy storage optimization configuration schemes are difficult to accurately match the random and short-term characteristics of temporary overloads.
A distributed energy storage optimization configuration model is constructed. By acquiring historical load data of distribution substations, reconstructing load data, calculating dynamic weighting coefficients and voltage sensitivity coefficients, establishing voltage deviation penalty costs, and iteratively solving for the energy storage configuration scheme that minimizes the total economic cost, the configuration of the energy storage system is optimized.
It has achieved efficient management of temporary heavy overload in distribution transformer areas, improved voltage stability and power reliability, and reduced the investment and operation and maintenance costs of energy storage.
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Figure CN121984046A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of distributed energy storage configuration technology for distribution networks, and particularly relates to a method and system for optimizing the configuration of distributed energy storage for temporary heavy overload management of distribution substations. Background Technology
[0002] With the rising electricity demand from urban and rural residents, the large-scale integration of new energy vehicle charging piles, and the increasing local consumption of distributed photovoltaic power, temporary heavy overloads frequently occur in distribution substations. This not only shortens the service life of equipment such as distribution transformers and lines but may also cause power outages, affecting the reliability of users' electricity supply. Traditional methods for mitigating temporary heavy overloads (such as line capacity expansion, load transfer, and temporary load shedding) can be effective in specific scenarios, but they suffer from high investment costs, slow response times, and insufficient flexibility. Distributed energy storage, with its flexible and controllable characteristics, has become a key technological direction for mitigating temporary heavy overloads.
[0003] In existing technologies, distributed energy storage optimization has been extensively studied in areas such as peak-valley regulation of distribution transformer area load and smoothing of new energy output fluctuations. However, few studies have explored in depth the optimized configuration of distributed energy storage under the specific scenario of temporary heavy overload. This makes it difficult for existing configuration schemes to accurately match the randomness and short-term characteristics of temporary overload, and thus cannot efficiently solve the problem of temporary heavy overload in the transformer area. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a distributed energy storage optimization configuration method and system for overload mitigation in distribution transformer areas. It fully considers the different voltage quality requirements of sensitive loads and conventional loads in distribution transformer areas, taking into account key factors such as network topology, voltage characteristics, line power supply capacity, energy storage investment and maintenance costs, and charging / discharging operation characteristics. A distributed energy storage configuration optimization model is constructed to achieve optimized configuration of distributed energy storage systems for temporary heavy overload mitigation.
[0005] The present invention adopts the following technical solution.
[0006] This invention proposes a distributed energy storage optimization configuration method for managing temporary heavy overload in distribution transformer areas, comprising: Historical load data of the distribution substation is acquired, and the load data of the day in which the temporary heavy overload event occurs is reconstructed. The reconstructed load data, distribution network topology, and load access nodes constitute a temporary heavy overload scenario. The dynamic weighting coefficient of the temporary heavy overload event and the voltage sensitivity coefficient of each node under each temporary heavy overload scenario are calculated. Based on the dynamic weighting coefficient and voltage sensitivity coefficient, a voltage deviation penalty cost is established. The total economic cost is composed of energy storage investment and operation and maintenance costs, voltage deviation penalty costs, penalty costs of various types of slack variables, and external grid power purchase costs. Under multiple constraints of the distribution network, the distributed energy storage configuration scheme that minimizes the total economic cost is iteratively solved.
[0007] In each iteration, based on the distributed energy storage configuration scheme obtained from the current iteration, the predicted load data of the distribution substation is determined, and the load data for the day in which the temporary heavy overload event occurs is reconstructed. The predicted heavy overload scenario is constructed based on the reconstructed load data, the distribution network topology, and the load access nodes. The predicted weight coefficients of the temporary heavy overload events of each node in the predicted heavy overload scenario are calculated, and the voltage sensitivity coefficient is corrected based on the deviation between the predicted weight coefficients and the dynamic weight coefficients. The voltage deviation penalty cost is updated using the predicted weight coefficients and the corrected voltage sensitivity coefficients. The iteration ends when the deviation between the predicted weight coefficients and the dynamic weight coefficients is less than a set threshold, thus obtaining the optimized distributed energy storage configuration scheme for the management of temporary heavy overload in the distribution substation.
[0008] Preferably, temporary heavy overload events are marked in the historical load data of the distribution area, and the duration of the temporary heavy overload events is determined; The load data for the duration of the temporary heavy overload event remains unchanged, while the load data for the period outside the duration of the temporary heavy overload event is taken from the load data of the normal days of the same period in history, so as to obtain the reconstructed daily load data.
[0009] Preferably, a temporary overload event is marked when the following conditions are met simultaneously: 1) Sudden condition: The first time the start time of the temporary heavy overload event is met. Compared to the previous moment When the apparent power increase of the load is not less than 30% of the rated capacity of the transformer, The start time of the temporary overload event is shown in the following formula:
[0010] In the formula, The start time of the temporary overload event Compared to the previous moment -1 is the apparent power increase of the load. The start time of the temporary overload event The apparent power of the load, The rated capacity of the transformer in the distribution area; 2) Peak load condition: There are n or more consecutive time intervals of apparent power of the load. Not less than the rated capacity of the transformer times, The overload factor is n, where n is a positive integer greater than 1, and the time interval is ≥1 hour. 3) Short-term condition: The duration of a temporary heavy overload event should not exceed 6 hours; 6 In the formula, Duration of the temporary heavy overload event; This is the start time of the temporary heavy overload event; This marks the end of a temporary heavy overload event.
[0011] Preferably, the dynamic weighting coefficient is as follows:
[0012] In the formula, , , These represent the start time, end time, and duration of a temporary heavy overload event. Indicates time The duration of the temporary heavy overload event; For nodes At any moment Dynamic weighting coefficients; For nodes At any moment The cumulative duration of heavy overload; This is a temporary overload duration adjustment factor; The temporary heavy overload multiple is the ratio between the average load during the duration of the temporary heavy overload event and the average normal load in the reconstructed daily load data. This is the adjustment coefficient for temporary heavy overload.
[0013] Preferably, a voltage deviation penalty cost is established based on the dynamic weighting coefficient and the voltage sensitivity coefficient, as shown in the following formula:
[0014] In the formula, Penalty cost for voltage deviation The cost of the penalty for deviation per unit voltage. This refers to the system's daily runtime. The total number of nodes. for Time Node voltage, For nodes Voltage sensitivity coefficient; Preferably, the slack variable penalty cost is constructed as shown in the following equation:
[0015] In the formula, The cost of penalizing slack variable bias. , , These are the relaxation penalty coefficients for voltage, active power, and reactive power, respectively. , The nodes at time t are respectively Voltage relaxation variables, active power relaxation variables, and reactive power relaxation variables.
[0016] Preferably, the first In the next iteration, the ratio of the difference between the predicted weight coefficient and the dynamic weight coefficient to the dynamic weight coefficient is calculated. ;according to The correction factor for the voltage sensitivity coefficient is fitted using an exponential function. As shown in the following formula: .
[0017] This invention also proposes a distributed energy storage optimization configuration system for managing temporary heavy overload in distribution transformer areas, comprising: The scenario generation module is used to obtain historical load data of the distribution network area, reconstruct the load data of the day in which the temporary heavy overload event occurs, and use the reconstructed load data, distribution network topology and load access nodes to form a temporary heavy overload scenario. The coefficient calculation module is used to calculate the dynamic weight coefficient of the temporary heavy overload event of each node and the voltage sensitivity coefficient of each node under each temporary heavy overload scenario. The optimization configuration module is used to establish voltage deviation penalty cost based on dynamic weight coefficient and voltage sensitivity coefficient; the total economic cost is composed of energy storage investment and operation and maintenance cost, voltage deviation penalty cost, penalty cost of various types of slack variables and external power grid purchase cost; under multiple constraints of the distribution network, the distributed energy storage configuration scheme that minimizes the total economic cost is iteratively solved.
[0018] The optimization configuration module is also used to determine the predicted load data of the distribution substation based on the distributed energy storage configuration scheme obtained in each iteration, and to reconstruct the load data of the day in which the temporary heavy overload event occurs. Based on the reconstructed load data, distribution network topology, and load access nodes, a predicted heavy overload scenario is constructed. The prediction weight coefficient of the temporary heavy overload event of each node in the predicted heavy overload scenario is calculated, and the voltage sensitivity coefficient is corrected based on the deviation between the prediction weight coefficient and the dynamic weight coefficient. The voltage deviation penalty cost is updated using the prediction weight coefficient and the corrected voltage sensitivity coefficient. The iteration ends when the deviation between the prediction weight coefficient and the dynamic weight coefficient is less than a set threshold, thus obtaining the distributed energy storage optimization configuration scheme for the management of temporary heavy overload in the distribution substation.
[0019] The present invention is also a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.
[0020] The present invention is also a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.
[0021] The beneficial effects of this invention are as follows: Compared with the prior art, this invention at least includes the following: It reconstructs the load data for the day in which the temporary heavy overload event occurs, avoiding interference from other influencing factors in the original load data for that day; it constructs a combined scenario with normal day load data as the background, highlighting the characteristics of the temporary heavy overload event itself; it calculates the dynamic weighting coefficient of the temporary heavy overload event for each node and the voltage sensitivity coefficient for each node under each temporary heavy overload scenario; based on the dynamic weighting coefficient and voltage sensitivity coefficient, it establishes a voltage deviation penalty cost, fully considering the core impacts of the differentiated voltage quality requirements of sensitive loads and regular loads in the distribution substation, network topology, voltage characteristics, line power supply capacity, and multi-dimensional key factors such as the economic cost of energy storage investment and operation, and charging and discharging operation characteristics, and provides a distributed energy storage configuration optimization model for temporary heavy overload management. The method involves several steps: In each iteration, based on the distributed energy storage configuration scheme obtained from the current iteration, the predicted load data for the distribution substation is determined, and the load data for the day of the temporary heavy overload event is reconstructed. The reconstructed load data, distribution network topology, and load access nodes form a predicted heavy overload scenario. The predicted weight coefficients for temporary heavy overload events at each node under the predicted heavy overload scenario are calculated, and the voltage sensitivity coefficient is corrected based on the deviation between the predicted weight coefficients and the dynamic weight coefficients. The voltage deviation penalty cost is updated using the predicted weight coefficients and the corrected voltage sensitivity coefficients. The iteration ends when the deviation between the predicted weight coefficients and the dynamic weight coefficients is less than a set threshold, resulting in an optimized distributed energy storage configuration scheme for managing temporary heavy overloads in distribution substations. This achieves optimized configuration of the distributed energy storage system for managing temporary heavy overloads. Attached Figure Description
[0022] Figure 1 This is a flowchart of the distributed energy storage optimization configuration method for managing temporary heavy overload in distribution areas proposed in this invention. Figure 2 This is a schematic diagram of the IEEE 33-node distribution network used in the embodiments of the present invention; Figure 3 This is a comparison diagram of node voltages before energy storage is configured in this embodiment of the invention; Figure 4 This is a comparison diagram of node voltages after energy storage is configured in an embodiment of the present invention; Figure 5 This is a voltage comparison diagram of node 33 during the overload period in an embodiment of the present invention. 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 with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.
[0024] This invention proposes a distributed energy storage optimization configuration method for managing temporary heavy overloads in distribution transformer substations. Addressing the "short-term and sudden" characteristics of temporary heavy overloads in distribution transformer substations, the method defines the identification conditions for such scenarios. Load curves are extracted from historical load data of the distribution transformer substation, and a system model of the temporary heavy overload scenario is constructed based on the distribution network topology. An optimization objective function is built, aiming to minimize the total economic cost, including energy storage investment and operation and maintenance costs, voltage deviation penalty costs considering the differentiated needs of sensitive and regular nodes, penalty costs for various types of slack variables, and external grid power purchase costs. Key constraints for distribution network operation, such as energy storage configuration and operation constraints, line voltage and capacity constraints, and node power balance constraints, are established. The constructed optimization model is solved to obtain an optimized distributed energy storage configuration scheme for managing temporary heavy overloads in distribution transformer substations.
[0025] like Figure 1 As shown, the method includes: Step 1: Obtain the historical load of the distribution network area, reconstruct the load data of the day in which the temporary heavy overload event occurs, and use the reconstructed load, distribution network topology, and load access nodes to constitute the temporary heavy overload scenario.
[0026] In response to the "short-term and sudden" characteristics of temporary heavy overload in distribution transformer substations, the identification conditions for temporary heavy overload scenarios are defined. Load curves are extracted from historical load data of distribution transformer substations, and a system model of temporary heavy overload scenarios in distribution transformer substations is constructed in combination with the distribution network topology. The constructed temporary heavy overload scenario of distribution transformer substations is composed of load, distribution network topology, and load demand access location.
[0027] Specifically, step 1 includes: Step 1.1: Mark temporary heavy overload events in the historical load data of the distribution transformer area and determine the duration of the temporary heavy overload events; In this embodiment, load data of the distribution substation over the past three years is acquired, with a time resolution of 15 minutes. The load data includes active power. and reactive power ; A temporary overload event is marked when all of the following conditions are met: 1) Sudden condition: The first time the start time of the temporary heavy overload event is met. Compared to the previous moment When the apparent power increase of the load is not less than 30% of the rated capacity of the transformer, The start time of the temporary overload event is shown in the following formula: (1) In the formula, The start time of the temporary overload event Compared to the previous moment -1 represents the apparent power increase of the load (in MVA). The start time of the temporary overload event Apparent power of load (unit: MVA). Rated capacity of transformers in the distribution area (unit: MVA); 2) Peak load condition: There are n or more consecutive time intervals of apparent power of the load. Not less than the rated capacity of the transformer times; In the embodiments, The overload factor is 1.5, which is adjusted according to the characteristics of the distribution area; n is 4, and the time interval is ≥1 hour. 3) Short-term condition: The duration of a temporary heavy overload event should not exceed 6 hours; 6(2) In the formula, Duration of the temporary heavy overload event (in hours); This is the start time of the temporary heavy overload event; The end time of a temporary heavy overload event is the time when the apparent power of the load continues to drop back to the rated power of the transformer in the distribution area.
[0028] Step 1.2: The load data for the duration of the temporary heavy overload event remains unchanged. The load data for the period outside the duration of the temporary heavy overload event is taken from the load data of the normal days of the same period in history, so as to obtain the reconstructed daily load data.
[0029] The original load data may be mixed with other interferences, such as extreme weather and power grid maintenance. The load during non-overload periods may not be typical normal operating conditions. By retaining the original true overload data during overload periods and replacing non-overload periods with historical data from the same period without typical normal loads, the influence of other interfering factors is eliminated. To avoid interference from other influencing factors in the original load data for the current day, a combined scenario based on normal daily load data is constructed to highlight the characteristics of the temporary heavy overload event itself. For each marked temporary heavy overload event, its duration is extracted. The load data is used as the core overload period load; for times other than the continuous period, the load curve of the normal day during the same period is used to fill in the gaps, finally forming a complete 24-hour heavy overload load scenario curve: (3) In the formula, , These are the time intervals after reconstruction. The active power (unit: MW) and reactive power (unit: MVar) of the load. , These are the active power (unit: MW) and reactive power (unit: MVar) of the load during a temporary heavy overload event, respectively. , For the same period in history The active power (unit: MW) and reactive power (unit: MVar) of the normal daily load.
[0030] Step 1.3: Obtain the typical topology node and line parameters of the distribution network, and construct the topology matrix. As shown in equation (4).
[0031] (4) In the formula, , The lines are respectively The corresponding start node number and end node number; , The lines are respectively The resistance and reactance values.
[0032] Step 1.4: Select the load access node according to the distribution network topology, and transfer the load active power. With reactive power The active power of the generated node load is allocated based on the number of access nodes. With reactive power and the active power of node loads. reactive power Line resistance Line reactance Normalization is performed.
[0033] Step 2: Calculate the dynamic weight coefficient of the temporary overload event for each node and the sensitivity coefficient of each node under each temporary overload scenario.
[0034] The dynamic weight coefficient under any temporary overload scenario is the dynamic weight coefficient of each node when it is in a temporary overload event, which is dynamically calculated based on the temporary overload state of node i at time t, the cumulative duration of temporary overload, and the temporary overload multiple. In response to the core characteristics of temporary heavy overload events—namely, their short duration, suddenness, and high impact (as defined in step 1.1 above: an increase of ≥30%, a peak apparent power of the load ≥1.5 times the rated capacity, and a duration ≤6 hours)—this invention introduces a dynamic weighting coefficient for temporary heavy overload events to avoid insufficient voltage control accuracy during overload periods caused by "indiscriminate optimization." The dynamic weight coefficients are based on node i at time i. Whether it is under heavy overload, the cumulative duration of heavy overload, and the heavy overload multiple are dynamically calculated so that the optimization model prioritizes the voltage stability requirements during heavy overload periods. The specific definitions are as follows.
[0035] (5) In the formula, , , These represent the start time, end time, and duration of a temporary heavy overload event. Indicates time The duration of the temporary heavy overload event; For nodes At any moment Dynamic weighting coefficients; For nodes At any moment Cumulative heavy overload duration (unit: hours); The temporary heavy overload duration adjustment coefficient is set to 0.8 in this example. It is adjusted according to the characteristics of the transformer area and is used to quantify the requirement that "the longer the heavy overload lasts, the more the voltage needs to be stabilized first". The temporary heavy overload multiple is the ratio between the average load during the duration of the temporary heavy overload event and the average normal load in the reconstructed daily load data. This is a temporary overload multiple adjustment factor, with a value of 0.5. It is adjusted according to the characteristics of the transformer area and is used to quantify that "the more severe the overload, the higher the priority of voltage stability".
[0036] The voltage sensitivity coefficient of a node is a core concept in power system analysis and control. It quantitatively describes how sensitive the voltage of a node in the system is to changes in the reactive or active power of another node.
[0037] Step 3: Based on the dynamic weighting coefficient and voltage sensitivity coefficient, establish the voltage deviation penalty cost; the total economic cost is composed of the energy storage investment and operation and maintenance cost, the voltage deviation penalty cost, the penalty cost of various types of slack variables, and the external power grid purchase cost.
[0038] Specifically, step 3 includes: Step 3.1, the investment and operation and maintenance costs of energy storage include: investment costs and operation and maintenance costs based on installed power and capacity; as shown in the following formula: (6) In the formula, Investment cost of energy storage system (unit: yuan); A set of nodes in the distribution network topology; The unit power investment cost of the energy storage system (unit: yuan / MW); For nodes Installed capacity of energy storage systems (unit: MW); The unit capacity investment cost of the energy storage system (unit: yuan / MWh); For nodes Installed capacity of energy storage systems (unit: MWh); The discount rate; For energy storage lifespan; The annual operation and maintenance cost of the energy storage system (unit: yuan); The annual operation and maintenance cost per unit power of the energy storage system (unit: yuan / MW); The unit capacity operation and maintenance cost of the energy storage system (unit: yuan / MWh).
[0039] Step 3.2: Based on the dynamic weighting coefficient and voltage sensitivity coefficient, establish the voltage deviation penalty cost, as shown in the following formula: (7) In the formula, Voltage deviation penalty cost (unit: yuan); The cost of penalizing a unit voltage deviation; This represents the system's daily runtime; in this example, it is taken as 24 hours. The total number of nodes; for Time Node Voltage (unit: pu); For nodes The voltage sensitivity coefficient.
[0040] By introducing a node voltage sensitivity coefficient to distinguish between sensitive and ordinary nodes, the high-precision voltage requirements of sensitive loads are addressed. Specifically, to address the high-precision voltage requirements of sensitive loads during temporary heavy overloads, a node voltage deviation penalty weighting coefficient is introduced to differentiate between sensitive and ordinary nodes. A higher voltage deviation penalty is applied to sensitive nodes, ensuring that the model prioritizes the voltage stability of sensitive loads.
[0041] Step 3.3: In optimization problems, strict constraints are usually imposed on variables. However, strictly satisfying all constraints can be very difficult. By introducing slack variables, the constraints are appropriately relaxed to ensure that the optimization problem does not become infeasible due to strict constraints. The slack variable penalty cost is constructed as shown in the following formula: (8) In the formula, Cost of slack variable bias penalty (unit: yuan); , , These are the relaxation penalty coefficients for voltage, active power, and reactive power, respectively. , The nodes at time t are respectively Voltage relaxation variables, active power relaxation variables, and reactive power relaxation variables.
[0042] Step 3.4: Construct the power purchase cost of the power injected from the external power grid, as shown in the following formula.
[0043] (9) In the formula, The total cost of purchasing electricity from the external power grid through the balancing node on a system operating day (unit: yuan); The active power electricity price (unit: yuan / MWh) is taken as 500 yuan / MWh in the example; The reactive power price is (unit: yuan / MVarh), and in this example, it is taken as 50 yuan / MVarh; The active power injected into the system from the external power grid at time t (unit: MW); The reactive power injected into the system from the external power grid at time t (unit: MVar).
[0044] Step 3.5 combines the investment and operation costs of the energy storage system's power and capacity, voltage deviation penalty costs, slack variable penalty costs, and external grid power purchase costs to construct an optimization objective function that aims to minimize the total economic cost of the system.
[0045] Step 4: Construct multiple constraints for the distribution network, including but not limited to: energy storage configuration and operation constraints, line voltage and capacity constraints, and node power balance constraints.
[0046] Specifically, step 4 includes: Step 4.1: Addressing the differences in voltage quality requirements between sensitive loads and conventional loads, differentiate between node load types and set voltage thresholds to construct voltage amplitude constraints for distribution network nodes, including voltage at conventional nodes. It should not be lower than the lower voltage limit. It should not exceed the upper voltage limit. As shown in equation (11); sensitive node voltage It should not be lower than the lower voltage limit. It should not exceed the upper voltage limit. As shown in equation (12).
[0047] , , , (11) , , , (12) In the formula, The lower limit of the conventional node voltage, pu; The upper limit of the conventional node voltage, pu; The lower limit of the sensitive node voltage, pu; pu represents the upper limit of the sensitive node voltage.
[0048] Assume node 1 is the balancing node, with a fixed voltage of 1.0 pu, as shown in equation (13): (13) Step 4.2: Construct voltage drop constraints for distribution network lines, for connection nodes. and the finish line The route The voltage drop should meet the following constraints, as shown in equations (14)-(15).
[0049] , , (14) , , (15) In the formula, , The lines at time t are respectively Voltages at the start and end nodes (unit: pu); , The lines at time t are respectively Transmitted active power (unit: MW) and reactive power (unit: MVar). Let be the voltage drop relaxation variable of line l at time t, and take the value 0.001 pu.
[0050] Step 4.3: Construct the operating constraints of the energy storage system, including installation logic constraints, capacity-power ratio constraints, charge-discharge power constraints, and state of charge constraints, as shown in equations (16)-(27).
[0051] Energy storage installation logic constraints: (16) In the formula, node Whether to connect energy storage, =1 indicates that at node Connect to energy storage, =0 indicates that at node Without energy storage, , For nodes The active power and reactive power.
[0052] Energy storage installation capacity and power constraints: (17) (18) In the formula, Configure the upper limit of energy storage capacity (unit: MWh); Upper limit for energy storage capacity (unit: MW); Energy storage installation power-capacity ratio constraints: (19) In the formula, This is the energy storage power capacity coefficient, to avoid configuration results of small capacity with high power or large capacity with low power.
[0053] Remaining energy constraints at the initial moment of energy storage: (20) In the formula, For nodes Remaining energy at the initial moment of energy storage (unit: MWh).
[0054] Energy storage charging and discharging power limitations: (twenty one) (twenty two) In the formula, , The nodes at time t are respectively Discharge power and charging power of energy storage (unit: MW).
[0055] Energy storage charging and discharging mutual exclusion constraints: (twenty three) (twenty four) (25) In the formula, , The node at time t 0-1 state variables of energy storage charging and discharging states; This indicates that node i is in a charging state at time t; This indicates that at time t, the energy storage at node i is in a discharging state. At the same time, the energy storage cannot be in a charging state or a discharging state.
[0056] Energy storage remaining power update constraints: (26) In the formula, The node at time t Remaining energy storage capacity (unit: MWh); , These are the energy storage charging efficiency and the discharging efficiency, respectively. The time interval for a single energy storage adjustment is set to 1 hour.
[0057] Upper and lower limits of remaining energy storage capacity: (27) In the formula, This is the lower limit of the energy storage SOC.
[0058] Step 4.4: The external power grid injects active power and reactive power into the system through the balancing node. The injected active power should not exceed 90% of the transformer's rated capacity. The active power injected by the external power grid is constrained as shown in Equation (28). The injected reactive power should change dynamically with the load to avoid insufficient reactive power under light load and excessive reactive power under heavy overload, as shown in Equation (29).
[0059] (28) (29) In the formula, This is the minimum reactive power base value of the system (unit: MVar), for small systems ( <2MVA), =0.05MVar; for medium-sized systems (2 < <10MVA), =0.1MVar; for large systems ( >10MVA), =0.2MVar.
[0060] Step 4.5: Construct power balance constraints for line nodes, as shown in equations (30)-(33).
[0061] (30) (31) (32) (33) In the formula, For all inflow nodes A collection of routes; For all outflow nodes A collection of routes; , The nodes at time t are respectively The active power (unit: MW) and reactive power (unit: MVar) of the load.
[0062] Step 4.6: In order to meet the different voltage quality requirements of sensitive nodes and conventional nodes, the upper and lower limits of voltage and the reactive power adjustment ratio of sensitive nodes and conventional nodes are distinguished. When the node voltage deviates from the corresponding threshold, the reactive power adjustment with different intensity is used to help stabilize the voltage, so as to prioritize the voltage stability of sensitive nodes and construct the node reactive power-voltage correlation constraint as shown in equations (34)-(37).
[0063] For sensitive nodes: (1) If the voltage of a sensitive node is lower than the lower voltage limit, the node voltage is increased by increasing the reactive power inflow.
[0064] , (34) (2) If the voltage of a sensitive node is higher than the upper voltage limit, the node voltage is suppressed by reducing reactive power inflow.
[0065] , (35) For regular nodes: (1) If the voltage of a normal node is lower than the lower voltage limit, the voltage of the node is increased by increasing the reactive power inflow.
[0066] , (36) (2) If the normal node voltage is higher than the voltage limit, the node voltage is suppressed by reducing reactive power inflow.
[0067] , (37) Step 5: Under multiple constraints of the distribution network, iteratively solve for the distributed energy storage configuration scheme that minimizes the total economic cost.
[0068] In each iteration, based on the distributed energy storage configuration scheme obtained from the current iteration, the predicted load data of the distribution substation is determined, and the load data for the day in which the temporary heavy overload event occurs is reconstructed. The predicted heavy overload scenario is constructed based on the reconstructed load data, the distribution network topology, and the load access nodes. The predicted weight coefficients of the temporary heavy overload events of each node in the predicted heavy overload scenario are calculated, and the voltage sensitivity coefficient is corrected based on the deviation between the predicted weight coefficients and the dynamic weight coefficients. The voltage deviation penalty cost is updated using the predicted weight coefficients and the corrected voltage sensitivity coefficients. The iteration ends when the deviation between the predicted weight coefficients and the dynamic weight coefficients is less than a set threshold, thus obtaining the optimized distributed energy storage configuration scheme for the management of temporary heavy overload in the distribution substation.
[0069] Solve the established optimization model to obtain the distributed energy storage optimization configuration scheme for the temporary heavy overload management of distribution network areas. The established distributed energy storage system planning optimization model for the temporary heavy overload management of distribution network is a mixed integer linear programming model, which can be solved directly by calling commercial solvers such as CPLEX and GUROBI to obtain the corresponding energy storage optimization configuration results.
[0070] No. In the next iteration, the ratio of the difference between the predicted weight coefficient and the dynamic weight coefficient to the dynamic weight coefficient is calculated. ;according to The correction factor for the voltage sensitivity coefficient is fitted using an exponential function. As shown in the following formula:
[0071] In the initial solution process, the voltage sensitivity coefficient of each node is calculated based on the network topology and historical data. If a normal node continuously and frequently experiences temporary heavy overload events with high dynamic weighting coefficients, the system can adaptively increase its voltage sensitivity coefficient, as this node may have effectively evolved into a newly emerging sensitive node. Conversely, if a sensitive node is in a state of extremely low load for a long period, its voltage sensitivity coefficient can be appropriately reduced. The correction factor obtained by fitting the exponential function is suitable for small... Insensitive to large The strong reaction effectively highlights emerging sensitive nodes.
[0072] This invention also proposes a distributed energy storage optimization configuration system for managing temporary heavy overload in distribution transformer areas, comprising: The scenario generation module is used to obtain historical load data of the distribution network area, reconstruct the load data of the day in which the temporary heavy overload event occurs, and use the reconstructed load data, distribution network topology and load access nodes to form a temporary heavy overload scenario. The coefficient calculation module is used to calculate the dynamic weight coefficient of the temporary heavy overload event of each node and the voltage sensitivity coefficient of each node under each temporary heavy overload scenario. The optimization configuration module is used to establish voltage deviation penalty cost based on dynamic weight coefficient and voltage sensitivity coefficient; the total economic cost is composed of energy storage investment and operation and maintenance cost, voltage deviation penalty cost, penalty cost of various types of slack variables and external power grid purchase cost; under multiple constraints of the distribution network, the distributed energy storage configuration scheme that minimizes the total economic cost is iteratively solved.
[0073] The optimization configuration module is also used to determine the predicted load data of the distribution substation based on the distributed energy storage configuration scheme obtained in each iteration, and to reconstruct the load data of the day in which the temporary heavy overload event occurs. Based on the reconstructed load data, distribution network topology, and load access nodes, a predicted heavy overload scenario is constructed. The prediction weight coefficient of the temporary heavy overload event of each node in the predicted heavy overload scenario is calculated, and the voltage sensitivity coefficient is corrected based on the deviation between the prediction weight coefficient and the dynamic weight coefficient. The voltage deviation penalty cost is updated using the prediction weight coefficient and the corrected voltage sensitivity coefficient. The iteration ends when the deviation between the prediction weight coefficient and the dynamic weight coefficient is less than a set threshold, thus obtaining the distributed energy storage optimization configuration scheme for the management of temporary heavy overload in the distribution substation.
[0074] Figures 2-5 Examples of implementation of the present invention are shown. To verify the implementation effect of the present invention, Figure 2 The IEEE standard 33-node distribution network shown is used as the research object for analysis. Nodes 5, 10, 15, and 21 are designated as sensitive load nodes, while nodes 2, 4, 7, 12, 18, 23, and 27 are designated as conventional load nodes. The Cplex solver is used to complete the optimization calculation. The resulting optimized distributed energy storage configuration is 1.25MW / 5MWh at node 2 and 0.3MW / 1.2MWh at node 15. For mitigation needs in temporary heavy overload scenarios, the node voltage comparison before and after energy storage configuration is as follows: Figure 3 and Figure 4 As shown, during the overload period, the voltage at node 33 is as follows: Figure 5 As shown, the results indicate that the above-mentioned energy storage management scheme can effectively solve the low voltage problem caused by temporary heavy overload in the distribution substation, and significantly improve the stability of the distribution network operation.
[0075] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.
[0076] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.
[0077] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.
[0078] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.
[0079] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.
Claims
1. A method for optimizing the allocation of distributed energy storage for managing temporary heavy overload in distribution transformer areas, characterized in that, include: Historical load data of the distribution network area is obtained, and the load data of the day in which the temporary heavy overload event occurs is reconstructed. The reconstructed load data, distribution network topology, and load access nodes constitute the temporary heavy overload scenario. Calculate the dynamic weighting coefficients of temporary heavy overload events for each node under various temporary heavy overload scenarios and the voltage sensitivity coefficients for each node; establish voltage deviation penalty costs based on the dynamic weighting coefficients and voltage sensitivity coefficients; construct the total economic cost by including energy storage investment and maintenance costs, voltage deviation penalty costs, penalty costs for various types of slack variables, and external grid power purchase costs; and iteratively solve for the distributed energy storage configuration scheme that minimizes the total economic cost under multiple constraints of the distribution network.
2. The distributed energy storage optimization configuration method for temporary heavy overload management of distribution transformer areas according to claim 1, characterized in that, In each iteration, based on the distributed energy storage configuration scheme obtained from the current iteration, the predicted load data of the distribution substation is determined, and the load data for the day in which the temporary heavy overload event occurs is reconstructed. The predicted heavy overload scenario is constructed based on the reconstructed load data, the distribution network topology, and the load access nodes. The predicted weight coefficients of the temporary heavy overload events of each node in the predicted heavy overload scenario are calculated, and the voltage sensitivity coefficient is corrected based on the deviation between the predicted weight coefficients and the dynamic weight coefficients. The voltage deviation penalty cost is updated using the predicted weight coefficients and the corrected voltage sensitivity coefficients. The iteration ends when the deviation between the predicted weight coefficients and the dynamic weight coefficients is less than a set threshold, thus obtaining the optimized distributed energy storage configuration scheme for the management of temporary heavy overload in the distribution substation.
3. The distributed energy storage optimization configuration method for temporary heavy overload management of distribution transformer areas according to claim 2, characterized in that, Mark temporary heavy overload events in the historical load data of the distribution area and determine the duration of the temporary heavy overload events; The load data for the duration of the temporary heavy overload event remains unchanged, while the load data for the period outside the duration of the temporary heavy overload event is taken from the load data of the normal days of the same period in history, so as to obtain the reconstructed daily load data.
4. The distributed energy storage optimization configuration method for temporary heavy overload management of distribution transformer areas according to claim 3, characterized in that, A temporary overload event is marked when all of the following conditions are met: 1) Sudden condition: The first time the start time of the temporary heavy overload event is met. Compared to the previous moment When the apparent power increase of the load is not less than 30% of the rated capacity of the transformer, The start time of the temporary overload event is shown in the following formula: In the formula, The start time of the temporary overload event Compared to the previous moment -1 is the apparent power increase of the load. The start time of the temporary overload event The apparent power of the load, The rated capacity of the transformer in the distribution area; 2) Peak load condition: There are n or more consecutive time intervals of apparent power of the load. Not less than the rated capacity of the transformer times, The overload factor is n, where n is a positive integer greater than 1, and the time interval is ≥1 hour. 3) Short-term condition: The duration of a temporary heavy overload event should not exceed 6 hours; 6 In the formula, Duration of the temporary heavy overload event; This is the start time of the temporary heavy overload event; This marks the end of a temporary heavy overload event.
5. The distributed energy storage optimization configuration method for temporary heavy overload management of distribution transformer areas according to claim 1, characterized in that, The dynamic weighting coefficients are shown in the following formula: In the formula, , , These represent the start time, end time, and duration of a temporary heavy overload event. Indicates time The duration of the temporary heavy overload event; For nodes At any moment Dynamic weighting coefficients; For nodes At any moment The cumulative duration of heavy overload; This is a temporary overload duration adjustment factor; The temporary heavy overload multiple is the ratio between the average load during the duration of the temporary heavy overload event and the average normal load in the reconstructed daily load data. This is the adjustment coefficient for temporary heavy overload.
6. The distributed energy storage optimization configuration method for temporary heavy overload management of distribution transformer areas according to claim 5, characterized in that, Based on the dynamic weighting coefficient and the voltage sensitivity coefficient, the voltage deviation penalty cost is established as shown in the following formula: In the formula, Penalty cost for voltage deviation The cost of the penalty for deviation per unit voltage. This refers to the system's daily runtime. The total number of nodes. for Time Node voltage, For nodes The voltage sensitivity coefficient.
7. The distributed energy storage optimization configuration method for temporary heavy overload management of distribution transformer areas according to claim 1, characterized in that, The slack variable penalty cost is constructed as shown in the following equation: In the formula, The cost of penalizing slack variable bias. , , These are the relaxation penalty coefficients for voltage, active power, and reactive power, respectively. , The nodes at time t are respectively Voltage relaxation variables, active power relaxation variables, and reactive power relaxation variables.
8. The distributed energy storage optimization configuration method for temporary heavy overload management of distribution transformer areas according to claim 2, characterized in that, No. In the next iteration, the ratio of the difference between the predicted weight coefficient and the dynamic weight coefficient to the dynamic weight coefficient is calculated. ;according to The correction factor for the voltage sensitivity coefficient is fitted using an exponential function. As shown in the following formula: 。 9. A distributed energy storage optimization configuration system for managing temporary heavy overload in distribution transformer areas, used to implement the method described in any one of claims 1 to 8, characterized in that, include: The scenario generation module is used to obtain historical load data of the distribution network area, reconstruct the load data of the day in which the temporary heavy overload event occurs, and use the reconstructed load data, distribution network topology and load access nodes to form a temporary heavy overload scenario. The coefficient calculation module is used to calculate the dynamic weight coefficient of the temporary heavy overload event of each node and the voltage sensitivity coefficient of each node under each temporary heavy overload scenario. The optimization configuration module is used to establish voltage deviation penalty cost based on dynamic weight coefficient and voltage sensitivity coefficient; the total economic cost is composed of energy storage investment and operation and maintenance cost, voltage deviation penalty cost, penalty cost of various types of slack variables and external power grid purchase cost; under multiple constraints of the distribution network, the distributed energy storage configuration scheme that minimizes the total economic cost is iteratively solved.
10. The distributed energy storage optimization configuration system for temporary heavy overload management of distribution transformer areas according to claim 9, characterized in that, The optimization configuration module is also used to determine the predicted load data of the distribution substation based on the distributed energy storage configuration scheme obtained in each iteration, and to reconstruct the load data of the day in which the temporary heavy overload event occurs. Based on the reconstructed load data, distribution network topology, and load access nodes, a predicted heavy overload scenario is constructed. The prediction weight coefficient of the temporary heavy overload event of each node in the predicted heavy overload scenario is calculated, and the voltage sensitivity coefficient is corrected based on the deviation between the prediction weight coefficient and the dynamic weight coefficient. The voltage deviation penalty cost is updated using the prediction weight coefficient and the corrected voltage sensitivity coefficient. The iteration ends when the deviation between the prediction weight coefficient and the dynamic weight coefficient is less than a set threshold, thus obtaining the distributed energy storage optimization configuration scheme for the management of temporary heavy overload in the distribution substation.
11. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-8.
12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-8.