Strategy determination method and device of power grid system and electronic equipment

Abstract

The invention discloses a strategy determination method and device of a power grid system and electronic equipment. The method comprises the steps that power grid state data corresponding to a target power grid system at the current moment are acquired, and the target power grid system comprises target equipment; a target function corresponding to the target power grid system and a target constraint corresponding to the target function are called, the target function takes total network loss minimization as a target, the target constraint comprises a voltage qualification rate constraint, and the target function comprises voltage functions and network loss data items corresponding to the multiple feeder lines respectively; the corresponding voltage function is a function taking the network loss minimum value of the corresponding feeder line as a target; and according to the power grid state data, under the target constraint, solving the target function to obtain an execution strategy corresponding to the target power grid system. According to the method and the device, the technical problem of poor system adaptability and economical efficiency easily caused when control is carried out according to the determined power grid system execution strategy in related technologies is solved.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more specifically, to a strategy determination method, apparatus, and electronic equipment for a power grid system. Background Technology

[0002] Traditional power grids, such as the main grid AVC system, typically rely on control strategies based on fixed rules, such as nine-zone diagrams and thirteen-zone diagrams. These strategies have long control cycles and highly discrete regulation commands, making it difficult to adapt to the second-level voltage fluctuations caused by the high proportion of distributed power sources connected to the grid.

[0003] In related technologies, multi-agent cooperative control methods based on reinforcement learning (such as MADDPG) can achieve hierarchical cooperation between substations and distribution networks. However, when they are controlled according to the determined power grid system execution strategy, they are prone to problems of poor system adaptability and economy.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a strategy determination method, apparatus, and electronic device for a power grid system, to at least solve the technical problem in the related art that when control is performed based on the determined power grid system execution strategy, it is easy to lead to poor system adaptability and economy.

[0006] According to one aspect of the present invention, a strategy determination method for a power grid system is provided, comprising: acquiring power grid state data corresponding to a target power grid system at the current time, wherein the target power grid system includes target equipment, the target equipment includes: transformer taps and capacitor banks, and the power grid state data includes transformer tap location data and capacitor bank switching status data; retrieving an objective function corresponding to the target power grid system and an objective constraint corresponding to the objective function, wherein the objective function aims to minimize total network loss, the objective constraint includes a voltage qualification rate constraint, the objective function includes voltage functions and network loss data items corresponding to multiple feeders respectively, the corresponding voltage function being a function aiming to minimize the network loss of the corresponding feeder, used to determine the corresponding operating voltage; and solving the objective function based on the power grid state data and under the objective constraints to obtain an execution strategy corresponding to the target power grid system, wherein the execution strategy includes a transformer tap location strategy and a capacitor bank switching strategy.

[0007] Optionally, based on the power grid state data, under the target constraints, the objective function is solved to obtain an execution strategy corresponding to the target power grid system, including: obtaining load forecast data corresponding to the multiple feeders for a predetermined future time period; based on the power grid state data and the load forecast data, under the target constraints, the objective function is solved to obtain an execution strategy corresponding to the target power grid system.

[0008] Optionally, before retrieving the objective function, the method further includes: determining the target voltage term and the target equipment action penalty term corresponding to each of the multiple feeders, wherein the target equipment action penalty term includes the action cost term and the gear change impact term corresponding to each of the target equipment; and constructing the objective function based on the target equipment action penalty term, the voltage function, the network loss data term, and the target voltage term corresponding to each of the multiple feeders.

[0009] Optionally, before retrieving the objective function, the method further includes: determining the target voltage term, reference voltage term, network loss data term, and adjustment weight term corresponding to each of the multiple feeders; for each feeder, determining the square of the difference between the target voltage term and the reference voltage term corresponding to each feeder to obtain the corresponding voltage difference term; determining the product of the voltage difference term and the corresponding adjustment weight term corresponding to each feeder to obtain the corresponding voltage adjustment term; and determining the sum of the voltage adjustment term and the network loss data term corresponding to each feeder to obtain the voltage function corresponding to each of the multiple feeders.

[0010] Optionally, before retrieving the target constraint corresponding to the objective function, the method further includes: determining an extreme voltage term, wherein the extreme voltage term is a term whose corresponding voltage is an extreme voltage determined from the operating voltage terms corresponding to the multiple feeders respectively; determining voltage deviation terms corresponding to the multiple feeders respectively at a predetermined time; determining upper voltage limit terms and lower voltage limit terms corresponding to the multiple feeders respectively based on the extreme voltage term and the voltage deviation terms corresponding to the multiple feeders respectively; and determining the voltage pass rate constraint based on the upper voltage limit terms and lower voltage limit terms corresponding to the multiple feeders respectively.

[0011] Optionally, determining the voltage deviation items corresponding to the plurality of feeders at a predetermined time includes: determining the maximum absolute deviation ratio item based on the extreme voltage item and the reference voltage item; determining the feeder capacity item corresponding to the plurality of feeders and the load change item at the predetermined time; and determining the voltage deviation items corresponding to the plurality of feeders at the predetermined time based on the maximum absolute deviation ratio item, the feeder capacity item corresponding to the plurality of feeders and the load change item at the predetermined time.

[0012] Optionally, before calling the objective function, the method further includes: for each feeder, determining the target voltage term corresponding to each feeder, the active power injection term, reactive power injection term, and resistance term of adjacent node line segments corresponding to multiple nodes on each feeder; and determining the network loss data terms corresponding to the multiple feeders based on the target voltage term corresponding to each feeder, the active power injection term, reactive power injection term, and resistance term of adjacent node line segments corresponding to multiple nodes on each feeder.

[0013] According to one aspect of the present invention, a strategy determination apparatus for a power grid system is provided, comprising: an acquisition module, configured to acquire power grid state data corresponding to a target power grid system at the current time, wherein the target power grid system includes target equipment, the target equipment includes: transformer taps and capacitor banks, and the power grid state data includes transformer tap location data and capacitor bank switching status data; a retrieval module, configured to retrieve an objective function corresponding to the target power grid system and an objective constraint corresponding to the objective function, wherein the objective function aims to minimize total network loss, the objective constraint includes a voltage qualification rate constraint, the objective function includes voltage functions and network loss data items corresponding to multiple feeders respectively, the corresponding voltage function being a function aiming to minimize the network loss of the corresponding feeder, used to determine the corresponding operating voltage; and a determination module, configured to solve the objective function based on the power grid state data and under the objective constraints to obtain an execution strategy corresponding to the target power grid system, wherein the execution strategy includes a transformer tap location strategy and a capacitor bank switching strategy.

[0014] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the strategy determination method for a power grid system as described in any of the preceding claims.

[0015] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the strategy determination method for a power grid system as described above.

[0016] In this embodiment of the invention, the power grid status data of the target power grid system at the current moment is obtained. The target power grid system includes target equipment, which includes transformer taps and capacitor banks. The power grid status data includes transformer tap location data and capacitor bank switching status data. An objective function corresponding to the target power grid system and corresponding objective constraints are retrieved. The objective function aims to minimize total network loss. The objective constraints include voltage qualification rate constraints. The objective function includes voltage functions and network loss data items corresponding to multiple feeders. The corresponding voltage functions are functions with the minimum network loss of the corresponding feeder as the objective, used to determine the corresponding operating voltage. Based on the power grid status data and under the objective constraints, the objective function is solved to obtain the execution strategy corresponding to the target power grid system. The execution strategy includes transformer tap location strategy and capacitor bank switching strategy. As can be seen, in this application, an objective function is used to determine the execution strategy. Since the voltage functions and network loss data items corresponding to multiple feeders are respectively considered, it is known that the operating voltage and network loss of multiple feeders are considered respectively. The impact of the differences in the economy and adaptability of different feeders on the network loss of the whole system is considered. Therefore, it can take all factors into consideration, so that the determined execution strategy has adaptability and economy. This solves the technical problem in related technologies that when the control is carried out according to the determined power grid system execution strategy, it is easy to cause poor system adaptability and economy. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a strategy determination method for a power grid system according to an embodiment of the present invention;

[0019] Figure 2 This is a flowchart illustrating the strategy execution method determined by an optional embodiment of the present invention;

[0020] Figure 3 This is a structural block diagram of a strategy determination device for a power grid system according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Example 1

[0024] According to an embodiment of the present invention, an embodiment of a strategy determination method for a power grid system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a flowchart of a strategy determination method for a power grid system according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0026] Step S102: Obtain the power grid status data of the target power grid system at the current moment. The target power grid system includes target equipment, which includes transformer taps and capacitor banks. The power grid status data includes transformer tap location data and capacitor bank switching status data.

[0027] In step S102 of this application, the power grid state data of the target power grid system at the current moment is obtained.

[0028] This involves a target power grid system, which refers to a specific power network. The system includes electrical equipment and lines within the power supply area, such as substations, feeders, capacitor banks, and transformer taps. Optionally, the target power grid system involved in this application can be an AVC (Automatic Voltage Control) power network.

[0029] This includes power grid status data, which refers to the actual operating parameters of the power network at a certain moment, such as voltage, current, power factor, transformer tap position, and capacitor bank switching status.

[0030] This involves target equipment, which refers to the equipment in the target power grid system that requires subsequent policy control.

[0031] This involves transformer tap changes. Transformers are used to regulate voltage in power grids, and the tap position is a key parameter affecting the output voltage. By changing the tap position, the transformer's transformation ratio can be adjusted, thereby controlling the grid voltage.

[0032] This involves capacitor banks, which are used in power systems to provide reactive power compensation. By switching capacitor banks on and off, the reactive power in the system can be adjusted, thereby affecting the grid voltage.

[0033] This includes transformer tap position data, which indicates the current position and tap level of the transformer tap.

[0034] This includes capacitor bank switching status data, which indicates whether each capacitor bank is in operation and at what level it is switched on or off. This data can be used to optimize reactive power distribution.

[0035] In step S102, the status data of the target power grid system is first acquired in real time. By obtaining the location of transformer taps and the switching status of capacitor banks, the operating status of the power grid can be understood in real time, including voltage levels and reactive power distribution. This provides accurate power grid operating status information for the subsequent formulation of AVC strategies, ensuring the real-time performance and accuracy of the control strategy.

[0036] Step S104: Retrieve the objective function corresponding to the target power grid system and the objective constraints corresponding to the objective function. The objective function aims to minimize the total network loss, and the objective constraints include voltage qualification rate constraints. The objective function includes voltage functions and network loss data items corresponding to multiple feeders. The corresponding voltage function is a function with the minimum network loss of the corresponding feeder as the objective, which is used to determine the corresponding operating voltage.

[0037] In step S104 of this application, the corresponding objective function and the corresponding constraints are retrieved.

[0038] This involves an objective function that aims to minimize total network loss, i.e., to reduce energy loss during power transmission through optimization strategies.

[0039] This involves objective constraints, which are used to constrain the objective function. The objective constraints in this step include voltage qualification rate constraints, which ensure that the grid voltage is within the specified safe range and avoid power quality problems caused by voltage exceeding the limit.

[0040] This involves voltage qualification rate constraints, which are an important indicator for measuring the voltage quality of a power system. It defines the percentage of times the voltage at each node in the power grid is within a specified range. Optionally, other constraints may also be included in the system. However, one of the system's objective constraints is to ensure the voltage qualification rate, that is, to ensure that the voltage at all nodes in the power grid is maintained within the allowable fluctuation range, avoiding damage to equipment or impact on power quality caused by excessively high or low voltage.

[0041] This involves multiple feeders. In a power system, multiple lines supplying power from one substation to different areas are called feeders. Each feeder may have different operating states and characteristics. By considering the characteristics of multiple feeders, this application can comprehensively optimize the operating states of all feeders, avoiding the optimization of a single feeder from leading to reduced efficiency of other feeders or overuse of equipment, ultimately minimizing network losses across the entire system and improving the overall economic operation of the power system.

[0042] This involves voltage functions corresponding to multiple feeders. Considering the differences in voltage control objectives and economics among different feeders, a specific voltage function was designed for each feeder to guide dynamic voltage optimization. Feeder network losses are directly related to their voltages, such as the head-end voltage. By constructing a voltage function with the goal of minimizing network losses, the economic operating voltage of each feeder can be accurately calculated. This means that, under the premise of satisfying power supply and voltage quality requirements, the feeder network losses reach a theoretically minimum, thereby saving energy losses during power transmission, reducing operating costs, and improving the economics of the power system.

[0043] This invention involves collecting and analyzing network loss data items corresponding to multiple feeders to minimize overall network loss. Compared to traditional power system network loss calculations that often operate on a system-wide or substation-by-station basis, this invention determines the network loss data items for each feeder, achieving more detailed network loss management. The network loss data items for each feeder are directly related to its operating efficiency. By analyzing and optimizing the network losses of each feeder, the system can dynamically adjust reactive power compensation and voltage regulation strategies to reduce energy loss during power transmission and achieve economical operation.

[0044] This involves operating voltage, which refers to the voltage level at various points in the power grid and is a key indicator for the stable operation of the power system. A reasonable operating voltage can reduce the operational stress on equipment and prevent damage or performance degradation caused by excessively high or low voltage. Ideally, the operating voltage should be within an optimal range, ensuring power quality while minimizing grid losses and improving economic efficiency.

[0045] In this step, by invoking a pre-defined objective function and constraints, with the core objective of minimizing total network loss, the system prioritizes minimizing energy loss during power transmission. Through a carefully designed optimization algorithm, the system can dynamically adjust the operating voltage of each feeder to approach or reach the economic operating point with minimum network loss, thereby significantly reducing the power company's operating costs and improving the grid's economic efficiency. The voltage compliance requirement in the objective constraints ensures that the power system's operating voltage is always maintained within a safe and compliant range. The voltage functions corresponding to multiple feeders determine the optimal operating voltage for each feeder. By combining the objective function of minimizing total network loss with the voltage functions of each feeder, the system can achieve global and local collaborative optimization. This means that while satisfying the requirement of minimizing total network loss, the system can also take into account the voltage characteristics and economic operating needs of each feeder, thereby improving the overall performance of the entire power grid system, including economy, power quality, and operational stability.

[0046] Step S106: Based on the power grid state data, under the target constraints, solve the objective function to obtain the execution strategy corresponding to the target power grid system. The execution strategy includes the transformer tap position strategy and the capacitor bank switching strategy.

[0047] In step S106 provided in this application, an objective function is solved under objective constraints based on power grid state data to obtain the transformer tap position strategy and capacitor bank switching strategy.

[0048] This involves execution strategies, which are the specific operational instructions calculated by the system based on optimization objectives and constraints. These execution strategies include transformer tap position adjustment strategies and capacitor bank switching strategies.

[0049] This involves the strategy for positioning transformer tap changers, as tap changer adjustments directly affect the voltage level on the transformer's secondary side. By optimizing tap changer positions, the AVC system can ensure that the grid voltage remains within acceptable limits, preventing excessively low or high voltage levels, thereby improving power quality and guaranteeing the normal operation of user equipment.

[0050] This involves capacitor bank switching strategies, which directly affect the reactive power compensation level of the power system. A reasonable switching strategy ensures that appropriate reactive power is provided under different load conditions, maintaining the system's power factor at an optimal level and reducing additional losses caused by reactive power flow. Capacitor banks can assist in voltage regulation by absorbing or releasing reactive power. Optimized switching strategies can more accurately control voltage levels, especially in situations requiring fine-tuning.

[0051] In this step, the system's execution strategy is obtained by solving an optimization problem. This strategy not only guides the adjustment of transformer tap positions but also determines the switching of capacitor banks, thereby achieving effective control over grid voltage and losses. This approach enables dynamic optimization and real-time control of the strategy, improving the economy and stability of grid operation.

[0052] Through steps S102-S106 above, the grid status data corresponding to the target grid system at the current moment is obtained. The target grid system includes target equipment, which includes transformer taps and capacitor banks. The grid status data includes transformer tap location data and capacitor bank switching status data. The objective function corresponding to the target grid system and the objective constraints corresponding to the objective function are retrieved. The objective function aims to minimize the total grid loss. The objective constraints include voltage qualification rate constraints. The objective function includes voltage functions and grid loss data items corresponding to multiple feeders. The corresponding voltage function is a function with the minimum grid loss of the corresponding feeder as the objective, used to determine the corresponding operating voltage. Based on the grid status data, under the objective constraints, the objective function is solved to obtain the execution strategy corresponding to the target grid system. The execution strategy includes transformer tap location strategy and capacitor bank switching strategy. As can be seen, in this application, an objective function is used to determine the execution strategy. Since the voltage functions and network loss data items corresponding to multiple feeders are respectively considered, it is known that the operating voltage and network loss of multiple feeders are considered respectively. The impact of the differences in the economy and adaptability of different feeders on the network loss of the whole system is considered. Therefore, it can take all factors into consideration, so that the determined execution strategy has adaptability and economy. This solves the technical problem in related technologies that when the control is carried out according to the determined power grid system execution strategy, it is easy to cause poor system adaptability and economy.

[0053] As an optional embodiment, based on the power grid state data, under target constraints, an objective function is solved to obtain an execution strategy corresponding to the target power grid system, including: obtaining load forecast data corresponding to multiple feeders for a predetermined future time period; and based on the power grid state data and load forecast data, under target constraints, an objective function is solved to obtain an execution strategy corresponding to the target power grid system.

[0054] In this embodiment, it is illustrated that the execution strategy is predicted while taking into account load forecasting parameters.

[0055] This involves a predetermined future time period, which refers to the period during which the system needs to predict and plan for power grid operation over a future timeframe (e.g., 15-30 minutes). Data within this predetermined period forms the basis for the system's forward-looking decision-making.

[0056] This includes load forecasting data, which is an estimate of future load demand on the power grid, including projected active and reactive power requirements. This data helps the system predict future grid conditions and plan equipment operations in advance.

[0057] In this step, the system obtains load forecast information for each feeder within the next 15-30 minutes from an external load forecasting system. This information includes projected active and reactive power demand, as well as potential load fluctuation trends. By acquiring load forecast data, the system can pre-plan equipment actions, reducing operational delays caused by sudden load changes. Forecasted load data helps reduce frequent equipment operations due to load fluctuations, extending equipment lifespan and reducing maintenance costs. Subsequently, the system comprehensively considers the current real-time status data of the power grid and future load forecast data, while adhering to objective constraints, and solves the objective function through an optimization algorithm to obtain the execution strategy. This ensures that the execution strategy considers the current status of the equipment and expected load demand, optimizes equipment scheduling, avoids unnecessary equipment actions, improves system responsiveness, and extends equipment lifespan.

[0058] Compared to related technologies that lack the ability to predict dynamic load trends, leading to control strategies lagging behind actual operating conditions, this system can plan equipment actions before load changes occur, adjusting operating parameters such as transformer tap positions or capacitor bank switching statuses in advance to cope with impending changes. This proactive control significantly reduces control lag, enabling the system to respond to load fluctuations more promptly and effectively, maintaining voltage within acceptable ranges and improving system stability and safety. The use of load forecast information allows the AVC system to make more accurate and timely equipment action decisions, avoiding frequent equipment adjustments. Based on load change predictions, the system can achieve the expected operating state with one or several adjustments, rather than continuous fine-tuning, thereby reducing mechanical and electrical stress on equipment, extending equipment lifespan, and reducing power system operation and maintenance costs.

[0059] As an optional embodiment, before retrieving the objective function, the method further includes: determining the target voltage terms corresponding to the multiple feeders and the target equipment action penalty terms, wherein the target equipment action penalty terms include the action cost terms and the gear change impact terms corresponding to the target equipment; and constructing the objective function based on the target equipment action penalty terms, the voltage functions corresponding to the multiple feeders, the network loss data terms, and the target voltage terms.

[0060] This embodiment illustrates the process of constructing the target function before calling it.

[0061] This includes a target voltage term, which can be the voltage at the beginning of the corresponding feeder.

[0062] This involves a penalty item for target equipment actions. This strategy optimization introduces the concept of equipment action penalties to reduce unnecessary equipment actions and avoid frequent gear adjustments. This penalty item includes an action cost item and a gear change impact item, aiming to quantify the negative impact of frequent equipment actions.

[0063] This includes an operational cost item, which reflects the economic cost of controlling equipment actions (such as transformer tap switching and capacitor bank switching). Frequent actions not only increase energy consumption but also accelerate equipment wear and tear, raising maintenance and replacement costs.

[0064] This includes the impact of tap change, which specifically refers to the potential impact of equipment (such as transformer tap changers) tap changes on system stability, equipment lifespan, and power quality. Large or frequent tap changes may cause transient instability in the system, affect the long-term performance of equipment, and cause voltage fluctuations.

[0065] In this step, target voltage terms were identified for multiple feeders. When optimizing the control strategy, the ideal voltage level for each feeder must first be determined, as different feeders may have different optimal voltage levels due to their load characteristics, line length, and equipment configuration. Setting target voltage terms allows the system to control each feeder more precisely, adapting to its specific operating conditions. Target voltage terms help maintain feeder voltage within the optimal operating range, reducing voltage fluctuations and improving power quality. Target equipment action penalty terms were also identified, including action cost terms for each target equipment and tap change impact terms. When optimizing the strategy, the penalty cost of equipment actions also needs to be considered, including the direct economic cost of equipment actions (action cost term) and the indirect impact on system stability, power quality, and equipment lifespan (taper change impact term). By quantifying the cost of equipment actions, the AVC system can avoid unnecessary equipment actions, thereby reducing the power company's expenses due to equipment wear and maintenance. The inclusion of tap change impact terms considers the harm of frequent equipment actions to their long-term performance, helping to extend equipment lifespan and reduce the risk of unexpected downtime. Reducing unnecessary equipment actions helps maintain system stability and avoids transient instability caused by equipment adjustments. By constructing an objective function based on the above considerations, we can further ensure that the system finds the optimal control strategy that satisfies both voltage stability and economic requirements while minimizing the number of equipment actions and costs.

[0066] As an optional embodiment, before retrieving the objective function, the method further includes: determining the target voltage term, reference voltage term, network loss data term, and adjustment weight term corresponding to each of the multiple feeders; for each feeder, determining the square of the difference between the target voltage term and the reference voltage term corresponding to each feeder to obtain the corresponding voltage difference term; determining the product of the voltage difference term and the corresponding adjustment weight term corresponding to each feeder to obtain the corresponding voltage adjustment term; and determining the sum of the voltage adjustment term and the network loss data term corresponding to each feeder to obtain the voltage function corresponding to each of the multiple feeders.

[0067] In this embodiment, the process of determining the voltage functions corresponding to each of the multiple feeders is described.

[0068] As an optional embodiment, before retrieving the target constraint corresponding to the objective function, the method further includes: determining an extreme voltage term, wherein the extreme voltage term is the term whose corresponding voltage is an extreme voltage determined from the operating voltage terms corresponding to the multiple feeders respectively; determining the voltage deviation term corresponding to the multiple feeders respectively at a predetermined time; determining the upper voltage limit term and the lower voltage limit term corresponding to the multiple feeders respectively based on the extreme voltage term and the voltage deviation term corresponding to the multiple feeders respectively; and determining the voltage qualification rate constraint based on the upper voltage limit term and the lower voltage limit term corresponding to the multiple feeders respectively.

[0069] In this embodiment, the process of determining the voltage compliance rate constraint is described.

[0070] This includes a reference voltage term, which sets an ideal voltage level that reflects the voltage requirements of the power system under ideal conditions. The reference voltage term may be determined based on various factors such as historical data, load forecasting, and equipment capacity.

[0071] This includes adjustment weighting terms, which are coefficients used in system control to balance voltage regulation and economic operation. Adjustment weighting terms reflect the priority and importance of different performance characteristics under a given operating condition, such as the priority and importance between voltage stability and economic efficiency.

[0072] This includes a voltage difference term, which is the square of the difference between the target voltage term and the reference voltage term, and measures the degree of deviation between the actual voltage state and the ideal state.

[0073] This includes a voltage regulation term, which is the product of the voltage difference term and the regulation weight term. This term is a key parameter in balancing voltage deviation and economical operation. The magnitude of the voltage regulation term reflects the urgency and cost of adjusting voltage deviation.

[0074] In this step, the process of constructing the voltage function by identifying and processing the target voltage term, reference voltage term, network loss data term, and adjustment weight term provides the system with multifaceted considerations, enabling it to quantify the economic cost of voltage regulation and the efficiency loss of power transmission based on real-time and predictive data. This not only optimizes the voltage regulation strategy and improves the economic operating efficiency of the power system, but also enhances system stability and power quality through clear control objectives and deviation identification, avoids over-regulation of equipment, effectively extends equipment lifespan, and provides a solid technical foundation for the intelligent operation of the power system.

[0075] As an optional embodiment, determining the voltage deviation items corresponding to multiple feeders at a predetermined time includes: determining the maximum absolute deviation ratio item based on the extreme voltage item and the reference voltage item; determining the feeder capacity item corresponding to each of the multiple feeders and the load change item at the predetermined time; and determining the voltage deviation items corresponding to each of the multiple feeders at the predetermined time based on the maximum absolute deviation ratio item, the feeder capacity item corresponding to each of the multiple feeders, and the load change item at the predetermined time.

[0076] In this embodiment, the process of determining the voltage deviation terms corresponding to the multiple feeders at a predetermined time is described.

[0077] This involves a predetermined time, which refers to a specific future time window in which the system makes predictions and control decisions. This time can be consistent with the future time based on load forecasts, such as 15-30 minutes.

[0078] This includes an extreme voltage term, which refers to the maximum or minimum value of the feeder voltage that may occur within a predetermined time period. This value is crucial for assessing voltage stability and can help the system determine whether there is a risk of voltage exceeding limits.

[0079] This includes a reference voltage item, which is the standard voltage level of the system under normal operating conditions. It is usually set at the midpoint of the acceptable voltage range and is the basis for measuring voltage deviation.

[0080] This includes the maximum absolute deviation ratio term, which refers to the ratio of the calculated voltage deviation to the reference voltage, representing the severity of voltage fluctuations and used to quantify voltage stability.

[0081] This includes a feeder capacity item, which is the maximum power capacity that each feeder can carry.

[0082] This includes a load change item, which represents the increase or decrease in feeder load (i.e., power demand) relative to the current moment within a predetermined time window, used to assess the dynamic changes in load.

[0083] This includes a voltage deviation item, which is the deviation between the actual feeder voltage and the reference voltage over a predetermined period of time. The voltage deviation item is a key basis for the AVC system to adjust the actions of equipment (such as tap changers and capacitor banks) to maintain voltage stability.

[0084] As an optional embodiment, before calling the objective function, the method further includes: for each feeder, determining the target voltage term corresponding to each feeder, the active power injection term, reactive power injection term, and resistance term of the adjacent node line segment corresponding to multiple nodes on each feeder; and determining the network loss data terms corresponding to multiple feeders based on the target voltage term corresponding to each feeder, the active power injection term, reactive power injection term, and resistance term of the adjacent node line segment corresponding to multiple nodes on each feeder.

[0085] In this embodiment, the process of determining the network loss data items corresponding to each of the multiple feeders is described.

[0086] This includes the target voltage item, which refers to the voltage level at the starting point of each feeder, usually the point connecting to the substation or the upstream power grid. Its voltage value is affected by the regulation of the upstream power grid or substation.

[0087] This includes active power injection and reactive power injection. These two items represent the active power (actual electrical energy consumed) and reactive power (electrical energy used to establish magnetic and electric fields, which is not directly converted into useful work) injected into each node in the feeder, respectively. These data reflect the load characteristics and power demand of the nodes.

[0088] This includes the resistance term of the line segment between adjacent nodes. This term represents the resistance value of the line between two adjacent nodes in the power grid and is one of the key parameters for calculating energy loss (network loss) in power transmission.

[0089] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0090] In related technologies, existing multi-agent cooperative control methods based on reinforcement learning (such as MADDPG) can achieve hierarchical coordination between substations and distribution networks, but they still have the following limitations: First, they lack the ability to predict dynamic load trends, causing control strategies to lag behind actual operating conditions. Second, when controlling multiple feeders, voltage qualification rate is used as the sole optimization objective, failing to fully consider the impact of economic differences among different feeders on the overall network loss. Furthermore, traditional methods lack refined modeling in terms of equipment action frequency constraints and multi-objective trade-offs, easily leading to conflicts between local optimization and global economic objectives. Therefore, there is an urgent need for an intelligent voltage control method that integrates load forecasting and economic optimization to improve the forward-looking decision-making capability and overall economic efficiency of the main grid AVC system. That is, traditional methods rely on current measurement data for decision-making, lacking the ability to predict load trends, causing control commands to lag behind actual operating conditions, especially in scenarios with random fluctuations in renewable energy output, where frequent equipment actions and short-term voltage exceedances are prominent issues. On the other hand, existing collaborative optimization models do not fully consider the differentiated economic needs of multiple feeders, and use a uniform voltage threshold or fixed economic weight coefficient, which leads to high-penetration photovoltaic feeders being forced to transmit reactive power in reverse to meet voltage constraints, causing a surge in network losses. Moreover, in traditional methods, the prediction results and control strategies are still decoupled, serving only as offline reference data, without forming a closed-loop optimization mechanism, making it difficult to dynamically correct the economic voltage target and equipment action timing.

[0091] In view of this, an optional embodiment of the present invention provides a method for dynamically optimizing the main grid AVC strategy based on load forecasting results. By constructing a closed-loop control architecture of "predictive data-driven - multi-objective collaborative optimization", it solves the problem of decreased control performance caused by response lag and insufficient economy in traditional methods. By using load forecasting results as prior information for AVC strategy optimization, the economic voltage reference value is dynamically corrected and the equipment action timing planning is guided, achieving proactive regulation and optimal global economy. Figure 2 This is a flowchart illustrating the strategy execution method determination provided by an optional embodiment of the present invention, such as... Figure 2 As shown, it will be introduced below:

[0092] S1, Obtain the power grid status data of the target power grid system at the current moment, wherein the target power grid system includes target equipment, and the target equipment includes: transformer taps and capacitor banks, and the power grid status data includes transformer tap location data T. i OLTC Capacitor bank switching status data T i CB Optionally, the power grid status data may also include the low-voltage side bus voltage data Vr of the transformer and the high-voltage side reactive power data Qr of the transformer.

[0093] S2, obtain load forecast data for multiple feeders for a predetermined future time period;

[0094] For example, it could receive load curves (including active / reactive power forecasts) for the next 15-30 minutes from an external load forecasting system, and combine them with real-time measurement data (voltage, power factor, equipment status) to form an extended state space. Specifically, a state vector is defined:

[0095]

[0096] Where: Vr and Qr are the reactive power of the low-voltage side bus and high-voltage side of the substation transformer, respectively; T i OLTC T i CB These represent the current transformer tap and the current capacitor bank tap, respectively; Ppred and Qpred are the predicted load values ​​within the future time step, respectively; ΔPerror is the average historical prediction error.

[0097] S3, retrieve the objective function corresponding to the target AVC power grid system. The objective function includes the objective constraints corresponding to the objective function, which aims to minimize the total network loss. The objective constraints include voltage qualification rate constraints. The objective function also includes voltage functions corresponding to each of the multiple feeders (also known as the economic voltage model for a single feeder). Network loss data item Target voltage term and the penalty items for the target device's actions The corresponding voltage function is a function with the minimum network loss of the corresponding feeder as the objective, which is used to determine the corresponding operating voltage. The target equipment action penalty item includes the action cost item and the gear change impact item corresponding to the target equipment respectively.

[0098] The objective function is In this context, based on the original overall feeder economic objective (minimizing network loss) and voltage qualification rate constraint, a network loss data item for separately analyzing the economics of multiple feeders, as well as a penalty item for the number of equipment actions, were introduced.

[0099] For the above function, where, This indicates the voltage level at the beginning of the k-th feeder. This represents the nominal voltage, the ideal reference voltage that the system is expected to achieve. For the network loss on the k-th feeder, , As an economic weight, the heavier the load of the feeder, the smaller the corresponding weight. This represents the voltage function of the k-th feeder. This indicates the number of times capacitor bank i has operated. This indicates the number of times transformer i operates.

[0100] It should be noted that, in addition to voltage qualification rate constraints, target constraints may also include voltage safety constraints, action time interval constraints, and the maximum number of daily actions of the equipment.

[0101] S4. Based on the power grid state data, under the objective constraints, solve the objective function to obtain the execution strategy corresponding to the target power grid system. The execution strategy includes the transformer tap position strategy and the capacitor bank switching strategy.

[0102] It should be noted that before calling the target function, the following steps are also included:

[0103] Determine the target voltage terms corresponding to each of the multiple feeders. Reference voltage term Network loss data items and adjusting weights ;

[0104] For each feeder, determine the target voltage term corresponding to each feeder. With reference voltage term The square of the difference yields the corresponding voltage difference term. ;

[0105] Determine the voltage difference term corresponding to each feeder. With the corresponding adjustment weight term The product of these terms yields the corresponding voltage regulation term. ;

[0106] Determine the voltage regulation term for each feeder. Sum of network loss data items The voltage functions corresponding to the multiple feeders are obtained respectively. .

[0107] It should be noted that before calling the target function, the following steps are also included:

[0108] For each feeder, determine the target voltage term corresponding to each feeder. The active power injection items corresponding to multiple nodes on each feeder Reactive power injection item Resistance terms of adjacent node line segments ;

[0109] Based on the target voltage term corresponding to each feeder, and the active power injection term, reactive power injection term, and resistance term of adjacent node line segments corresponding to multiple nodes on each feeder, the network loss data terms corresponding to multiple feeders are determined. , where I is a node on the k-th feeder, and Nk represents the set of nodes on the k-th feeder.

[0110] It should be noted that before retrieving the objective constraints corresponding to the objective function, the following steps are also included:

[0111] Based on the extreme voltage term And the voltage deviation terms corresponding to each of the multiple feeders. Determine the upper voltage limit for each of the multiple feeders. and voltage lower limit item ;

[0112] like ,in, and These are the lower and upper voltage limits of the AVC system at time t. Minimum value of all feeders at the economic reference voltage at time t; The maximum value of all feeders at the economic reference voltage at time t; This refers to the dynamically adjusted voltage deviation.

[0113] The voltage qualification rate constraint is determined based on the upper and lower voltage limits corresponding to each of the multiple feeders.

[0114] It should be noted that before retrieving the objective constraints corresponding to the objective function, the following steps are also included:

[0115] Determine the extreme voltage term, where the extreme voltage term is the term whose corresponding voltage is the extreme voltage determined from the operating voltage terms corresponding to multiple feeders respectively;

[0116] Based on the extreme voltage term and the reference voltage term Determine the percentage term of the maximum absolute deviation, such as ;

[0117] Determine the feeder capacity items corresponding to each of the multiple feeders. and the load variation item under the predetermined time. ;

[0118] Based on the maximum absolute deviation ratio term, the feeder capacity terms corresponding to each of the multiple feeders, and the load change term at a predetermined time, determine the voltage deviation terms corresponding to each of the multiple feeders at the predetermined time. .

[0119] like, ,in, The reference voltage deviation is α, and β are weighting coefficients. The change in load over time t; For feeder capacity; The maximum absolute deviation between the economic reference voltage and the nominal voltage Un for all feeders is determined. If the calculated lower voltage limit is less than the minimum safe voltage value of the distribution network system, then the minimum safe voltage value shall be selected as the lower voltage limit.

[0120] It should be noted that after determining the execution strategy, key indicators such as voltage and power factor of the main grid can be continuously monitored after execution to evaluate the control effect. Data collected during execution, such as the number of equipment actions and changes in network losses, serves as input for subsequent model optimization, enabling model iteration and strategy adjustment. For example, based on the evaluation results, the parameters of the load forecasting model and the weighting coefficients α and β in the economic voltage model can be adjusted to improve forecast accuracy and the flexibility of the control strategy.

[0121] The optimized parameters can then be used to recalculate the AVC strategy, forming a new prediction-optimization-control closed loop. It is then checked whether the preset control cycle endpoint has been reached or whether specific exit conditions are met (e.g., the grid state is stable and frequent adjustments are no longer needed). If the termination conditions are not met, the process returns to the initial steps for the next round of data collection and state space initialization. If the termination conditions are met, it indicates that the current control cycle has been completed, and the process ends. This process not only demonstrates the comprehensiveness and systematic nature of the technical solution of this invention but also ensures that the AVC control system can continuously adjust its strategy through the prediction-optimization-control closed loop under load fluctuation environments, thereby maintaining the efficient and economical operation of the power grid, extending equipment lifespan, and enhancing the system's ability to absorb new energy sources.

[0122] Through the aforementioned optional implementation methods, this invention addresses the problems of frequent equipment operation, increased network losses, and limited renewable energy absorption caused by the delayed response to load fluctuations in traditional AVC systems. By embedding load forecasting results into a multi-feeder collaborative optimization model, a prediction-optimization-control closed-loop architecture is designed to achieve dynamic adjustment of the main grid AVC strategy. This method guides equipment operation timing planning and dynamic correction of economic voltage reference values ​​using load forecasting data, significantly reducing control lag and improving system operating economy and equipment lifespan. It achieves at least the following beneficial effects: through specific technical means such as dynamic voltage deviation calculation, multi-feeder joint optimization model, equipment operation suppression strategy, and parameter adaptive mechanism, it solves the shortcomings of traditional AVC systems in terms of flexibility, economy, equipment lifespan, and adaptability. The combined application of these technical means makes this invention exhibit significant advantages in complex power grid environments.

[0123] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0125] Example 2

[0126] According to an embodiment of the present invention, an apparatus for implementing the above-described strategy determination method for a power grid system is also provided. Figure 3 This is a structural block diagram of a power grid system strategy determination device according to an embodiment of the present invention, such as... Figure 3 As shown, the device includes: an acquisition module 302, a retrieval module 304, and a determination module 306. The device will be described in detail below.

[0127] The acquisition module 302 is used to acquire the power grid status data of the target power grid system at the current time. The target power grid system includes target equipment, which includes transformer taps and capacitor banks. The power grid status data includes transformer tap location data and capacitor bank switching status data.

[0128] The retrieval module 304, connected to the acquisition module 302, is used to retrieve the objective function corresponding to the target power grid system and the objective constraints corresponding to the objective function. The objective function aims to minimize the total network loss, and the objective constraints include voltage qualification rate constraints. The objective function includes voltage functions and network loss data items corresponding to multiple feeders. The corresponding voltage function is a function with the minimum network loss of the corresponding feeder as the objective, which is used to determine the corresponding operating voltage.

[0129] The determination module 306, connected to the aforementioned retrieval module 304, is used to solve the objective function based on the power grid state data and under the target constraints, to obtain the execution strategy corresponding to the target power grid system. The execution strategy includes the transformer tap position strategy and the capacitor bank switching strategy.

[0130] It should be noted that the above-mentioned acquisition module 302, retrieval module 304 and determination module 306 correspond to steps S102 to S106 in the strategy determination method for implementing the power grid system. The multiple modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0131] Example 3

[0132] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the power grid system strategy determination method of any of the above embodiments.

[0133] Example 4

[0134] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the power grid system strategy determination method described above.

[0135] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0136] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0141] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A strategy determination method for a power grid system, characterized in that, include: Obtain the power grid status data of the target power grid system at the current moment, wherein the target power grid system includes target equipment, the target equipment includes: transformer taps and capacitor banks, and the power grid status data includes transformer tap location data and capacitor bank switching status data; The objective function corresponding to the target power grid system and the objective constraints corresponding to the objective function are retrieved. The objective function aims to minimize the total network loss, and the objective constraints include voltage qualification rate constraints. The objective function includes voltage functions and network loss data items corresponding to multiple feeders respectively. The corresponding voltage function is a function with the minimum network loss of the corresponding feeder as the objective, which is used to determine the corresponding operating voltage. Based on the power grid state data, under the target constraints, the objective function is solved to obtain the execution strategy corresponding to the target power grid system. The execution strategy includes the transformer tap position strategy and the capacitor bank switching strategy.

2. The method according to claim 1, characterized in that, Based on the power grid state data, under the target constraints, the objective function is solved to obtain the execution strategy corresponding to the target power grid system, including: Obtain load forecast data for each of the multiple feeders for a predetermined future time period; Based on the power grid status data and the load forecast data, the objective function is solved under the target constraints to obtain the execution strategy corresponding to the target power grid system.

3. The method according to claim 1, characterized in that, Before calling the target function, the following is also included: Determine the target voltage item and the target equipment action penalty item corresponding to each of the multiple feeders, wherein the target equipment action penalty item includes the action cost item and the gear change impact item corresponding to each target equipment; The target function is constructed based on the target device action penalty term and the voltage function, network loss data term, and target voltage term corresponding to the multiple feeders.

4. The method according to claim 1, characterized in that, Before calling the target function, the following is also included: Determine the target voltage term, reference voltage term, network loss data term, and adjustment weight term corresponding to each of the multiple feeders; For each feeder, the square of the difference between the target voltage term and the reference voltage term corresponding to each feeder is determined to obtain the corresponding voltage difference term. The product of the voltage difference term and the corresponding adjustment weight term for each feeder is determined to obtain the corresponding voltage regulation term; The sum of the voltage regulation term and the network loss data term corresponding to each feeder is determined to obtain the voltage function corresponding to each of the multiple feeders.

5. The method according to claim 1, characterized in that, Before retrieving the objective constraints corresponding to the objective function, the process also includes: Determine the extreme voltage term, wherein the extreme voltage term is the term whose corresponding voltage is an extreme voltage determined from the operating voltage terms corresponding to multiple feeders respectively; Determine the voltage deviation terms corresponding to the multiple feeders at a predetermined time. Based on the extreme voltage term and the voltage deviation term corresponding to the multiple feeders, determine the upper voltage limit term and the lower voltage limit term corresponding to the multiple feeders respectively; The voltage qualification rate constraint is determined based on the upper voltage limit and lower voltage limit items corresponding to the multiple feeders.

6. The method according to claim 5, characterized in that, The voltage deviation terms corresponding to the multiple feeders at a predetermined time are determined, including: Based on the extreme voltage term and the reference voltage term, determine the maximum absolute deviation ratio term; Determine the feeder capacity item and the load change item under the predetermined time corresponding to the multiple feeders respectively; Based on the maximum absolute deviation ratio term, the feeder capacity term corresponding to each of the multiple feeders, and the load change term under the predetermined time, the voltage deviation term corresponding to each of the multiple feeders under the predetermined time is determined.

7. The method according to any one of claims 1 to 6, characterized in that, Before calling the target function, the following is also included: For each feeder, determine the target voltage term corresponding to each feeder, and the active power injection term, reactive power injection term, and resistance term of the adjacent node line segment corresponding to multiple nodes on each feeder. Based on the target voltage item corresponding to each feeder, the active power injection item, reactive power injection item, and resistance item of adjacent node line segments corresponding to multiple nodes on each feeder, the network loss data items corresponding to the multiple feeders are determined.

8. A strategy determination device for a power grid system, characterized in that, include: The acquisition module is used to acquire the power grid status data of the target power grid system at the current moment. The target power grid system includes target equipment, which includes transformer taps and capacitor banks. The power grid status data includes transformer tap location data and capacitor bank switching status data. The retrieval module is used to retrieve the objective function corresponding to the target power grid system and the objective constraints corresponding to the objective function. The objective function aims to minimize the total network loss, and the objective constraints include voltage qualification rate constraints. The objective function includes voltage functions and network loss data items corresponding to multiple feeders respectively. The corresponding voltage function is a function with the minimum network loss of the corresponding feeder as the objective, used to determine the corresponding operating voltage. The determination module is used to solve the objective function based on the power grid state data and under the target constraints to obtain the execution strategy corresponding to the target power grid system. The execution strategy includes the transformer tap position strategy and the capacitor bank switching strategy.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the strategy determination method for a power grid system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the strategy determination method for the power grid system as described in any one of claims 1 to 7.

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