A peak regulation method and system for an energy storage power station in a power system

Abstract

This application discloses a peak-shaving dispatching method and system for energy storage power stations in a power system, relating to the field of power system operation dispatching and optimization control technology. The system accurately identifies power flow crossing conditions by synchronously collecting data and quantifies the resulting settlement deviations. It then directly links the charging and discharging dispatching of energy storage power stations with the economic objective of minimizing settlement deviations. By solving an optimization model, it obtains the optimized charging and discharging power plan for the energy storage power station within a future dispatching cycle. This optimized power plan is then distributed to the energy management system of the energy storage power station to control the station to perform corresponding charging and discharging operations. This effectively compensates for settlement errors caused by complex power flow in the grid, improves the fairness and accuracy of new energy power trading, and provides grid operators with a refined and automated means of controlling settlement deviations.

Description

Technical Field

[0001] This application relates to the field of power system operation scheduling and optimization control technology, specifically a peak-shaving scheduling method and system for energy storage power stations in a power system. Background Technology

[0002] Existing technologies have yielded relatively abundant research on the optimized scheduling of energy storage power stations for grid peak shaving. Common methods mainly focus on optimizing the charging and discharging strategies of energy storage systems to achieve goals such as peak shaving and valley filling, smoothing renewable energy output, reducing grid losses, and delaying grid upgrade investments. These traditional methods can improve the economic efficiency and security of grid operation to a certain extent.

[0003] However, with the deepening of power market reforms and the expansion of new energy participation in market transactions, the problem of settlement deviations under complex power flow conditions is becoming increasingly prominent in traditional physical dispatching. Current energy storage peak-shaving dispatching methods generally lack specific consideration for this particular problem. This means that in scenarios where "power flow crossing" occurs frequently, the dispatching decisions of energy storage systems may not be able to effectively correct settlement errors caused by complex power flow, affecting the fairness and accuracy of new energy power trading, and failing to provide grid operators with a refined and automated settlement deviation control tool. Summary of the Invention

[0004] In a first aspect, one embodiment of this application provides a peak-shaving dispatch method for an energy storage power station in a power system. The method includes: deploying multiple energy metering devices at new energy grid connection points, key lines, and settlement points in the power system to synchronously collect grid operation data from each node; identifying whether a power flow crossing condition has occurred based on the grid operation data and grid topology, and determining the type and impact path of the power flow crossing condition; determining a quantitative relationship model between power flow crossing and settlement power deviation based on the energy data of each metering point in the power system, the corresponding comprehensive error of the metering devices, and system line loss data, according to the principle of energy conservation; and combining the types of power flow crossing conditions... The model quantifies the relationship between the power flow type and the impact path to assess the current or predictable deviation in the settlement power volume caused by power flow crossing conditions. Using the charging and discharging power of the energy storage power station as the decision variable, an optimized scheduling model is constructed. Minimizing the settlement power volume deviation is one of the objective functions of the optimized scheduling model. Based on the power flow crossing condition type and the impact path, corresponding physical and operational constraints are set. The optimized scheduling model is solved to obtain the optimized charging and discharging power plan for the energy storage power station in the next scheduling cycle. This optimized charging and discharging power plan is then distributed to the energy management system of the energy storage power station to control the station to perform corresponding charging and discharging operations.

[0005] In conjunction with the first aspect, in some implementations of the first aspect, after the optimized charging and discharging power plan is sent to the energy management system of the energy storage power station, it also includes: evaluating the scheduling effect based on the newly collected grid operation data, and updating and optimizing the parameters of the quantitative relationship model and / or the optimized scheduling model.

[0006] In conjunction with the first aspect, in some implementations of the first aspect, based on grid operation data and grid topology, it is determined whether a power flow crossing condition has occurred in the grid, and the type and impact path of the power flow crossing condition are identified. This includes: real-time monitoring of electrical quantity changes at each grid-connected node based on grid topology and node power measurements; analysis of electrical quantity changes to identify random fluctuation patterns in new energy power generation, where the fluctuation patterns at least include random changes in power direction and random fluctuations in power magnitude; analysis of the current grid operation based on the identified random fluctuation patterns, combined with decision tree or support vector machine intelligent recognition algorithms, to determine whether a power flow crossing condition has occurred and to identify the crossing type of the power flow crossing condition; and reconstruction analysis of the actual power flow direction and power transmission path under the power flow crossing condition based on grid topology and real-time power data to determine the impact path of the power flow crossing condition.

[0007] In conjunction with the first aspect, in certain implementations of the first aspect, based on the electricity data of each metering point in the power system, the corresponding comprehensive error of the metering device, and the system line loss data, a quantitative relationship model for the deviation between power flow crossing and the settled electricity is determined according to the principle of electricity conservation. This includes: establishing constraint equations based on the electricity data of each metering point in the power system and according to the principle of electricity conservation. In the constraint equations, the comprehensive error of the metering device is the sum of the errors caused by the electricity meter, the transformer, the secondary circuit, and other factors in the power system; the system line loss data is calculated using the root mean square current method; and the constraint equations, the comprehensive error of the metering device, and the system line loss are combined to construct a deviation calculation model for the settled electricity under the power flow crossing condition, which serves as the quantitative relationship model.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, the physical constraints include at least: the upper and lower limits of the charging and discharging power of the energy storage power station itself, the operating range of the state of charge, and the limit on the number of charging and discharging conversions.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the operational constraints include: energy storage device power constraints and energy storage device state of energy constraints; wherein, the energy storage device power constraints are the absolute values ​​of the charging and discharging power of the energy storage power station in each time period, which shall not exceed the maximum allowable power of the corresponding power conversion device; the energy storage device state of energy constraints are the state of charge of the energy storage power station in each time period, which shall be maintained within the safe range allowed by the rated capacity and meet the state requirements at the beginning and end of the dispatch cycle.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, the charging and discharging power of the energy storage power station is used as the decision variable to construct an optimized scheduling model. Minimizing the settlement power deviation value is taken as one of the objective functions of the optimized scheduling model. This includes: taking minimizing the settlement power deviation value as the core objective, and combining the operating cost objective of the energy storage power station with the system safety auxiliary objective to form a comprehensive multi-objective function. Among them, the calculation of the settlement power deviation objective is related to the type of power flow crossing condition, the charging and discharging power of the energy storage power station, as well as the comprehensive error of the metering device and the system line loss data. The operating cost objective of the energy storage power station includes the charging and discharging power conversion loss cost and the power exchange cost with the grid. The system safety auxiliary objective is quantified by evaluating the adjustment effect of the charging and discharging behavior of the energy storage power station on the load rate and node voltage level of the critical line on the power flow crossing condition. When constructing the optimized scheduling model, the identified power flow crossing condition type is used as the input parameter of the model, so that the weight coefficients of different optimization objectives in the comprehensive multi-objective function and the benchmark value of the settlement power deviation objective are adaptively adjusted according to the condition type, so that the optimization objective matches the actual grid operation requirements.

[0011] In conjunction with the first aspect, in certain implementations of the first aspect, the optimized scheduling model is solved to obtain the optimized charging and discharging power plan of the energy storage power station within a future scheduling cycle. This includes: introducing a working condition trend prediction module based on a long short-term memory network, using the predicted probability distribution of crossing working conditions for multiple future scheduling cycles as the rolling time-domain boundary condition for solving the optimized scheduling model; adopting a hybrid solution strategy that integrates an improved chaotic particle swarm optimization algorithm and a branch-and-bound method, using physical constraints and operational constraints as hard constraints of the model, and solving them simultaneously with a comprehensive multi-objective function. The improved chaotic particle swarm optimization algorithm is used to quickly find the optimal solution in the continuous decision space, while the branch-and-bound method is used to make accurate decisions on the discrete charging and discharging states of the energy storage power station; during the solution process, the latest collected grid operation data is accessed in real time to perform online rolling corrections on the settlement power deviation and physical constraints in the optimized scheduling model; after the solution is completed, an optimized charging and discharging power plan is generated, which includes the charging and discharging power for each time period within a future scheduling cycle, the expected adjustment effect assessment, and the confidence interval.

[0012] Secondly, this application provides a peak-shaving dispatch system for an energy storage power station in a power system. The system includes: a data synchronization acquisition module configured to deploy multiple energy metering devices at new energy grid connection points, key lines, and settlement points in the power system to synchronously collect grid operation data at each node; a power flow crossing condition identification module configured to identify whether a power flow crossing condition has occurred in the grid based on grid operation data and grid topology, and determine the type and impact path of the power flow crossing condition; a quantitative relationship modeling module configured to determine a quantitative relationship model between power flow crossing and settlement power deviation based on the power data of each metering point in the power system, the corresponding comprehensive error of the metering devices, and system line loss data, according to the principle of power conservation; and a settlement power deviation evaluation module configured to... The system analyzes the types and impact paths of power flow crossing conditions, using a quantitative relationship model to assess current or predict future settlement power deviations caused by these conditions. An optimized scheduling model construction module is configured to use the charging and discharging power of the energy storage power station as the decision variable, constructing an optimized scheduling model. Minimizing the settlement power deviation is used as one of the objective functions of the optimized scheduling model, and corresponding physical and operational constraints are set based on the power flow crossing condition type and impact path. An optimization solution and command issuance module is configured to solve the optimized scheduling model, obtain the optimized charging and discharging power plan for the energy storage power station in the next scheduling cycle, and issue the optimized charging and discharging power plan to the energy management system of the energy storage power station to control the energy storage power station to perform corresponding charging and discharging operations.

[0013] In conjunction with the second aspect, in some implementations of the second aspect, the system further includes: an effect evaluation and model update module, configured to evaluate the scheduling effect based on the newly collected grid operation data after the optimized charging and discharging power plan is issued to the energy management system of the energy storage power station, and update and optimize the parameters of the quantitative relationship model and / or the optimized scheduling model.

[0014] This embodiment provides a closed-loop, proactive solution for managing settlement power deviations. By synchronously collecting data, it accurately identifies power flow crossing conditions and quantifies the resulting settlement deviations. This directly links the charging and discharging scheduling of energy storage power stations with the economic goal of minimizing settlement deviations. By solving an optimization model to generate optimal charging and discharging commands, it can effectively compensate for settlement errors caused by complex power flow in the grid, improve the fairness and accuracy of new energy power trading, and provide grid operators with a refined and automated means of controlling settlement deviations. Attached Figure Description

[0015] Figure 1 The diagram shown is a flowchart illustrating a peak-shaving dispatch method for an energy storage power station in a power system, provided in an exemplary embodiment of this application.

[0016] Figure 2The diagram shown is a structural schematic of a peak-shaving dispatch system for an energy storage power station in a power system, provided by an exemplary embodiment of this application.

[0017] Figure 3 The diagram shown is a structural schematic of a peak-shaving dispatch system for an energy storage power station in a power system, provided by an exemplary embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] Figure 1 The diagram shown is a flowchart illustrating a peak-shaving dispatch method for an energy storage power station in a power system, provided in an exemplary embodiment of this application. Figure 1 As shown, a peak-shaving dispatch method for an energy storage power station in a power system includes the following steps.

[0020] Step 100: Deploy multiple power metering devices at new energy grid connection points, key lines, and settlement checkpoints in the power system to synchronously collect grid operation data at each node.

[0021] For example, an electric power system refers to an overall network for the production, transmission, distribution, and consumption of electrical energy, consisting of power generation, transmission, transformation, distribution, and consumption.

[0022] For example, the new energy grid connection point refers to the physical location of the common connection point for new energy power generation systems such as wind power generation and photovoltaic power generation to connect to the power grid.

[0023] It should be understood that in the electricity market, the settlement point, which is used for trade settlement and to determine the electricity charges for both the buyer and seller, is usually the boundary point of property rights and responsibilities.

[0024] For example, power grid operation data is a physical quantity used to reflect the real-time status of the power grid. Power grid operation data includes at least the voltage, current, active power, reactive power, and metering point electricity data of each node.

[0025] Step 200: Based on the power grid operation data and power grid topology, identify whether the power flow crossing condition has occurred in the power grid, and determine the type and impact path of the power flow crossing condition.

[0026] Power flow crossing of new energy power plants refers to the phenomenon where, in complex grid topologies involving multiple voltage levels and multiple power plants connected to the grid, the actual power flow path differs from the power flow direction (usually grid connection or disconnection) preset at the settlement metering point. This leads to discrepancies between the electricity measured at specific settlement points (such as the power plant's grid connection point) and the theoretical values ​​calculated based on power generation and consumption, and is a significant source of settlement errors.

[0027] The specific implementation steps for step 200 are as follows: (1) Based on the power grid topology and node power measurement values, monitor the changes in electrical quantities of each grid-connected node in real time; (2) Analyze the changes in electrical quantities and identify the random fluctuation pattern of new energy power generation. The fluctuation pattern includes at least the random change law of power direction and the random fluctuation characteristics of power magnitude. (3) Based on the identified random fluctuation pattern, combined with decision tree or support vector machine intelligent recognition algorithm, analyze the current operating condition of the power grid to determine whether the power flow crossing condition has occurred and identify the crossing type of the power flow crossing condition. (4) Based on the power grid topology and real-time power data, the actual power flow direction and power transmission path under the power flow crossing condition are reconstructed and analyzed to determine the influence path of the power flow crossing condition.

[0028] In one specific embodiment, the system continuously and synchronously collects real-time data such as voltage, current, active power P, and reactive power Q at each node through synchronous power metering devices deployed at the main outlet M1 of the photovoltaic power station, the inlet M2 of line A, and the inlet M3 of line B. The system's backend analysis module performs statistical analysis on the real-time power sequence, identifying the rapid drop and rise in the magnitude of renewable energy generation power due to factors such as cloud cover, as well as the random reversal of power direction on specific lines (such as M2). Subsequently, the system uses intelligent recognition algorithms such as support vector machines to intelligently analyze the current power grid operating conditions. For example, when a sudden drop in power at M1 and a reversal of the power direction at M2 are identified, the algorithm can determine that a specific "local reverse flow" power flow crossing condition has occurred. Finally, based on the grid topology and real-time power data (such as M1=+150kW, M2=-50kW, M3=+200kW), the system can perform physical reconstruction analysis of the actual power flow direction and power transmission path under the crossing condition, thereby accurately determining its impact path. For example, it can identify the core impact path of "main grid -> M2 metering point -> local network on the photovoltaic power station side", providing key input for subsequent settlement power deviation quantification and compensation control.

[0029] Step 300: Based on the power data of each metering point in the power system, the corresponding comprehensive error of the metering device, and the system line loss data, and in accordance with the principle of power conservation, determine the quantitative relationship model between power flow crossing and settlement power deviation.

[0030] Based on the electricity data of each metering point in the power system, and in accordance with the principle of electricity conservation, constraint equations are established. In these constraint equations, the comprehensive error of the metering device is the sum of the errors caused by the electricity meter, the transformer, the secondary circuit, and other factors in the power system. The system line loss data is calculated using the root mean square current method. By combining the constraint equations, the comprehensive error of the metering device, and the system line loss, a deviation calculation model for the settled electricity under the power flow crossing condition is constructed, which serves as the quantitative relationship model.

[0031] In one specific embodiment, the error of the electricity meter is mainly caused by the meter structure and operating principle. The error of the current transformer is mainly determined by the ratio difference and phase difference generated by the current transformer during measurement; the secondary circuit will create additional ratio difference and phase difference for the current transformer, thus affecting the error of the current transformer. The voltage at the secondary port of the voltage transformer should be equal to the voltage on the voltage coil of the electricity meter. The error in the secondary circuit is caused by factors such as fuse switches, terminal blocks, wires, test junction boxes, and contact resistance in the secondary circuit, resulting in inconsistencies in their values ​​and phases. This error is related to the secondary load of the voltage circuit, power factor, and connection method. At the same time, other factors such as high-order harmonics of the power system and the field environment will also bring certain measurement errors to the electricity metering device. Therefore, the actual calculated error mainly includes four aspects: the error of the electricity meter, the combined error of the current transformer, the combined error of its secondary circuit, and the error caused by other factors of the power system. ; In the formula, ε s For the error of the electricity meter; ε h For mutual inductor synthesis error; ε d For the secondary circuit synthesis error; ε p Errors caused by other factors in the power system.

[0032] On the other hand, in the process of electricity trading and settlement, it is necessary to accurately calculate the actual electricity consumption and to make reasonable and effective calculations of line losses. Taking line losses into account when applying the principle of conservation of settled electricity is essential to ensure the accuracy of power calculations.

[0033] The root mean square (RMS) current method, as the most basic and simplest method for theoretical calculation of distribution network line losses, works by equating the energy loss caused by the RMS current flowing through the transmission line with the energy loss generated by the actual load over the same period. The calculation of the energy loss is shown below: ; In the formula, Δw is the calculated power loss; I if R is the root mean square current of the line; R is the sum of the resistances of the line and the electrical equipment; t is the time during which losses occur.

[0034] Among them I if The following formula can be used for calculation: ; In the formula, I if I is the root mean square current flowing through this line; i This represents the current flowing through the corresponding period of the selected representative daily load.

[0035] If the active and reactive power and voltage parameters of the load carried by the line are known, then the root mean square current I flowing through the line is... if It can be expressed by the following formula: ; In the formula, P i Q is the active power at point i; i U is the reactive power at point i; i Let i be the line voltage at point i.

[0036] From the perspective of electrical principles, the distribution area within the region where new energy is connected to the grid satisfies the principle of energy conservation, that is, the amount of new energy generated is equal to the sum of the amount of electricity fed into the grid, the amount of electricity consumed by users, plus the system line loss. ; In the formula, w j w0 w i These represent the electricity consumption values ​​of the new energy generation sub-meter, the grid-connected electricity meter, and the user sub-meter, respectively; ε j ,ε0,ε i These represent the measurement errors of the corresponding electricity values ​​for the new energy power generation sub-meters, the main meter, and each user's sub-meter; Δw represents the system line loss.

[0037] The Support Vector Machine (SVM) algorithm is used to transform the problem into a dual problem for solution. By selecting an appropriate kernel function K(x, z) and parameter C, the power flow direction and type are identified. ;

[0038] : This indicates that the Lagrange multiplier vector α to be optimized (one for each sample) ).

[0039] : The sum of all Lagrange multipliers.

[0040] : This is the core margin maximization term.

[0041] : The kernel function. It is responsible for mapping the original power grid operation data (such as voltage, current, power) to a high-dimensional feature space, making the power flow patterns that were "intertwined" on the two-dimensional plane linearly separable in the high-dimensional space. For example, the Gaussian kernel can capture the non-linear characteristics of the power flow crossing.

[0042] : The class labels of the samples (e.g., +1 represents "power flow crossing occurs", -1 represents "not occurred").

[0043] : The product of this term ensures that only the inner products between samples of the same class (or support vectors) contribute to the margin, essentially calculating the distance between sample points in the high-dimensional feature space.

[0044] By maximizing this expression, the SVM tries to find a hyperplane that pushes the sample points of different classes as far apart as possible (i.e., the larger the margin), thereby improving the generalization ability of the model.

[0045] By solving the above optimization problem, a set of optimal Lagrange multipliers can be obtained. .

[0046] Select a positive component of α * such that 0 < < C: Select an α from the optimal solution that satisfies 0 < < C, which means the corresponding sample * is a support vector and lies on the margin boundary.

[0047] Using the selected support vectors and the optimal multipliers , calculate the bias term: ; After obtaining α * and b * , the classification decision function of the SVM can be determined, and this function can judge which class the input sample belongs to according to its features.

[0048] Based on this, considering line losses and meter errors in accordance with the principle of electricity conservation settlement, determine the quantitative relationship model between power flow crossing and settlement electricity deviation.

[0049] ​Step 400: Combining the type and impact path of the tidal current crossing condition, a quantitative relationship model is used to assess the current or predict the future settlement power deviation caused by the tidal current crossing condition.

[0050] Settlement electricity deviation refers to the difference between the electricity measured by the meter at the settlement point and the theoretically due electricity that should be settled, caused by factors such as power flow crossing phenomena, comprehensive errors of metering devices, and system line losses.

[0051] Step 500: Using the charging and discharging power of the energy storage power station as the decision variable, construct an optimized scheduling model, take minimizing the settlement power deviation value as one of the objective functions of the optimized scheduling model, and set corresponding physical constraints and operational constraints based on the power flow crossing condition type and influence path.

[0052] The core objective is to minimize the settlement electricity deviation, combined with the operating cost objective of the energy storage power station and the system safety auxiliary objective, forming a comprehensive multi-objective function. The calculation of the settlement electricity deviation objective is related to the type of power flow crossing condition, the charging and discharging power of the energy storage power station, as well as the comprehensive error of the metering device and system line loss data. The operating cost objective of the energy storage power station includes the cost of charging and discharging power conversion losses and the cost of energy exchange with the grid. The system safety auxiliary objective is quantified by evaluating the effect of the energy storage power station's charging and discharging behavior on the adjustment of the load rate and node voltage level of critical lines along the power flow crossing condition.

[0053] When constructing the optimized scheduling model, the identified power flow crossing conditions are used as input parameters of the model. This allows the weight coefficients of different optimization objectives in the integrated multi-objective function, as well as the benchmark value of the settlement power deviation objective, to be adaptively adjusted according to the operating condition type, so that the optimization objectives match the actual power grid operation requirements.

[0054] The optimization scheduling model aims to minimize a weighted comprehensive objective function. The decision variable is the charging and discharging power of the energy storage power station in each time period t within the dispatch cycle. The formula for calculating the comprehensive multi-objective function is as follows: ; , , These are the weighting coefficients for settlement power deviation, operating costs, and system safety auxiliary objectives, respectively. Their values ​​represent the power flow crossing condition type. This function is the core of the model's "adaptive adjustment." For example, in the "local backfeed" operating condition, it can be set... , High, The lower limit is to prioritize ensuring fair settlement and grid security.

[0055] The target for calculating electricity volume deviation aims to minimize the deviation caused by tidal current crossing. The calculation formula is as follows: ; Where t and T represent the time period index and the total number of time periods in the scheduling cycle. : The actual metered electricity volume at the affected settlement point during time period t. : The calculation function for the theoretically calculated reference value of electricity consumption. The type of working condition that the current flows through determines the applicable scenarios for the calculation model. The charging and discharging power (kW) of the energy storage power station is the core decision variable of the model. : Overall error vector of the metering device. System line loss (kWh). Function The expected output (i.e., the target value when the deviation is zero) will vary depending on the operating condition type. Adaptive adjustment. For example, for reverse flow conditions, the reference value may be set to 0 or a small positive number to guide the energy storage discharge to counteract the reverse flow.

[0056] The operating cost objective is to minimize the economic cost of operating energy storage. The calculation formula is as follows: ; : Charging and discharging power conversion loss coefficient. The absolute value of charging and discharging power (kW) represents the power conversion process. Cost per unit power conversion loss (RMB / kW). Duration of the time period (hours). : The grid electricity price (yuan / kWh) for time period t. This item reflects the "cost of exchanging electricity with the grid," which is negative (profit) when discharging and selling electricity, and positive (expenditure) when charging and purchasing electricity.

[0057] As a system safety auxiliary objective, this objective quantifies the improvement effect of energy storage behavior on grid safety indicators along the power flow crossing path. The calculation formula is: ; At time t, the load factor of critical lines along the power flow path is affected. The energy storage power, Pess(t), influences this value by altering the power flow distribution. : Safety threshold for critical line load rate. Voltage level of critical nodes (such as grid connection points) affected by the crossing during time period t. : Reference value for node voltage. The load rate and voltage adjustment weighting coefficients can be adjusted according to the focus of the operating conditions.

[0058] For example, physical constraints include at least the upper and lower limits of the energy storage power station's own charging and discharging power, the range of its state of charge operation, and the limit on the number of charging and discharging conversions.

[0059] Upper and lower limits of charge and discharge power constraints: ; in, : Maximum charging power and maximum discharging power (kW) of the energy storage power station.

[0060] State of Charge (SOC) operating range constraints: ; in, The state of charge of the stored energy at the beginning of time period t. Its dynamics are determined by the following equation: ; in, , These represent the lower and upper limits (e.g., 20% and 90%) that the SOC is allowed to operate at. Rated capacity (kWh) of the energy storage power station. : The initial SOC at the start of the scheduling cycle.

[0061] Charge / discharge conversion cycle limit constraint: ; in, : A binary variable representing the operating state of energy storage. It is usually defined as: u(t)=1 represents discharging (Pess(t)≥0), u(t)=0 represents charging or standby (Pess(t)≤0). This constraint is handled by introducing auxiliary variables and linearization methods. The maximum number of charge / discharge state transitions allowed within a scheduling cycle.

[0062] For example, the operating constraints include: energy storage device power constraints and energy storage device state of energy constraints; wherein, the energy storage device power constraints are the absolute values ​​of the charging and discharging power of the energy storage power station in each time period, which shall not exceed the maximum allowable power of the corresponding power conversion device; the energy storage device state of energy constraints are the state of charge of the energy storage power station in each time period, which shall be maintained within the safe range allowed by the rated capacity and meet the state requirements at the beginning and end of the dispatch cycle.

[0063] Energy storage device power constraints (power conversion device limitations): ; : The maximum permissible power (kW) of the power conversion device. This constraint ensures that the absolute value of the charging and discharging power does not exceed the hardware capability.

[0064] Operating range constraints for energy state constraints of energy storage devices: These are already included in the SOC operating range of physical constraints. Scheduling cycle start and end state requirements: ; : The target SOC value required at the end of the scheduling cycle.

[0065] Step 600: Solve the optimized scheduling model to obtain the optimized charging and discharging power plan of the energy storage power station in the next scheduling cycle. Send the optimized charging and discharging power plan to the energy management system of the energy storage power station to control the energy storage power station to perform the corresponding charging and discharging operations.

[0066] A working condition trend prediction module based on a long short-term memory network is introduced. The predicted probability distribution of traversal working conditions for multiple future scheduling cycles is used as the rolling time-domain boundary condition for solving the optimization scheduling model. A hybrid solution strategy combining an improved chaotic particle swarm optimization algorithm and a branch-and-bound method is adopted. Physical and operational constraints are used as hard constraints of the model and solved simultaneously with a comprehensive multi-objective function. The improved chaotic particle swarm optimization algorithm is used for rapid optimization in the continuous decision space, while the branch-and-bound method is used for accurate decision-making on the discrete charging and discharging states of the energy storage power station.

[0067] An improved chaotic particle swarm optimization algorithm (for power optimization) initializes a swarm of "particles," each representing a complete 96-dimensional power plan vector [Pess(1),Pess(2),...,Pess(96)]. The algorithm introduces chaotic perturbations to avoid getting trapped in local optima and rapidly searches within a continuous power decision space (e.g., from -50MW to +50MW) to minimize the comprehensive objective function. .

[0068] In each iteration, when the algorithm evaluates particles, it calls the power flow calculation program to assess the impact of the power plan on settlement deviations, line loads, and voltage levels, and rigorously verifies physical and operational constraints.

[0069] Branch and bound (for precise state decision-making): Works in conjunction with the particle swarm optimization algorithm. For the optimal power sequence generated by the particle swarm, the branch and bound method precisely optimizes the discrete state u(t). By constructing a search tree, systematically enumerating and pruning, it determines the optimal discrete state for each time period (e.g., whether to discharge at maximum power or charge at medium power in a given time period), while strictly adhering to the limit on the number of charge-discharge transitions (e.g., no more than 20 times per cycle).

[0070] The two algorithms achieve fusion solution through information exchange (such as particle swarm optimization providing initial solutions for branch and bound, and branch and bound providing feasible region guidance under discrete constraints for particle swarm optimization).

[0071] During the solution process, the latest collected power grid operation data is accessed in real time, and a rolling time-domain control strategy is used to perform online rolling corrections on the settlement power deviation and physical constraints in the optimized scheduling model. After the solution is completed, an optimized charging and discharging power plan is generated, which includes the charging and discharging power for each time period in the next scheduling cycle, the expected regulation effect assessment, and the confidence interval.

[0072] This embodiment provides a closed-loop, proactive solution for managing settlement power deviations. By synchronously collecting data, it accurately identifies power flow crossing conditions and quantifies the resulting settlement deviations. This directly links the charging and discharging scheduling of energy storage power stations with the economic goal of minimizing settlement deviations. By solving an optimization model to generate optimal charging and discharging commands, it can effectively compensate for settlement errors caused by complex power flow in the grid, improve the fairness and accuracy of new energy power trading, and provide grid operators with a refined and automated means of controlling settlement deviations.

[0073] Step 700: Based on the newly collected power grid operation data, evaluate the scheduling effect and update and optimize the parameters of the quantitative relationship model and / or the optimized scheduling model.

[0074] By comparing the expected scheduling results with the actual grid data after execution, the parameters in the "quantitative relationship model" and "optimized scheduling model" can be calibrated and updated online. This effectively overcomes the impact of uncertainties such as inaccurate grid models, errors in renewable energy output and load forecasting, and changes in equipment performance, enabling the entire system to learn and evolve, thereby maintaining high-precision deviation assessment and efficient scheduling control performance over the long term, and improving the practicality and robustness of the solution.

[0075] Figure 2 The diagram shown is a structural schematic of a peak-shaving dispatch system for an energy storage power station in a power system, provided in an exemplary embodiment of this application. Figure 2 As shown, the peak-shaving dispatch system of the energy storage power station in the power system includes: a data synchronization acquisition module, a power flow crossing condition identification module, a quantitative relationship modeling module, a settlement power deviation assessment module, an optimized dispatch model construction module, and an optimization solution and instruction issuance module.

[0076] The data synchronization acquisition module is configured to deploy multiple power metering devices at new energy grid connection points, key lines, and settlement points in the power system to synchronously collect grid operation data at each node.

[0077] The data synchronization acquisition module is the system's perception layer, and multiple high-performance power metering devices can be deployed at renewable energy grid connection points, critical lines, and settlement checkpoints. These devices employ a new type of multi-node grid-connected power metering and flow analysis device for power systems, achieving precise synchronization of power data across multiple nodes (e.g., through fiber optic communication and high-precision clock units). Its core task is to synchronously collect grid operation data such as voltage, current, and active / reactive power at each node according to a unified timescale, providing a high-fidelity, highly synchronized data foundation for subsequent analysis.

[0078] The power flow crossing condition identification module is configured to identify whether a power flow crossing condition has occurred in the power grid based on power grid operation data and power grid topology, and to determine the type and impact path of the power flow crossing condition.

[0079] Based on the power grid operation data acquired by the data synchronization acquisition module and the known power grid topology, the module performs real-time intelligent identification of power flow crossing conditions. This module first analyzes the random changes in power direction and the fluctuations in power magnitude. Then, combining intelligent algorithms such as decision trees or SVMs, it analyzes the current operating conditions of the power grid, accurately determines whether a power flow crossing has occurred, and identifies the specific type of crossing (such as local backflow). Simultaneously, based on the topology and real-time power data, it reconstructs and analyzes the actual power transmission path of the power flow crossing, i.e., determines its influencing path, providing target points for subsequent accurate assessment and directional control.

[0080] The quantitative relationship modeling module is configured to determine the quantitative relationship model between power flow crossing and settlement power deviation based on the power data of each metering point in the power system, the corresponding comprehensive error of the metering device, and the system line loss data, and in accordance with the principle of power conservation.

[0081] The settlement power deviation assessment module is configured to combine the type and impact path of the tidal current crossing condition, and use a quantitative relationship model to assess the current or predict the future settlement power deviation caused by the tidal current crossing condition.

[0082] The optimization scheduling model construction module is configured to use the charging and discharging power of the energy storage power station as the decision variable to build an optimization scheduling model. Minimizing the settlement power deviation value is taken as one of the objective functions of the optimization scheduling model. Based on the power flow crossing condition type and the influence path, corresponding physical constraints and operational constraints are set.

[0083] The optimization solution and command issuance module is configured to solve the optimization scheduling model to obtain the optimized charging and discharging power plan of the energy storage power station in the next scheduling cycle, and to issue the optimized charging and discharging power plan to the energy management system of the energy storage power station to control the energy storage power station to perform the corresponding charging and discharging operations.

[0084] The system optimizes the construction and solution of the scheduling model to generate control strategies, and finally issues commands to complete the control actions. This system effectively solves the core problem addressed in the document, namely, "the difficulty in accurately measuring the electricity consumption during power flow crossings in new energy power plants," and generates a workable engineering system with practical control capabilities.

[0085] Figure 3 The diagram shown is a structural schematic of a peak-shaving dispatch system for an energy storage power station in a power system, provided in an exemplary embodiment of this application. Figure 3 As shown, the system also includes an effect evaluation and model update module.

[0086] The effect evaluation and model update module is configured to evaluate the scheduling effect based on the newly collected grid operation data after the optimized charging and discharging power plan is sent to the energy management system of the energy storage power station, and update and optimize the parameters of the quantitative relationship model and / or the optimized scheduling model.

[0087] After the energy storage power station executes the optimized dispatch command, the effect evaluation and model update module actively collects a new round of actual grid operation data. By comparing this data with the expected results before dispatch, it quantitatively evaluates the actual execution effect of the dispatch strategy. Based on this evaluation result, the module can perform online calibration and rolling updates of the internal parameters of the quantitative relationship model (used to calculate deviations) and the optimized dispatch model (used to generate commands) in the system. This mechanism can effectively overcome the impact of uncertainties such as inaccurate grid models, changes in equipment performance, and long-term errors in renewable energy output and load forecasting. This enables the entire system to have the ability to "learn" and "evolve," ensuring that the dispatch strategy can continuously track actual changes in the grid, maintain high-precision deviation assessment capabilities and efficient compensation control performance over the long term, and greatly improve the system's robustness, adaptability, and practical value in complex operating environments.

[0088] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0089] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0090] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0091] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0092] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A peak-shaving dispatch method for an energy storage power station in a power system, characterized in that, include: Multiple electricity metering devices are deployed at new energy grid connection points, key lines, and settlement checkpoints in the power system to synchronously collect grid operation data at each node; Based on the power grid operation data and power grid topology, identify whether the power flow crossing condition has occurred in the power grid, and determine the type and impact path of the power flow crossing condition. Based on the power data of each metering point in the power system, the corresponding comprehensive error of the metering device, and the system line loss data, a quantitative relationship model between power flow crossing and settlement power deviation is determined according to the principle of power conservation. By combining the type and impact path of the tidal current crossing condition, the quantitative relationship model is used to assess the current or predict the future settlement power deviation caused by the tidal current crossing condition. Using the charging and discharging power of the energy storage power station as the decision variable, an optimized scheduling model is constructed. Minimizing the settlement power deviation value is taken as one of the objective functions of the optimized scheduling model. Based on the power flow crossing condition type and influence path, corresponding physical constraints and operational constraints are set. The optimized scheduling model is solved to obtain the optimized charging and discharging power plan of the energy storage power station in the next scheduling cycle. The optimized charging and discharging power plan is then sent to the energy management system of the energy storage power station to control the energy storage power station to perform the corresponding charging and discharging operations.

2. The method according to claim 1, characterized in that, After the optimized charge and discharge power plan is sent to the energy management system of the energy storage power station, the method further includes: Based on the newly collected power grid operation data, the scheduling effect is evaluated, and the parameters of the quantitative relationship model and / or the optimized scheduling model are updated and optimized.

3. The method according to claim 1, characterized in that, The process of identifying whether a power flow crossing condition has occurred in the power grid based on the power grid operation data and power grid topology, and determining the type and impact path of the power flow crossing condition, includes: Based on the power grid topology and node power measurements, the electrical quantities of each grid-connected node are monitored in real time. The changes in electrical quantities are analyzed to identify the random fluctuation patterns of new energy power generation. The fluctuation patterns include at least the random variation law of power direction and the random fluctuation characteristics of power magnitude. Based on the identified random fluctuation patterns, combined with decision tree or support vector machine intelligent recognition algorithms, the current operating conditions of the power grid are analyzed to determine whether a power flow crossing condition has occurred and to identify the crossing type of the power flow crossing condition. Based on the power grid topology and real-time power data, the actual power flow direction and power transmission path under the power flow crossing condition are reconstructed and analyzed to determine the influence path of the power flow crossing condition.

4. The method according to any one of claims 1 to 3, characterized in that, Based on the power data of each metering point in the power system, the corresponding comprehensive error of the metering device, and the system line loss data, and in accordance with the principle of power conservation, a quantitative relationship model is determined between power flow crossing and the deviation of the settled power volume, including: Based on the electricity data of each metering point in the power system, and in accordance with the principle of electricity conservation, a constraint equation is established. In the constraint equation, the comprehensive error of the metering device is the sum of the electricity meter error, the transformer combined error, the secondary circuit combined error, and the errors caused by other factors in the power system. The system line loss data was calculated using the root mean square current method. By combining the constraint equations, the comprehensive error of the metering device, and the system line loss, a deviation calculation model for the settled electricity under the power flow crossing condition is constructed, which serves as the quantitative relationship model.

5. The method according to any one of claims 1 to 3, characterized in that, The physical constraints include at least the upper and lower limits of the energy storage power station's own charging and discharging power, the range of its state-of-charge operation, and the limit on the number of charging and discharging conversions.

6. The method according to claim 1, characterized in that, The operational constraints include: energy storage device power constraints and energy storage device energy state constraints; The power constraint of the energy storage device is the absolute value of the charging and discharging power of the energy storage power station in each time period, which shall not exceed the maximum allowable power of the corresponding power conversion device. The energy state constraint of the energy storage device is that the state of charge of the energy storage power station at each time period must be maintained within the safe range allowed by the rated capacity and meet the state requirements at the beginning and end of the dispatch cycle.

7. The method according to claim 1, characterized in that, The step of constructing an optimized scheduling model using the charging and discharging power of the energy storage power station as the decision variable, and minimizing the settlement power deviation value as one of the objective functions of the optimized scheduling model, includes: The core objective is to minimize the deviation of the settled electricity volume, and this is combined with the operating cost objective and system safety auxiliary objective of the energy storage power station to form a comprehensive multi-objective function; The calculation of the settlement power deviation target is related to the type of power flow crossing condition, the charging and discharging power of the energy storage power station, the comprehensive error of the metering device, and the system line loss data. The operating cost target of the energy storage power station includes the cost of charging and discharging power conversion loss and the cost of exchanging electricity with the grid; The system safety auxiliary objective is quantified by evaluating the effect of the energy storage power station's charging and discharging behavior on the adjustment of critical line load rate and node voltage level on the power flow crossing path. When constructing the optimized scheduling model, the identified power flow crossing conditions are used as input parameters of the model. The weight coefficients of different optimization objectives in the integrated multi-objective function and the benchmark value of the settlement power deviation objective are adaptively adjusted according to the operating condition type, so that the optimization objectives match the actual power grid operation requirements.

8. The method according to claim 7, characterized in that, Solving the optimized scheduling model to obtain the optimized charging and discharging power plan of the energy storage power station in a future scheduling cycle includes: A working condition trend prediction module based on long short-term memory network is introduced, and the predicted probability distribution of the traversal working conditions in multiple future scheduling cycles is used as the rolling time domain boundary condition for solving the optimized scheduling model. A hybrid solution strategy combining an improved chaotic particle swarm optimization algorithm and a branch-and-bound method is adopted. The physical constraints and operational constraints are used as hard constraints of the model and solved simultaneously with the comprehensive multi-objective function. The improved chaotic particle swarm optimization algorithm is used to quickly find the optimal solution in the continuous decision space, while the branch-and-bound method is used to make accurate decisions on the discrete charging and discharging states of the energy storage power station. During the solution process, the latest collected power grid operation data is accessed in real time to perform online rolling corrections on the settlement power deviation and physical constraints in the optimized scheduling model; After the solution is completed, the optimized charge and discharge power plan is generated. The optimized charge and discharge power plan includes the charge and discharge power for each time period in the next scheduling cycle, the expected adjustment effect assessment and confidence interval.

9. A peak-shaving dispatching system for an energy storage power station in a power system, characterized in that, include: The data synchronization acquisition module is configured to deploy multiple power metering devices at the new energy grid connection points, key lines and settlement points of the power system to synchronously collect power grid operation data at each node; The power flow crossing condition identification module is configured to identify whether a power flow crossing condition has occurred in the power grid based on the power grid operation data and the power grid topology, and to determine the type and impact path of the power flow crossing condition. The quantitative relationship modeling module is configured to determine the quantitative relationship model between power flow crossing and settlement power deviation based on the power data of each metering point in the power system, the corresponding comprehensive error of the metering device, and the system line loss data, and in accordance with the principle of power conservation. The settlement power deviation assessment module is configured to combine the type and impact path of the tidal current crossing condition with the quantitative relationship model to assess the current or predict the future settlement power deviation value caused by the tidal current crossing condition. The optimized scheduling model construction module is configured to construct an optimized scheduling model with the charging and discharging power of the energy storage power station as the decision variable, and to take minimizing the settlement power deviation value as one of the objective functions of the optimized scheduling model. Based on the power flow crossing condition type and the influence path, corresponding physical constraints and operational constraints are set. The optimization solution and instruction issuance module is configured to solve the optimization scheduling model to obtain the optimized charging and discharging power plan of the energy storage power station in a future scheduling cycle, and to issue the optimized charging and discharging power plan to the energy management system of the energy storage power station to control the energy storage power station to perform corresponding charging and discharging operations.

10. The system according to claim 9, characterized in that, Also includes: The effect evaluation and model update module is configured to evaluate the scheduling effect based on the newly collected grid operation data after the optimized charging and discharging power plan is sent to the energy management system of the energy storage power station, and update and optimize the parameters of the quantitative relationship model and / or the optimized scheduling model.

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