A power system operation scheduling optimization method and device, electronic equipment and storage medium

Abstract

The present application relates to the technical field of wind, fire and storage combined operation scheduling and power market participation optimization, and discloses a power system operation scheduling optimization method and device, electronic equipment and storage medium, the present application introduces the wind power uncertainty output scene to depict the random fluctuation characteristics of wind power output, relies on the day-ahead-day-in two-stage constraint to build a maximum benefit optimization model, and can output the day-ahead bidding scheme and the day-in real-time scheduling instruction of the combined system. The present application can fully tap the wind, fire and storage multi-source complementary regulation potential, effectively suppress the wind power output fluctuation, reduce the output deviation penalty caused by wind power uncertainty, and improve the overall market benefit of the combined system. At the same time, the multi-time scale operation constraint is considered, the frequency modulation response tracking accuracy is guaranteed, which is beneficial to improving the power grid operation stability and reducing the social electricity cost.

Description

Technical Field

[0001] This invention relates to the field of wind, thermal, and energy storage joint operation and dispatching and power market participation optimization technology, specifically to a power system operation and dispatch optimization method, device, electronic equipment, and storage medium. Background Technology

[0002] In the existing electricity market participation mechanism, various generating units participate in market transactions independently based solely on their rated output capacity. This model fails to comprehensively leverage the differentiated regulatory advantages of different units and makes it difficult to form a coordinated control system. Faced with the dynamic fluctuations in renewable energy output, the independent participation of a single unit in the market is extremely unsuitable. It not only fails to effectively mitigate grid power fluctuations and exacerbates grid instability risks, but also leads to a waste of regulatory resources for each unit, failing to maximize electricity market benefits and further increasing overall electricity costs. This makes it difficult to adapt to the operational needs of a new power system with a high proportion of renewable energy integrated into the grid. Summary of the Invention

[0003] This invention provides a method, apparatus, electronic device, and storage medium for optimizing power system operation and scheduling, in order to solve the problems that existing technologies cannot effectively mitigate power grid fluctuations, exacerbate the risk of grid instability, waste the regulation resources of each unit, fail to maximize electricity market revenue, further increase the overall electricity cost of society, and are difficult to adapt to the operation requirements of new power systems with a high proportion of renewable energy grid connection.

[0004] In a first aspect, the present invention provides a power system operation and scheduling optimization method, the method comprising: Obtain the basic boundary data of the current market; wherein, the basic boundary data includes at least historical wind power forecast errors and wind power point forecast results; Using the historical wind power prediction error and the wind power point prediction results, a wind power uncertainty output scenario is constructed. Under the preset day-ahead and intraday operating constraints, a power system operation optimization model is constructed with the goal of maximizing the expected total revenue of the joint system under the various wind power uncertain output scenarios. The power system operation optimization model is solved, and the day-ahead market basic bidding scheme and intraday market real-time dispatch instructions are determined according to the optimal solution.

[0005] This invention characterizes the random fluctuations in wind power output by introducing uncertain output scenarios. Based on day-ahead and intraday two-stage constraints, it constructs a revenue-maximizing optimization model, which can output day-ahead bidding schemes and intraday real-time dispatch instructions for the joint system. This invention can fully tap the complementary regulation potential of wind, thermal, and energy storage systems, effectively smooth wind power output fluctuations, reduce output deviation penalties caused by wind power uncertainty, and improve the overall market returns of the joint system. Simultaneously, it takes into account multi-timescale operational constraints, ensuring frequency regulation response tracking accuracy, which is conducive to improving grid operation stability and reducing social electricity costs.

[0006] In one optional implementation, the acquisition of day-ahead market baseline boundary data; wherein, the baseline boundary data includes at least historical wind power forecasting errors and wind power point forecasting results, including: Obtain the basic boundary data of the day-ahead market; wherein, the basic boundary data includes at least market rules, unit boundary conditions, historical market clearing prices, historical wind power forecasting errors, wind power point forecasting results, and market frequency regulation signals; Data preprocessing is performed on the market rules, the unit boundary conditions, the historical market clearing price, the historical wind power prediction error, the wind power point prediction result, and the market frequency regulation signal.

[0007] This invention, by comprehensively collecting and preprocessing multi-dimensional fundamental boundary data such as market rules, unit boundaries, electricity prices, wind power forecasts, and frequency regulation signals, provides a reliable and complete input data source for subsequent generation of uncertain scenarios and the construction of optimization models. It avoids the problem of model distortion caused by missing data or noise in the original data, ensuring that the characterization of wind power uncertainties and the setting of unit constraints align with actual engineering and market transaction rules, thereby improving the engineering feasibility and reliability of the optimization model's output bidding schemes and dispatch instructions.

[0008] In one optional implementation, the step of constructing a wind power uncertainty output scenario using the historical wind power prediction error and the wind power point prediction result includes: Extract the statistical characteristics of the historical wind power prediction error and the wind power point prediction results; Distribution fitting is performed on each of the statistical features, and wind power sample scenarios are extracted from the fitted multivariate distribution using the Latin hypercube sampling method. Clustering is performed on each of the wind power sample scenarios to obtain multiple wind power uncertain output scenarios; wherein, the wind power uncertain output scenario corresponds to the wind power output sequence and its occurrence probability for each time period within a complete scheduling cycle.

[0009] This invention extracts the statistical characteristics of wind power prediction errors and power prediction results, and through distribution fitting, Latin hypercube sampling, and clustering, obtains wind power output scenarios with probabilistic uncertainty. This efficiently characterizes the random fluctuations in wind power output, reduces the number of scenarios while preserving the statistical regularity of the original data, and avoids excessive computational burden on the model due to an excessively large scenario set. It provides reasonable uncertainty input for subsequent model optimization, balancing computational efficiency and modeling accuracy, which helps reduce market penalties caused by wind power prediction deviations and improves the robustness of scheduling decisions.

[0010] In one optional implementation, the day-ahead operating constraints include day-ahead operating constraints for thermal power plants, day-ahead operating constraints for energy storage power stations, day-ahead operating constraints for wind farms, and day-ahead combined system operating constraints. The aforementioned operating constraints of thermal power plants include thermal power plant start-up status constraints, thermal power plant start-up and shutdown duration constraints, thermal power plant output constraints, thermal power plant ramping constraints, and thermal power plant frequency regulation output constraints. The operational constraints of the day-ahead energy storage power station include energy storage charge and discharge state constraints, energy storage charge and discharge power constraints, state of charge constraints, frequency regulation power limit constraints, coupling constraints between frequency regulation capacity and charge and discharge power, constraints of state of charge on frequency regulation capacity, and constraints on the relationship between frequency regulation capacity and bidding capacity. The operational constraints of the day-ahead wind farm include the operational constraint body of the day-ahead wind farm and the ramping constraint of the day-ahead wind farm.

[0011] This invention establishes multi-level day-ahead operating constraints for thermal power plants, energy storage, wind farms, and integrated systems, comprehensively covering multiple dimensions of physical boundaries, including unit start-up and shutdown, output, ramp-up, state of charge, and frequency regulation capacity coupling. It not only reflects the actual physical limitations of each device's operation but also considers the capacity coupling relationship between electrical energy and the frequency regulation market, avoiding invalid solutions that violate actual unit operating conditions and ensuring the physical feasibility of day-ahead bidding schemes. It provides complete boundary conditions for the optimization model, enhancing the engineering practicality of bidding decisions.

[0012] In one optional implementation, the intraday operating constraints include operating constraints of thermal power plants, operating constraints of energy storage power stations, operating constraints of wind farms operating in each of the wind power uncertain output scenarios, and intraday joint system operating constraints. The intraday operating constraints of thermal power plants include thermal power plant start-up status constraints, thermal power plant start-up and shutdown duration constraints, thermal power plant output constraints, thermal power plant ramping constraints, and thermal power plant frequency regulation output constraints. The operational constraints of the intraday energy storage power station include energy storage charge and discharge state constraints, energy storage charge and discharge power constraints, state of charge constraints, frequency regulation power limit constraints, coupling constraints between frequency regulation capacity and charge and discharge power, constraints of state of charge on frequency regulation capacity, and constraints on the relationship between frequency regulation capacity and bidding capacity. The calculation formula for the operating constraints of the wind farm operating in the uncertain wind power output scenario is as follows:

[0013] In the formula, s Different wind power scenarios within the day; For the real-time operation phase within the day; For wind power to participate in the bidding capacity of the intraday real-time energy market; To determine the actual daily output of wind power, the output prediction curve considering the uncertainty of wind power in the aforementioned wind power output uncertainty scenario is adopted. , This refers to the up-and-down frequency regulation capacity for wind power to participate in the intraday real-time frequency regulation market.

[0014] This invention provides intraday operational constraints tailored to various uncertain wind power output scenarios, configuring constraints for thermal power, energy storage, wind farms, and integrated systems. It fully replicates the actual operational boundaries of each unit, including start-up and shutdown, output, ramp-up, state of charge, and frequency regulation capacity coupling. By differentiating wind farm operating conditions for different wind power scenarios, intraday scheduling adapts to various random wind power output conditions, avoiding scheduling results that violate equipment physical limits. It achieves compatibility between intraday real-time scheduling and the day-ahead bidding constraint system, ensuring the feasibility of intraday scheduling commands in multiple scenarios and improving the adaptability of scheduling schemes to wind power fluctuations.

[0015] In one optional implementation, the step of constructing a power system operation optimization model under preset daytime and intraday operating constraints, with the objective of maximizing the expected total revenue of the joint system under each of the aforementioned wind power uncertain output scenarios, includes: Under the preset day-ahead and intraday operating constraints, a power system operation optimization model is constructed with the objective of maximizing the expected total revenue of the joint system under each wind power uncertain output scenario. The expected total revenue of the joint system under each wind power uncertain output scenario represents the probability-weighted sum of the actual revenue of the joint system under each wind power uncertain output scenario. The actual benefits include market gains and deviation penalties.

[0016] This invention uses the probability-weighted sum of the actual revenue of the joint system under various uncertain wind power output scenarios as the target for maximizing the expected total revenue, while also taking into account market revenue and deviation penalties. It comprehensively considers the revenue fluctuations caused by random wind power output and the costs of bidding deviations, avoiding overly aggressive decision-making that focuses solely on the revenue of a single scenario. By combining day-ahead and intraday constraints for optimization, it can balance profitability and penalty risks while meeting the physical operating boundaries of the units, thereby improving the overall expected revenue of the joint system and enhancing the adaptability of dispatch decisions to wind power uncertainties.

[0017] In one alternative implementation, the deviation penalty can be expressed by the following formula:

[0018] In the formula, Penalty fees for deviations in the energy market; For deviation penalty fees in the FM market; The deviation penalty coefficient for the real-time operating output of the joint system exceeding the energy market bid volume; The deviation penalty coefficient for insufficient energy market bids due to insufficient real-time output of the joint system; The current energy market price; Real-time bidding capacity for the joint system energy market; For day-ahead bidding capacity in the Joint System Energy Market; The deviation penalty coefficient for the real-time operation output of the joint system exceeding the bidding volume in the frequency modulation market; Penalty for deviations in the frequency modulation market bidding volume due to insufficient real-time output of the joint system; This refers to the frequency modulation mileage factor. This refers to the current FM mileage price in the FM market. This refers to the current daytime FM market capacity price; Real-time operating capacity of the joint system frequency regulation capacity market; The day-ahead tender capacity for the joint system frequency modulation capacity market; The time resolution for the real-time operation phase within the day is set to 15 minutes.

[0019] This invention constructs bidirectional deviation penalty calculation logic for both the energy market and the frequency regulation market, distinguishing between two operating conditions: exceeding and falling short of bidding capacity. It introduces corresponding penalty coefficients, market prices, and time scale parameters to quantify penalty costs. This allows for precise quantification of bidding deviation costs in both the energy and frequency regulation markets, incorporating deviation penalties quantitatively into optimization objectives. It constrains dispatch results to avoid overly aggressive bidding strategies, suppresses bidding deviations caused by wind power uncertainties, reduces default penalty expenditures during actual operation, and improves the economic efficiency and compliance of market decisions within the joint system.

[0020] In a second aspect, the present invention provides a power system operation and dispatch optimization device, the device comprising: The acquisition module is used to acquire the basic boundary data of the day-ahead market; wherein, the basic boundary data includes at least historical wind power prediction errors and wind power point prediction results; The scenario construction module is used to construct a wind power uncertainty output scenario by using the historical wind power prediction error and the wind power point prediction result; The model building module is used to construct a power system operation optimization model under preset daytime and intraday operating constraints, with the goal of maximizing the expected total revenue of the joint system under the operation of each wind power uncertainty output scenario. The solution module is used to solve the power system operation optimization model and determine the day-ahead market basic bidding scheme and intraday market real-time dispatch instructions according to the optimal solution.

[0021] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the power system operation scheduling optimization method of the first aspect or any corresponding embodiment described above.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the power system operation scheduling optimization method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the first type of power system operation scheduling optimization method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of the power system operation scheduling optimization method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a wind power output uncertainty scenario obtained by Latin hypercube sampling and k-means aggregation method according to an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the planned day-ahead power output of each generating unit participating in the electricity market according to Mode 1 of the present invention. Figure 5 This is a schematic diagram illustrating the planned day-ahead power output of each generating unit participating in the electricity market according to Mode 2 of the present invention. Figure 6 This is a schematic diagram illustrating the planned output of each generating unit in the frequency regulation ancillary services market according to Mode 1 of the present invention; Figure 7This is a schematic diagram illustrating the planned output of each generating unit in the frequency regulation ancillary services market under Mode 2 according to an embodiment of the present invention. Figure 8 This is a structural block diagram of a power system operation scheduling optimization device according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] This invention provides a power system operation and dispatch optimization method. By introducing the uncertain output scenario of wind power, it characterizes the random fluctuation characteristics of wind power output. Based on day-ahead and intraday two-stage constraints, it constructs a revenue-maximizing optimization model, which can output day-ahead bidding schemes and intraday real-time dispatch instructions for the joint system. This invention can fully tap the complementary regulation potential of wind, thermal, and energy storage, effectively smooth wind power output fluctuations, reduce the output deviation penalty caused by wind power uncertainty, and improve the overall market revenue of the joint system. Simultaneously, it takes into account multi-timescale operational constraints, ensuring frequency regulation response tracking accuracy, which is conducive to improving grid operation stability and reducing social electricity costs.

[0029] This embodiment provides a power system operation and scheduling optimization method. Figure 1 This is a flowchart of a power system operation and scheduling optimization method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Obtain the basic boundary data of the day-ahead market; wherein, the basic boundary data includes at least the historical wind power forecast error and the wind power point forecast result.

[0030] It should be noted that the basic boundary data for the day-ahead market refers to all the original input data required for day-ahead market optimization calculations. Historical wind power forecasting error refers to the sequence of differences between actual wind power output and wind power forecasting results during historical periods; wind power point forecasting results refer to the deterministic single-point forecast values ​​of wind power output for each period within the dispatch cycle.

[0031] In this embodiment of the invention, the basic boundary data of the day-ahead market is obtained. The basic boundary data includes at least historical wind power prediction errors and wind power point prediction results. The obtained historical wind power prediction errors and wind power point prediction results are used as the input data source for the subsequent construction of wind power uncertain output scenarios, so as to extract corresponding statistical features based on this part of the data and provide original data support for the characterization of the random characteristics of subsequent wind power output.

[0032] Step S102: Using historical wind power prediction errors and wind power point prediction results, construct a wind power uncertainty output scenario.

[0033] It should be noted that the wind power uncertainty output scenario refers to multiple sets of output samples generated from historical wind power prediction errors and wind power point prediction results. Each set of scenarios includes the wind power output sequence for each time period of the complete scheduling cycle and the corresponding probability of occurrence, which is used to characterize the random uncertainty of wind power output.

[0034] In this embodiment of the invention, statistical features corresponding to the two sets of data, namely historical wind power prediction error and wind power point prediction results, are extracted and distribution fitting is performed. Wind power sample scenarios are extracted by Latin hypercube sampling, and then the wind power sample scenarios are clustered to generate multiple sets of wind power uncertainty output scenarios with occurrence probability. This depicts the random fluctuation characteristics of wind power output and provides uncertainty input conditions for subsequent optimization models.

[0035] Step S103: Under the preset daytime and intraday operating constraints, a power system operation optimization model is constructed with the goal of maximizing the expected total revenue of the joint system under each wind power uncertain output scenario.

[0036] It should be noted that day-ahead operating constraints refer to the set of constraints that must be followed during the day-ahead market bidding phase. Intraday operating constraints refer to the set of constraints that must be followed during the intraday real-time dispatch phase, adapting to uncertain wind power output scenarios, constraining variables such as unit output and frequency regulation capacity at different times of the day, and ensuring the physical feasibility of intraday dispatch schemes. A joint system refers to a system entity composed of wind power, thermal power, and energy storage units participating in the electricity market as a whole, participating in the energy market and frequency regulation ancillary services market. Expected total revenue refers to the expected revenue obtained by weighting and summing the actual revenue of the joint system under various uncertain wind power output scenarios according to the probability of occurrence of each scenario, serving as the objective function of the optimization model. The power system operation optimization model refers to a mathematical optimization model with the objective of maximizing the expected total revenue of the joint system, constrained by both day-ahead and intraday operating constraints, used to solve market bidding and dispatch instructions.

[0037] In this embodiment of the invention, under the preset day-ahead and intraday operating constraints, with the goal of maximizing the expected total revenue of the joint system under each wind power uncertain output scenario, the actual revenue corresponding to each wind power uncertain output scenario is weighted and summed according to the probability of the scenario occurrence to obtain the expected total revenue. At the same time, market revenue and deviation penalty fees are included. The physical operating boundaries of each unit and market transaction constraints are integrated to build a power system operation optimization model. Based on this model, the optimization solution of the bidding and dispatch scheme is completed.

[0038] Step S104: Solve the power system operation optimization model and determine the day-ahead market basic bidding scheme and intraday market real-time dispatch instructions according to the optimal solution.

[0039] It should be noted that the day-ahead market basic bidding scheme refers to the day-ahead market bidding results output by the model, which includes the bidding parameters of each unit's electrical energy and frequency regulation capacity, and is used for day-ahead market bidding; the intraday market real-time dispatch instructions refer to the intraday operation instructions output by the model for each time period, which includes dispatch parameters such as each unit's output and frequency regulation capacity, and is used for intraday actual operation execution.

[0040] In this embodiment of the invention, the power system operation optimization model is solved to obtain the optimal solution corresponding to the model. Based on the optimal solution, the application parameters of the electrical energy and frequency regulation capacity of each unit in the joint system are extracted to obtain the basic bidding scheme for the day-ahead market. At the same time, the output and frequency regulation capacity operation parameters of each unit under different wind power uncertainty output scenarios are analyzed to generate intraday market real-time dispatch instructions adapted to wind power fluctuation conditions, thereby completing the entire set of decision outputs for the joint system to participate in the power market.

[0041] This embodiment provides a power system operation and scheduling optimization method. Figure 2 This is a flowchart of a power system operation and scheduling optimization method according to an embodiment of the present invention, such as... Figure 2As shown, the process includes the following steps: Step S201: Obtain the basic boundary data of the day-ahead market; wherein, the basic boundary data includes at least the historical wind power forecast error and the wind power point forecast result.

[0042] Specifically, step S201 includes: Step S2011: Obtain the basic boundary data of the day-ahead market; wherein, the basic boundary data includes at least market rules, unit boundary conditions, historical market clearing prices, historical wind power forecasting errors, wind power point forecasting results, and market frequency regulation signals.

[0043] In this embodiment of the invention, the basic boundary data of the day-ahead market is obtained, including market rules, unit boundary conditions, historical market clearing prices, historical wind power forecasting errors, wind power point forecasting results, and market frequency regulation signals.

[0044] Specifically, referencing the rules of the electricity trading market, a trading structure combining day-ahead and real-time markets is adopted: the day-ahead market submits hourly bids for energy and frequency regulation on a one-hour basis on day D-1; the real-time market, on day D, uses a 15-minute rolling cycle to clear and adjust the trading results for the next two hours based on data such as wind power forecasts and load forecasts, while simultaneously accepting normalized AGC frequency regulation signals, which are tracked and executed in real time by the joint system. This joint system participates in both the energy market and the frequency regulation ancillary services market as a whole. Its day-ahead bids for participating capacity in each market are relatively small compared to the power system capacity, and its impact on market clearing prices is not considered; therefore, its energy and frequency regulation bids are fully absorbed by the dispatch center.

[0045] The boundary conditions of the units cover the rated capacity, upper and lower limits of output and gradeability of the wind turbine, the rated charging and discharging power, capacity, upper and lower limits of state of charge, charging and discharging efficiency, initial and final state of charge constraints of the energy storage system, as well as the lifetime loss parameters considering the number of charge and discharge cycles, and the grid connection point power constraints and frequency regulation capacity and power output coupling distribution constraints of the wind-storage combined system.

[0046] Historical market clearing price data includes day-ahead market energy clearing price time series, frequency regulation capacity price time series, and frequency regulation mileage price time series published by the power trading center. This historical price data covers a sufficiently long historical period to ensure the adequacy of subsequent statistical modeling. Historical wind power forecasting error data originates from the deviation records between historical wind power forecasts and measured output values ​​of a wind farm in Northwest China, used to establish the marginal probability distribution and time-series correlation structure of forecasting errors for each time period. Wind power point forecasting results are deterministic wind power output forecast sequences for each time period within the dispatch cycle (24 hours, 1-hour resolution) output by a wind power forecasting system driven by numerical weather prediction. Historical market frequency regulation signal data is the real-time high-frequency AGC frequency regulation command sequence of a typical day in the PJM market in 2025.

[0047] Step S2012 involves preprocessing data on market rules, unit boundary conditions, historical market clearing prices, historical wind power forecasting errors, wind power point forecasting results, and market frequency regulation signals.

[0048] In this embodiment of the invention, data preprocessing is performed on market rules, unit boundary conditions, historical market clearing prices, historical wind power prediction errors, wind power point prediction results, and market frequency regulation signals. The data preprocessing includes normalization processing.

[0049] Step S202: Using historical wind power prediction errors and wind power point prediction results, construct a wind power uncertainty output scenario.

[0050] Specifically, step S202 includes: Step S2021: Extract the statistical characteristics of historical wind power prediction errors and wind power point prediction results.

[0051] In this embodiment of the invention, statistical features of historical wind power prediction errors and wind power point prediction results are extracted, and the statistical features of historical wind power prediction errors and wind power point prediction results are modeled.

[0052] Step S2022: Perform distribution fitting on each statistical feature, and use Latin hypercube sampling method to extract wind power sample scenarios from the fitted multivariate distribution.

[0053] In this embodiment of the invention, distribution fitting is performed on each statistical feature, and then Latin hypercube sampling is used to extract wind force sample scenarios on a large scale from the fitted multivariate distribution.

[0054] Step S2023: Cluster the wind power sample scenarios to obtain multiple wind power uncertain output scenarios; wherein, the wind power uncertain output scenario corresponds to the wind power output sequence and its occurrence probability for each time period within a complete scheduling cycle.

[0055] In this embodiment of the invention, a large number of wind power sample scenarios generated are aggregated into multiple representative typical wind power scenario sets by k-means clustering. Each typical scenario corresponds to the wind power output sequence and its occurrence probability for each time period within a complete scheduling cycle, thereby incorporating the uncertainty of wind power output into the subsequent optimization model in a scenario-based manner.

[0056] In some alternative implementations, day-ahead operating constraints include day-ahead operating constraints for thermal power plants, day-ahead operating constraints for energy storage power plants, day-ahead operating constraints for wind farms, and day-ahead combined system operating constraints.

[0057] It should be noted that a two-stage robust optimization model (i.e., a power system operation optimization model) is constructed with the objective of maximizing the total revenue of the day-ahead and intraday real-time markets. This model establishes operational constraints for both the day-ahead bidding and intraday real-time dispatch stages.

[0058] In this embodiment of the invention, the operational constraints of the current day-to-date tender mainly include the operational constraints of thermal power plants, the operational constraints of energy storage power stations, the operational constraints of wind farms, and the operational constraints of the combined system.

[0059] The current operational constraints on thermal power plants include constraints on the start-up status of thermal power plants, constraints on the start-up and shutdown duration of thermal power plants, constraints on the output of thermal power plants, constraints on the ramp-up of thermal power plants, and constraints on the frequency regulation output of thermal power plants.

[0060] In this embodiment of the invention, the operating constraints of thermal power plants are as follows: 1) Constraints on the start-up status of thermal power plants:

[0061] In the formula, This represents the operating variables of the thermal power plant during time period t, where 1 indicates that it is running and 0 indicates that it is not running. For thermal power units in t The start-up variable for the time period, where 1 indicates the start-up status and 0 indicates that it is not in the start-up status; The shutdown variable for thermal power units during time period t, where 1 indicates shutdown status and 0 indicates not in shutdown status.

[0062] 2) Constraints on start-up and shutdown time of thermal power plants: ,

[0063] In the formula, This is the minimum startup time for thermal power units; This is the minimum downtime for thermal power units.

[0064] 3) Output constraints of thermal power plants: ; ; In the formula, , This refers to the lower and upper limits of the output of thermal power plants. For thermal power plants to participate in bidding for energy market capacity; The frequency regulation capacity that can be provided to thermal power plants; This serves as the baseline for the actual output of thermal power plants, used to calculate coal consumption.

[0065] 4) Climbing constraint:

[0066] In the formula, , This represents the maximum ramp rate of thermal power generation. The time resolution for the current running phase is 60 minutes.

[0067] 5) Frequency regulation output constraints of thermal power plants: ; ; In the formula, , These refer to the up and down frequency regulation capacities that thermal power plants can provide; This refers to the bidding capacity of thermal power plants in the frequency regulation market.

[0068] The current operational constraints of energy storage power stations include constraints on energy storage charge and discharge status, energy storage charge and discharge power, state of charge constraints, frequency regulation power limit constraints, coupling constraints between frequency regulation capacity and charge and discharge power, constraints on frequency regulation capacity of state of charge, and constraints on the relationship between frequency regulation capacity and bidding capacity.

[0069] In this embodiment of the invention, the operating constraints of the energy storage power station are as follows: 1) Energy storage charge / discharge state constraints:

[0070] In the formula, , These represent the discharge and charging states of the energy storage system at time t, respectively. The time indicates that there is no discharge at time t. Time indicates the discharge at time t; Time t indicates that charging is not performed at time t. Time indicates that charging occurs at time t.

[0071] 2) Energy storage charging and discharging power constraints:

[0072]

[0073] In the formula, , These represent the charging and discharging power of the energy storage system at time t, respectively. , These represent the maximum charging and discharging power of the energy storage at time t; It provides actual power to the energy storage power station.

[0074] 3) SOC (State of Charge) constraint:

[0075]

[0076] In the formula, Let t be the charge of the energy storage system. Let be the charge of the energy storage system at time t-1. , and , respectively, the charging and discharging efficiency of the energy storage system at time t; , These represent the lower limit and upper limit of the charge of the energy storage system at time t, respectively.

[0077] 4) Constraints related to energy storage frequency regulation capacity: Frequency modulation power limit constraints: ; Coupling constraints between frequency modulation capacity and charge / discharge power: ; SOC constraints on frequency modulation capacity: ; Constraints on the relationship between frequency modulation capacity and bidding capacity: ; In the formula, , This represents the up and down frequency regulation power that the energy storage power station can provide at time t; The time resolution for the current operational phase is set to 60 minutes. , The up and down frequency modulation rate; , The bidding capacity for energy storage power stations participating in the energy market is divided into two states: discharging and charging. Bidding capacity for energy storage in the frequency regulation market.

[0078] The day-ahead wind farm's operational constraints include the day-ahead wind farm's operational constraints body and the day-ahead wind farm's ramping constraints.

[0079] In this embodiment of the invention, 1) Operational constraints of the wind farm:

[0080] In the formula, For wind power to participate in day-ahead energy market bidding capacity; The day-ahead wind power forecast curve is used to represent the actual output of wind power as predicted by the day-ahead forecast. , This refers to the up- and down-regulation capacity for wind power to participate in the day-ahead frequency regulation market.

[0081] Climbing speed constraint:

[0082] In the formula, This represents the maximum adjustable frequency ratio for wind power. This represents the available power of wind power.

[0083] It is worth mentioning that the operational constraints in the recent tender also included joint system operational constraints; ;

[0084] In the formula, The total bidding capacity for the joint system to participate in the day-ahead energy market; This represents the total bidding capacity of the joint system participating in the frequency modulation market.

[0085] In some alternative implementations, intraday operating constraints include operating constraints for thermal power plants, operating constraints for energy storage power stations, operating constraints for wind farms operating in each wind power uncertainty output scenario, and intraday joint system operating constraints.

[0086] In this embodiment of the invention, intraday real-time operating constraints are established for different wind power uncertainty output scenarios. Except for the operating constraints of wind farms, the constraints of other units are no different from those in the day-ahead bidding stage except for the time scale. Among them, the intraday operating constraints include the operating constraints of thermal power plants, the operating constraints of energy storage power stations, the operating constraints of wind farms operating in each wind power uncertainty output scenario, and the intraday joint system operating constraints.

[0087] The daily operating constraints of thermal power plants include constraints on the plant's start-up status, start-up and shutdown duration, output, ramp-up, and frequency regulation output.

[0088] For details, please refer to the above-described embodiments for the operating constraints of thermal power plants that were tendered previously; these will not be repeated here.

[0089] The operational constraints of intraday energy storage power stations include energy storage charge and discharge state constraints, energy storage charge and discharge power constraints, state of charge constraints, frequency regulation power limit constraints, coupling constraints between frequency regulation capacity and charge and discharge power, constraints of state of charge on frequency regulation capacity, and constraints on the relationship between frequency regulation capacity and bidding capacity.

[0090] For details, please refer to the operational constraints of the energy storage power station that was tendered in the above embodiment, which will not be repeated here.

[0091] The formula for calculating the operating constraints of a wind farm operating under uncertain wind power output scenarios is as follows:

[0092] In the formula, s Different wind power scenarios within the day; For the real-time operation phase within the day; For wind power to participate in the bidding capacity of the intraday real-time energy market; To determine the actual daily output of wind power, the output prediction curve considering the uncertainty of wind power output in the scenario is adopted. , This refers to the up-and-down frequency regulation capacity for wind power to participate in the intraday real-time frequency regulation market.

[0093] In this embodiment of the invention, considering the uncertain power output of wind power, wind farm operation constraints are established:

[0094] In the formula, s Different wind power scenarios within the day; For the real-time operation phase within the day; For wind power to participate in the bidding capacity of the intraday real-time energy market; To determine the actual daily output of wind power, the output prediction curve considering the uncertainty of wind power output in the scenario is adopted. , This refers to the up-and-down frequency regulation capacity for wind power to participate in the intraday real-time frequency regulation market.

[0095] Step S203: Under the preset daytime and intraday operating constraints, a power system operation optimization model is constructed with the goal of maximizing the expected total revenue of the joint system under each wind power uncertain output scenario.

[0096] Specifically, step S203 includes: Step S2031: Under the preset day-ahead and intraday operating constraints, a power system operation optimization model is constructed with the goal of maximizing the expected total revenue of the joint system under each wind power uncertain output scenario. The expected total revenue of the joint system under each wind power uncertain output scenario represents the probability-weighted sum of the actual revenue of the joint system under each wind power uncertain output scenario.

[0097] In this embodiment of the invention, an objective function for maximizing the expected total revenue of the joint system is established: ; ; ; ; In the formula, The expected total revenue of the joint system under multiple scenarios (i.e., wind power output uncertainty scenarios) is the probability-weighted sum of the actual revenue under each scenario. The actual revenue includes two parts: market revenue and deviation penalty. s Different wind power scenarios within the day; S This represents the total number of wind power scenarios; The probability of occurrence for each wind power scenario; For the real-time operation phase within the day; T 1 represents the total real-time operating time during the day; For the combined system's revenue in the intraday real-time energy market; For the combined system's revenue in the intraday real-time frequency modulation market; This represents the real-time daily coal consumption cost for thermal power plants. Penalty fees for deviations in the energy market; For deviation penalty fees in the FM market; Real-time energy market price for the day; The total contribution of the joint system to the intraday real-time energy market; The time resolution for the real-time operation phase within the day is set to 15 minutes. The price of frequency modulation capacity in the intraday real-time frequency modulation market; This is the FM mileage factor, which is calculated from the market FM signal; The price of FM mileage in the intraday real-time FM market; To contribute to the overall efforts of the joint system in the intraday real-time frequency modulation market; This refers to the unit price of coal consumption. For thermal power plants Real-time output during a given time period.

[0098] Step S2032, the actual benefits include market benefits and deviation penalties.

[0099] In this embodiment of the invention, the actual revenue includes market revenue and deviation penalty. Market revenue is the transaction revenue obtained by the joint system from participating in the electricity market and the frequency regulation ancillary service market. Deviation penalty is the penalty expenditure incurred in the electricity market and the frequency regulation market due to the deviation of the actual operating conditions from the day-ahead bid capacity. The two are combined to jointly characterize the revenue level of the joint system in a single scenario.

[0100] In some alternative implementations, the deviation penalty can be expressed by the following formula:

[0101] In the formula, Penalty fees for deviations in the energy market; For deviation penalty fees in the FM market; The deviation penalty coefficient for the real-time operating output of the joint system exceeding the energy market bid volume; The deviation penalty coefficient for insufficient energy market bids due to insufficient real-time output of the joint system; The current energy market price; Real-time bidding capacity for the joint system energy market; For day-ahead bidding capacity in the Joint System Energy Market; The deviation penalty coefficient for the real-time operation output of the joint system exceeding the bidding volume in the frequency modulation market; Penalty for deviations in the frequency modulation market bidding volume due to insufficient real-time output of the joint system; This refers to the frequency modulation mileage factor. This refers to the current FM mileage price in the FM market. This refers to the current daytime FM market capacity price; Real-time operating capacity of the joint system frequency regulation capacity market; The day-ahead tender capacity for the joint system frequency modulation capacity market; The time resolution for the real-time operation phase within the day is set to 15 minutes.

[0102] In this embodiment of the invention, the bidding capacity submitted by the joint system in the day-ahead market serves as the basis for scheduling execution on the following day. However, the uncertainty of wind power output during real-time operation in various scenarios inevitably leads to actual output deviating from the day-ahead bidding value. Therefore, a deviation penalty needs to be established. The objective function result will minimize the deviation between the day-ahead bidding and intraday scheduling in each scenario based on the optimal solution. The deviation penalty can be expressed by the following formula:

[0103] In the formula, Penalty fees for deviations in the energy market; For deviation penalty fees in the FM market; The deviation penalty coefficient for the real-time operating output of the joint system exceeding the energy market bid volume; The deviation penalty coefficient for insufficient energy market bids due to insufficient real-time output of the joint system; The current energy market price; Real-time bidding capacity for the joint system energy market; For day-ahead bidding capacity in the Joint System Energy Market; The deviation penalty coefficient for the real-time operation output of the joint system exceeding the bidding volume in the frequency modulation market; Penalty for deviations in the frequency modulation market bidding volume due to insufficient real-time output of the joint system; This refers to the frequency modulation mileage factor. This refers to the current FM mileage price in the FM market. This refers to the current daytime FM market capacity price; Real-time operating capacity of the joint system frequency regulation capacity market; The day-ahead tender capacity for the joint system frequency modulation capacity market; The time resolution for the real-time operation phase within the day is set to 15 minutes.

[0104] Step S204: Solve the power system operation optimization model and determine the day-ahead market basic bidding scheme and intraday market real-time dispatch instructions according to the optimal solution.

[0105] In this embodiment of the invention, a power system operation optimization model is solved using a mixed integer linear programming solver (such as Gurobi, CPLEX, etc.) to obtain the day-ahead market basic bidding scheme and the intraday real-time market real-time dispatch instructions, and the expected profit is calculated based on these.

[0106] After completing scenario generation and model construction, the day-ahead and real-time two-stage optimization problem of the wind-thermal-storage integrated system is transformed into a mixed-integer linear programming model and solved efficiently using a commercial solver. This yields the basic day-ahead market bidding scheme and the intraday real-time market real-time dispatch instructions for each scenario. The output of the basic day-ahead market bidding scheme includes: the bidding capacity of wind farms, thermal power plants, and energy storage power stations in the energy market and frequency regulation market respectively within 24 time periods of the operating day, as well as the start-up and shutdown plans of thermal power units and energy storage power stations for each time period and the expected total revenue of the integrated system. The intraday real-time market real-time dispatch instructions include: the real-time output of wind farms, thermal power plants, and energy storage power stations in the energy market, the real-time up-frequency regulation capacity and down-frequency regulation capacity in the frequency regulation market respectively within 96 time periods at 15-minute intervals, the actual frequency regulation mileage, the actual deviation penalty cost, and the real-time operating revenue of the integrated system for each time period.

[0107] It is worth mentioning that the model is built on the MATLAB R2023b platform, uses the YALMIP toolbox for mathematical modeling and constraint expression, and calls the Gurobi 10.0 commercial solver to perform branch and bound and cutting plane algorithms to complete the solution in a computing environment with an Intel Core i7-8700 processor (3.19 GHz) and 8GB DDR4 memory. The solution efficiency is sufficient to support the day-ahead-real-time two-stage joint optimization of multiple combinations consisting of multiple wind power scenarios and one frequency modulation signal scenario.

[0108] The beneficial effects of this invention are as follows: Enhancing overall market profitability and regulatory flexibility: This invention fully leverages the deep peak-shaving capabilities of thermal power, the rapid charging and discharging characteristics of energy storage, and the low marginal cost advantage of wind power. By jointly optimizing the allocation of energy base points and frequency regulation capacity, the system achieves an optimal trade-off between frequency regulation performance and energy supply. Compared to independent operation, joint operation can reduce the losses from frequent start-ups and shutdowns of thermal power through internal complementarity, while utilizing energy storage to absorb the penalty risk caused by wind power forecasting deviations, maximizing the total revenue of the consortium in both markets.

[0109] To effectively address wind power uncertainties and reduce real-time deviation penalties, this invention introduces a two-stage scheduling framework based on robust optimization, encompassing day-ahead and intraday phases. The day-ahead phase formulates bidding strategies considering the worst-case wind power output scenario, ensuring the robust feasibility of the bidding schemes. The intraday real-time phase incorporates a deviation penalty mechanism for rolling corrections. Compared to traditional deterministic optimization, this invention significantly reduces real-time energy deficit penalties and frequency regulation capacity deficit penalties caused by wind power forecasting errors, ensuring the stability of revenue.

[0110] Achieving multi-timescale coordination to ensure grid safety and stability: This invention clarifies the coupling constraints between hourly day-ahead bidding and 15-minute real-time operation. By setting constraints on thermal power ramp rate, dynamic changes in energy storage SOC, and frequency regulation mileage response, the wind-thermal-storage integrated system achieves better tracking accuracy when responding to grid AGC frequency regulation commands. Simulations show that this invention can smooth wind power output fluctuations and avoid the risk of grid frequency drops due to insufficient frequency regulation capacity.

[0111] Specific application examples are as follows: This study uses a combined system comprising a wind farm, a thermal power plant, and a lithium battery energy storage power station as the research object to verify the effectiveness of the method of this invention. The system is configured with a wind power installed capacity of 2000MW, a thermal power installed capacity of 300MW (minimum output 30%), and an energy storage system installed capacity of 250MW, with a utilization period of 2 hours. Market data used are typical daily electricity prices and PJM frequency regulation signal data from a certain province's electricity market.

[0112] Compare the following two scheduling modes: Mode 1 (Comparative): Traditional separate operation mode, where wind power, thermal power and energy storage each participate independently in the electricity market and frequency regulation ancillary service market, without considering joint complementarity.

[0113] Mode 2 (Method of the present invention): The two-stage robust optimization of the present invention is adopted, and the participants jointly participate in the power and frequency regulation market, taking into account the deviation penalty mechanism.

[0114] Figure 3 The paper presents a scenario of uncertain wind power output obtained by Latin hypercube sampling and k-means aggregation, and solves the day-ahead bidding plan under two modes based on the above scenario. Figure 4 and Figure 5The power output plans of each unit participating in the electricity market under the two modes were presented respectively. Figure 6 and Figure 7 The output plans of each unit participating in the frequency regulation ancillary services market under two different modes are presented. Through optimization, the total profit comparison between the two modes is shown in Table 1. The expected total profit of Mode 2 (joint optimization) is 2.96% higher than that of Mode 1 (traditional separation), and the profit standard deviation is smaller (i.e., the revenue volatility is lower), which reflects the robustness of the model in dealing with the uncertainty of wind power.

[0115] Table 1. Comparison of Expected Profits Between the Two Models

[0116] The capacity allocation for the electricity market and the frequency regulation ancillary service market reflects the price game between the two markets, with each generating unit choosing the market with the higher profit margin to participate in day-ahead bidding. Compared to Mode 1, where energy storage can only profit from price peaks and troughs, Mode 2 can fully utilize the combined and complementary effects of various generating units. Energy storage addresses the output fluctuations caused by the uncertainty of wind power, storing energy when wind power output exceeds the bidding volume and generating electricity when wind power output is lower than the bidding volume, fully utilizing new energy sources while avoiding deviation penalties. Simultaneously, thermal power can also play a regulatory role when energy storage capacity is insufficient. Furthermore, energy storage can also achieve time-shifting of thermal power output to obtain more energy market revenue.

[0117] This embodiment also provides a power system operation scheduling optimization device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0118] This embodiment provides a power system operation and dispatch optimization device, such as... Figure 8 As shown, it includes: The acquisition module 301 is used to acquire the basic boundary data of the day-ahead market; wherein, the basic boundary data includes at least the historical wind power forecast error and the wind power point forecast result; The scenario construction module 302 is used to construct a wind power uncertainty output scenario by using historical wind power prediction errors and wind power point prediction results; The model building module 303 is used to build a power system operation optimization model under preset day-ahead and intraday operation constraints, with the goal of maximizing the expected total revenue of the joint system under various wind power uncertainty output scenarios. The solver module 304 is used to solve the power system operation optimization model and determine the day-ahead market basic bidding scheme and intraday market real-time dispatch instructions according to the optimal solution.

[0119] In some optional implementations, the acquisition module 301 includes: The acquisition unit is used to acquire the basic boundary data of the day-ahead market; wherein, the basic boundary data includes at least market rules, unit boundary conditions, historical market clearing prices, historical wind power forecasting errors, wind power point forecasting results, and market frequency regulation signals; The preprocessing unit is used to preprocess data such as market rules, unit boundary conditions, historical market clearing prices, historical wind power forecasting errors, wind power point forecasting results, and market frequency regulation signals.

[0120] In some alternative implementations, the scene construction module 302 includes: The extraction unit is used to extract the statistical characteristics of historical wind power prediction errors and wind power point prediction results; The fitting unit is used to fit the distribution of each statistical feature and to extract wind power sample scenarios from the multivariate distribution obtained by the Latin hypercube sampling method. Clustering units are used to cluster various wind power sample scenarios to obtain multiple wind power uncertain output scenarios; among them, the wind power uncertain output scenario corresponds to the wind power output sequence and its occurrence probability for each time period within a complete scheduling cycle.

[0121] In some alternative implementations, day-ahead operating constraints include day-ahead operating constraints for thermal power plants, day-ahead operating constraints for energy storage power plants, day-ahead operating constraints for wind farms, and day-ahead combined system operating constraints. The current operational constraints for thermal power plants include constraints on the start-up status of thermal power plants, constraints on the start-up and shutdown duration of thermal power plants, constraints on the output of thermal power plants, constraints on the ramp-up of thermal power plants, and constraints on the frequency regulation output of thermal power plants. The current operational constraints of energy storage power stations include energy storage charge and discharge state constraints, energy storage charge and discharge power constraints, state of charge constraints, frequency regulation power limit constraints, coupling constraints between frequency regulation capacity and charge and discharge power, constraints of state of charge on frequency regulation capacity, and constraints on the relationship between frequency regulation capacity and bidding capacity. The day-ahead wind farm's operational constraints include the day-ahead wind farm's operational constraints body and the day-ahead wind farm's ramping constraints.

[0122] In some optional implementations, intraday operating constraints include operating constraints for thermal power plants, operating constraints for energy storage power stations, operating constraints for wind farms operating in each wind power uncertainty output scenario, and intraday joint system operating constraints. The daily operating constraints of thermal power plants include thermal power plant start-up status constraints, thermal power plant start-up and shutdown duration constraints, thermal power plant output constraints, thermal power plant ramping constraints, and thermal power plant frequency regulation output constraints. The operational constraints of intraday energy storage power stations include energy storage charge and discharge state constraints, energy storage charge and discharge power constraints, state of charge constraints, frequency regulation power limit constraints, coupling constraints between frequency regulation capacity and charge and discharge power, constraints of state of charge on frequency regulation capacity, and constraints on the relationship between frequency regulation capacity and bidding capacity. The formula for calculating the operating constraints of a wind farm operating under uncertain wind power output scenarios is as follows:

[0123] In the formula, s Different wind power scenarios within the day; For the real-time operation phase within the day; For wind power to participate in the bidding capacity of the intraday real-time energy market; To determine the actual daily output of wind power, the output prediction curve considering the uncertainty of wind power output in the scenario is adopted. , This refers to the up-and-down frequency regulation capacity for wind power to participate in the intraday real-time frequency regulation market.

[0124] In some alternative implementations, the model building module 303 includes: The model building unit is used to construct a power system operation optimization model under preset day-ahead and intraday operation constraints, with the goal of maximizing the expected total revenue of the joint system under each wind power uncertain output scenario; wherein, the expected total revenue of the joint system under each wind power uncertain output scenario represents the probability weighted sum of the actual revenue of the joint system under each wind power uncertain output scenario. Actual revenue unit, used for actual revenue including market revenue and deviation penalty.

[0125] In some alternative implementations, the deviation penalty can be expressed by the following formula:

[0126] In the formula, Penalty fees for deviations in the energy market; For deviation penalty fees in the FM market; The deviation penalty coefficient for the real-time operating output of the joint system exceeding the energy market bid volume; The deviation penalty coefficient for insufficient energy market bids due to insufficient real-time output of the joint system; The current energy market price; Real-time bidding capacity for the joint system energy market; For day-ahead bidding capacity in the Joint System Energy Market; The deviation penalty coefficient for the real-time operation output of the joint system exceeding the bidding volume in the frequency modulation market; Penalty for deviations in the frequency modulation market bidding volume due to insufficient real-time output of the joint system; This refers to the frequency modulation mileage factor. This refers to the current FM mileage price in the FM market. This refers to the current daytime FM market capacity price; Real-time operating capacity of the joint system frequency regulation capacity market; The day-ahead tender capacity for the joint system frequency modulation capacity market; The time resolution for the real-time operation phase within the day is set to 15 minutes.

[0127] The power system operation scheduling optimization device provided in this embodiment of the invention can execute the power system operation scheduling optimization method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0128] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0129] The following is a detailed reference. Figure 9 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0130] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 9 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0131] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the power system operation scheduling optimization method of the embodiments of the present invention.

[0132] Figure 9 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0133] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the power system operation scheduling optimization method shown in the above embodiments is implemented.

[0134] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for optimizing the operation and scheduling of a power system, characterized in that, The method includes: Obtain the basic boundary data of the current market; wherein, the basic boundary data includes at least historical wind power forecast errors and wind power point forecast results; Using the historical wind power prediction error and the wind power point prediction results, a wind power uncertainty output scenario is constructed. Under the preset day-ahead and intraday operating constraints, a power system operation optimization model is constructed with the goal of maximizing the expected total revenue of the joint system under the various wind power uncertain output scenarios. The power system operation optimization model is solved, and the day-ahead market basic bidding scheme and intraday market real-time dispatch instructions are determined according to the optimal solution.

2. The method according to claim 1, characterized in that, The acquisition of the day-ahead market baseline data; wherein, the baseline data includes at least historical wind power forecast errors and wind power point forecast results, including: Obtain the basic boundary data of the day-ahead market; wherein, the basic boundary data includes at least market rules, unit boundary conditions, historical market clearing prices, historical wind power forecasting errors, wind power point forecasting results, and market frequency regulation signals; Data preprocessing is performed on the market rules, the unit boundary conditions, the historical market clearing price, the historical wind power prediction error, the wind power point prediction result, and the market frequency regulation signal.

3. The method according to claim 1, characterized in that, The method of constructing a wind power uncertainty output scenario by using the historical wind power prediction error and the wind power point prediction result includes: Extract the statistical characteristics of the historical wind power prediction error and the wind power point prediction results; Distribution fitting is performed on each of the statistical features, and wind power sample scenarios are extracted from the fitted multivariate distribution using the Latin hypercube sampling method. Clustering is performed on each of the wind power sample scenarios to obtain multiple wind power uncertain output scenarios; wherein, the wind power uncertain output scenario corresponds to the wind power output sequence and its occurrence probability for each time period within a complete scheduling cycle.

4. The method according to claim 1, characterized in that, The day-ahead operating constraints include day-ahead operating constraints for thermal power plants, day-ahead operating constraints for energy storage power stations, day-ahead operating constraints for wind farms, and day-ahead combined system operating constraints. The aforementioned operating constraints of thermal power plants include thermal power plant start-up status constraints, thermal power plant start-up and shutdown duration constraints, thermal power plant output constraints, thermal power plant ramping constraints, and thermal power plant frequency regulation output constraints. The operational constraints of the day-ahead energy storage power station include energy storage charge and discharge state constraints, energy storage charge and discharge power constraints, state of charge constraints, frequency regulation power limit constraints, coupling constraints between frequency regulation capacity and charge and discharge power, constraints of state of charge on frequency regulation capacity, and constraints on the relationship between frequency regulation capacity and bidding capacity. The operational constraints of the day-ahead wind farm include the operational constraint body of the day-ahead wind farm and the ramping constraint of the day-ahead wind farm.

5. The method according to claim 1, characterized in that, The intraday operating constraints include the operating constraints of thermal power plants, the operating constraints of energy storage power stations, the operating constraints of wind farms operating in each of the aforementioned wind power uncertain output scenarios, and the intraday joint system operating constraints. The intraday operating constraints of thermal power plants include thermal power plant start-up status constraints, thermal power plant start-up and shutdown duration constraints, thermal power plant output constraints, thermal power plant ramping constraints, and thermal power plant frequency regulation output constraints. The operational constraints of the intraday energy storage power station include energy storage charge and discharge state constraints, energy storage charge and discharge power constraints, state of charge constraints, frequency regulation power limit constraints, coupling constraints between frequency regulation capacity and charge and discharge power, constraints of state of charge on frequency regulation capacity, and constraints on the relationship between frequency regulation capacity and bidding capacity. The calculation formula for the operating constraints of the wind farm operating in the uncertain wind power output scenario is as follows: In the formula, s Different wind power scenarios within the day; For the real-time operation phase within the day; For wind power to participate in the bidding capacity of the intraday real-time energy market; To determine the actual daily output of wind power, the output prediction curve considering the uncertainty of wind power in the aforementioned wind power output uncertainty scenario is adopted. , This refers to the up-and-down frequency regulation capacity for wind power to participate in the intraday real-time frequency regulation market.

6. The method according to claim 1, characterized in that, Under preset daytime and intraday operating constraints, and with the objective of maximizing the expected total revenue of the joint system under various uncertain wind power output scenarios, a power system operation optimization model is constructed, including: Under the preset day-ahead and intraday operating constraints, a power system operation optimization model is constructed with the objective of maximizing the expected total revenue of the joint system under each wind power uncertain output scenario. The expected total revenue of the joint system under each wind power uncertain output scenario represents the probability-weighted sum of the actual revenue of the joint system under each wind power uncertain output scenario. The actual benefits include market gains and deviation penalties.

7. The method according to claim 6, characterized in that, The deviation penalty can be expressed by the following formula: In the formula, Penalty fees for deviations in the energy market; For deviation penalty fees in the FM market; The deviation penalty coefficient for the real-time operating output of the joint system exceeding the energy market bid volume; The deviation penalty coefficient for insufficient energy market bids due to insufficient real-time output of the joint system; The current energy market price; Real-time bidding capacity for the joint system energy market; For day-ahead bidding capacity in the Joint System Energy Market; The deviation penalty coefficient for the real-time operation output of the joint system exceeding the bidding volume in the frequency modulation market; Penalty for deviations in the frequency modulation market bidding volume due to insufficient real-time output of the joint system; This refers to the frequency modulation mileage factor. This refers to the current FM mileage price in the FM market. This refers to the current daytime FM market capacity price. Real-time operating capacity of the joint system frequency regulation capacity market; The current day's bidding capacity for the joint system frequency modulation capacity market; The time resolution for the real-time operation phase within the day is set to 15 minutes.

8. A power system operation and dispatch optimization device, characterized in that, The device includes: The acquisition module is used to acquire the basic boundary data of the day-ahead market; wherein, the basic boundary data includes at least historical wind power prediction errors and wind power point prediction results; The scenario construction module is used to construct a wind power uncertainty output scenario by using the historical wind power prediction error and the wind power point prediction result; The model building module is used to construct a power system operation optimization model under preset daytime and intraday operating constraints, with the goal of maximizing the expected total revenue of the joint system under the operation of each wind power uncertainty output scenario. The solution module is used to solve the power system operation optimization model and determine the day-ahead market basic bidding scheme and intraday market real-time dispatch instructions according to the optimal solution.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the power system operation and scheduling optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the power system operation and scheduling optimization method according to any one of claims 1 to 7.

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