Cascade power station scheduling strategy generation method and system based on spot market

Abstract

The invention relates to the field of cascade power station scheduling strategy planning, in particular to a cascade power station scheduling strategy generation method and system based on a spot market. By establishing an economic mapping mechanism of a unit performance quantitative model and a market rule, the problem that a traditional static proportional distribution strategy neglects the dynamic characteristic difference of the unit is solved, AGC instructions can be dynamically distributed according to the real-time adjustment capability of each unit, the AGC comprehensive performance index of a power station in a spot market environment is remarkably improved, and the power station performance is improved. The power station not only can avoid the assessment penalty caused by substandard performance, but also can make full use of the adjustment potential of a high-performance unit to obtain more compensation benefits, so that the maximization of economic benefits is realized; the problems that system-level performance degradation, economic loss, equipment-level risk and resource waste are possibly caused by the fact that an automatic power generation control system of a cascade hydropower station generally adopts a static distribution strategy based on an adjusting capacity proportion to distribute AGC adjusting tasks are solved.

Description

Technical Field

[0001] This invention relates to the field of cascade power plant dispatch strategy planning, specifically a method and system for generating cascade power plant dispatch strategies based on the spot market. Background Technology

[0002] With the deepening development of the electricity spot market, the function of the Automatic Generation Control (AGC) system in cascade hydropower stations has transformed from simply ensuring frequency stability to a core ancillary service that directly impacts the economic benefits of the power station. In the spot market, especially the real-time market, the power grid rigorously assesses the performance indicators of AGC, such as regulation accuracy and response speed, and directly links these performance indicators to economic compensation and penalties. High-performance AGC services can receive additional mileage compensation, while substandard performance will face economic penalties.

[0003] Currently, in order to cope with the fluctuations in the spot market, the allocation of AGC adjustment tasks in the intraday or real-time rolling dispatch layer within the power plant generally adopts a static allocation strategy based on the proportion of adjustment capacity. This strategy assumes that all units in the power plant have similar dynamic response capabilities, and thus simply allocates the total AGC instructions issued by the grid according to the proportion of the adjustable capacity of each unit.

[0004] However, in-depth research and practice in this field have shown that this seemingly fair and simple static proportional allocation strategy completely ignores the inherent and significant differences in dynamic response characteristics (such as response delay, regulation rate, settling time, etc.) between different AGC units within the same power plant when allocating tasks. This results in an excessive number of regulation task commands being allocated to units with slow dynamic responses. This subtle but real technical problem can lead to the following negative consequences: 1. System-level performance degradation and economic losses: Slow-speed units cannot accurately and quickly track their assigned commands, directly leading to a decline in the overall AGC performance indicators of the entire power station (such as regulation accuracy and response speed). Under spot market rules, this not only prevents the power station from obtaining performance-based mileage compensation, but also incurs economic penalties for failing to meet assessment standards, resulting in the predicament of "wanting to make money but losing money."

[0005] 2. Equipment-level risks and resource waste: In order to keep up with commands that cannot be completed, slow-speed units may be forced to frequently traverse vibration zones or operate under suboptimal conditions, exacerbating mechanical wear and fatigue risks. At the same time, those units in the power plant that respond quickly and have excellent performance are rendered useless by this rigid allocation strategy, with their rapid regulation capabilities being idled, resulting in a huge waste of high-quality regulation resources. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for generating scheduling strategies for cascade hydropower stations based on the spot market. This solves the problem that the automatic generation control systems of cascade hydropower stations generally adopt a static allocation strategy based on the proportion of regulation capacity when allocating AGC regulation tasks, which may lead to system-level performance degradation and economic losses, as well as equipment-level risks and resource waste.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for generating a cascade power plant dispatching strategy based on the spot market, the method specifically including the following steps: S1. Real-time acquisition of real-time operating status data of each AGC unit in the cascade power station, and receiving total AGC regulation instructions issued in real-time by the electricity spot market for several future control periods; S2. Based on the real-time operating status data and historical operating data of each AGC unit, the real-time comprehensive adjustment performance score of each AGC unit is dynamically calculated through the performance quantification model. Based on the real-time comprehensive adjustment performance score and the AGC performance compensation and assessment rules of the spot market, the expected marginal revenue corresponding to the unit adjustment command of each AGC unit is determined. S3. Establish a control optimization model with the objective function of maximizing the total expected marginal revenue of the cascade power station. Take the total AGC regulation command, each expected marginal revenue and the dynamic constraints of cascade hydraulic coupling as inputs, and solve the optimal command for each AGC unit in several future control periods in a rolling manner. S4. The optimal instruction is issued to the corresponding AGC unit for execution according to its corresponding control time period.

[0008] Preferably, in step S2, the specific steps for dynamically calculating the real-time comprehensive adjustment performance score of each AGC unit using a performance quantification model are as follows: S211. For each AGC unit, extract the operating data sequence within a preset historical time window from its historical operating database. The operating data sequence includes, but is not limited to: historical AGC command values, actual output values, adjustment rate time series, and steady-state error time series. S212. Based on the operating data sequence, calculate several basic performance indicators that reflect the dynamic adjustment performance of the AGC unit. The basic performance indicators include at least the average command response delay, the average adjustment rate, and the average steady-state error absolute value. S213. Normalize several basic performance indicators and map them to the same dimensionless numerical range. S214. Weighted fusion of several normalized basic performance indicators to calculate the real-time comprehensive regulation performance score of the AGC unit.

[0009] Preferably, in step S2, the specific steps for determining the expected marginal revenue are as follows: S221. Obtain the AGC performance compensation and assessment rules published in the spot market, and extract key economic parameters including the unit performance score compensation price, the unit power penalty coefficient when the adjustment accuracy is not up to standard, and the unit power penalty coefficient when the response delay is not up to standard. S222. Calculate the instruction allocation weight of each AGC unit based on the ratio of the real-time comprehensive adjustment performance score of each AGC unit to the sum of the real-time comprehensive adjustment performance scores of each AGC unit. S223. Based on the instruction allocation weights of each AGC unit, calculate the expected marginal revenue corresponding to each unit's adjustment instruction. The calculation formula is as follows:

[0010] in, This represents the expected marginal benefit of a single adjustment command from AGC unit i. This indicates the instruction allocation weight for AGC unit i. This represents the unit price for performance score compensation. This represents the probability that the adjustment accuracy of AGC unit i meets the standard. This represents the probability that the response delay of AGC unit i meets the standard. This represents the penalty coefficient per unit power when the adjustment accuracy is not up to standard. This represents the unit power penalty factor when the response delay fails to meet the standard. S224. Normalize the expected marginal revenue corresponding to the unit adjustment command of each AGC unit so that the expected marginal revenue of all units is on the same order of magnitude, so as to obtain the final expected marginal revenue corresponding to the unit adjustment command of each AGC unit and output it.

[0011] Preferably, step S3 specifically includes the following steps: S31. Establish a control optimization model with the objective function of maximizing the total expected marginal revenue of the cascade power station. Take the total AGC regulation command, each expected marginal revenue and the dynamic constraints of cascade hydraulic coupling as inputs, and solve the optimal command for each AGC unit in several future control periods in a rolling manner. S32. Construct the objective function of the control optimization model. The objective function is to maximize the total expected marginal revenue of the cascade hydropower stations, and its expression is:

[0012] In the above formula, Indicates that the cascade hydropower stations are from Time's up The total expected marginal revenue at any given time, This represents the expected marginal benefit of AGC unit i at time t per unit adjustment command. There are a total of M AGC units. Let represent the decision variable, indicating the change in instructions for AGC unit i at time t. Indicates the penalty coefficient. This represents the risk cost item of cascade hydraulic coupling at time t; S33. Construct a set of constraints for the control optimization model, wherein the set of constraints includes power constraint balance, upper and lower limits of unit output, unit ramp rate constraint, and dynamic constraints of cascade hydraulic coupling. S34. At each moment, based on historical data of the real-time electricity spot market, a time series prediction algorithm is used to generate the predicted value of the total AGC regulation command for each period in the future prediction time domain. Based on the historical operating status data of the power plant, the operating status data for each period in the future is predicted, including the predicted values ​​of the head of each unit, upstream and downstream flow, etc. S35. Based on the latest real-time operating status data of each AGC unit and the total AGC adjustment command, calculate the corresponding predicted data, and solve the control optimization model to obtain the optimal command for each AGC unit in the future several control periods.

[0013] Preferably, the power constraint balance means that the sum of the output of all AGC units at any given time is equal to the total planned output value of AGC issued by the real-time electricity spot market at that time; The above and below limits of unit output mean that the output of any AGC unit at any given time must be between the upper and lower limits of the unit output. The unit ramp rate constraint means that the absolute value of the difference in output between any two adjacent moments of any AGC unit is less than a preset maximum ramp rate. The expression for the cascade hydraulic coupling dynamic constraint is:

[0014] In the above formula, This represents the inflow rate of downstream power plant j at time t. Indicates the upstream power station at time Outbound flow The time delay of water flow from the upstream power station to the downstream power station. Let be the discharge flow rate of the downstream power station j at time t. This indicates that all tailwater generated after power generation will flow directly into the AGC unit assembly in reservoir j of the downstream power station. This represents the power generation flow of unit i at time t.

[0015] Preferably, the operating status data includes at least the predicted values ​​of the head of each unit, the outflow from the upstream power station, and the inflow from the downstream power station.

[0016] The technical solution also provides a system for implementing the aforementioned method for generating cascade power plant scheduling strategies based on the spot market, including a processor and a memory. The memory is used to store a computer program, which, when executed by the processor, implements the aforementioned method for generating cascade power plant scheduling strategies based on the spot market.

[0017] Compared with the prior art, the present invention provides a method and system for generating cascade power plant scheduling strategies based on the spot market, which has the following beneficial effects: 1. This invention achieves a precise transformation from technical performance to economic value by establishing a quantitative model of unit performance and an economic mapping mechanism of market rules. It solves the problem that the traditional static proportional allocation strategy ignores the differences in dynamic characteristics of units. It can dynamically allocate AGC commands according to the real-time adjustment capabilities of each unit, which significantly improves the comprehensive performance index of power plants in the spot market environment. This allows power plants to avoid assessment penalties due to substandard performance and fully utilize the adjustment potential of high-performance units to obtain more compensation benefits, thereby maximizing economic benefits.

[0018] 2. This invention constructs a multi-dimensional risk cost model that includes dynamic constraints of hydraulic coupling, and quantifies safety factors such as unit vibration risk, equipment life loss, and the impact of hydraulic coupling into economic costs. It achieves synergistic optimization of economic benefits and operational safety in the optimization objectives, effectively avoiding the contradiction between economy and safety in traditional methods. It not only ensures the safe operation of equipment but also prevents operational risks that may be caused by hydraulic coupling, significantly improving the safety and reliability of cascade power station dispatch.

[0019] 3. This invention adopts a model predictive control framework combined with an efficient solution algorithm. Through a rolling optimization mechanism, it realizes forward-looking decision-making in multiple time periods, taking into account both the optimization needs at the current moment and the potential impact of future time periods. At the same time, through intelligent decomposition and coordination strategies and real-time guarantee mechanisms, it significantly improves computational efficiency while ensuring optimization quality, enabling complex optimization problems to be solved in a short time required by the power market, thus ensuring the real-time performance and practicality of the scheduling strategy. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of the method for generating cascade power plant scheduling strategies based on the spot market according to the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0022] Those skilled in the art will understand that all or part of the steps in the methods of the following embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0023] To address the common problem in cascade hydropower station automatic generation control systems that employ a static allocation strategy based on regulation capacity ratios for AGC (Automatic Generation Control) tasks, which can lead to system-level performance degradation, economic losses, equipment-level risks, and resource waste, this invention provides a cascade power station scheduling strategy generation method based on the spot market. This method can accurately quantify and dynamically respond to AGC task allocation strategies based on unit performance differences, thereby overcoming the economic and safety dilemmas caused by the current static proportional allocation strategy. Figure 1 As shown, the method specifically includes the following steps: S1. Real-time acquisition of operating status data of each AGC unit in the cascade power station, and receiving total AGC adjustment commands issued by the electricity spot market for several future control periods. In the actual data acquisition process, data can be acquired according to the following scheme: 1. Electrical measurements can use 0.2S-class smart meters with a voltage and current sampling rate of 4kHz and a power calculation cycle of 80ms; 2. Mechanical condition monitoring can be achieved by installing a vibration sensor network, with monitoring points including the upper guide, lower guide, and water guide bearings, and a frequency response range of 0.5Hz-1kHz; 3. Hydraulic monitoring can use a multi-channel ultrasonic flow meter to obtain several flow velocity points at the measurement cross-section; 4. Water level monitoring can be performed using a radar water level gauge. Additionally, for subsequent calculations, data cleaning and standardization can be performed as needed, which will not be elaborated here.

[0024] S2. In the electricity spot market environment, traditional AGC systems adopt a fixed proportional allocation strategy. This approach ignores the essential differences in the dynamic regulation characteristics between generating units. In actual operation, different generating units exhibit significant differences in regulation performance due to factors such as equipment aging, maintenance conditions, and hydraulic conditions. Treating high-performance units and low-performance units equally wastes valuable resources and increases assessment risks. Therefore, it is necessary to establish a quantitative mapping system from technical performance to economic value. First, based on the real-time operating status data and historical operating data of each AGC unit, the real-time comprehensive regulation performance score of each AGC unit is dynamically calculated through a performance quantification model. Based on the real-time comprehensive regulation performance score and the AGC performance compensation and assessment rules of the spot market, the expected marginal revenue corresponding to each unit's regulation command is determined. The specific steps for dynamically calculating the real-time comprehensive regulation performance score of each AGC unit through the performance quantification model are as follows: S211. The regulation performance of a generator unit is a multi-dimensional concept. A single indicator cannot fully reflect its comprehensive capabilities. This invention constructs a complete evaluation system from three dimensions: time (response speed), amplitude (regulation capability), and accuracy (steady-state performance) to ensure the comprehensiveness and accuracy of the evaluation results. For each AGC generator unit, the operating data sequence within a preset historical time window is extracted from its historical operating database. The operating data sequence includes, but is not limited to: historical AGC command values, actual output values, regulation rate time series, and steady-state error time series. S212. Qualitative descriptions cannot support precise optimization decisions. The dynamic characteristics of the unit must be transformed into comparable numerical indicators. Therefore, based on the operating data sequence, several basic performance indicators reflecting the dynamic regulation performance of the AGC unit are calculated. The basic performance indicators include at least the average command response delay, the average regulation rate, and the average steady-state error absolute value. Regulation performance is a complex concept that needs to be quantitatively characterized from multiple perspectives to avoid one-sided evaluation. The three basic performance indicators designed in this step comprehensively reflect the unit's regulation capability from different dimensions to ensure the scientific nature of the evaluation results.

[0025] S213. Normalize several basic performance indicators and map them to the same dimensionless numerical range. For example, use a per-unit value system and normalize them based on the rated parameters of the equipment.

[0026] S214. The normalized basic performance indicators are weighted and fused to calculate the real-time comprehensive regulation performance score of the AGC unit. The weighting coefficients are determined by regression analysis of the historical best regulation process.

[0027] In addition, the specific steps for determining expected marginal revenue are as follows: S221. Obtain the AGC performance compensation and assessment rules published in the spot market. Since the AGC performance compensation and assessment rules in the electricity spot market are usually described in natural language, it is necessary to extract the key economic parameters, including the unit performance score compensation price, the unit power penalty coefficient when the regulation accuracy is not up to standard, and the unit power penalty coefficient when the response delay is not up to standard, through rule parsing and parameter extraction, so as to lay the foundation for subsequent analysis.

[0028] S222. Since traditional proportional or capacity-based allocation methods cannot reflect the market principle of "high quality, high price", this step calculates the command allocation weight of each AGC unit based on the ratio of the real-time comprehensive regulation performance score of each AGC unit to the sum of the real-time comprehensive regulation performance scores of each AGC unit. Through performance-oriented weight calculation, it ensures that high-performance AGC units get more regulation opportunities, thereby maximizing the overall benefits of the power plant.

[0029] S223. Based on the instruction allocation weights of each AGC unit, calculate the expected marginal revenue corresponding to each unit's adjustment instruction. The calculation formula is as follows:

[0030] in, This represents the expected marginal benefit of a single adjustment command from AGC unit i. This indicates the instruction allocation weight for AGC unit i. This represents the unit price for performance score compensation. This represents the probability that the adjustment accuracy of AGC unit i meets the standard. This represents the probability that the response delay of AGC unit i meets the standard. This represents the penalty coefficient per unit power when the adjustment accuracy is not up to standard. This represents the unit power penalty factor when the response delay fails to meet the standard.

[0031] S224. The expected marginal revenue of different AGC units may differ by orders of magnitude. Directly using this for optimization calculations can lead to numerical computation problems. The marginal revenue of high-performance AGC units may be several times that of low-performance AGC units. This difference may cause matrix ill-conditioning problems during the optimization process, affecting the efficiency and stability of the solution. Therefore, the expected marginal revenue corresponding to a unit adjustment command for each AGC unit is normalized to ensure that the expected marginal revenue of all units is on the same order of magnitude. This allows us to obtain and output the final expected marginal revenue corresponding to a unit adjustment command for each AGC unit. Unnormalized marginal revenue can lead to an overemphasis on high-return AGC units during the optimization process, neglecting the overall coordination of the power plant. This step provides a balanced input basis for the control optimization model through scale unification.

[0032] S3. The AGC (Automatic Guided Control) scheduling of cascade hydropower stations involves multiple complex factors such as unit combination, hydraulic coupling, and market rules. Optimal decision-making is difficult to achieve solely through empirical rules or simple heuristic methods. This step aims to transform the complex decision-making problem into a solvable optimization problem by establishing a precise mathematical model: A control optimization model is established with the objective function of maximizing the total expected marginal revenue of the cascade hydropower stations. The total AGC regulation command, each expected marginal revenue, and the dynamic constraints of cascade hydraulic coupling are used as inputs to coordinate multiple objectives such as economy, safety, and stability. The optimal command for each AGC unit is solved in a rolling manner over several future control periods. Specifically, the steps include: S31. Establish a control optimization model with the objective function of maximizing the total expected marginal revenue of the cascade power stations. Take the total AGC regulation command, each expected marginal revenue and the dynamic constraints of cascade hydraulic coupling as inputs, and solve the optimal command for each AGC unit in several future control periods in a rolling manner.

[0033] S32. Construct the objective function of the control optimization model. The objective function is to maximize the total expected marginal revenue of the cascade hydropower stations. Its expression is:

[0034] In the above formula, Indicates that the cascade hydropower stations are from The total expected marginal revenue from time point to time point. This represents the expected marginal benefit of AGC unit i at time t per unit adjustment command. There are a total of M AGC units. Let represent the decision variable, indicating the change in instructions for AGC unit i at time t. This represents the penalty coefficient, balancing returns and risks. The risk cost item representing the cascade hydraulic coupling at time t generally includes hydraulic risk cost, equipment risk cost, and market risk cost. The hydraulic risk cost can be quantified by the weighted sum of squares of two key components. The first component characterizes the degree to which the reservoir water level deviates from the optimal operating head, calculated using the square of the water level deviation. The second component reflects the degree to which the power generation flow deviates from the optimal flow, measured using the square of the flow deviation. The two components are multiplied by their respective weighting coefficients and then summed. The result reflects the expected risk cost arising from the deviation of the hydraulic operating condition from the optimal operating condition. The equipment risk cost item consists of two main parts. The first part measures the proximity of the unit's current operating point to the boundary of the vibration zone, calculated by the square of the distance between the output and the boundary of the vibration zone, used to quantify the mechanical fatigue risk caused by operating through or near the vibration zone. The second part assesses the severity of the adjustment process, calculated by the square of the change in output, used to reflect the cumulative wear and tear on equipment lifespan caused by frequent adjustments. The two parts are multiplied by their respective coefficients and then summed to constitute the comprehensive risk cost at the equipment level. The market risk cost is quantified using a product of volatility and adjustment amount. Volatility represents the uncertainty of the current market environment, while adjustment amount is the sum of the absolute values ​​of all unit output changes, reflecting the overall adjustment intensity of the power plant. Multiplying these two factors by a risk price coefficient yields the potential assessment risk cost of power regulation under volatile market conditions. After calculating the risk costs across these three dimensions, a weighted summation is used to form the total risk cost. The weights of each component are dynamically adjusted based on the current system operating status. For example, during flood season, the focus is on preventing hydraulic risks; after equipment maintenance, priority is given to equipment risks; and during periods of severe market volatility, market risks are emphasized. This dynamic weighting mechanism ensures that the risk cost assessment remains consistent with the actual situation, providing an accurate risk-return balance for optimization decisions. AGC scheduling has significant temporal correlation; current decisions affect future returns. This step achieves a balance between short-term and long-term returns through multi-period optimization.

[0035] S33. Power system operation must strictly adhere to physical laws, such as power balance and equipment capacity limitations. If optimization results violate these fundamental physical constraints, they will be unenforceable in the actual system and may even lead to safety accidents. Therefore, it is necessary to construct a constraint set for the control optimization model. This constraint set includes power constraint balance, unit output upper and lower limit constraints, unit ramp rate constraints, and cascade hydraulic coupling dynamic constraints. This constraint set ensures that the calculation results guarantee that all equipment operates within safe limits, preventing equipment damage or system collapse. Specifically, power constraint balance means that the sum of the outputs of all AGC units at any given time equals the total AGC planned output command value issued by the real-time electricity spot market at that time; unit output upper and lower limit constraints mean that the output of any AGC unit at any given time must be between the upper and lower limits of the unit output; unit ramp rate constraints mean that the absolute value of the difference between the outputs of any AGC unit at any two adjacent times is less than a preset maximum ramp rate; the expression for the cascade hydraulic coupling dynamic constraints is:

[0036] In the above formula, This represents the inflow rate of downstream power plant j at time t. Indicates the upstream power station at time Outbound flow The time delay of water flow from the upstream power station to the downstream power station. Let be the discharge flow rate of the downstream power station j at time t. This indicates that all tailwater generated after power generation will flow directly into the AGC unit assembly in reservoir j of the downstream power station. This represents the power generation flow of unit i at time t.

[0037] S34. At each time point, based on historical real-time electricity spot market data, a time series forecasting algorithm is used to generate predicted values ​​for the total AGC (Automatic Guided Control) regulation commands for each time period within the future forecast time domain. Furthermore, based on historical power plant operating status data, the operating status data for each future time period is predicted. This operating status data includes at least predicted values ​​for the head of each unit, the outflow from the upstream power plant, and the inflow from the downstream power plant. The core of Model Predictive Control (MPC) lies in making forward-looking decisions based on predictive information. Traditional methods only use current state information and cannot cope with the rapid fluctuations in the electricity spot market and the hydraulic delay characteristics of cascade hydropower stations. This step generates high-quality predictive data to provide the necessary time foresight for optimization decisions.

[0038] S35. Based on the latest real-time operating status data of each AGC unit and the total AGC adjustment command, calculate the corresponding predicted data and solve the control optimization model to obtain the optimal command for each AGC unit in the future control period. Since the established optimization model contains a large number of variables and constraints, it belongs to a large-scale nonlinear programming problem. A special efficient solution algorithm must be used to obtain a high-quality solution in a limited time. Here, a hybrid strategy combining the interior point method and heuristic algorithm can be used to solve the problem.

[0039] S4. Issue the optimal instruction to the corresponding AGC unit for execution according to its corresponding control time period.

[0040] The technical solution also provides a system for implementing a method for generating cascade power plant scheduling strategies based on the spot market, including a processor and a memory. The memory is used to store computer programs, and when the computer programs are executed by the processor, they implement the method for generating cascade power plant scheduling strategies based on the spot market.

[0041] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for generating a cascade power plant scheduling strategy based on the spot market, characterized in that, The method specifically includes the following steps: S1. Real-time acquisition of real-time operating status data of each AGC unit in the cascade power station, and receiving total AGC regulation instructions issued in real-time by the electricity spot market for several future control periods; S2. Based on the real-time operating status data and historical operating data of each AGC unit, the real-time comprehensive adjustment performance score of each AGC unit is dynamically calculated through the performance quantification model. Based on the real-time comprehensive adjustment performance score and the AGC performance compensation and assessment rules of the spot market, the expected marginal revenue corresponding to the unit adjustment command of each AGC unit is determined. S3. Establish a control optimization model with the objective function of maximizing the total expected marginal revenue of the cascade power station. Take the total AGC regulation command, each expected marginal revenue and the dynamic constraints of cascade hydraulic coupling as inputs, and solve the optimal command for each AGC unit in several future control periods in a rolling manner. S4. The optimal instruction is issued to the corresponding AGC unit for execution according to its corresponding control time period.

2. The method for generating a cascade hydropower station dispatching strategy according to claim 1, characterized in that, In step S2, the specific steps for dynamically calculating the real-time comprehensive adjustment performance score of each AGC unit using the performance quantification model are as follows: S211. For each AGC unit, extract the operating data sequence within a preset historical time window from its historical operating database. The operating data sequence includes, but is not limited to: historical AGC command values, actual output values, adjustment rate time series, and steady-state error time series. S212. Based on the operating data sequence, calculate several basic performance indicators that reflect the dynamic adjustment performance of the AGC unit. The basic performance indicators include at least the average command response delay, the average adjustment rate, and the average steady-state error absolute value. S213. Normalize several basic performance indicators and map them to the same dimensionless numerical range. S214. Weighted fusion of several normalized basic performance indicators to calculate the real-time comprehensive regulation performance score of the AGC unit.

3. The method for generating a cascade hydropower station dispatching strategy according to claim 1, characterized in that, In step S2, the specific steps for determining the expected marginal revenue are as follows: S221. Obtain the AGC performance compensation and assessment rules published in the spot market, and extract key economic parameters including the unit performance score compensation price, the unit power penalty coefficient when the adjustment accuracy is not up to standard, and the unit power penalty coefficient when the response delay is not up to standard. S222. Calculate the instruction allocation weight of each AGC unit based on the ratio of the real-time comprehensive adjustment performance score of each AGC unit to the sum of the real-time comprehensive adjustment performance scores of each AGC unit. S223. Based on the instruction allocation weights of each AGC unit, calculate the expected marginal revenue corresponding to each unit's adjustment instruction. The calculation formula is as follows: ;in, This represents the expected marginal benefit of a single adjustment command from AGC unit i. This indicates the instruction allocation weight for AGC unit i. This represents the unit price for performance score compensation. This represents the probability that the adjustment accuracy of AGC unit i meets the standard. This represents the probability that the response delay of AGC unit i meets the standard. This represents the penalty coefficient per unit power when the adjustment accuracy is not up to standard. This represents the unit power penalty factor when the response delay fails to meet the standard. S224. Normalize the expected marginal revenue corresponding to the unit adjustment command of each AGC unit so that the expected marginal revenue of all units is on the same order of magnitude, so as to obtain the final expected marginal revenue corresponding to the unit adjustment command of each AGC unit and output it.

4. The method for generating a cascade hydropower station dispatching strategy according to claim 1, characterized in that, Step S3 specifically includes the following steps: S31. Establish a control optimization model with the objective function of maximizing the total expected marginal revenue of the cascade power station. Take the total AGC regulation command, each expected marginal revenue and the dynamic constraints of cascade hydraulic coupling as inputs, and solve the optimal command for each AGC unit in several future control periods in a rolling manner. S32. Construct the objective function of the control optimization model. The objective function is to maximize the total expected marginal revenue of the cascade hydropower stations, and its expression is: In the above formula, Indicates that the cascade hydropower stations are from Time's up The total expected marginal revenue at any given time, This represents the expected marginal benefit of AGC unit i at time t per unit adjustment command. There are a total of M AGC units. Let represent the decision variable, indicating the change in instructions for AGC unit i at time t. Indicates the penalty coefficient. This represents the risk cost item of cascade hydraulic coupling at time t; S33. Construct a set of constraints for the control optimization model, wherein the set of constraints includes power constraint balance, upper and lower limits of unit output, unit ramp rate constraint, and dynamic constraints of cascade hydraulic coupling. S34. At each moment, based on real-time historical data of the electricity spot market, a time series forecasting algorithm is used to generate the total AGC regulation command forecast value for each period in the future forecast time domain, and based on the historical operating status data of the power plant, the operating status data for each period in the future is predicted. S35. Based on the latest real-time operating status data of each AGC unit and the total AGC adjustment command, calculate the corresponding predicted data, and solve the control optimization model to obtain the optimal command for each AGC unit in the future several control periods.

5. The method for generating a cascade hydropower station dispatching strategy according to claim 4, characterized in that, The power constraint balance means that at any given moment, the sum of the output of all AGC units is equal to the total planned output command value of AGC issued by the real-time electricity spot market at that moment; The above and below limits of unit output are constraints that mean that the output of any AGC unit at any given time must be between the upper and lower limits of the unit output. The unit ramp rate constraint means that the absolute value of the difference in output between any two adjacent moments of any AGC unit is less than a preset maximum ramp rate. The expression for the cascade hydraulic coupling dynamic constraint is: In the above formula, This represents the inflow rate of downstream power plant j at time t. Indicates the upstream power station at time Outbound flow The time delay of water flow from the upstream power station to the downstream power station. Let be the discharge flow rate of the downstream power station j at time t. This indicates that all tailwater generated after power generation will flow directly into the AGC unit assembly in reservoir j of the downstream power station. This represents the power generation flow of unit i at time t.

6. The method for generating a cascade hydropower station dispatching strategy according to claim 4, characterized in that, The operational status data includes at least the predicted values ​​of the water head of each unit, the outflow from the upstream power station, and the inflow from the downstream power station.

7. A system for implementing the method for generating a cascade power station dispatching strategy based on the spot market as described in any one of claims 1-6, characterized in that, It includes a processor and a memory, the memory being used to store a computer program, which, when executed by the processor, implements the method for generating a cascade power station dispatch strategy based on the spot market as described in any one of claims 1-6.

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