A method and system for joint clearing of an energy-frequency-climbing market by a cascade hydropower station group

Abstract

This invention belongs to the technical field of market-oriented operation of cascade hydropower, and discloses a joint clearing method for cascade hydropower station groups participating in the electricity-frequency regulation-ramping market. The invention includes introducing scenario analysis to generate typical wind power output scenarios, calculating system ramping demand based on day-ahead forecast information, establishing a Stackelberg two-level optimization model for cascade hydropower and the market trading center, setting constraints for the upper and lower level models, and using KKT conditions and the McCormick envelope method to transform the optimization problem into a single-level mixed-integer linear optimization model, which is then solved using the CPLEX solver to obtain the optimal bidding strategy for cascade hydropower and the clearing price and quantity for each market. This method can effectively reduce system operating costs in multi-product trading scenarios, quantify the regulatory value of cascade hydropower in frequency regulation and ramping services, and significantly improve the operating revenue of cascade hydropower.

Description

Technical Field

[0001] This invention belongs to the technical field of hydropower participation in the electricity market operation, and in particular relates to a method and system for joint clearing of the power energy-frequency regulation-ramp market by a group of cascade hydropower stations. Background Technology

[0002] Cascade hydropower stations have advantages such as adjustable output, fast response, basin-wide coordinated operation, and optimized allocation of hydropower time sequence, making them an important flexible regulation resource in high-proportion new energy power systems. Under the current market mechanism, the coordination mechanism for cascade hydropower to participate in the electricity market and ancillary service market is still imperfect, and its regulation capacity and basin-wide joint operation value have not been fully reflected. It is urgent to coordinate multiple market products such as electricity, frequency regulation, and ramping under a unified framework to realize the measurable and settlementable expression of the multidimensional value of cascade hydropower. Reference: Zhang Lizi, Chen Haoxuan, Huang Xianchao, et al. Research on the Coordination Mechanism of Electricity Trading Product Sub-markets [J]. Proceedings of the CSEE, 2024, 44(16): 6320-6334. The paper systematically divides electricity trading products and market trading mechanisms, and divides ancillary service products into three categories: frequency regulation, reserve, and ramping.

[0003] Reference: Liu Dexu, Yang Ying, Huang Hongxu, et al. Review and prospect of dispatching, operation and control of large-scale pumped storage power stations in new power systems [J]. Proceedings of the CSEE, 2025, 45(01):80-98. This paper introduces the dispatching and operation strategies of different types of pumped storage power stations and large-scale pumped storage power stations under new power systems.

[0004] Reference: Liu Fei, Che Yanying, Tian Xu, et al. Cost mitigation mechanism of pumped storage power station under new power system: review and prospect [J]. Journal of Shanghai Jiaotong University, 2023, 57(7):757-768. The total revenue model of pumped storage participating in the power energy and ancillary services market was studied. By comparing the revenue of the full market model with the current two-part electricity price, it was verified that the full market model is conducive to cost mitigation of pumped storage.

[0005] Reference: Zhao Yue, Cai Qiuna, Wang Long, et al. Market clearing model for ramp-up ancillary services considering different demand elasticities [J]. Automation of Electric Power Systems, 2024, 48(05):48-57. This paper details the demand generation and clearing model of the ramp-up market, providing a theoretical reference for the construction of the ramp-up ancillary services market.

[0006] Reference: Liu Meng, Wang Tianliang, et al. Bidding strategy for grid-side energy storage power stations to participate in the spot joint market [J]. Power System Technology, 2021, 45(9): 3398-3408. A master-slave game model for energy storage power stations to participate in the electricity market bidding was established, but the impact of pumped storage power stations on market electricity prices was not considered.

[0007] In summary, existing research has the following shortcomings in exploring the value of pumped storage resources:

[0008] (1) There is a lack of systematic characterization of the linkage and revenue coupling of the bidding and reporting of energy, frequency regulation and ramping in multiple markets of cascade hydropower;

[0009] (2) Existing research has not paid enough attention to the basin-wide coordinated regulation capacity, time-series hydropower optimization allocation capacity, and multi-market coordinated benefit mechanism of cascade hydropower station groups. There is an urgent need to form a market-oriented bidding and settlement mechanism that is compatible with the operating characteristics of cascade hydropower. Summary of the Invention

[0010] To address the problems existing in the prior art, this invention provides a method for a cascade hydropower station group to participate in the joint clearing of the electricity-frequency regulation-ramp market.

[0011] This invention is implemented as follows: a method for a group of cascade hydropower stations to participate in the joint clearing of the electricity-frequency regulation-ramp market includes:

[0012] Step 1: Sampling is performed using the Monte Carlo algorithm to generate wind power scenarios, and then the K-means clustering algorithm is used to reduce the number of clusters, generating a finite number of typical wind power output scenarios and the probability of each scenario;

[0013] Step 2: Calculate the system ramp-up requirements through wind power scenario calculations, and market participants submit price-volume declarations;

[0014] Step 3: Establish a Stackelberg two-layer optimization model architecture. The upper layer model is the cascade hydropower operation entity, and the lower layer model is the joint market trading center to achieve joint clearing of the electricity market and the ancillary services market.

[0015] Step 4: Set the constraints for the lower-level model; the constraints for the lower-level model include market-related constraints, thermal power unit-related constraints, and cascade hydropower-related constraints, etc.

[0016] Step 5: Set upper-level model constraints, including constraints on electricity market quotation and pricing, constraints on ancillary service market quotation and pricing, and constraints on the start-up, shutdown, and operation switching of cascade hydropower station groups;

[0017] Step 6: After transforming the optimization problem into a single-layer mixed integer linear optimization model using KKT conditions and McCormick envelope method, solve it using the CPLEX solver.

[0018] Furthermore, in step 1, the Monte Carlo scene generation method can fully capture the randomness and volatility of wind power, and as the number of scenes increases, the results become closer to the real distribution; K-means clustering reduction can significantly reduce the computational complexity of subsequent optimization or evaluation models, while retaining the main statistical features of the original scene set.

[0019] Furthermore, in step 2, the system ramp-up demand during time period t is the sum of the upward change in the system net load during time period t, the upper limit of the load prediction error during time period t, and the lower limit of the new energy output prediction error during time period t; as shown in equation (1);

[0020] (1);

[0021] In equation (1), This is for the system's ramp-up requirements during time period t; Let t be the change in net load of the system during time period t. This represents the upper limit of the load forecasting error for time period t. This represents the lower limit of the wind power output prediction error for time period t.

[0022] In step 2, each market participant submits its volume and price quotation decisions to the market trading center. Wind power generation is subject to natural conditions and has significant uncertainties, so it participates in the electricity market by submitting volume but not price quotations to avoid additional risks to wind farms due to price fluctuations. Cascade hydropower participates by submitting volume and price quotations. Thermal power units typically participate in market competition using a marginal cost pricing mechanism.

[0023] Furthermore, in step 3, the Stackelberg bilevel optimization problem, often referred to as a master-slave game or bilevel programming, involves a decision-making process divided into an upper level (leader) and a lower level (follower). The lower level's decisions depend on the upper level's decisions. The upper level model is a cascade hydropower project, aiming to maximize operating revenue by making optimal decisions regarding bidding quantities. The lower level model is a joint market trading center, aiming to minimize total operating costs by achieving joint clearing of the electricity market and ancillary services market. The day-ahead market clearing process is simulated through the mutual transfer of decision quantities between the upper and lower levels.

[0024] The optimization objective of the joint market clearing model is to minimize the total system operating cost; where the system operating cost includes the electricity purchase cost of each market participating in the market transaction and the penalty cost, as shown in equation (2):

[0025] (2);

[0026] In equation (2), Minimize the objective function of the lower-level joint market; , , , These represent the electricity purchase costs in the electricity market, frequency regulation market, frequency regulation mileage market, and ramping ancillary services market, respectively. This indicates the costs of various penalties;

[0027] The revenue of cascade hydropower operators is divided into revenue from the electricity market, revenue from the frequency regulation market, and revenue from the ramp-up market. The decision variables are the bid prices in each market and the planned volume in each market. The specific revenue function can be expressed as:

[0028] (3);

[0029] In equation (3), the objective function of the upper-level model is to maximize the total profit of the cascade hydropower project, denoted as... ; The total revenue of cascade hydropower in various markets, The start-up and shutdown costs for cascade hydropower.

[0030] Furthermore, in step 4, the constraints of the lower-level model (joint market trading center) specifically include energy balance constraints, frequency regulation capacity / mileage balance constraints, ramping capacity balance constraints, thermal power unit related constraints, actual dispatch constraints of cascade hydropower, and clearing price constraints, etc.

[0031] The core of the power balance constraint in the power market is that power production and consumption must be equal at every instant. Similarly, the frequency regulation capacity / mileage balance constraint and the ramp capacity balance constraint also require the corresponding power market products to maintain supply and demand balance. The shadow price corresponding to the balance constraint is the clearing price of the market, as shown in equations (4), (5), and (6).

[0032] (4);

[0033] (5);

[0034] (6);

[0035] In equation (4), The actual output of hydropower station i at time t. The actual output of thermal power unit i at time t. The output of the wind turbine at time t. Let the unit output be fixed at time t. Total system load requirements Let be the dual variable of this equation, representing the shadow price of the cleared electricity price at time t; in equation (5), and These represent the joint market frequency modulation capacity and frequency modulation mileage demand at time t, respectively; dual variables and Let be the shadow prices of the frequency regulation capacity clearing price and the frequency regulation mileage clearing price in the joint market at time t, respectively; in equation (6), Let be the slope relaxation variable, representing the slope relaxation requirement that is not met at time t. The dual variable of this equation represents the shadow price of the clearing electricity price in the ramping market at time t; the relevant constraints of thermal power units include thermal power frequency regulation capacity, mileage operation constraints, upper and lower limits of thermal power unit output and ramping rate constraints, and ramping capacity constraints of thermal power units.

[0036] The actual demand constraint for cascade hydropower is that the market demand volume shall not exceed the output declared by the operators of the upper-level cascade hydropower station group. At the same time, the reservoir capacity change of the hydropower station shall be calculated based on the actual market demand volume, and the conditions of reservoir capacity return and reservoir capacity not exceeding the limit shall be met.

[0037] To ensure that the actual clearing price is more in line with the current electricity market environment without changing the economic meaning and solvability of the two-level optimization, upper and lower price limits are set to restrict the electricity prices in each market to a reasonable range.

[0038] Furthermore, in step 5, the upper-level model constraints include electricity market quotation and pricing constraints, and ancillary service market quotation and pricing constraints.

[0039] The quotation and pricing constraints for the electricity market are that cascade hydropower should submit reasonable electricity and price declarations based on the upper and lower limits of the unit's output. The quotation and pricing constraints for the ancillary services market are that cascade hydropower should submit reasonable electricity and price declarations based on the remaining capacity of the unit after deducting the electricity output.

[0040] In step 6, the KKT conditions are a set of necessary conditions for determining the optimal solution of an optimization problem with equality and inequality constraints. The main conditions are manifested in three aspects: stability condition, that is, the gradient of the Lagrange function is zero; primordial / dual feasibility, that is, the solution satisfies the constraints and the multiplier is non-negative; complementary relaxation condition, that is, the product of the multiplier and its corresponding inequality constraint is 0.

[0041] McCormick's envelope method is a convex relaxation technique for handling bilinear terms. It transforms non-convex problems into linear or convex problems, allowing for efficient solutions using established linear programming or convex optimization solvers.

[0042] Another objective of this invention is to provide a joint clearing system for a cascade hydropower station group participating in the electricity-frequency regulation-ramp market, comprising:

[0043] The clustering reduction module is used to sample and generate wind power scenarios using the Monte Carlo algorithm, and then use the K-means clustering algorithm to reduce the number of clusters, generating a finite number of typical wind power output scenarios and the probability of each scenario;

[0044] The calculation module is used to obtain the system ramp-up requirements through wind power scenario calculations, and market participants submit price-volume declarations.

[0045] The model architecture building module is used to build a Stackelberg two-layer optimization model architecture. The upper-layer model is a cascade hydropower operator, and the lower-layer model is a joint market trading center to achieve joint clearing of the electricity market and the ancillary services market.

[0046] The lower-level model constraint setting module is used to set the constraints of the lower-level model; the lower-level model constraints include market-related constraints, thermal power unit-related constraints, and T-cascade hydropower station group-related constraints, etc.

[0047] The upper-level model constraint setting module is used to set the upper-level model constraints, which include constraints on electricity market quotation and pricing, and constraints on ancillary service market quotation and pricing.

[0048] The transformation module is used to transform this optimization problem into a single-layer mixed integer linear optimization model using KKT conditions and the McCormick envelope method, and then solve it using the CPLEX solver.

[0049] Another object of the present invention is to provide a computer device comprising a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for the joint clearing of the power-frequency regulation-ramp market for the cascade hydropower station group.

[0050] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for the joint clearing of the power-frequency regulation-ramp market for the t-cascade hydropower station group.

[0051] Another objective of this invention is to provide an information data processing terminal, which is used to enable the cascade hydropower station group to participate in the joint clearing system of the power energy-frequency regulation-ramp market.

[0052] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0053] This invention enables cascade hydropower stations to participate in the electricity market through a quantity-based bidding method. The resulting optimal joint market bidding strategy significantly increases the total profit of hydropower station operators, demonstrating the strategic advantages of cascade hydropower in the joint market and facilitating the transmission of regulation value and cost recovery of hydropower stations.

[0054] This invention establishes a relatively complete electricity market clearing mechanism. Electricity market prices exhibit a structure of "low price in the early morning – stable price at midday – peak price at night." In the ancillary services market, prices rise significantly with scarcity, reflecting the consistency between the marginal cost-based pricing mechanism and the system's scarcity state. Cascaded hydropower becomes a key flexible resource for balancing supply and demand and smoothing out the intermittent impacts of new energy sources.

[0055] This invention utilizes the higher operating efficiency, faster frequency response, and stronger power regulation capabilities of cascade hydropower to rationally schedule units mainly composed of wind, thermal, and hydropower, effectively reducing the total power purchase cost and penalty cost of the system. The proposed method can replace some of the high marginal cost output and ramp-up redundancy of thermal power with a more cost-effective and flexible supply, achieving a dual improvement in the revenue of cascade hydropower and the economic efficiency of the system.

[0056] This invention provides a joint clearing method for a cascade hydropower station group participating in the electricity-frequency regulation-ramping market. A Stackelberg model is constructed for t cascade hydropower stations participating in the joint market: the upper layer consists of the cascade hydropower stations, aiming to maximize revenue by bidding for electricity prices and quantities in each market; the lower layer is the joint market trading center, aiming to minimize the total system operating cost by clearing electricity prices and winning bids in each market. The two-layer model is transformed into a single-layer optimization model using KKT conditions, and the bilinear revenue term is handled using McCormick envelopes. Finally, the solution is obtained within the CPLEX / Yalmip framework. The proposed method can effectively reduce system operating costs in multi-product trading scenarios, quantify the regulatory value of cascade hydropower in frequency regulation and ramping services, and significantly improve the operating revenue of t cascade hydropower stations.

[0057] (1) The expected benefits and commercial value of the technical solution of this invention after transformation are as follows:

[0058] This invention addresses the joint clearing scenario of cascade hydropower stations participating in the electricity, frequency regulation, and ramp-up ancillary services markets. It transforms the traditional operation model, primarily reliant on single-source power generation revenue, into a diversified revenue model emphasizing "electricity revenue + ancillary service revenue + regulation value compensation," significantly improving the profitability stability and resource allocation efficiency of cascade hydropower stations in a spot market environment. This technical solution helps improve the responsiveness of cascade hydropower stations to fluctuations in renewable energy, load fluctuations, and system ramp-up demands, enhancing their market competitiveness and bargaining power. Furthermore, it helps reduce the overall system electricity purchase cost, decrease ramp-up redundancy and regulation reserves of high-cost thermal power units, and improve the overall economic efficiency and security of the new power system. For power generation groups, grid dispatching agencies, market operators, and river basin hydropower asset management entities, this invention has strong engineering application prospects and can be further extended into product forms such as joint clearing software platforms, market declaration and decision-making systems, and cascade hydropower digital operation systems, possessing significant promotional value, industrial value, and commercial monetization potential.

[0059] (2) The technical solution of this invention fills a technical gap in the industry both domestically and internationally:

[0060] (3) Whether the technical solution of the present invention solves the technical problem that people have long wanted to solve but have never been able to solve successfully:

[0061] This invention essentially addresses a long-standing and difficult-to-solve key issue in the construction of the electricity market: how to enable cascade hydropower stations to participate in market competition based on their actual regulation capacity under a high proportion of renewable energy integration, while simultaneously fulfilling the three functions of power supply, frequency support, and ramp-up guarantee under a unified mechanism, ultimately achieving simultaneous improvement in operational revenue and system economics. For a long time, although cascade hydropower has been recognized as an important flexible resource, in actual market mechanisms, it has often suffered from insufficient value expression, crude bidding strategies, fragmented cross-market revenue, and a disconnect between dispatch and price formation, making it difficult to transform its resource advantages into clear, stable, and sustainable market revenue. This invention, by establishing a unified joint clearing framework and operational decision-making mechanism, elevates cascade hydropower stations from the traditional role of "passively obeying dispatch" to proactive market players "capable of participating in multi-market collaborative bidding and obtaining reasonable value recovery," effectively solving the long-standing technical challenge of making the regulatory value of cascade hydropower explicit, monetizable, and institutionalized.

[0062] (4) The technical solution of the present invention overcomes technical bias:

[0063] This invention overcomes, to some extent, several inherent technical biases within the industry regarding the participation of cascade hydropower stations in market-based joint clearing. Firstly, traditional views often hold that cascade hydropower is more suited to planned power generation and is less likely to participate in the competitive landscape of ancillary services as precisely as flexible energy storage or rapid-fire units. Secondly, there is a prevailing perception that "while cascade hydropower has strong regulation capabilities, its complex basin coupling, unclear declaration boundaries, and difficulties in value settlement make it unsuitable for inclusion in a unified multi-market bidding framework." Thirdly, some argue that the ancillary service market is better suited to thermal power, pumped storage, or energy storage, with cascade hydropower only suitable as a supplementary resource. This invention, by constructing a systematic method for cascade hydropower stations to participate in the joint market for electricity, frequency regulation, and ramp-up, demonstrates that cascade hydropower is not limited to passive regulation resources but can become a significant market player possessing energy supply value, rapid regulation value, and system support value. This breaks the existing perception that "cascade hydropower is difficult to participate in with precision in the market," providing direct technical support for its market function reshaping and institutional positioning optimization. Attached Figure Description

[0064] Figure 1 This is a flowchart of the method for joint clearing of the electricity-frequency regulation-ramp market provided in the embodiments of the present invention for cascade hydropower stations.

[0065] Figure 2 This is a structural block diagram of the joint clearing system of the cascade hydropower station participating in the electricity-frequency regulation-ramp market provided in the embodiments of the present invention.

[0066] Figure 3 This is a diagram of a two-layer optimization model architecture for cascade hydropower participating in joint market bidding, provided in an embodiment of the present invention.

[0067] Figure 4 This is a flowchart of the solution process for the two-layer optimization model provided in this embodiment of the invention.

[0068] Figure 5 This is a modified IEEE 30 network structure diagram provided in an embodiment of the present invention.

[0069] Figure 6 This is a day-ahead wind power and load forecast data diagram provided in an embodiment of the present invention.

[0070] Figure 7 This is a frequency modulation mileage factor curve provided in the frequency modulation requirements of this invention.

[0071] Figure 8 This is a wind power scenario and probability diagram provided in an embodiment of the present invention.

[0072] Figure 9 This is a calculation curve of the ramp demand provided by an embodiment of the present invention.

[0073] Figure 10 These are the inflow curves of various hydropower plants.

[0074] Figure 11 This is a diagram illustrating the pricing strategy for cascade hydropower in the electricity market, provided in an embodiment of the present invention.

[0075] Figure 12 This is a diagram illustrating the pricing strategy for cascade hydropower in the frequency regulation capacity market, provided in an embodiment of the present invention.

[0076] Figure 13 This is a diagram illustrating the pricing strategy for t-stage hydropower in the frequency regulation mileage market, provided by an embodiment of the present invention.

[0077] Figure 14 This is a diagram illustrating the pricing strategy for cascade hydropower in the ramp-up capacity market, provided by an embodiment of the present invention.

[0078] Figure 15 This is a diagram showing the results of the electricity market clearing process provided in an embodiment of the present invention.

[0079] Figure 16 This is a diagram showing the market clearing results for frequency modulation capacity provided in an embodiment of the present invention.

[0080] Figure 17This is a diagram showing the market clearing results for frequency modulation mileage provided in an embodiment of the present invention.

[0081] Figure 18 This is a diagram showing the market clearing results provided in an embodiment of the present invention. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0083] like Figure 1 As shown in the figure, the method for a cascade hydropower station group to participate in the joint clearing of the power energy-frequency regulation-ramp market provided by the present invention includes the following steps:

[0084] S101: Wind power scenarios are generated by sampling using the Monte Carlo algorithm, and then clustered and reduced using the K-means clustering algorithm to generate a finite number of typical wind power output scenarios and the probability of each scenario;

[0085] S102: The system ramp-up requirements are obtained through wind power scenario calculations, and market participants submit price-quantity declarations.

[0086] S103: Establish a Stackelberg two-layer optimization model architecture, with the upper layer model being VSPS and the lower layer model being a joint market trading center, to achieve joint clearing of the electricity market and the ancillary services market;

[0087] S104: Set constraints for the lower-level model; the lower-level model constraints include market-related constraints, thermal power unit-related constraints, and cascade hydropower-related constraints, etc.

[0088] S105: Set constraints for the upper-level model, including constraints on electricity market quotation and pricing, and constraints on ancillary service market quotation and pricing.

[0089] S106: The optimization problem is transformed into a single-layer mixed integer linear optimization model using KKT conditions and McCormick envelope method, and then solved by the CPLEX solver.

[0090] In S101 provided by the present invention, the Monte Carlo scene generation method can fully capture the randomness and volatility of wind power, and as the number of scenes increases, the results are closer to the real distribution; K-means clustering reduction can significantly reduce the computational complexity of subsequent optimization or evaluation models, while retaining the main statistical features of the original scene set.

[0091] In S102 provided in this embodiment of the invention, the system ramp-up demand in time period t is the sum of the upward change of the system net load in time period t, the upper limit of the load prediction error in time period t, and the lower limit of the new energy output prediction error in time period t; as shown in equation (1);

[0092] (1);

[0093] In equation (1), This is for the system's ramp-up requirements during time period t; Let t be the change in net load of the system during time period t. This represents the upper limit of the load forecasting error for time period t. This represents the lower limit of the wind power output prediction error for time period t.

[0094] In step 2, each market participant submits its volume and price quotation decisions to the market trading center. Wind power generation is subject to natural conditions and has significant uncertainties, so it participates in the electricity market by submitting volume but not price quotations to avoid additional risks to wind farms due to price fluctuations. Cascade hydropower participates by submitting volume and price quotations. Thermal power units typically participate in market competition using a marginal cost pricing mechanism.

[0095] In S103 of this embodiment, the Stackelberg bilevel optimization problem, often referred to as a master-slave game or bilevel programming, involves a decision-making process divided into an upper level (leader) and a lower level (follower). The lower level's decisions depend on the upper level's decisions. The upper level model is a cascade hydropower station group, aiming to maximize operating revenue by making optimal decisions on bidding quantities. The lower level model is a joint market trading center, aiming to minimize total operating costs by achieving joint clearing of the electricity market and ancillary services market. The day-ahead market clearing process is simulated through the mutual transfer of decision quantities between the upper and lower levels.

[0096] The optimization objective of the joint market clearing model is to minimize the total system operating cost; where the system operating cost includes the electricity purchase cost of each market participating in the market transaction and the penalty cost, as shown in equation (2):

[0097] (2);

[0098] In equation (2), Minimize the objective function of the lower-level joint market; , , , These represent the electricity purchase costs in the electricity market, frequency regulation market, frequency regulation mileage market, and ramping ancillary services market, respectively. This indicates the costs of various penalties;

[0099] The revenue of cascade hydropower operators is divided into revenue from the electricity market, revenue from the frequency regulation market, and revenue from the ramp-up market. The decision variables are the bid prices in each market and the planned volume in each market. The specific revenue function can be expressed as:

[0100] (3);

[0101] In equation (3), the objective function of the upper-level model is to maximize the total profit of the cascade hydropower project, denoted as... ; The total revenue of cascade hydropower in various markets, Start-up and shutdown costs.

[0102] In S104 provided in this embodiment of the invention, the constraints of the lower-level model (joint market trading center) specifically include energy balance constraints, frequency regulation capacity / mileage balance constraints, ramping capacity balance constraints, thermal power unit related constraints, actual dispatch constraints of cascade hydropower, and clearing price constraints, etc.

[0103] The core of the power balance constraint in the power market is that power production and consumption must be equal at every instant. Similarly, the frequency regulation capacity / mileage balance constraint and the ramp capacity balance constraint also require the corresponding power market products to maintain supply and demand balance. The shadow price corresponding to the balance constraint is the clearing price of the market, as shown in equations (4), (5), and (6).

[0104] (4);

[0105] (5);

[0106] (6);

[0107] In equation (4), This represents the total output of hydropower station group i at time t; Let be the dual variable of this equation, representing the shadow price of the cleared electricity price at time t; in equation (5), and These represent the joint market frequency modulation capacity and frequency modulation mileage demand at time t, respectively; dual variables and Let be the shadow prices of the frequency regulation capacity clearing price and the frequency regulation mileage clearing price in the joint market at time t, respectively; in equation (6), Let be the slope relaxation variable, representing the slope relaxation requirement that is not met at time t. The dual variable of this equation represents the shadow price of the clearing electricity price in the ramping market at time t; the relevant constraints of thermal power units include thermal power frequency regulation capacity, mileage operation constraints, upper and lower limits of thermal power unit output and ramping rate constraints, and ramping capacity constraints of thermal power units.

[0108] The actual call-up constraint for a cascade hydropower station group is that the market call-up volume shall not exceed the output declared by the upstream cascade hydropower operators, and the water balance shall be calculated based on the actual market call-up volume.

[0109] To ensure that the actual clearing price is more in line with the current electricity market environment without changing the economic meaning and solvability of the two-level optimization, upper and lower price limits are set to restrict the electricity prices in each market to a reasonable range.

[0110] In S105 provided in this embodiment of the invention, the upper-level model constraints include electricity market quotation constraints and ancillary service market quotation constraints.

[0111] The quotation and pricing constraints in the electricity market are that cascade hydropower operators submit reasonable electricity and price declarations based on the upper and lower limits of unit output, while the quotation and pricing constraints in the ancillary services market are that cascade hydropower stations submit reasonable electricity and price declarations based on the remaining capacity of the units after deducting electricity output.

[0112] In step 6, the KKT conditions are a set of necessary conditions for determining the optimal solution of an optimization problem with equality and inequality constraints. The main conditions are manifested in three aspects: stability condition, that is, the gradient of the Lagrange function is zero; primordial / dual feasibility, that is, the solution satisfies the constraints and the multiplier is non-negative; complementary relaxation condition, that is, the product of the multiplier and its corresponding inequality constraint is 0.

[0113] McCormick's envelope method is a convex relaxation technique for handling bilinear terms. It transforms non-convex problems into linear or convex problems, allowing for efficient solutions using established linear programming or convex optimization solvers.

[0114] like Figure 2 As shown in the figure, an embodiment of the present invention provides a joint clearing system for a cascade hydropower station group participating in the electricity-frequency regulation-ramp market, comprising:

[0115] The clustering reduction module is used to sample and generate wind power scenarios using the Monte Carlo algorithm, and then use the K-means clustering algorithm to reduce the number of clusters, generating a finite number of typical wind power output scenarios and the probability of each scenario;

[0116] The calculation module is used to obtain the system ramp-up requirements through wind power scenario calculations, and market participants submit price-volume declarations.

[0117] The model architecture building module is used to build a Stackelberg two-layer optimization model architecture. The upper-layer model is the cascade hydropower station operator, and the lower-layer model is the joint market trading center to achieve joint clearing of the electricity market and the ancillary services market.

[0118] The lower-level model constraint setting module is used to set the constraints of the lower-level model; the lower-level model constraints include market-related constraints, thermal power unit-related constraints, and cascade hydropower-related constraints, etc.

[0119] The upper-level model constraint setting module is used to set the upper-level model constraints, which include constraints on electricity market quotation and pricing, and constraints on ancillary service market quotation and pricing.

[0120] The transformation module is used to transform this optimization problem into a single-layer mixed integer linear optimization model using KKT conditions and the McCormick envelope method, and then solve it using the CPLEX solver.

[0121] Another object of the present invention is to provide a computer device comprising a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for the joint clearing of the power-frequency regulation-ramp market for the cascade hydropower station group.

[0122] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for the joint clearing of the power-frequency regulation-ramp market for the cascade hydropower station group.

[0123] Another objective of this invention is to provide an information data processing terminal, which is used to enable the cascade hydropower station group to participate in the joint clearing system of the power energy-frequency regulation-ramp market.

[0124] Specific implementation of the present invention:

[0125] Figure 3 A two-layer optimization model architecture diagram for cascade hydropower participating in joint market bidding.

[0126] Figure 4 Flowchart for solving the two-layer optimization model.

[0127] A joint clearing method for a cascade hydropower station group participating in the electricity-frequency regulation-ramp market is proposed. This method constructs a Stackelberg model for cascade hydropower participation in the joint market: the upper layer consists of cascade hydropower station operators, aiming to maximize revenue by bidding for electricity prices and volumes in each market; the lower layer is the joint market trading center, aiming to minimize the total system operating cost by clearing electricity prices and winning bid volumes in each market. The two-layer model is transformed into a single-layer optimization model using KKT conditions, and the bilinear revenue term is handled using McCormick envelopes. Finally, the solution is obtained within the CPLEX / Yalmip framework. This method effectively quantifies the regulatory value of t-cascade hydropower in the ancillary services market and achieves a win-win situation for both sides in the game. Specific implementation details are as follows:

[0128] Step 1: Introduce scenario analysis to generate typical scenarios where wind power may be generated. Use Monte Carlo algorithm to sample and generate scenarios, and then use K-means clustering algorithm to cluster and reduce the number of typical scenarios for wind power output and the probability of each scenario.

[0129] In step 1, when describing the uncertainty of wind power output, the wind power output deviation can be considered as having a mean of zero and a variance of... The normal distribution random variable. The variance is related to the predicted wind power output and installed capacity, as shown in equation (7).

[0130] (7);

[0131] In equation (7), Let t be the wind power prediction deviation. The predicted wind power output at time t.

[0132] Based on the wind power prediction model in equation (7), the Monte Carlo algorithm is used to sample and generate scenarios, and then the K-means clustering algorithm is used to reduce the number of clusters, generating a finite number of typical wind power output scenarios and the probability of each scenario.

[0133] Step 2: Compare the wind power output scenario generated in Step 1 with the day-ahead wind power forecast output to form a forecast error sample for each time period. Based on this, obtain the lower limit of the wind power forecast error for each time period. The power dispatching agency determines the system net load for each time period of the operating day based on the day-ahead forecast information, and then calculates the system ramp-up demand.

[0134] In step 2, the wind power scenario and its general characteristics are compared with the day-ahead wind power forecast output to form a forecast error sample for each time period, thereby obtaining the lower limit of the wind power forecast error for each time period. Next, the upper limit of the load forecast error is obtained from the forecast error distribution within the historical rolling window. Finally, the power dispatching agency determines the system net load for each time period of the operating day based on the day-ahead forecast information, as shown in equation (8).

[0135] (8);

[0136] In equation (8), The system net load for time period t. Let t be the total system load during time period t. This represents the output of the stationary unit at time t.

[0137] System ramping requirements refer to the minimum comprehensive ramp rate and available capacity required to maintain power balance and safe operation under a given time resolution. Its magnitude is driven by net load changes and wind power forecasting uncertainties, as shown in equation (9).

[0138] (9);

[0139] In equation (9), This is for the system's ramp-up requirements during time period t. Let t be the change in net load of the system during time period t. This represents the upper limit of the load forecasting error for time period t. This represents the lower limit of the wind power output prediction error for time period t.

[0140] Step 3: Establish a Stackelberg two-layer optimization model. The upper-layer model is VSPS, which makes the optimal decision on bidding and quantity with the goal of maximizing operating revenue. The lower-layer model is a joint market trading center, which achieves joint clearing of the electricity market and ancillary services market with the goal of minimizing total operating costs.

[0141] In step 3, the objective function of the lower-level model is to minimize the electricity purchase cost and penalty cost in the electricity market, frequency regulation market, and ramp market, as shown in equation (10); the objective function of the upper-level model is to maximize the total profit of cascade hydropower, as shown in equation (11).

[0142] (10);

[0143] (11);

[0144] In equation (10), Minimize the objective function of the lower-level joint market; , , , These represent the electricity purchase costs in the electricity market, frequency regulation market, frequency regulation mileage market, and ramping ancillary services market, respectively. This represents various penalty costs. n represents the number of thermal power units. , These represent the bid price and electricity volume of thermal power unit i in the electricity market at time t; T is the total number of trading sessions throughout the day. The price at which the cascade hydropower projects are declared in the electricity market at time t. , These represent the frequency regulation capacity and frequency regulation mileage declared by thermal power unit i in the frequency regulation ancillary service market at time t; , These are the bid prices for frequency regulation capacity and frequency regulation mileage of thermal power unit i at time t in the frequency regulation ancillary service market; , The declared prices are for the frequency regulation capacity and frequency regulation mileage of cascade hydropower, respectively. R represents the frequency regulation capacity of the cascade hydropower stations actually used by market operators during time period t; R is the ratio of frequency regulation mileage to frequency regulation capacity. , These represent the price and capacity declared by thermal power unit i in the ramp-climbing auxiliary service market at time t. The price declared by the cascade hydropower project in the climbing auxiliary services market at time t. The actual ramp-up capacity of the cascade hydropower station group at time t. , These are the wind curtailment and uphill relaxation penalty coefficients, respectively. , These represent the actual wind power output and the slack variable during ramping at time t.

[0145] In equation (11), the total profit is obtained by subtracting the start-up and shutdown costs from the total revenue of the cascade hydropower project. The objective function of the upper-level model is to maximize the total profit of the cascade hydropower project, denoted as... . , , , These represent the electrical energy, frequency regulation capacity, frequency regulation mileage, ramp-up capacity, and actual market-cleared electricity price at time t. This represents the start-up and shutdown cost coefficient for cascade hydropower.

[0146] Step 4: Set constraints for the lower-level model, including energy balance constraints, frequency regulation capacity / mileage balance constraints, ramping capacity balance constraints, thermal power frequency regulation capacity, mileage operation constraints, upper and lower limits of thermal power unit output and ramping rate constraints, thermal power unit ramping capacity constraints, actual dispatch constraints of cascade hydropower, water volume balance constraints of cascade hydropower, and clearing price constraints.

[0147] In step 4, the electrical energy balance constraint, frequency regulation capacity / mileage balance constraint, and ramp capacity balance constraint are shown in equations (12), (13), and (14), respectively.

[0148] (12);

[0149] (13);

[0150] (14);

[0151] In equation (12) The actual output of hydropower station i at time t. The actual output of thermal power unit i at time t. The output of the wind turbine at time t. Let the unit output be fixed at time t. This represents the total system load requirement. Let be the dual variable of this equation, representing the shadow price of the cleared electricity price at time t; in equation (13), and These represent the joint market frequency modulation capacity and frequency modulation mileage demand at time t, respectively; dual variables and Let be the shadow prices of the frequency regulation capacity clearing price and the frequency regulation mileage clearing price in the joint market at time t, respectively. In equation (14), Let be the slope relaxation variable, representing the slope relaxation requirement that is not met at time t. Let be the dual variable of this equation, representing the shadow price of the clearing electricity price in the ramp-up market at time t.

[0152] In step 4, the constraints on the frequency regulation capacity and mileage operation of thermal power units, the upper and lower limits of output of thermal power units and the climbing rate constraints, and the climbing capacity constraints of thermal power units are shown in equations (15), (16), and (17), respectively.

[0153] (15);

[0154] (16);

[0155] (17);

[0156] In equation (15), This represents the maximum frequency regulation capacity of thermal power unit i at time t; Let be the ratio of the frequency regulation mileage to the frequency regulation capacity of thermal power unit i at time t; These are the dual variables of the corresponding constraints. In equation (16), and These represent the maximum and minimum generating power of generator set i, respectively; Represents the percentage of the ramp rate of generator set i; These are the dual variables of the corresponding constraints. In equation (17), Let i be the maximum ramp rate of thermal power unit i. These are the dual variables of the corresponding constraints.

[0157] In step 4, the constraints on the actual use of cascade hydropower and the constraints on the balance of hydropower and water volume are as shown in equations (18) and (19).

[0158] (18);

[0159] (19);

[0160] In equation (18), The pumping capacity declared by hydropower station i at time t. Let be the maximum power generation of hydropower station i at time t. , These are the declared frequency regulation power and the maximum frequency regulation power of hydropower station i at time t, respectively. , These represent the declared frequency regulation mileage and the maximum frequency regulation mileage of hydropower station i at time t, respectively. , These are the declared ramp power and maximum ramp power of hydropower station i at time t, respectively. It is the ratio of frequency modulation mileage to frequency modulation capacity. The maximum power generation capacity of hydropower unit i at water level t. Equation (19) represents the water balance constraint. Let be the reservoir capacity of hydropower unit i at time t. This represents the water inflow of hydropower unit i at time t. Indicates the amount of water transferred from upstream. Let be the discharge flow rate of hydropower unit i at time t. Let be the amount of water discharged by hydropower unit i at time t. This is a consolidated item for evaporation losses.

[0161] In step 4, in order to make the actual clearing price more in line with the current electricity market environment without changing the economic meaning and solvability of the two-level optimization, upper and lower price limits are set as shown in equation (20).

[0162] (20);

[0163] In equation (20), , , , These represent the actual clearing price of the market at time t, including electrical energy, frequency regulation capacity, frequency regulation mileage, and ramp-up capacity. The optimal clearing price and the clearing volume of each generating unit are generated through day-ahead spot market clearing. The clearing result is influenced not only by the joint market clearing model but also by the scheduling mode of the cascade hydropower units.

[0164] Step 5: Set constraints for the upper-level model, including constraints on electricity market quotation and pricing, and constraints on ancillary service market quotation and pricing.

[0165] In step 5, the quotation constraints for the upper-level model's electricity market and ancillary service market are shown in equations (21), (22), and (23), respectively.

[0166] (twenty one) ;

[0167] (twenty two) ;

[0168] (twenty three) ;

[0169] In equation (21), , These are the minimum and maximum generating capacities of hydropower unit i, respectively. , Let $i$ be the minimum and maximum power generation bid prices for hydropower unit $i$, respectively. In equation (22), The frequency regulation capacity declared by the cascade hydropower unit i at time t. This is the ratio of frequency modulation mileage to frequency modulation capacity. , , , The minimum and maximum frequency regulation capacity declaration prices and frequency regulation mileage declaration prices for the cascade hydropower station i are given in equation (23). Let i be the ramp-up capacity declared by hydropower unit i at time t. This represents the maximum climbing ability of cascade hydropower station i. , Quotation for the maximum and minimum ramping capacity of hydropower unit i.

[0170] Step 6: After transforming the optimization problem into a single-layer mixed integer linear optimization model using KKT conditions and McCormick envelope method, solve it using the CPLEX solver to obtain the optimal bidding strategy for cascade hydropower and the clearing price and power volume for each market.

[0171] In step 6, the optimization problem is transformed into a single-level mixed-integer linear optimization model using KKT conditions. The objective function of the new optimization model is the same as the objective function of the original upper-level model, and the constraints include the constraints of the original upper-level model, KKT conditions, etc. Since the upper-level model contains a bilinear term of cleared electricity price multiplied by cleared electricity quantity, the McCormick envelope approximation is used. That is, a new variable is introduced and a McCormick envelope is applied to each product that needs to be linearized. For general triples... and its interval , The McCormick envelope is shown in equations (24) and (25). The linearized upper objective function replaces the original product term with the z variable, as shown in equation (26).

[0172] (twenty four) ;

[0173] (25);

[0174] (26);

[0175] Verification Implementation Examples

[0176] The example is based on the IEEE 30-node system, and introduces four market participants: one cascade hydropower station group, two thermal power units, and one wind power cluster, all of which are bidding units. The two thermal power units have a total installed capacity of 600MW; the wind power cluster has an installed capacity of 500MW; and the cascade hydropower station group has an installed capacity of 400MW. Detailed parameters of the cascade hydropower station group and the thermal power units are provided in the appendix. The modified IEEE 30-node network is attached. Figure 5 As shown: Among them, nodes 11, 13, 18, and 24 are equipped with two bidding thermal power units, a cascade hydropower station group, and a wind power cluster, respectively, while the remaining units are fixed output units.

[0177] The system baseline capacity is taken as 100MVA, and the baseline voltage is taken as the average rated voltage; the maximum transmission power of the line is obtained based on the line impedance parameters; day-ahead load and wind power forecasts are attached. Figure 6 The operating parameters of the cascade hydropower station units are shown in Table 1. The system frequency regulation demand accounts for 10% of the total load. The system frequency regulation mileage factor variation curve is attached. Figure 6 As shown in the attached figure, 200 wind power output scenarios were generated using the Monte Carlo method, and 5 typical scenarios and their probabilities were obtained by combining them with the K-means clustering method. Figure 7 As shown. The climbing requirement is calculated according to formula (5), and the result is shown in the appendix. Figure 8 As shown in Table 2, the market price data for electricity from thermal power units is shown in Table 3, and the market price data for ancillary services is shown in Table 4. The scheduling cycle is set to 24 hours, with a total of 24 trading sessions.

[0178] Table 1 Basic parameters of cascade hydropower stations

[0179]

[0180] Table 2. Market Price Parameters for Electricity from Thermal Power Units

[0181]

[0182] Table 3. Pricing parameters for the auxiliary services market of thermal power units

[0183]

[0184] Analysis of the results of the joint market clearing

[0185] The results of the electricity market clearing are attached. Figure 14As shown, the system load was low at night, wind power availability was good and all of it was absorbed; there were no wind curtailment penalties throughout the day. The highest electricity price of the day occurred during the 20:00 time slot, at 803.68 yuan; the lowest electricity price occurred during the 7:00 time slot, at 150.00 yuan. 8:00 was the morning peak load period, and the system's marginal cost increased, forming a price peak around the 8:00 time slot. From 9:00 to 16:00, the net load was stable, wind power continued to be absorbed, marginal units returned to the middle marginal cost range, and the electricity price remained at a moderate level. During the evening peak, the load increased significantly, and wind power output was not as advantageous as in the early morning. The two thermal power units were increased to a higher load, and pumped storage units continuously supported the peak load at near-maximum power generation from 19:00 to 21:00, subsequently gradually decreasing as the load declined. From 22:00 to 24:00, the load declined, the ramp-up pressure eased, and the electricity price gradually returned to a low to medium level.

[0186] The recent market clearing results for frequency regulation capacity and mileage, along with the price curves for the cleared frequency regulation capacity and mileage, are attached. Figure 15 and attached Figure 16 As shown in the results of the frequency regulation market clearing, cascade hydropower stations dominate frequency regulation capacity and mileage, with thermal power only filling a small share on a few hours. Since frequency regulation demand is positively correlated with system load, both the frequency regulation capacity price and the frequency regulation mileage price are relatively low during the low-load period in the early morning, fluctuating flexibly during the day depending on the changes in cascade hydropower pricing, and remaining high during the evening peak period.

[0187] The results of the recent market clearing-out for hill-climbing assistance services are attached. Figure 17 As shown. No ramp-up slack penalty occurred throughout the entire period. Daytime load and net load changes were relatively stable, and the system's ramp-up demand was moderate, with cascade hydropower continuing to play a leading role. As the net load rapidly increased from 17:00 to 21:00 in the evening, ramp-up demand increased. Cascade hydropower had to handle both energy clearing and providing frequency regulation and ramp-up capabilities; therefore, some thermal power units were called up to meet the ramp-up demand. Figure 18 It can be seen that the ramp-up electricity price is generally consistent with the marginal supply and demand status of the system, which is in line with the pricing mechanism based on marginal cost: when the demand for ramp-up increases, the marginal supply shifts to resources with higher costs, and the price rises accordingly; conversely, it falls. In particular, at 7:00 and 18:00, the demand for ramp-up surges, and the available margin of cascade hydropower and thermal power simultaneously tightens, pushing the price up to near the price cap.

[0188] Analysis of cascade hydropower revenue and joint market costs

[0189] Table 4 compares the revenue and total system cost of a cascade hydropower station group after participating in various market transactions. As shown in Table 1, with the increasing variety of market transactions, the revenue structure of the cascade hydropower station group has gradually shifted from being dominated by peak-valley arbitrage to being centered on flexible ancillary services. With frequency regulation and ramping being included in joint clearing, electricity revenue has slightly decreased due to reserved bandwidth, but ancillary service revenue has increased from zero and further after the introduction of ramping, significantly improving the total profit of the power station. At the system level, as cascade hydropower provides rapidly adjustable capacity and ramping capabilities at lower costs, some high-marginal-cost thermal power output and ramping redundancy are replaced, suppressing prices during scarce periods. This causes the total operating cost to gradually decrease as market varieties expand, while the costs of wind curtailment and ramping slack penalties are significantly alleviated.

[0190] Table 4. Revenue and System Operating Costs of the Cascade Hydropower Station Group Participating in Various Markets

[0191]

[0192] The mechanism behind the improved profitability lies in the multi-market coupling framework constructed in this paper, which comprehensively and effectively quantifies the rapid regulation advantages of cascade hydropower. Through a reasonable multi-market bidding strategy, the upper-level bidding and lower-level clearing are coordinated in a timely manner, thereby precisely allocating limited reservoir capacity and bandwidth to the time periods and categories where the system lacks the most flexibility. Therefore, the strategy of cascade hydropower station groups participating in joint market bidding not only significantly improves the profitability of cascade hydropower operators but also simultaneously reduces the overall operating costs of the system, verifying the dual effectiveness of the proposed method in improving both individual resource profitability and the overall system economy.

[0193] This invention belongs to the technical field of hydropower participation in the electricity market operation, and in particular relates to a method for joint clearing of the electricity energy-frequency regulation-ramp market for a group of cascade hydropower stations.

[0194] Addressing the limitations of my country's current electricity market development and the cost allocation issues in the market-oriented operation of cascade hydropower, this paper innovatively proposes a joint clearing method for the three markets of electricity energy, frequency regulation, and ramping, based on optimizing the bidding strategy for cascade hydropower station groups. The McCormick envelope approximation is used to handle the bilinear terms, ensuring they satisfy the KKT conditions. The following conclusions are drawn through theoretical and numerical analysis:

[0195] (1) After participating in the joint market by cascade hydropower stations through the bidding method, compared with participating in the single electricity market, the electricity revenue decreased slightly from RMB 694,300 to RMB 678,300, and the pumping cost increased from RMB 615,200 to RMB 838,900. However, the newly added frequency regulation and ramp-up revenue totaled RMB 432,900, which increased the total profit from RMB 137,300 to RMB 269,200, which is 1.96 times the original profit. This reflects the strategic advantage of cascade hydropower station groups in the joint market and helps to transmit the regulation value of hydropower and recover costs.

[0196] (2) From the results of the joint market clearing, the electricity market price shows a structure of "low in the early morning - stable at noon - peak at night"; frequency regulation demand is generally positively correlated with system load, and the two types of price curves are similar in shape; the ramp market price rises significantly with scarcity, reflecting the consistency between the pricing mechanism based on marginal cost and the scarcity state of the system. Cascade hydropower has become a key flexible resource for balancing supply and demand and smoothing the intermittent impact of new energy sources.

[0197] (3) From a system perspective, as the combined market product range expanded from electrical energy to "electric energy – frequency regulation – ramping", the total operating cost decreased from RMB 2,215,793 to RMB 2,170,212; the wind curtailment and ramping relaxation penalties were significantly reduced. This verifies that the proposed method can replace the high marginal cost output of thermal power with more cost-effective and flexible resources, further realizing a win-win situation for cascade hydropower and the system.

[0198] This paper currently considers only the cascade hydropower station group as the sole strategic entity, treating thermal power, renewable energy, and demand-side resources as price takers, without explicitly depicting their strategic interactions and uncertainties. Furthermore, the installed capacity of the cascade hydropower station group in the example is considered a predetermined planning result, focusing on multi-market bidding and joint clearing strategies under a given capacity configuration, without conducting a systematic comparison of different installed capacity ratios. In the actual electricity market, the bidding strategies of other generating units and the installed capacity of the cascade hydropower stations will significantly influence clearing and price formation. Future work could extend the model to a multi-leader-multi-follower equilibrium framework, further incorporating service types such as reserve, reactive power voltage support, and black start, and employing appropriate solution methods to ensure solvability, thereby more closely reflecting the real market environment and improving the fairness and rationality of transactions.

[0199] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0200] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for a cascade hydropower station group to participate in the joint clearing of the electricity-frequency regulation-ramp market, characterized in that, Includes the following steps: Step 1: Sampling is performed using the Monte Carlo algorithm to generate wind power scenarios, and then the K-means clustering algorithm is used to reduce the number of clusters, generating a finite number of typical wind power output scenarios and the probability of each scenario; Step 2: Calculate the system ramp-up requirements through wind power scenario calculations, and market participants submit price-volume declarations; Step 3: Establish a Stackelberg two-layer optimization model architecture, with the upper layer model being a cascade hydropower station group and the lower layer model being a joint market trading center to achieve joint clearing of the electricity market and the ancillary services market; Step 4: Set the constraints for the lower-level model; the constraints for the lower-level model include market-related constraints, thermal power unit-related constraints, and cascade hydropower-related constraints, etc. Step 5: Set the upper-level model constraints, which include constraints on electricity market quotation and pricing, constraints on ancillary service market quotation and pricing, and constraints on the balance of hydropower volume in cascade hydropower. Step 6: After transforming the optimization problem into a single-layer mixed integer linear optimization model using KKT conditions and McCormick envelope method, solve it using the CPLEX solver.

2. The method for joint clearing of the electricity-frequency regulation-ramp market by a group of cascade hydropower stations as described in claim 1, characterized in that, In step 1, the Monte Carlo scene generation method can fully capture the randomness and volatility of wind power, and as the number of scenes increases, the results become closer to the real distribution; K-means clustering reduction can significantly reduce the computational complexity of subsequent optimization or evaluation models, while retaining the main statistical features of the original scene set.

3. The method for joint clearing of the electricity-frequency regulation-ramp market by a group of cascade hydropower stations as described in claim 1, characterized in that, In step 2, the system's ramp-up demand during time period t is the sum of the upward change in the system's net load during time period t, the upper limit of the load prediction error during time period t, and the lower limit of the new energy output prediction error during time period t; as shown in equation (1); （1）； In equation (1), This is for the system's ramp-up requirements during time period t; Let t be the change in net load of the system during time period t. This represents the upper limit of the load forecasting error for time period t. This represents the lower limit of the wind power output prediction error for time period t. In step 2, each market participant submits its volume and price quotation decision to the market trading center. Wind power generation is subject to natural conditions and has great uncertainty. It participates in the electricity market by submitting volume but not price, so as to avoid additional risks to wind farms due to price fluctuations. Variable speed pumped storage participates by submitting volume and price. Thermal power units usually participate in market competition by using the marginal cost pricing mechanism.

4. The method for joint clearing of the electricity-frequency regulation-ramp market by a group of cascade hydropower stations as described in claim 1, characterized in that, In step 3, the Stackelberg bilevel optimization problem, often referred to as a master-slave game or bilevel programming, involves a decision-making process divided into an upper level (leader) and a lower level (follower). The lower level's decisions depend on the results of the upper level's decisions. The upper level model is a cascade hydropower project, aiming to maximize operating revenue by making optimal decisions regarding bidding and quantity. The lower level model is a joint market trading center, aiming to minimize total operating costs by achieving joint clearing of the electricity market and ancillary services market. The day-ahead market clearing process is simulated through the mutual transfer of decision quantities between the upper and lower levels. The optimization objective of the joint market clearing model is to minimize the total system operating cost; where the system operating cost includes the electricity purchase cost of each market participating in the market transaction and the penalty cost, as shown in equation (2): （2）； s In equation (2), Minimize the objective function of the lower-level joint market; , , , These represent the electricity purchase costs in the electricity market, frequency regulation market, frequency regulation mileage market, and ramping ancillary services market, respectively. This indicates the costs of various penalties; The revenue of cascade hydropower operators is divided into revenue from the electricity market, revenue from the frequency regulation market, and revenue from the ramp-up market. The decision variables are the bid prices in each market and the planned volume in each market. The specific revenue function can be expressed as: （3）； In equation (3), the objective function of the upper-level model is to maximize the total profit of the cascade hydropower project, denoted as... ; The total revenue of cascade hydropower in various markets, The start-up and shutdown costs for cascade hydropower.

5. The method for joint clearing of the electricity-frequency regulation-ramp market by a group of cascade hydropower stations as described in claim 1, characterized in that, In step 4, the constraints of the lower-level model (joint market trading center) specifically include: power balance constraints, frequency regulation capacity / mileage balance constraints, ramping capacity balance constraints, thermal power unit related constraints, actual dispatch constraints of cascade hydropower, water volume balance constraints of cascade hydropower, and clearing price constraints, etc. The core of the power balance constraint in the power market is that power production and consumption must be equal at every instant. Similarly, the frequency regulation capacity / mileage balance constraint and the ramp capacity balance constraint also require the corresponding power market products to maintain supply and demand balance. The shadow price corresponding to the balance constraint is the clearing price of the market, as shown in equations (4), (5), and (6). （4）； （5）； （6）； In equation (4), This represents the total output of hydropower station group i at time t; Let be the dual variable of this equation, representing the shadow price of the cleared electricity price at time t; in equation (5), and These represent the joint market frequency modulation capacity and frequency modulation mileage demand at time t, respectively; dual variables and Let be the shadow prices of the frequency regulation capacity clearing price and the frequency regulation mileage clearing price in the joint market at time t, respectively; in equation (6), Let be the slope relaxation variable, representing the slope relaxation requirement that is not met at time t. The dual variable of this equation represents the shadow price of the clearing electricity price in the ramping market at time t; the relevant constraints of thermal power units include thermal power frequency regulation capacity, mileage operation constraints, upper and lower limits of thermal power unit output and ramping rate constraints, and ramping capacity constraints of thermal power units. The actual call-up constraint for a cascade hydropower station group is that the market call-up volume shall not exceed the output declared by the upstream cascade hydropower operators, and the water balance shall be calculated based on the actual market call-up volume. To ensure that the actual clearing price is more in line with the current electricity market environment without changing the economic meaning and solvability of the two-level optimization, upper and lower price limits are set to restrict the electricity prices in each market to a reasonable range.

6. The method for joint clearing of the electricity-frequency regulation-ramp market by a group of cascade hydropower stations as described in claim 1, characterized in that, In step 5, the upper-level model constraints include electricity market quotation constraints, ancillary service market quotation constraints, and cascade hydropower water balance constraints. The quotation and pricing constraints in the electricity market are that cascade hydropower stations should submit reasonable electricity and price declarations based on the upper and lower limits of unit output. The quotation and pricing constraints in the ancillary services market are that cascade hydropower stations should submit reasonable electricity and price declarations based on the remaining capacity of the units after deducting electricity output. In step 6, the KKT conditions are a set of necessary conditions for determining the optimal solution of an optimization problem with equality and inequality constraints. The main conditions are manifested in three aspects: stability condition, that is, the gradient of the Lagrange function is zero; primordial / dual feasibility, that is, the solution satisfies the constraints and the multiplier is non-negative; complementary relaxation condition, that is, the product of the multiplier and its corresponding inequality constraint is 0. McCormick's envelope method is a convex relaxation technique for handling bilinear terms. It transforms non-convex problems into linear or convex problems, allowing for efficient solutions using established linear programming or convex optimization solvers.

7. A cascade hydropower participation in a combined clearing system for the electricity-frequency regulation-ramp market, implementing the clearing method as described in any one of claims 1-6, characterized in that, The cascade hydropower station group participates in the joint clearing system of the electricity-frequency regulation-ramp market, including: The clustering reduction module is used to sample and generate wind power scenarios using the Monte Carlo algorithm, and then use the K-means clustering algorithm to reduce the number of clusters, generating a finite number of typical wind power output scenarios and the probability of each scenario; The calculation module is used to obtain the system ramp-up requirements through wind power scenario calculations, and market participants submit price-volume declarations. The model architecture building module is used to build a Stackelberg two-layer optimization model architecture. The upper-layer model is the operator of the cascade hydropower station group, and the lower-layer model is the joint market trading center to achieve joint clearing of the power market and the ancillary services market. The lower-level model constraint setting module is used to set the constraints of the lower-level model; the lower-level model constraints include market-related constraints, thermal power unit-related constraints, and cascade hydropower-related constraints, etc. The upper-level model constraint setting module is used to set the upper-level model constraints, which include constraints on electricity market quotation and pricing, constraints on ancillary service market quotation and pricing, and constraints on the balance of hydropower volume in cascade hydropower. The transformation module is used to transform this optimization problem into a single-layer mixed integer linear optimization model using KKT conditions and the McCormick envelope method, and then solve it using the CPLEX solver.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the joint clearing method for the cascade hydropower station group participating in the power energy-frequency regulation-ramp market as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the joint clearing method for a cascade hydropower station group participating in the power-frequency regulation-ramp market as described in any one of claims 1-6.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the joint clearing system of the cascade hydropower station group participating in the power energy-frequency regulation-ramp market as described in claim 7.

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