A method and device for clearing a pumped storage power station

Abstract

This invention pertains to the power industry and discloses a clearing method and apparatus for pumped storage power stations. The method includes: establishing an optimization objective function aimed at minimizing the total system operating cost; constructing multiple constraints; and solving a mixed-integer nonlinear programming problem involving the objective function and constraints using an adaptive alternating direction multiplier method based on KKT conditions. The method iteratively updates the original residuals and dual residuals, and adaptively adjusts the penalty parameters until a preset convergence tolerance is met, yielding the optimal clearing result. By integrating DC power flow equations, node power balance, and comprehensive N-1 safety verification constraints, the method ensures that the clearing result meets the actual power grid's safe operation requirements while preventing safety risks such as line overload caused by improper market clearing results, thus improving system reliability. Furthermore, the adaptive alternating direction multiplier method based on KKT conditions improves computational efficiency while maintaining solution accuracy.

Description

Technical Field

[0001] This invention belongs to the field of electric power, and in particular relates to a clearing method and apparatus for a pumped storage power station. Background Technology

[0002] With the high proportion of renewable energy sources, such as wind and solar power, being integrated into the grid, the volatility and peak-shaving pressure of the power system have increased dramatically. Pumped storage hydropower stations, as the most mature large-scale energy storage and flexible regulation resource, are increasingly demonstrating their value in improving system security and promoting the consumption of new energy sources. In the context of the electricity market, how to guide pumped storage hydropower stations to participate in multi-market transactions, maximize their regulation potential, and ensure the overall economic operation of the system has become a critical issue that urgently needs to be addressed.

[0003] In related technologies, most models are optimized only for a single market (such as the spot market) or use sequential optimization decoupling methods, leading to conflicting bidding strategies for pumped storage in different markets, poor opportunity cost transmission, and suboptimal resource allocation. Furthermore, they commonly employ simplified line capacity constraints or introduce AC power flow models with excessively high computational complexity, making it difficult to simultaneously achieve the accuracy of N-1 safety verification and practical solution efficiency in clearing models. In addition, the handling of uncertainties in wind and solar power output often ignores the spatiotemporal correlation of prediction errors, resulting in insufficient robustness of the models to risks such as localized congestion and insufficient reserves. Summary of the Invention

[0004] In view of this, the present invention discloses a clearing method and apparatus for a pumped storage power station, which can solve the shortcomings of related technologies.

[0005] To achieve the above objectives, the present invention discloses the following technical solution: According to a first aspect of the present invention, a clearing method for a pumped storage power station is proposed, comprising: An optimization objective function is established with the goal of minimizing the total operating cost of the system. The objective function includes at least the fuel cost, start-up and shutdown cost, ramp-up cost of thermal power units, pumping cost of pumped storage power stations, as well as the trading revenue in the spot market, the reserve capacity revenue in the ancillary services market, the cost of wind and solar curtailment penalties and the cost of load shedding, and takes into account the constraints of medium and long-term contract electricity volume and price. Multiple constraints are constructed, and the system of multiple constraints includes at least: network security constraints including DC power flow constraints and N-1 security constraints; energy dynamic constraints, operating state logic constraints and minimum start-up and shutdown time constraints of pumped storage power stations; power balance and medium- and long-term contract execution constraints between markets; upper and lower limits of output, minimum start-up and shutdown time and ramping constraints of traditional thermal power units; power balance, curtailment and multi-scenario uncertainty constraints of renewable energy; and system reserve capacity demand constraints. An adaptive alternating direction multiplier method based on KKT conditions is used to solve the mixed integer nonlinear programming problem of the objective function and constraints. The original residual and dual residual are updated iteratively, and the penalty parameter is adaptively adjusted until the preset convergence tolerance is met, so as to obtain the optimal clearing result.

[0006] According to a second aspect of the present invention, a clearing device for a pumped storage power station is provided, comprising: The first building unit is to establish an optimization objective function with the goal of minimizing the total operating cost of the system. The objective function includes at least the fuel cost, start-up and shutdown cost, ramp-up cost of thermal power units, pumping cost of pumped storage power stations, as well as the trading revenue in the spot market, the reserve capacity revenue in the ancillary services market, the cost of wind and solar curtailment penalties and the cost of load shedding, and takes into account the constraints of medium and long-term contract electricity volume and price. The second building unit: Constructs multiple constraints, the multiple constraint system including at least: network security constraints including DC power flow constraints and N-1 security constraints, energy dynamic constraints, operating state logic constraints and minimum start-up and shutdown time constraints of pumped storage power stations, power balance and medium- and long-term contract execution constraints between markets, output upper and lower limits, minimum start-up and shutdown time and ramping constraints of traditional thermal power units, power balance, curtailment and multi-scenario uncertainty constraints of renewable energy, and system reserve capacity demand constraints; Solution Unit: The adaptive alternating direction multiplier method based on KKT conditions is used to solve the mixed integer nonlinear programming problem of the objective function and constraints. The original residual and dual residual are updated iteratively, and the penalty parameter is adaptively adjusted until the preset convergence tolerance is met, so as to obtain the optimal clearing result.

[0007] According to a third aspect of the present invention, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in the first aspect by running the executable instructions.

[0008] According to a fourth aspect of the invention, a computer-readable storage medium is provided having computer instructions stored thereon that, when executed by a processor, implement the steps of the method as described in the first aspect.

[0009] As can be seen from the above technical solutions, the beneficial effects of the pumped storage power station clearing method and apparatus disclosed in this invention are as follows: On the one hand, by integrating DC power flow equations, node power balance, and comprehensive N-1 security check constraints, this invention constructs a refined power grid security modeling system. Compared to a simplified model that only uses line capacity limits, this method can more accurately reflect network power flow, losses, and security bottlenecks under fault conditions. It ensures that the clearing results meet the actual power grid's security operation requirements while preventing security risks such as line overload caused by improper market clearing results, thus improving system reliability. On the other hand, this invention designs an adaptive alternating direction multiplier method (ADMM) based on KKT conditions. This algorithm, through problem decomposition, residual-driven dynamic adjustment of penalty parameters, and multiple convergence criteria, significantly improves computational efficiency while ensuring solution accuracy. This makes the model, which contains a large number of security constraints and integer variables, applicable to rapid clearing calculations for large-scale real-world systems. Attached Figure Description

[0010] Figure 1 This is a flowchart of a clearing method for a pumped storage power station provided in an exemplary embodiment; Figure 2 This is a schematic diagram of an optimized clearing model for pumped storage power plants that considers multi-market coupling, provided as an exemplary embodiment. Figure 3 This is a flowchart of an adaptive alternating direction multiplier method based on KKT conditions provided in an exemplary embodiment; Figure 4 This is a schematic structural diagram of a device provided in an exemplary embodiment; Figure 5 This is a block diagram of a clearing device for a pumped storage power station, provided as an exemplary embodiment. Detailed Implementation

[0011] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.

[0012] It should be noted that in other embodiments, the corresponding methods are not necessarily performed in the order shown and described in this invention. The method comprises steps. In some other embodiments, the method may include more or fewer steps than those described in this invention. Furthermore, a single step described in this invention may be broken down into multiple steps in other embodiments; and multiple steps described in this invention may be combined into a single step in other embodiments.

[0013] With the rapid growth of renewable energy installed capacity, the uncertainty and peak-shaving pressure of power system operation are increasing. Pumped storage power stations, due to their flexible regulation capabilities and rapid response characteristics, have become a key resource for improving system reliability and promoting the consumption of new energy. However, under the existing market mechanism, the optimized operation of pumped storage power stations faces multiple technical bottlenecks: Traditional optimization models for pumped storage power plants participating in market transactions often focus on a single market type or ignore grid security constraints, resulting in systemic flaws. For example, day-ahead market clearing models based on linear programming or mixed-integer linear programming, while capable of handling unit combination problems, fail to effectively couple the coordination between medium- and long-term contract execution and the real-time ancillary service market. This leads to conflicting bidding strategies for pumped storage power plants in different markets, hindering the full realization of their regulatory potential. Some models considering multi-market participation employ sequential optimization methods, optimizing the energy market first and then the ancillary service market. This decoupling approach ignores the transmission of opportunity costs between markets, resulting in suboptimal resource allocation.

[0014] In power grid security constraint modeling, existing methods generally suffer from oversimplification. Most studies use transmission line capacity constraints instead of complete power flow constraints, failing to accurately reflect the impact of node voltage phase angle and network losses on the clearing results; the few models that introduce DC power flow constraints are too computationally complex for practical application. In particular, in handling N-1 security constraints, traditional methods typically add key constraints after screening for anticipated accidents. This approach may miss critical security bottlenecks when topology changes or load distribution changes abruptly.

[0015] In terms of uncertainty handling, while existing techniques based on scenario trees or robust optimization can address renewable energy prediction errors, they often neglect the impact of the spatiotemporal correlation of prediction errors on pumped storage operation strategies. For example, the correlation of wind power prediction errors across regions can exacerbate local congestion problems, and existing models lack an effective description of this spatiotemporal correlation structure.

[0016] Further analysis reveals that the operational characteristics of pumped storage power plants present unique modeling challenges in a multi-market environment: the spatiotemporal coupling of energy states requires the model to accurately describe the energy transfer process at different time scales; the discrete nature of operating states (pumping, power generation, and standby) increases the complexity of the optimization problem; and the collaborative optimization with thermal power units and renewable energy requires the model to be capable of handling mixed-integer nonlinear problems.

[0017] Because related technologies have failed to establish a unified multi-market coupling framework, lack precise descriptions of grid security constraints, and lack robust methods for handling uncertainties, they generally suffer from problems such as low clearing efficiency, insufficient safety margin, and poor economic efficiency in the practice of pumped storage power stations participating in multi-market transactions. As a result, they are unable to meet the needs of multi-market collaborative optimization and safe operation under the new power system.

[0018] To address the shortcomings in related technologies, this invention proposes a clearing method, apparatus, equipment, and storage medium for pumped storage power stations.

[0019] Figure 1 This is a flowchart illustrating a clearing method for a pumped storage power station, as provided in an exemplary embodiment. Figure 2 As shown, the method may include the following steps: Step 101: Establish an optimization objective function with the goal of minimizing the total operating cost of the system. The objective function shall include at least the fuel cost, start-up and shutdown cost, ramp-up cost of thermal power units, pumping cost of pumped storage power stations, transaction revenue in the spot market, reserve capacity revenue in the ancillary services market, wind and solar curtailment penalty cost and load shedding cost, and consider the constraints of medium and long-term contract electricity volume and price.

[0020] like Figure 2 As shown, the present invention can establish an optimal clearing model for pumped storage power plants that considers multi-market coupling. This model may include the following core modules: Market Coupling Module: Establishes a power and price coupling mechanism among the medium- and long-term, spot, and ancillary service markets to ensure that medium- and long-term contract power is prioritized in the spot market, pumped storage power stations participate in energy trading in the spot market, and provide frequency regulation and reserve capacity in the ancillary service market; Grid Security Module: Integrates DC power flow constraints, N-1 security checks, and node injection balancing to ensure that the clearing results meet the requirements for safe grid operation; Unit Combination Module: Handles start-up and shutdown decisions, minimum operating time, and ramp-up constraints for traditional thermal power units; Uncertainty Handling Module: Uses a scenario tree method to handle prediction errors in wind power and photovoltaic output and constructs a multi-scenario optimization model.

[0021] The expression for the optimization objective function is: ; in, Indicates the total number of time periods in the scheduling cycle. This indicates the number of traditional thermal power units. This represents the fuel cost of unit i at time t. This indicates the startup action of unit i at time t. This represents the startup cost of unit i at time t. This indicates the ramp-up status of unit i during time period t. This represents the ramp-up cost of unit i during time period t. This indicates the fuel cost consumed when a pumped-storage power station pumps water. This represents the pumping power of a pumped storage power station during time period t. This indicates the pumping capacity reserved for auxiliary services by the pumped storage power station during time period t. This represents the spot market price during time period t. This indicates the power generation capacity of a pumped storage power station in the spot market during time period t. The price of ancillary services will be increased during time period t. This indicates the increased reserve capacity provided by the pumped storage power station during time period t. This indicates a reduction in the price of ancillary services for t. This indicates the reduced reserve capacity provided by the pumped storage power station during time period t. This represents the penalty coefficient for abandoning wind and solar power. This represents the wind curtailment power during time period t. This represents the amount of solar power curtailed during time period t. Loss of load value The load shedding power during time period t, This indicates the contract price in the medium- to long-term market during period d. This represents the contracted power generation during period d in the medium- to long-term market. This indicates the amount of liquidity withdrawn from contracts during the d-period of the medium- to long-term market.

[0022] Step 102: Construct multiple constraints. The multiple constraint system includes at least the following: network security constraints including DC power flow constraints and N-1 security constraints; energy dynamic constraints, operating state logic constraints and minimum start-up and shutdown time constraints of pumped storage power stations; power balance and medium- and long-term contract execution constraints between markets; upper and lower limits of output, minimum start-up and shutdown time and ramping constraints of traditional thermal power units; power balance, curtailment and multi-scenario uncertainty constraints of renewable energy; and system reserve capacity demand constraints.

[0023] The expression for the energy dynamic constraints of a pumped storage power station is: ; in, This indicates the energy storage status of a pumped storage power station during time period t. Indicates pumping efficiency. Indicates power generation efficiency. Indicates the duration of each time period. This represents the self-discharge loss of a pumped storage power station during time period t.

[0024] The expression for the operational state logical constraint is: ; in, This indicates that the pumped storage power station is in a shutdown state during time period t. This indicates that the pumped storage power station is in the pumping state during time period t. This indicates that the pumped storage power station is in power generation mode during time period t. This indicates that the pumped storage power station starts during time period t. The pumped storage power station was shut down during the period t.

[0025] The expression for the minimum start / stop time constraint is: ; in, Minimum operating time of pumped storage power stations This indicates the minimum downtime of a pumped storage power station.

[0026] The expression for the power balance constraint between markets is: ; in, This represents the output of a traditional thermal power unit i in the spot market during time period t. This indicates the reserved output of a traditional thermal power unit i for auxiliary services during time period t. This represents the actual power output of the wind farm during time period t. This indicates the actual output of the photovoltaic power station during time period t. This represents the load demand during time period t. This represents the network loss during time period t.

[0027] The expression for the execution constraints of medium- and long-term contracts is: ; in, This represents the medium- to long-term contract output of traditional thermal power unit i during time period t. This represents the total contracted electricity volume of traditional thermal power unit i during the contract period d. This represents the set of time periods within the contract period d.

[0028] The expressions for the upper and lower limits of output, minimum start-up and shutdown time, and ramping constraints of traditional thermal power units are as follows: ; in, This indicates the operating status of a traditional thermal power unit i during time period t.

[0029] ; in This represents the minimum operating time of unit i. This represents the minimum downtime of unit i. This indicates the start-up action of unit i. This indicates the shutdown action of unit i.

[0030] ; in This represents the maximum uphill rate of unit i. This represents the maximum downhill / climb rate of unit i.

[0031] The expression for the power balance constraint of renewable energy is: ; in This represents the predicted power output of the wind farm in time period t. This indicates the predicted output of the photovoltaic power plant during time period t.

[0032] The expression for the power curtailment constraint is: ; Multi-scenario uncertainty constraints are used to handle biases caused by inaccurate renewable energy output forecasts, and their expression is: ; in, This indicates a scenario of uncertainty. This represents the prediction error of the wind farm at time t. This indicates the prediction error of the photovoltaic power plant at time period t. Indicates scene index, The expected value, representing the probability of scenario s, is used as the penalty cost for wind and solar power curtailment and substituted into the optimization objective function. .

[0033] The core output of this constraint is to transform the "wind and solar curtailment penalty cost" from a deterministic value into an expected value, and substitute it into the objective function. This makes the optimization objective minimize the sum of the "deterministic operating cost" and the "expected curtailment penalty cost". When scheduling the start-up, shutdown, and output of thermal power units and pumped storage power stations, the optimizer automatically weighs the "flexibility cost reserved to adapt to future fluctuations" against the "average cost of wind and solar curtailment due to inability to adapt to fluctuations", ultimately arriving at a decision scheme with the lowest expected total cost in the long run.

[0034] DC power flow constraints are used to calculate network power flow and losses, including line power flow equations, node power balance equations, and the definition of net injected power at nodes. Their expressions are as follows: ; in This represents the power flow of line ij during time period t. This represents the susceptance of line ij. This represents the voltage phase angle of node i during time period t. This represents the loss coefficient of line ij. This represents the loss of line ij during time period t. This indicates the conductance of line ij. Let i represent the set of nodes directly connected to node i. This represents the net injection power of node i during time period t. This represents the loss of node i during time period t. This represents the generator set located at node i. This represents a wind farm located at node i. This represents the pumped storage power station located at node i.

[0035] The N-1 safety constraint is used to ensure that the power flow of all lines in the system does not exceed the limit under any anticipated line fault condition, and its expression is: ; in This represents the power flow of line ij under fault state k during time period t. This represents the susceptance of line ij under fault state k. This represents the voltage phase angle of node i under fault state k.

[0036] The expression for the reserve capacity constraint is: ; in This represents the reserve capacity provided by a traditional thermal power unit i during time period t. This represents the reserve capacity provided by a traditional thermal power unit i during time period t. This indicates the system's reserve requirement during time period t. This indicates the system's backup downsizing demand during time period t.

[0037] Step 103: The mixed integer nonlinear programming problem of the objective function and constraints is solved by using the adaptive alternating direction multiplier method based on KKT conditions. The original residual and dual residual are updated iteratively, and the penalty parameter is adjusted adaptively until the preset convergence tolerance is met, so as to obtain the optimal clearing result.

[0038] Specifically, such as Figure 3As shown, an adaptive alternating direction multiplier method based on KKT conditions is used to solve the mixed-integer nonlinear programming problem of the objective function and constraints. This includes: constructing an augmented Lagrangian function to couple power balance and node injection balance constraints; in each iteration, calculating the original and dual residuals of power balance and node injection balance, and updating the penalty parameter in the augmented Lagrangian function according to the relative magnitudes of the original and dual residuals; and determining that the algorithm has converged when the norms of the original residual, dual residual, and KKT conditions are all less than their respective set convergence tolerances.

[0039] The expression for the augmented Lagrange function is: ; in, This represents the cost function of a traditional thermal power unit. This represents the cost function of a pumped storage power station. This represents network loss and load shedding costs. Indicates the cost of curtailing renewable energy. The dual constraint variables representing power balance, The dual constraint variable representing the node-injected balancing power is represented. This represents the penalty parameter of the augmented Lagrange function.

[0040] Residual definition: ; in, Indicates the number of iterations. This represents the original power balance residuals for time period t. This represents the original residual of the node injection power balance in time period t. This represents the power balance dual residual at time t. This represents the node injection power balance dual residual during time period t.

[0041] Adaptive parameter update: ; in, This indicates the multiplier by which the penalty parameter increases. This indicates the factor by which the penalty parameter is reduced. This represents the residual balance threshold.

[0042] Convergence condition: ; in, This indicates the convergence tolerance of the original residuals. This indicates the convergence tolerance of the dual residuals. This indicates the conditional convergence tolerance.

[0043] On one hand, this invention constructs a refined power grid security modeling system by integrating DC power flow equations, node power balance, and comprehensive N-1 security check constraints. Compared to a simplified model that only uses line capacity limits, this method can more accurately reflect network power flow, losses, and security bottlenecks under fault conditions. It ensures that the clearing results meet the actual power grid's safe operation requirements while preventing security risks such as line overload caused by inappropriate market clearing results, thus improving system reliability. On the other hand, this invention designs an adaptive alternating direction multiplier method based on KKT conditions. This algorithm, through problem decomposition, residual-driven dynamic adjustment of penalty parameters, and multiple convergence criteria, significantly improves computational efficiency while ensuring solution accuracy. This makes the model, which contains numerous security constraints and integer variables, applicable to rapid clearing calculations for large-scale real-world systems.

[0044] Figure 4 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 4 At the hardware level, the device includes a processor 402, an internal bus 404, a network interface 406, memory 408, and non-volatile memory 410, and may also include other hardware required for its functions. One or more embodiments of the present invention can be implemented in software, for example, the processor 402 reads the corresponding computer program from the non-volatile memory 410 into memory 408 and then runs it. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0045] Please refer to Figure 5 A clearing device for a pumped storage power station can be applied to, for example... Figure 5 The device shown, in order to implement the technical solution of the present invention, includes: The first building unit 501 is used to establish an optimization objective function with the goal of minimizing the total operating cost of the system. The objective function includes at least the fuel cost, start-up and shutdown cost, ramp-up cost of thermal power units, pumping cost of pumped storage power stations, as well as the trading revenue in the spot market, the reserve capacity revenue in the ancillary services market, the cost of wind and solar curtailment penalties and the cost of load shedding, and takes into account the constraints of medium and long-term contract electricity volume and price. The second building unit 502 is used to build multiple constraints. The multiple constraint system includes at least: network security constraints including DC power flow constraints and N-1 security constraints; energy dynamic constraints, operating state logic constraints and minimum start-up and shutdown time constraints of pumped storage power stations; power balance and medium- and long-term contract execution constraints between markets; upper and lower limits of output, minimum start-up and shutdown time and ramping constraints of traditional thermal power units; power balance, curtailment and multi-scenario uncertainty constraints of renewable energy; and system reserve capacity demand constraints. The solver unit 503 is used to solve the mixed integer nonlinear programming problem of the objective function and constraints by using the adaptive alternating direction multiplier method based on KKT conditions. It iteratively updates the original residual and dual residual, and adaptively adjusts the penalty parameter until the preset convergence tolerance is met, so as to obtain the optimal clearing result.

[0046] Optionally, the expression for the optimization objective function is: ; in, Indicates the total number of time periods in the scheduling cycle. This indicates the number of traditional thermal power units. This represents the fuel cost of unit i at time t. This indicates the startup action of unit i at time t. This represents the startup cost of unit i at time t. This indicates the ramp-up status of unit i during time period t. This represents the ramp-up cost of unit i during time period t. This indicates the fuel cost consumed when a pumped-storage power station pumps water. This represents the pumping power of a pumped storage power station during time period t. This indicates the pumping capacity reserved for auxiliary services by the pumped storage power station during time period t. This represents the spot market price during time period t. This indicates the power generation capacity of a pumped storage power station in the spot market during time period t. The price of ancillary services will be increased during time period t. This indicates the increased reserve capacity provided by the pumped storage power station during time period t. This indicates a reduction in the price of ancillary services for t. This indicates the reduced reserve capacity provided by the pumped storage power station during time period t. This represents the penalty coefficient for abandoning wind and solar power. This represents the wind curtailment power during time period t. This represents the amount of solar power curtailed during time period t. Loss of load value The load shedding power during time period t, This indicates the contract price in the medium- to long-term market during period d. This represents the contracted power generation during period d in the medium- to long-term market. This indicates the amount of liquidity withdrawn from contracts during the d-period of the medium- to long-term market.

[0047] Furthermore, The DC power flow constraints are used to calculate network power flow and losses, including line power flow equations, node power balance equations, and the definition of net injected power at nodes, and their expressions are as follows: ; in This represents the power flow of line ij during time period t. This represents the susceptance of line ij. This represents the voltage phase angle of node i during time period t. This represents the loss coefficient of line ij. This represents the loss of line ij during time period t. This indicates the conductance of line ij. Let i represent the set of nodes directly connected to node i. This represents the net injection power of node i during time period t. This represents the loss of node i during time period t. This represents the generator set located at node i. This represents a wind farm located at node i. This represents a pumped storage power station located at node i; The N-1 safety constraint is used to ensure that, under any anticipated line fault condition, the power flow of all lines in the system does not exceed the limit, and its expression is: ; in This represents the power flow of line ij under fault state k during time period t. This represents the susceptance of line ij under fault state k. This represents the voltage phase angle of node i under fault state k.

[0048] Optionally, the multi-scenario uncertainty constraint is used to handle the bias caused by inaccurate renewable energy output forecasting, and its expression is: ; in, This indicates a scenario of uncertainty. This represents the prediction error of the wind farm at time t. This indicates the prediction error of the photovoltaic power plant at time period t. Indicates scene index, Let represent the probability of scenario s, and the obtained expected value is used as the penalty cost for wind and solar power curtailment and substituted into the optimization objective function.

[0049] Optionally, the solving unit 503 is specifically used for: Construct an augmented Lagrangian function to couple power balance and node injection balance constraints; In each iteration, the original residual and dual residual of power balance and node injection balance are calculated, and the penalty parameter in the augmented Lagrangian function is updated according to the relative magnitude of the original residual and the dual residual. The algorithm is considered convergent when the norms of the original residual, the dual residual, and the KKT condition are all less than their respective set convergence tolerances.

[0050] Furthermore, the expression for the augmented Lagrange function is: ; in, This represents the cost function of a traditional thermal power unit. This represents the cost function of a pumped storage power station. This represents network loss and load shedding costs. Indicates the cost of curtailing renewable energy. The dual constraint variables representing power balance, The dual constraint variable representing the node-injected balancing power is represented. This represents the penalty parameter of the augmented Lagrange function.

[0051] Optional, The expression for the energy dynamic constraint is: ; in, This indicates the energy storage status of a pumped storage power station during time period t. Indicates pumping efficiency. Indicates power generation efficiency. Indicates the duration of each time period. This represents the self-discharge loss of a pumped storage power station during time period t.

[0052] The expression for the operational state logical constraint is: ; in, This indicates that the pumped storage power station is in a shutdown state during time period t. This indicates that the pumped storage power station is in the pumping state during time period t. This indicates that the pumped storage power station is in power generation mode during time period t. This indicates that the pumped storage power station starts during time period t. The pumped storage power station was shut down during the period t.

[0053] The expression for the minimum start / stop time constraint is: ; in, Minimum operating time of pumped storage power stations This indicates the minimum downtime of a pumped storage power station.

[0054] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0055] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0056] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0057] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0058] For any computer-readable medium (or computer-readable storage medium) as described above or otherwise, computer instructions may be stored thereon, which, when executed by a processor, implement one or more of the above embodiments, thereby realizing the technical solution of the present invention.

[0059] The present invention also proposes a computer program that, when executed by a processor, implements one or more of the embodiments described above, thereby realizing the technical solution of the present invention. This computer program may be specifically recorded on the above-described or other computer-readable media, and the present invention does not impose any limitations thereon.

[0060] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0061] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0062] The terminology used in one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0063] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of the present invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0064] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of one or more embodiments of the present invention.

Claims

1. A method of outflow of a pumped storage power station, characterized by, include: An optimization objective function is established with the goal of minimizing the total operating cost of the system. The objective function includes at least the fuel cost, start-up and shutdown cost, ramp-up cost of thermal power units, pumping cost of pumped storage power stations, as well as the trading revenue in the spot market, the reserve capacity revenue in the ancillary services market, the cost of wind and solar curtailment penalties and the cost of load shedding, and takes into account the constraints of medium and long-term contract electricity volume and price. Multiple constraints are constructed, and the system of multiple constraints includes at least: network security constraints including DC power flow constraints and N-1 security constraints; energy dynamic constraints, operating state logic constraints and minimum start-up and shutdown time constraints of pumped storage power stations; power balance and medium- and long-term contract execution constraints between markets; upper and lower limits of output, minimum start-up and shutdown time and ramping constraints of traditional thermal power units; power balance, curtailment and multi-scenario uncertainty constraints of renewable energy; and system reserve capacity demand constraints. An adaptive alternating direction multiplier method based on KKT conditions is used to solve the mixed integer nonlinear programming problem of the objective function and constraints. The original residual and dual residual are updated iteratively, and the penalty parameter is adaptively adjusted until the preset convergence tolerance is met, so as to obtain the optimal clearing result.

2. The method of claim 1, wherein, The expression for the optimization objective function is: ； in, Indicates the total number of time periods in the scheduling cycle. This indicates the number of traditional thermal power units. This represents the fuel cost of unit i at time t. This indicates the startup action of unit i at time t. This represents the startup cost of unit i at time t. This indicates the ramp-up status of unit i during time period t. This represents the ramp-up cost of unit i during time period t. This indicates the fuel cost consumed when a pumped-storage power station pumps water. This represents the pumping power of a pumped storage power station during time period t. This indicates the pumping capacity reserved for auxiliary services by the pumped storage power station during time period t. This represents the spot market price during time period t. This indicates the power generation capacity of a pumped storage power station in the spot market during time period t. The price of ancillary services will be increased during time period t. This indicates the increased reserve capacity provided by the pumped storage power station during time period t. This indicates a reduction in the price of ancillary services for t. This indicates the reduced reserve capacity provided by the pumped storage power station during time period t. This represents the penalty coefficient for abandoning wind and solar power. This represents the wind power curtailment rate during time period t. This represents the amount of solar power curtailed during time period t. Loss of load value The load shedding power during time period t, This indicates the contract price in the medium- to long-term market during period d. This represents the contracted power generation during period d in the medium- to long-term market. This indicates the amount of liquidity withdrawn from contracts during the d-period of the medium- to long-term market.

3. The method according to claim 2, characterized in that, The DC power flow constraints are used to calculate network power flow and losses, including line power flow equations, node power balance equations, and the definition of net injected power at nodes, and their expressions are as follows: ； in This represents the power flow of line ij during time period t. This represents the susceptance of line ij. This represents the voltage phase angle of node i during time period t. This represents the loss coefficient of line ij. This represents the loss of line ij during time period t. This indicates the conductance of line ij. Let i represent the set of nodes directly connected to node i. This represents the net injection power of node i during time period t. This represents the loss of node i during time period t. This represents the generator set located at node i. This represents a wind farm located at node i. This represents a pumped storage power station located at node i; The N-1 safety constraint is used to ensure that, under any anticipated line fault condition, the power flow of all lines in the system does not exceed the limit, and its expression is: ； in This represents the power flow of line ij under fault state k during time period t. This represents the susceptance of line ij under fault state k. This represents the voltage phase angle of node i under fault state k.

4. The method according to claim 2, characterized in that, The multi-scenario uncertainty constraint is used to handle the bias caused by inaccurate renewable energy output forecasting, and its expression is: ； in, This indicates a scenario of uncertainty. This represents the prediction error of the wind farm at time t. This indicates the prediction error of the photovoltaic power plant at time period t. Indicates scene index, Let represent the probability of scenario s. The expected value obtained is used as the penalty cost for wind and solar power curtailment and substituted into the optimization objective function.

5. The method according to claim 1, characterized in that, The method of using an adaptive alternating direction multiplier method based on KKT conditions to solve the mixed integer nonlinear programming problem of the objective function and constraints includes: Construct an augmented Lagrangian function to couple power balance and node injection balance constraints; In each iteration, the original residual and dual residual of power balance and node injection balance are calculated, and the penalty parameter in the augmented Lagrangian function is updated according to the relative magnitude of the original residual and the dual residual. The algorithm is considered convergent when the norms of the original residual, the dual residual, and the KKT condition are all less than their respective set convergence tolerances.

6. The method according to claim 5, characterized in that, The expression for the augmented Lagrange function is: ； in, This represents the cost function of a traditional thermal power unit. The cost function of a pumped storage power station is represented by the following: This represents network loss and load shedding costs. Indicates the cost of curtailing renewable energy. The dual constraint variables representing power balance, The dual constraint variable representing the node-injected balancing power is represented. This represents the penalty parameter of the augmented Lagrange function.

7. The method according to claim 1, characterized in that, The expression for the energy dynamic constraint is: ； in, This indicates the energy storage status of a pumped storage power station during time period t. Indicates pumping efficiency. Indicates power generation efficiency. Indicates the duration of each time period. This represents the self-discharge loss of a pumped storage power station during time period t. The expression for the operational state logical constraint is: ； in, This indicates that the pumped storage power station is in a shutdown state during time period t. This indicates that the pumped storage power station is in the pumping state during time period t. This indicates that the pumped storage power station is in power generation mode during time period t. This indicates that the pumped storage power station starts during time period t. The pumped storage power station was shut down during the period t. The expression for the minimum start / stop time constraint is: ； in, Minimum operating time of pumped storage power stations This indicates the minimum downtime of a pumped storage power station.

8. A clearing device for a pumped storage power station, characterized in that, include: The first building unit is to establish an optimization objective function with the goal of minimizing the total operating cost of the system. The objective function includes at least the fuel cost, start-up and shutdown cost, ramp-up cost of thermal power units, pumping cost of pumped storage power stations, as well as the trading revenue in the spot market, the reserve capacity revenue in the ancillary services market, the cost of wind and solar curtailment penalties and the cost of load shedding, and takes into account the constraints of medium and long-term contract electricity volume and price. The second building unit: Constructs multiple constraints, the multiple constraint system including at least: network security constraints including DC power flow constraints and N-1 security constraints, energy dynamic constraints, operating state logic constraints and minimum start-up and shutdown time constraints of pumped storage power stations, power balance and medium- and long-term contract execution constraints between markets, output upper and lower limits, minimum start-up and shutdown time and ramping constraints of traditional thermal power units, power balance, curtailment and multi-scenario uncertainty constraints of renewable energy, and system reserve capacity demand constraints; Solution Unit: The adaptive alternating direction multiplier method based on KKT conditions is used to solve the mixed integer nonlinear programming problem of the objective function and constraints. The original residual and dual residual are updated iteratively, and the penalty parameter is adaptively adjusted until the preset convergence tolerance is met, so as to obtain the optimal clearing result.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in any one of claims 1-7 by running the executable instructions.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.

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