Pumped storage and new energy storage collaborative market participation revenue optimization system

Abstract

The present application relates to the technical field of power system energy storage, in particular to a pumped storage and new energy storage collaborative market participation benefit optimization system, comprising a data acquisition and preprocessing module that acquires multi-source heterogeneous data and generates a standardized data set; a market information and policy analysis module that constructs market constraints and policy constraints; a collaborative scheduling optimization module that constructs and solves a multi-objective collaborative optimization model with the goals of maximizing comprehensive benefits, minimizing operating costs, and maximizing carbon emission reduction, generates a joint scheduling strategy; a benefit calculation and intelligent distribution module that calculates collaborative incremental benefits and performs compliance distribution; a strategy execution and closed-loop optimization module that quantifies execution deviation and benefit deviation, triggering online iterative updating of the model. The present application also integrates a pumped storage power station distribution module to achieve fair cross-regional allocation of capacity electricity charges, and a cold region adaptation control module to use pumped storage waste heat to keep new energy storage warm.

Description

Technical Field

[0001] This invention relates to the field of power system energy storage technology, specifically to a revenue optimization system for the coordinated participation of pumped hydro storage and novel energy storage in the market. Background Technology

[0002] With the rapid growth of installed capacity of new energy power generation, the demand for flexible regulation resources in the power system is becoming increasingly urgent. Pumped storage, as a mature large-scale energy storage method, has advantages such as large capacity, long lifespan, and low operating costs, but it also has disadvantages such as slow response speed and site selection limitations. New energy storage, represented by electrochemical energy storage, features fast response speed and flexible deployment, but it suffers from problems such as limited cycle life, high unit capacity cost, and significant performance degradation at low temperatures. The two technologies are highly complementary in terms of their technical characteristics. How to achieve synergistic and optimized operation of pumped storage and new energy storage has become a research hotspot in the field of power system operation.

[0003] Meanwhile, my country's power market reform continues to deepen, and a multi-tiered market system, including the electricity market, ancillary services market, capacity market, and carbon market, is gradually being improved. Pumped storage and new energy storage, as market players, are seeing increasingly diversified revenue sources, including peak-valley price arbitrage, frequency regulation ancillary service revenue, capacity compensation revenue, and carbon trading revenue. Against this backdrop, optimizing the synergistic revenue streams of pumped storage and new energy storage has become crucial for improving the economic viability of energy storage projects.

[0004] Existing technologies have addressed the coordinated dispatch of pumped storage and new energy storage technologies, but several shortcomings remain: Firstly, existing coordinated dispatch methods primarily focus on power complementarity at the technical level, failing to fully couple price signals from the electricity market and multi-market revenue mechanisms, resulting in poor economic efficiency and energy waste. Secondly, existing methods lack consideration for cross-regional capacity cost allocation. Pumped storage power plants typically serve multiple regions, and the reasonable allocation of capacity costs among these regions directly impacts the optimization boundary of the dispatch strategy. Thirdly, existing methods have failed to establish a closed-loop optimization mechanism between dispatch strategy and revenue accounting. Fourthly, the lack of effective deviation quantification feedback and model iterative updates after dispatch strategy execution makes it difficult to adapt to dynamic changes in market rules and electricity pricing policies. Summary of the Invention

[0005] In view of this, the present invention aims to provide a revenue optimization system for the coordinated participation of pumped hydro storage and novel energy storage in the market, so as to solve the above-mentioned problems existing in the prior art.

[0006] This application provides a revenue optimization system for the coordinated participation of pumped storage and new energy storage in the market, including a data acquisition and preprocessing module with communication connection, a market information and policy analysis module, a coordinated scheduling optimization module, a revenue calculation and intelligent allocation module, and a strategy execution and closed-loop optimization module.

[0007] The data acquisition and preprocessing module is used to acquire multi-source heterogeneous data and perform preprocessing. The market information and policy analysis module is used to analyze the requirements of electricity market trading rules and electricity price policies (i.e., electricity guidance documents) and construct dual participation constraints of market constraints and policy constraints. The collaborative scheduling optimization module is used to construct and solve a multi-objective collaborative optimization model based on the standardized data output by the data acquisition and preprocessing module and the dual constraints constructed by the market information and policy analysis module, with the objectives of maximizing comprehensive benefits, minimizing operating costs, and maximizing carbon emission reduction, and to generate a joint scheduling strategy for pumped storage and new energy storage. The revenue calculation and intelligent allocation module is used to perform refined calculation of multi-dimensional revenue based on the actual transaction data after the joint scheduling strategy is executed, and to allocate revenue in compliance with electricity price policy requirements. The strategy execution and closed-loop optimization module is used to distribute the joint scheduling strategy to the energy storage execution agency, collect actual operation data and revenue data, quantify and calculate the execution deviation and revenue deviation of the scheduling strategy, and trigger the online iterative update and parameter tuning of the multi-objective collaborative optimization model when the deviation value exceeds the preset threshold or when a change in the external environment is detected.

[0008] Optionally, it also includes a pumped storage power station distribution module, the pumped storage power station distribution module comprising: The pumped storage power station digital unit is used to convert the available capacity and corresponding capacity electricity fee parameters of the pumped storage power station into standard data objects and broadcast them on the power trading network platform. The subscription instruction response unit is used to receive capacity subscription request data from nodes in different regions, update the occupancy status of the pumped storage power station according to the request timestamp and priority, and generate a pumped storage power station usage right allocation signal; the subscribed pumped storage power station only provides services to its subscriber. The remaining cost dynamic allocation unit is used to collect data on the remaining pumped storage power stations that have not been subscribed. The remaining pumped storage power stations serve various regions. Based on the load distribution weight data of each region's nodes, the allocation coefficient of the remaining pumped storage power stations is calculated and input as a cost item into the collaborative scheduling optimization module.

[0009] Optionally, the market information and policy analysis module includes: The market rule analysis unit is used to analyze the trading rules, price signals, and clearing mechanisms of the electricity market, capacity market, ancillary services market, and carbon market. The policy parameter dynamic adaptation unit is used to build in and update policy parameters such as the pumped storage revenue sharing ratio, the peak capacity conversion ratio of new energy storage, and the capacity electricity price reduction rules in real time. The market constraints are formed by the time-series market signals parsed by the market rule parsing unit; the policy constraints are formed by the real-time policy parameters provided by the policy parameter dynamic adaptation unit as boundaries.

[0010] Optionally, the revenue calculation and intelligent allocation module includes: The multi-dimensional revenue splitting unit is used to split and statistically analyze the comprehensive revenue according to market type, energy storage type, and scheduling time. It calculates the independent revenue generated by pumped storage and new energy storage in each market, and calculates the incremental revenue of collaboration by constructing two sets of parallel simulations of collaborative operation mode and independent operation mode and comparing the total revenue difference of the two sets of simulations under the same market conditions in the same period. The carbon asset and curtailment accounting unit is used to complete the linkage calculation of carbon asset accounting and the cost of new energy curtailment losses; The policy-based revenue distribution unit is used to distribute various revenues among pumped storage power station operators and new energy storage power station operators based on real-time policy parameters, and to offset the system operating costs with the policy-required revenue retention portion, while simultaneously calculating the electricity bill reduction revenue for power users and generating compliant revenue distribution ledgers and vouchers.

[0011] Optionally, the strategy execution and closed-loop optimization module includes: The deviation quantization unit is used to collect actual operating condition data of the equipment and calculate the tracking deviation rate between the actual power and the commanded power. The profit deviation quantification unit is used to collect actual market transaction profit data and calculate the profit deviation rate between actual and expected profits. The model iterative optimization unit is used to automatically initiate online iterative updates of the multi-objective collaborative optimization model when the tracking deviation rate or revenue deviation rate exceeds a preset threshold and the duration exceeds a preset duration, or when external environmental abrupt signals such as power market rule adjustments, electricity price policy updates, or drastic changes in grid operation status are detected. The unit adjusts the objective function weights, core calculation parameters, and constraint thresholds of the model, and then solidifies the new parameters into the model after simulation verification.

[0012] Optionally, it also includes a cold-region adaptation and control module, which includes an environmental sensing unit, a waste heat recovery module installed in the pumped storage unit, and a constant temperature protection unit installed in the new energy storage unit. The cold-region adaptation and control module is used to adjust the constant temperature protection unit in real time based on the low-temperature environment data collected by the environmental sensing unit and the waste heat parameters of the pumped storage unit collected by the waste heat recovery module, so as to provide heat preservation for the new energy storage unit using the waste heat of the pumped storage unit.

[0013] Optionally, the collaborative scheduling optimization module adopts a two-layer model architecture, including: The upper-level scheduling strategy model is used to determine the joint scheduling strategy of pumped storage and new energy storage, with the core of coordinated peak shaving of pumped storage and new energy storage, combined with grid load demand, new energy output forecast, cold region environmental constraints, and unit operation limitations, with the goal of optimizing the overall system operating efficiency and the best peak shaving effect. The lower-level cost accounting model is used to calculate the actual operating costs of pumped storage units and new energy storage units under the current scheduling mode. The lower-level cost accounting model correlates the operating energy consumption cost of the constant temperature protection unit with the operating status of the corresponding pumped storage unit. The correlation calculation includes dynamically deducting the energy consumption cost of the constant temperature protection unit based on the available waste heat of the waste heat recovery module of the pumped storage unit. The upper-level scheduling strategy model and the lower-level cost accounting model are solved collaboratively using a genetic algorithm to output the optimal joint scheduling strategy.

[0014] Optionally, the joint dispatch strategy of pumped hydro storage and novel energy storage includes: When the adjustment demand is a short-term frequency adjustment at the millisecond or second level, a coordinated response strategy is generated, which is independently responded by the power-type energy storage module in the new energy storage unit and monitored by the pumped storage unit in standby mode. When the regulation demand is minute-level peak shaving, a coordinated response strategy is generated, with the energy-type energy storage module in the new energy storage unit as the main response and the pumped storage unit as the auxiliary response, and the pumped storage unit gradually taking over the regulation load. When the regulation demand is hourly long-term peak shaving, a collaborative response strategy is generated, with the pumped storage unit as the main responder and the new energy storage unit as the auxiliary responder. The new energy storage unit compensates for the regulation delay of the pumped storage unit and smooths out the high-frequency fluctuations in the output of new energy sources.

[0015] Optionally, the objective function of the multi-objective collaborative optimization model includes: The objective function for maximizing overall benefits includes revenue from the electricity market, capacity market, ancillary services market, and carbon trading. The objective function for minimizing operating costs includes pumped storage operating costs and new energy storage operating costs. The pumped storage operating costs include pumping electricity costs, start-up and shutdown costs, and head loss costs. The new energy storage operating costs include charging and discharging loss costs, cycle life decay costs, and constant temperature protection unit costs. The objective function for maximizing carbon emission reduction is calculated by substituting the carbon emission reduction from thermal power generation units.

[0016] Compared with the prior art, the present invention has the following beneficial effects: Multi-objective collaborative optimization to enhance overall benefits: By constructing a multi-objective collaborative optimization model with the goals of maximizing overall benefits, minimizing operating costs, and maximizing carbon emission reductions, the model comprehensively considers the benefits of multiple markets such as the electricity market, capacity market, ancillary services market, and carbon market, and achieves the economic optimality of the coordinated operation of pumped storage and new energy storage.

[0017] Reasonable allocation of capacity costs across regions: Through the cross-regional capacity cost allocation module, the capacity electricity cost of pumped storage power stations is reasonably allocated among the benefiting regions, forming scheduling boundary conditions associated with physical connection line constraints, thereby improving the feasibility and economy of scheduling strategies.

[0018] Closed-loop optimization mechanism, adaptive dynamic adjustment: Through the strategy execution and closed-loop optimization module, the execution deviation and revenue deviation of the scheduling strategy are quantified. When the deviation exceeds the threshold or the external environment changes, the model is automatically triggered to update online, enabling the system to adapt to the dynamic changes of market rules and electricity price policies.

[0019] Enhanced adaptability to cold regions: By using a cold-region adaptability control module, the waste heat from pumped storage units is utilized to provide insulation for the new energy storage, reducing the energy consumption cost of the constant temperature protection unit. Furthermore, a dual-layer model architecture is used to achieve dynamic offsetting of waste heat and energy consumption costs, effectively improving the system's operating efficiency and economy under cold-region conditions.

[0020] Collaborative Incremental Revenue Calculation and Fair Distribution: The incremental revenue of collaborative operation compared to independent operation is calculated using parallel simulation methods. Combined with policy parameters, the revenue is distributed in a compliant manner, providing a scientific basis for the distribution of benefits among multiple stakeholders.

[0021] Multi-temporal and spatial scale hierarchical coordinated response: For regulation needs at different time scales such as millisecond / second frequency regulation, minute-level peak shaving, and hour-level peak shaving, differentiated hierarchical coordinated response strategies are designed to fully leverage the complementary advantages of pumped hydro storage and new energy storage technologies. Attached Figure Description

[0022] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0023] Figure 1 The structural block diagram of the revenue optimization system for pumped hydro storage and novel energy storage to participate in the market in a coordinated manner, as provided in the embodiments of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Figure 1 This is a structural block diagram of a revenue optimization system for the synergistic participation of pumped hydro storage and novel energy storage in the market, provided in an embodiment of the present invention. Figure 1 As shown, this embodiment provides a revenue optimization system for the coordinated participation of pumped hydro storage and new energy storage in the market. The system includes a data acquisition and preprocessing module 1, a market information and policy analysis module 2, a coordinated scheduling optimization module 3, a revenue calculation and intelligent allocation module 4, and a strategy execution and closed-loop optimization module 5, all connected via communication. These five modules are interconnected through data interfaces, forming a complete data flow chain from data acquisition to strategy execution and then to closed-loop optimization.

[0026] The input terminals of the data acquisition and preprocessing module are connected to the data interfaces of the power grid dispatching system, new energy power generation units, pumped storage power station monitoring systems, new energy storage power station monitoring systems, environmental monitoring stations, and the power trading center, respectively, for collecting multi-source heterogeneous data. This multi-source heterogeneous data includes power grid operating status data (grid frequency, node voltage, tie-line power), new energy output data (real-time wind and solar power output), pumped storage operating parameters (unit start-up / shutdown status, power generation, pumping power, head), new energy storage operating parameters (state of charge, charge / discharge power, battery temperature), environmental parameters (ambient temperature), and market price data (time-of-use pricing, ancillary service pricing, carbon trading prices). After acquisition, the module performs preprocessing operations such as data cleaning, filtering and noise reduction, and spatiotemporal synchronization correction to generate a standardized operating dataset, which is then output to the collaborative scheduling optimization module.

[0027] The market information and policy analysis module is used to analyze electricity market trading rules and pricing policy requirements, constructing dual participation constraints of market constraints and policy constraints. This module analyzes the trading rules, price signals, and clearing mechanisms of the energy market, capacity market, ancillary services market, and carbon market through the market rule analysis unit, generating time-series market signals. It also incorporates and updates policy parameters such as pumped storage revenue sharing ratios, peak capacity conversion ratios for new energy storage, and capacity price reduction rules in real time through the policy parameter dynamic adaptation unit. Market constraints are composed of time-series market signals, while policy constraints are formed by real-time policy parameters as boundaries. Both types of constraints are jointly output to the collaborative dispatch optimization module.

[0028] The collaborative scheduling optimization module receives standardized operational datasets output from the data acquisition and preprocessing module, as well as dual constraints constructed by the market information and policy analysis module, to build and solve a multi-objective collaborative optimization model. This model aims to maximize overall revenue, minimize operating costs, and maximize carbon emission reductions. Overall revenue includes revenue from the electricity market, capacity market, ancillary services market, and carbon trading. Operating costs include pumped storage operating costs (pumping electricity fees, start-up and shutdown costs, and head loss costs) and new energy storage operating costs (charge and discharge loss costs, cycle life decay costs, and constant temperature protection unit costs). Carbon emission reductions are calculated based on the reduction in carbon emissions from replacing thermal power units. The module uses a multi-objective optimization algorithm to solve the model, generating a joint scheduling strategy for pumped storage and new energy storage, including key parameters such as start-up and shutdown times, power generation, and pumping power of pumped storage units, and charge and discharge power and target state of charge values ​​for new energy storage.

[0029] The revenue calculation and intelligent allocation module is used to perform refined multi-dimensional revenue calculations based on actual transaction data after the execution of the joint dispatch strategy, and to allocate revenue in compliance with electricity price policy requirements. This module breaks down and statistically analyzes comprehensive revenue by market type, energy storage type, and dispatch time. It also calculates incremental collaborative revenue by comparing the difference in total revenue between two parallel simulations of collaborative and independent operation modes. Simultaneously, this module performs linked calculations of carbon asset accounting and renewable energy curtailment loss costs, and allocates various revenues between pumped storage power station operators and new energy storage power station operators based on real-time policy parameters, generating compliant revenue allocation ledgers and vouchers.

[0030] The strategy execution and closed-loop optimization module distributes the joint scheduling strategy generated by the collaborative scheduling optimization module to the energy storage execution agencies (including pumped storage unit control systems and new energy storage converter control systems), and collects actual operating data and revenue data to quantify and calculate the execution deviation and revenue deviation of the scheduling strategy. This module calculates the tracking deviation rate between actual power and commanded power through the execution deviation quantification unit, and the revenue deviation rate between actual revenue and expected revenue through the revenue deviation quantification unit. When the tracking deviation rate or revenue deviation rate exceeds a preset threshold, or when changes in the external environment such as adjustments to electricity market rules, updates to electricity pricing policies, or drastic changes in the grid's operating status are detected, the model iteration optimization unit automatically initiates online iterative updates of the multi-objective collaborative optimization model, adjusting the objective function weights, core calculation parameters, and constraint thresholds. After simulation verification, the new parameters are solidified into the model, achieving adaptive optimization of the system.

[0031] Through the communication connection and collaborative operation of the above five modules, this embodiment realizes the optimization of the entire process of pumped storage and new energy storage participating in the market, covering key links such as data collection, market analysis, optimized scheduling, revenue calculation, and closed-loop optimization, forming a complete closed-loop control system.

[0032] In some embodiments, the system further includes a pumped storage power station allocation module, which comprises: a pumped storage power station digitization unit, used to convert the available capacity and corresponding capacity charge parameters of the pumped storage power station into standard data objects and broadcast them on the power trading network platform; a subscription instruction response unit, used to receive capacity subscription request data from nodes in different regions, update the occupancy status of the pumped storage power station according to the request timestamp and priority, and generate a pumped storage power station usage right allocation signal; the subscribed pumped storage power station only provides services to its subscriber; and a residual cost dynamic allocation unit, used to collect data on the remaining unsubscribed pumped storage power stations, which serve various regions, calculate the allocation coefficient of the remaining pumped storage power stations based on the load distribution weight data of nodes in each region, and input it as a cost item into the collaborative scheduling optimization module.

[0033] Specifically, the pumped storage power station digitization unit is used to convert the available capacity and corresponding capacity charge parameters of a pumped storage power station into standard data objects, which are then broadcast on the power trading network platform. Taking a pumped storage power station as an example, the station has a total installed capacity of 1200MW and an annual capacity charge of 200 million yuan. The pumped storage power station digitization unit digitally encapsulates the station's capacity resources, generating standard data objects containing fields such as station identification, available capacity, total capacity charge, billing cycle, and listing time. These objects are then broadcast to provincial power grid companies within the region through the power trading network platform for their purchase.

[0034] The subscription instruction response unit receives capacity subscription request data from nodes in different regions, updates the occupancy status of pumped storage power stations based on request timestamps and priorities, and generates a pumped storage power station usage right allocation signal. The subscription rules follow the "whole station subscription" principle, meaning that when a provincial power grid company subscribes to a pumped storage power station, it directly subscribes to the entire capacity of that power station. The subscription instruction response unit processes subscription requests according to the priority order of request timestamps, with the node that submits the request earliest obtaining the right to use the entire capacity of the power station. The subscribed pumped storage power station only provides services to its subscriber, who bears the entire capacity electricity cost. For example, if the Liaoning Provincial Power Grid Company submits the earliest subscription request for a pumped storage power station, then the entire 1200MW capacity of that power station belongs to Liaoning Province, and its annual capacity electricity cost of 200 million yuan is borne by Liaoning Province.

[0035] The residual cost dynamic allocation unit is used to collect data on unsubscribed pumped storage power stations. For residual pumped storage power stations not subscribed by any provincial power grid company, this unit determines that they serve all regions together and no longer specifically serve any particular subscriber. Based on the load distribution weight data of each regional node (such as the previous year's electricity consumption percentage of each region), the unit calculates the allocation coefficient for the residual pumped storage power stations and allocates the residual capacity electricity cost among the regions. For example, if the annual capacity electricity cost of a residual pumped storage power station is 0.9 billion yuan, and the electricity consumption percentages of each region are 40% in Liaoning, 30% in Jilin, 20% in Heilongjiang, and 10% in Inner Mongolia, then the allocated amounts for each region are 0.36 billion yuan, 0.27 billion yuan, 0.18 billion yuan, and 0.09 billion yuan, respectively. The unit inputs the allocated cost items into the collaborative scheduling optimization module as a component of the operating cost accounting.

[0036] Through the collaborative work of the above three units, the pumped storage power station allocation module realizes a capacity resource allocation mechanism of "whole station subscription, exclusive service for the subscribed power station, and shared service for the remaining power stations", providing complete cost boundary conditions for the collaborative scheduling optimization module.

[0037] In some embodiments, the market information and policy analysis module includes a market rule analysis unit and a policy parameter dynamic adaptation unit, used to analyze the electricity market trading rules and electricity price policy requirements, and to construct dual participation constraints of market constraints and policy constraints.

[0038] The market rule analysis unit is used to analyze the trading rules, price signals, and clearing mechanisms of the electricity market, capacity market, ancillary services market, and carbon market. In the electricity market, this unit analyzes the time-of-use pricing mechanism, generating a 24-hour time-of-use price forecast sequence, enabling the collaborative dispatch optimization module to formulate arbitrage strategies for off-peak charging and peak-hour discharging. In the capacity market, this unit analyzes the definition of capacity products, compensation standards, and entry thresholds, transforming them into capacity application minimum constraints and revenue calculation parameters. In the ancillary services market, this unit analyzes the pricing mechanisms and clearing rules for frequency regulation and reserve services, transforming them into frequency regulation capacity constraints and revenue calculation models. In the carbon market, this unit analyzes the carbon emission quota allocation mechanism and carbon trading prices, transforming them into carbon emission reduction revenue calculation parameters.

[0039] The policy parameter dynamic adaptation unit is used to internally and update policy parameters such as the revenue sharing ratio for pumped storage, the peak capacity conversion ratio for new energy storage, and the capacity pricing reduction rules in real time. This unit synchronously updates the revenue sharing ratio for pumped storage from the policy database; for example, a province stipulates that 30% of the electricity cost for pumped storage capacity is borne by the provincial power grid, and 70% is shared by the beneficiaries. This unit also updates the peak capacity conversion ratio for new energy storage; for example, the rated power of new energy storage is converted into peak capacity at 80%. This unit also updates the capacity pricing reduction rules; for example, when the annual availability factor of pumped storage is below 95%, the capacity price is reduced proportionally. The unit maintains a connection with the policy database through an interface and automatically updates parameters when policies are adjusted.

[0040] The dual constraints are constructed as follows: Market constraints consist of time-series market signals obtained from the market rule parsing unit, including upper and lower limits of electricity prices for each time period, capacity market entry thresholds, and ancillary service pricing caps. Policy constraints are formed by real-time policy parameters provided by the policy parameter dynamic adaptation unit, including policy boundary parameters such as the pumped storage revenue sharing ratio, the peak capacity conversion ratio of new energy storage, and the capacity price reduction coefficient. Both types of constraints are input into the collaborative scheduling optimization module, serving as the economic and institutional boundaries of the multi-objective collaborative optimization model, ensuring that the generated joint scheduling strategy conforms to both market rules and policy requirements.

[0041] In some embodiments, the revenue calculation and intelligent allocation module includes a multi-dimensional revenue splitting unit, a carbon asset and curtailment accounting unit, and a policy-based revenue allocation unit, which are used to perform refined multi-dimensional revenue calculation based on actual transaction data after the joint dispatch strategy is executed, and to allocate revenue in compliance with electricity price policy requirements.

[0042] The multi-dimensional revenue breakdown unit is used to break down and statistically analyze comprehensive revenue by market type, energy storage type, and dispatch time. By market type, it breaks down revenue into electricity market revenue, capacity market revenue, ancillary service market revenue, and carbon trading revenue. By energy storage type, it calculates the independent revenue generated by pumped hydro storage and new energy storage in each market. By dispatch time, it refines the revenue contribution for each time period with 15-minute intervals. This unit also constructs two parallel simulations—one for coordinated operation and one for independent operation—and compares the total revenue difference between the two simulations under the same market conditions within the same time period to calculate the incremental revenue from coordinated operation. For example, if the total revenue from coordinated operation is 1.5 million yuan and the total revenue from independent operation is 1.2 million yuan, then the incremental revenue from coordinated operation is 300,000 yuan, reflecting the added value brought by coordinated dispatch.

[0043] The carbon asset and curtailment accounting unit is used to link carbon asset accounting with the calculation of the cost of curtailment losses from renewable energy. Regarding carbon asset accounting, this unit calculates carbon emission reductions based on energy storage discharge and the carbon emission intensity of thermal power units, and converts this into carbon asset revenue by incorporating carbon trading prices. Regarding the calculation of curtailment loss costs, this unit calculates the amount of renewable energy curtailed electricity absorbed by energy storage charging, multiplying it by the renewable energy grid connection price to calculate the reduction in curtailment loss costs. The two are calculated in conjunction; for example, when energy storage charging absorbs renewable energy curtailment, it reduces curtailment losses and also replaces thermal power to achieve carbon emission reductions, both of which are included in the overall revenue calculation.

[0044] The policy-based revenue distribution unit is used to legally allocate various revenues between pumped storage power station operators and new energy storage power station operators based on real-time policy parameters. This unit obtains policy parameters such as the pumped storage revenue sharing ratio, the peak capacity conversion ratio for new energy storage, and the rules for distributing incremental revenue through collaboration from the market information and policy analysis module. It then distributes revenues from the electricity market, capacity market, ancillary services market, carbon trading, and incremental collaboration according to policy regulations. Simultaneously, this unit uses the policy-restricted revenue retention portion to offset system operating costs, calculates electricity bill reductions for power users, and ultimately generates compliant revenue distribution ledgers and vouchers, ensuring the entire revenue distribution process is traceable and auditable.

[0045] In some embodiments, the strategy execution and closed-loop optimization module includes an execution deviation quantification unit, a revenue deviation quantification unit, and a model iterative optimization unit, which are used to distribute the joint scheduling strategy to the energy storage execution agency, collect actual operation data and revenue data, quantify and calculate the execution deviation and revenue deviation of the scheduling strategy, and trigger online iterative update and parameter tuning of the multi-objective collaborative optimization model when the deviation value exceeds a preset threshold or when a change in the external environment is detected.

[0046] The execution deviation quantification unit is used to collect actual operating condition data of the equipment and calculate the tracking deviation rate between the actual power and the commanded power. This unit acquires actual power data in real time from the pumped storage unit control system and the new energy storage converter control system, compares it with the commanded power issued by the collaborative scheduling optimization module, and calculates the tracking deviation rate using the formula: Tracking Deviation Rate = |Actual Power - Commanded Power| / Commanded Power × 100%. For example, if the command requires a pumped storage discharge power of 300MW during a certain period, and the actual discharge power is 285MW, then the tracking deviation rate is 5%. This deviation rate reflects the execution accuracy of the scheduling command and is an important indicator for measuring the executability of the scheduling strategy.

[0047] The revenue deviation quantification unit is used to collect actual market transaction revenue data and calculate the revenue deviation rate between actual and expected revenue. This unit obtains actual settlement revenue data from the power trading center and compares it with the expected revenue predicted by the revenue calculation and intelligent allocation module before dispatching. The formula is: Revenue Deviation Rate = |Actual Revenue - Expected Revenue| / Expected Revenue × 100%. For example, if the expected revenue for a certain period is 500,000 yuan and the actual settlement revenue is 470,000 yuan, then the revenue deviation rate is 6%. This deviation rate reflects the accuracy of revenue prediction and is an important indicator for measuring the economic effectiveness of dispatching strategies.

[0048] The model iterative optimization unit is used to automatically initiate online iterative updates of the multi-objective collaborative optimization model when trigger conditions are met. Trigger conditions include two categories: first, when the tracking deviation rate or revenue deviation rate exceeds a preset threshold and the duration exceeds a preset time limit, for example, the tracking deviation rate threshold is set to 3%, the revenue deviation rate threshold is set to 5%, and the duration threshold is set to 30 minutes; second, when a sudden change in the external environment is detected, including adjustments to electricity market rules (such as changes in time-of-use pricing periods), updates to electricity pricing policies (such as adjustments to capacity compensation standards), and drastic changes in the grid's operating status (such as line tripping or unplanned unit outages).

[0049] When the triggering conditions are met, the model iterative optimization unit automatically starts the online iterative update process. The updates include: adjusting the weights of the objective function, for example, increasing the weight of the carbon emission reduction target if the carbon market price rises sharply; correcting core calculation parameters, such as updating the energy storage efficiency decay coefficient and cycle life decay coefficient based on actual operating data; and adjusting constraint thresholds, such as adjusting the maximum charge and discharge power limit and the upper and lower limits of the state of charge based on the aging of the equipment.

[0050] The updated model is validated in a simulation environment. After confirming the improved optimization effect through backtesting with historical data or simulation runs, the new parameters are solidified into the model, achieving adaptive optimization and closed-loop control of the system. Through the collaborative work of the above units, this embodiment ensures the execution accuracy and effectiveness of the scheduling strategy, enabling the system to adapt to changes in internal and external conditions and continuously maintain optimal operating status.

[0051] In some embodiments, the above-described revenue optimization system further includes a cold-region adaptation and control module to address the performance degradation of novel energy storage in cold environments, utilizing the waste heat generated by the pumped-storage unit to provide insulation for the novel energy storage. This module includes an environmental sensing unit, a waste heat recovery module installed in the pumped-storage unit, and a constant-temperature protection unit installed in the novel energy storage unit.

[0052] The environmental sensing unit is used to collect low-temperature environmental data in real time. Multiple temperature sensors are deployed within the pumped-storage power station and the new energy storage power station to monitor parameters such as ambient temperature, humidity, and wind speed, with a sampling frequency of once per minute. When the ambient temperature is detected to be below a preset threshold (e.g., 0℃), the cold-region adaptation control process is triggered, activating waste heat recovery and constant-temperature protection functions. Simultaneously, the unit uploads the ambient temperature data in real time to the collaborative scheduling and optimization module as input parameters for cold-region environmental constraints.

[0053] The waste heat recovery module is installed in the pumped storage unit to collect waste heat parameters generated during the operation of the pumped storage unit. During power generation, the turbine generator and bearing system generate a large amount of heat, with cooling water temperatures typically reaching 35-45°C. During pumping, the mechanical friction between the pumps and turbines also generates heat, with lubricating oil temperatures reaching 40-50°C. The waste heat recovery module collects this waste heat through plate heat exchangers or shell-and-tube heat exchangers, transferring the heat to the circulating working fluid (such as water or antifreeze). The waste heat parameters collected by this module include waste heat temperature (°C), available heat capacity (kWh), and supply stability (continuous heating duration), and are uploaded in real time to the collaborative scheduling and optimization module for subsequent correlation calculations.

[0054] A constant-temperature protection unit is installed in the novel energy storage unit to maintain the operating temperature of the device. Novel energy storage devices (such as lithium-ion batteries) experience severe performance degradation at low temperatures; the ideal operating temperature range is 15-25°C. The constant-temperature protection unit includes an electric heating device, an insulation layer, a temperature control system, and a heat exchange interface. The heat exchange interface is connected to the waste heat recovery module of the pumped hydro storage unit, allowing it to receive waste heat for heating; the electric heating device serves as a backup heat source; and the temperature control system adjusts the heat supply in real time based on the battery temperature to maintain the battery within its optimal operating temperature range.

[0055] The workflow of the cold region adaptation and control module is as follows: When the environmental sensing unit detects that the ambient temperature is below 0℃, the cold-region adaptation and control module is activated. First, this module acquires the available waste heat and temperature from the waste heat recovery module. If the pumped-storage unit is operating and the waste heat temperature is above 35℃, the waste heat is prioritized for heating the new energy storage unit. The waste heat heats the circulating working fluid through a heat exchanger and is then transported via pipeline to the constant-temperature protection unit of the new energy storage unit, where it heats the battery system through the heat exchange interface. At this time, the electric heating device of the constant-temperature protection unit is in standby mode, serving only as a backup heat source.

[0056] If the pumped-storage unit is not operating, or the waste heat temperature is insufficient and the available waste heat cannot meet the heating demand, the constant-temperature protection unit will activate the electric heating device to provide heating. Simultaneously, the lower-level cost accounting model of the collaborative scheduling optimization module will link the energy consumption cost of the constant-temperature protection unit with the waste heat supply from the pumped-storage unit: when the pumped-storage unit is operating and providing waste heat, the system automatically deducts a portion of the electricity cost of the constant-temperature protection unit, with the deduction amount calculated based on the waste heat and electricity price; when the waste heat is insufficient, the remaining portion is borne by the new energy storage operator.

[0057] Through the above-mentioned control mechanism, the cold region adaptation control module achieves three technical effects: First, it effectively utilizes the waste heat resources of pumped storage units to reduce the energy consumption cost of constant temperature protection for new energy storage; second, it ensures that the new energy storage maintains a suitable operating temperature under cold region conditions, avoiding performance degradation and shortened lifespan caused by low temperatures; and third, through the correlation accounting mechanism, it quantifies the value of waste heat into cost deduction, providing more accurate cost parameters for the collaborative scheduling optimization module.

[0058] In some embodiments, the collaborative scheduling optimization module adopts a two-layer model architecture, including an upper-layer scheduling strategy model and a lower-layer cost accounting model, and performs collaborative solution through a genetic algorithm to output the optimal joint scheduling strategy.

[0059] The upper-level scheduling strategy model is used to determine the joint scheduling strategy of pumped storage and new energy storage, with the core of coordinated peak shaving of pumped storage and new energy storage, combined with grid load demand, new energy output forecast, cold region environmental constraints, and unit operation limitations, with the goal of optimizing the overall system operating efficiency and the best peak shaving effect.

[0060] Decision variables: The upper-level scheduling strategy model constructs a scheduling decision sequence for the next 24 hours with a 15-minute time resolution, comprising 96 time periods. Decision variables include: Pumped storage unit start-up and shutdown state variables: 1 indicates power generation mode, and 0 indicates shutdown or pumping mode (pumping mode is set as a separate variable). Pumped storage pumping state variables: 1 indicates pumping operation, and 0 indicates shutdown or power generation operation. Pumped storage power generation capacity: Continuous variables Pumped storage pumping power: Continuous variables New energy storage charging and discharging power: Continuous variables New energy storage state of charge: Continuous variables Constraints: Grid load balance constraints: The net output (discharge power minus charging power) of pumped storage and new energy storage systems must meet the system's peak-shaving requirements, i.e. ,in The system peak-shaving demand is for time period t.

[0061] Constraints on Renewable Energy Output: Energy storage systems need to smooth out high-frequency fluctuations in renewable energy output, i.e. ;in, The rate of change in the output of new energy sources.

[0062] Cold region environmental constraints: When the ambient temperature is below 0℃, the usable capacity of new energy storage is reduced by a temperature reduction factor. To reduce, that is ,and The corresponding reduction.

[0063] Pumped storage unit operation restrictions include: minimum start-up time (≥2 hours), minimum downtime (≥1 hour), maximum number of start-ups and shutdowns (≤4 times / day), and power ramp-up rate limit (±50MW / 15min).

[0064] New energy storage operation limitations: including upper and lower limits of state of charge Maximum charge / discharge power limit, mutual exclusion constraint of charge / discharge state .

[0065] Objective function: The objective function of the upper-level scheduling strategy model is to optimize the overall system operating efficiency and achieve the best peak-shaving effect.

[0066] Operating efficiency is measured by the overall efficiency of the energy storage system: The goal is to maximize .

[0067] Peak shaving effect is measured by the peak-to-valley reduction rate: ,in The original load is set to be maximized. .

[0068] The lower-level cost accounting model is used to calculate the actual operating costs of pumped storage units and new energy storage units under the current scheduling mode. This model correlates the operating energy consumption cost of the constant temperature protection unit with the corresponding operating status of the pumped storage unit, including dynamically deducting the energy consumption cost of the constant temperature protection unit based on the available waste heat from the waste heat recovery module of the pumped storage unit.

[0069] Pumped storage operation costs: Electricity cost for pumping water: in Let t be the electricity price for time period t.

[0070] Start-up and shutdown costs: ,in For the number of times it can be started, Cost per startup (including mechanical wear and tear and additional energy consumption).

[0071] Head loss cost: ,in This is the head loss coefficient. The actual water head, The reference head is used.

[0072] Operating costs of new energy storage: Charge and discharge loss costs:

[0073] ,in This refers to the charge / discharge efficiency.

[0074] Cycle life decay cost: ,in The initial investment cost of the new energy storage is denoted as , and the cycle life is denoted as at a specific depth of discharge.

[0075] Cost of constant temperature protection unit: ,in This refers to the electric heating power of the constant temperature protection unit.

[0076] Correlational Accounting Mechanism: The lower-level cost accounting model correlates the operating energy consumption cost of the constant temperature protection unit with the operating status of the pumped storage unit. When the pumped storage unit is running, the waste heat recovery module collects the waste heat generated by the unit, and the usable waste heat... Calculate using the following formula: ,in The waste heat recovery coefficient (usually taken as 0.1-0.3) represents the usable waste heat generated per unit of electrical power (kWh / kW).

[0077] Theoretical heating requirements of the constant temperature protection unit According to ambient temperature and battery temperature target calculate: ,in denoted as the heat transfer coefficient of the insulation layer, and AA as the heat dissipation area.

[0078] Related deduction rules: If Then the constant temperature protection unit does not require electric heating. , ;like Then electric heating will supplement the remaining portion. , Calculated based on actual electric heating output. Meanwhile, the heat revenue earned by the pumped storage unit from providing waste heat is deducted: ,in For reference to heating prices, this deduction is deducted from the operating costs of pumped storage hydroelectric power plants.

[0079] Genetic Algorithm Collaborative Solution Mechanism The upper-level scheduling strategy model and the lower-level cost accounting model are solved collaboratively through a genetic algorithm to achieve joint optimization of scheduling strategy and cost accounting.

[0080] Encoding scheme: A hybrid approach of real number encoding and binary encoding is used. Binary encoding is used for the start-up and shutdown status of pumped storage units. and pumping status Real number encoding is used for continuous variables. Each individual contains all decision variables for 96 time periods, and the chromosome length is 96 × (2 + 4) = 576 genes.

[0081] Population initialization: The population is initialized using a combination of random generation and heuristic rules, with a population size of 200. Heuristic rules include: prioritizing charging during off-peak hours, prioritizing discharging during peak hours, and reserving new energy storage capacity during periods of frequency regulation demand.

[0082] Fitness function: The fitness function combines the upper-level objective (optimal operating efficiency and best peak-shaving effect) and the lower-level objective (minimum operating cost), and adopts a weighted summation method: in, Weighting coefficients It can be dynamically adjusted according to operational objectives. For example, the weight of operational efficiency can be increased when carbon market prices rise, and the weight of peak-shaving effectiveness can be increased when peak-shaving demand is urgent.

[0083] Selection operation: A tournament selection strategy is adopted, in which 5 individuals are randomly selected each time, and the individual with the highest fitness is selected to enter the next generation.

[0084] Crossover operation: Simulated binary crossover (SBX) is used, with a crossover probability of 0.9 and a distribution exponent of 20.

[0085] Mutation operation: Use multinomial mutation, set the mutation probability to 0.1, and the distribution exponent to 20.

[0086] Iteration termination condition: Set the maximum number of iterations to 500 generations, or terminate the iteration when the fitness value changes by less than 0.1% for 50 consecutive generations.

[0087] Algorithm flow: Initialize the population (200 individuals). For each individual, the scheduling strategy is decoded and input into the upper-level scheduling strategy model to calculate the running efficiency and peak-shaving effect. The scheduling strategy is passed to the lower-level cost accounting model to calculate the operating costs of pumped storage and new energy storage, and waste heat deduction-related accounting is performed. Calculate fitness values ​​based on upper-level and lower-level objectives. Selection, crossover, and mutation generate a new generation of population. Repeat the above steps until the termination condition is met. The individual with the highest fitness is selected as the optimal joint scheduling strategy.

[0088] This embodiment achieves joint optimization of scheduling strategy and cost accounting through a two-layer model architecture. The upper-layer scheduling strategy model aims to optimize the overall system operating efficiency and peak-shaving effect, generating a preliminary joint scheduling strategy. The lower-layer cost accounting model accurately calculates various operating costs, especially by quantifying the value of pumped storage waste heat into deductions for new energy storage costs through a correlation accounting mechanism, making cost accounting more realistic and accurate. A genetic algorithm iteratively optimizes between the two models to find the Pareto optimal solution set. The final output joint scheduling strategy, while meeting the system's peak-shaving requirements, has both high operating efficiency and low overall operating cost, realizing the economic efficiency and high efficiency of the coordinated operation of pumped storage and new energy storage.

[0089] Furthermore, the joint dispatch strategy of pumped storage and new energy storage adopts a hierarchical collaborative response mechanism to provide differentiated responses to the grid regulation needs at different time scales, giving full play to the complementary technological advantages of pumped storage and new energy storage.

[0090] I. Short-time frequency modulation response strategy at the millisecond / second level When the grid frequency deviation exceeds a preset threshold (e.g., ±0.05Hz), the regulation demand is identified as a short-term frequency adjustment at the millisecond or second level. At this time, the collaborative scheduling optimization module generates a collaborative response strategy in which the power-type energy storage modules in the new energy storage unit respond independently.

[0091] Power-type energy storage modules, comprising flywheel energy storage and supercapacitors, are characterized by fast response speed (milliseconds), high power density, and long cycle life. These modules respond within milliseconds, rapidly absorbing or releasing active power to smooth frequency fluctuations. For example, when the grid frequency suddenly drops, the power-type energy storage module completes its discharge response within 50 milliseconds, releasing active power to support frequency recovery; when the frequency suddenly rises, it completes its charging response within 50 milliseconds, absorbing excess power.

[0092] Under this strategy, the pumped storage unit remains in standby monitoring mode and does not participate in instantaneous response. Due to mechanical inertia, pumped storage units typically require 1-3 minutes to reach full power output from standby, which cannot meet the millisecond / second frequency regulation time requirement. Therefore, the pumped storage unit only synchronously monitors the frequency regulation effect and does not perform charging and discharging actions, avoiding the impact of mechanical response delay on frequency regulation accuracy. Through this division of labor, high-frequency frequency regulation demands are met by power-type energy storage for rapid response, ensuring frequency regulation effectiveness while avoiding mechanical losses caused by frequent start-stop operations of the pumped storage unit.

[0093] II. Minute-level Mid-Time Peak Shaving Response Strategy When the grid load or the output of new energy sources fluctuates at the minute level, the regulation demand is identified as minute-level mid-time peak shaving. At this time, the collaborative scheduling optimization module generates a collaborative response strategy with the energy storage module in the new energy storage unit as the main responder and the pumped storage unit as the auxiliary responder.

[0094] Energy storage modules (such as lithium-ion batteries) are characterized by fast response times (on the order of seconds) and high energy density. In the initial stages of strategy execution, these modules rapidly output or absorb energy within seconds to meet initial adjustment needs. For example, when the output of new energy sources suddenly decreases, the energy storage module begins discharging within 1-2 seconds to fill the power gap.

[0095] Pumped-storage units synchronously adjust their operating conditions, gradually transitioning from standby to power generation or pumping, and progressively taking over the regulation load. Since it takes 1-3 minutes for a pumped-storage unit to reach full power output from startup, within minutes of the energy storage module responding, the pumped-storage unit gradually increases its output, taking over the regulation task from the new energy storage. This coordinated approach of "new energy storage responding first, pumped-storage taking over later" avoids deep charging and discharging (i.e., prolonged high-power charging and discharging) of the new energy storage, effectively extending its cycle life. For example, if minute-level peak-shaving demand lasts for 30 minutes, the energy storage only undertakes the main regulation task for the first 5 minutes, with the pumped-storage taking over for the subsequent 25 minutes, and the new energy storage only providing auxiliary support.

[0096] III. Hourly-level long-term peak-shaving response strategy When the power grid experiences a sustained peak-valley difference regulation demand lasting for several hours, the regulation demand is identified as hourly long-term peak regulation. At this time, the collaborative scheduling optimization module generates a collaborative response strategy with pumped storage units as the main responders and new energy storage units as auxiliary responders.

[0097] Pumped-storage hydroelectric units serve as the main body for large-capacity, long-duration regulation, leveraging their advantages of large capacity, low cost, and long continuous operating time to complete hourly charge-discharge cycles. For example, during off-peak hours at night, pumped-storage units continuously pump water at rated power for 6-8 hours, converting electrical energy into the potential energy of water for storage; during peak hours during the day, they continuously generate electricity for 6-8 hours, releasing the stored energy. The unit energy storage cost of pumped-storage (approximately 0.2-0.3 yuan / kWh) is far lower than that of new energy storage (approximately 0.5-0.8 yuan / kWh), making it suitable for long-term peak-shaving tasks.

[0098] In this strategy, novel energy storage units play a supporting role, compensating for the regulation delay of pumped-storage hydropower and smoothing out high-frequency fluctuations in renewable energy output. There is a 1-3 minute delay between the start-up command and full-power output of pumped-storage units. During this delay, novel energy storage responds quickly to fill the power gap. Simultaneously, renewable energy output exhibits high-frequency fluctuations on a minute-level timescale, which pumped-storage units struggle to accurately track. Novel energy storage utilizes its rapid response capabilities to smooth these high-frequency fluctuations, resulting in a smoother combined output. Through this synergistic approach of "pumped-storage as the primary regulator and novel energy storage as the secondary regulator," peak-shaving costs are minimized while renewable energy consumption is maximized.

[0099] IV. Coordinated Switching Mechanism of the Three Strategies The collaborative scheduling optimization module automatically switches between the three strategies mentioned above based on the real-time identification results of grid regulation demand. Regulation demand identification is based on a comprehensive judgment of indicators such as grid frequency deviation, load change rate, and renewable energy output fluctuation rate. When the absolute value of the frequency deviation exceeds 0.05Hz, it is determined to be a short-time frequency modulation requirement, and the response strategy is switched to a millisecond / second-level short-time frequency modulation response strategy.

[0100] When the frequency deviation is less than 0.05Hz but the load change rate exceeds 5% / min, it is determined to be a medium-time peak shaving demand, and the system switches to a minute-level medium-time peak shaving response strategy.

[0101] When the load change rate is less than 5% / min but the peak-to-valley difference exceeds 20% of the system capacity, it is determined to be a long-term peak-shaving demand, and the system switches to an hourly long-term peak-shaving response strategy.

[0102] Through the aforementioned hierarchical and coordinated response mechanism, this embodiment achieves functional complementarity between pumped hydro storage and new energy storage at different time scales: power-type energy storage is responsible for high-frequency regulation, energy-type energy storage is responsible for medium-term peak shaving, and pumped hydro storage is responsible for long-term peak shaving. The three strategies work together to ensure timely response to grid regulation needs while optimizing the lifespan and operational economy of various types of energy storage.

[0103] The objective function of the multi-objective collaborative optimization model includes three optimization objectives: maximizing comprehensive benefits, minimizing operating costs, and maximizing carbon emission reduction. The model is solved by a multi-objective optimization algorithm to achieve the comprehensive optimality of the collaborative operation of pumped storage and new energy storage.

[0104] I. Objective Function for Maximizing Overall Returns The overall benefit maximization objective includes four components: electricity market benefits, capacity market benefits, ancillary services market benefits, and carbon trading benefits.

[0105] Electricity market revenue refers to the peak-valley price difference revenue obtained by energy storage systems through off-peak charging and peak-peak discharging. The calculation formula is as follows:

[0106] in The total discharge power during time period t. The total charging power during time period t. Electricity price during peak hours. The figure represents the electricity price during off-peak hours, and Δt represents the time interval. This return reflects the energy storage system's ability to arbitrage using electricity price fluctuations.

[0107] Capacity market revenue refers to the capacity compensation revenue obtained by an energy storage system from participating in capacity market bidding. Its calculation formula is as follows: ;in For the capacity won in the m-th capacity market, This refers to the capacity compensation price in the corresponding market. Pumped storage, due to its large capacity and long continuous discharge time, is suitable for participating in the annual or monthly capacity market; new energy storage, due to its fast response and flexible adjustment, is suitable for participating in the short-term capacity market.

[0108] Ancillary services market revenue refers to the revenue obtained by energy storage systems from participating in ancillary services such as frequency regulation and backup. The calculation formula is as follows: ,in The revenue from frequency modulation services includes two parts: capacity compensation and mileage compensation. For frequency modulation capacity, For frequency modulation capacity price, For frequency modulation mileage, Price based on FM mileage; Revenue from standby services is calculated based on standby capacity and standby price.

[0109] Carbon trading revenue refers to the revenue earned in the carbon market from the reduction in carbon emissions caused by energy storage systems replacing thermal power units for electricity generation. The calculation formula is as follows: ,in For carbon emission reductions, The carbon trading price. Carbon emission reductions are calculated based on the amount of thermal power replaced by energy storage discharge: , This refers to the average carbon emission intensity of thermal power units (typically 0.8-1.0 tons of CO2 / MWh).

[0110] The objective function for maximizing overall returns is: .

[0111] The goal of minimizing operating costs includes two components: the operating costs of pumped storage and the operating costs of new energy storage technologies.

[0112] The operating costs of pumped storage hydroelectric power include pumping electricity costs, start-up and shutdown costs, and head loss costs. Pumping electricity costs refer to the expenses incurred for consuming electricity during pumping operations, calculated using the following formula: Start-up and shutdown costs refer to the mechanical losses and additional energy consumption incurred during the start-up and shutdown of the unit. The calculation formula is as follows: , For the number of times it can be started, This refers to the cost of a single startup (typically 50,000-100,000 RMB per startup). Head loss cost refers to the cost of efficiency loss due to changes in reservoir head, calculated using the following formula: Where β is the head loss coefficient. The actual water head, The reference head is used.

[0113] The operating costs of new energy storage systems include charge / discharge loss costs, cycle life degradation costs, and the cost of the temperature control unit. Charge / discharge loss costs refer to the costs incurred due to energy conversion efficiency losses during the charge / discharge process, and are calculated using the following formula: in , This refers to charge / discharge efficiency. Cycle life degradation cost refers to the depreciation cost caused by the reduction in battery life due to charge / discharge cycles, calculated using the following formula: ,in The initial investment cost for new energy storage This refers to the cycle life at a specific depth of discharge. The cost of the constant temperature protection unit refers to the cost of electrical heating consumed to maintain the battery's operating temperature under cold-climate conditions; the calculation formula is: in, The cost of the electric heating power of the constant temperature protection unit is linked to the cold region adaptation and control module, and can be deducted when the waste heat from pumped water storage is available.

[0114] The objective function for minimizing operating costs is: .

[0115] The target for maximizing carbon emission reductions is calculated by substituting carbon emissions from thermal power plants for electricity generation. This target reflects the environmental contribution of energy storage systems and is an important indicator for assessing the green value of energy storage systems.

[0116] The formula for calculating carbon emission reduction is:

[0117] ,in, Carbon emission intensity of thermal power units. This represents the average carbon emission intensity of the power grid. Since the electricity used for energy storage charging may come from clean energy or thermal power, the net carbon emission reduction is the emission reduction from replacing thermal power with discharge minus the carbon emissions corresponding to the electricity used for charging.

[0118] In most cases, energy storage charging occurs primarily during off-peak hours, when thermal power typically accounts for a higher proportion of electricity generation. However, overall, the operation of energy storage systems still achieves net carbon emission reductions, especially when energy storage absorbs surplus electricity from renewable energy sources, where the carbon reduction effect is even more significant. The objective function for maximizing carbon emission reductions is: .

[0119] Combining the three objective functions mentioned above, the multi-objective collaborative optimization model can be expressed as:

[0120] The model employs multi-objective optimization algorithms (such as the non-dominated sorting genetic algorithm NSGA-II or the multi-objective particle swarm optimization algorithm MOPSO) to obtain a Pareto optimal solution set. The collaborative scheduling optimization module can select the optimal solution based on the operator's preferences. For example, it can choose the solution with higher carbon emission reduction when carbon market prices rise, and choose the solution with higher operating efficiency when peak-shaving demand is urgent, thereby achieving a flexible trade-off between different objectives.

[0121] Through the synergistic optimization of the three objective functions mentioned above, this embodiment achieves a balance between the economy, efficiency, and environmental friendliness of the coordinated operation of pumped storage and new energy storage, providing scientific decision support for energy storage systems to participate in the electricity market.

[0122] In summary, this application provides a revenue optimization system for the coordinated participation of pumped storage and new energy storage in the market. This system includes a data acquisition and preprocessing module with communication connections, a market information and policy analysis module, a coordinated scheduling optimization module, a revenue calculation and intelligent allocation module, and a strategy execution and closed-loop optimization module. The data acquisition and preprocessing module collects multi-source heterogeneous data and generates a standardized operational dataset. The market information and policy analysis module analyzes the trading rules and policy parameters of the electricity market, capacity market, ancillary services market, and carbon market, constructing dual constraints of market and policy. The coordinated scheduling optimization module, based on the standardized data and dual constraints, constructs and solves a multi-objective coordinated optimization model aiming to maximize comprehensive revenue, minimize operating costs, and maximize carbon emission reduction. A two-layer model architecture is used to generate a joint scheduling strategy for pumped storage and new energy storage through a genetic algorithm. This strategy employs a hierarchical coordinated response mechanism: during short-term frequency regulation at the millisecond / second level, the power-type energy storage module in the new energy storage responds independently; during medium-term peak regulation at the minute level, the energy-type energy storage module responds... The system employs a primary and secondary response mechanism of pumped storage, prioritizing pumped storage during long-term hourly peak shaving and supplementing it with new energy storage. The revenue calculation and intelligent allocation module uses parallel simulation to calculate incremental revenue and performs linked calculations of carbon assets and curtailment losses, allocating revenue in compliance with real-time policy parameters. The strategy execution and closed-loop optimization module quantifies and tracks deviations in revenue and triggers online iterative updates when deviations exceed thresholds or external environmental changes occur. Furthermore, the system includes a pumped storage power station allocation module, adopting a "whole-station subscription" model, where the subscribed power station is dedicated to... The system serves the subscribers, while the remaining unsubscribed power stations serve all regions and share costs according to load distribution weights. It also includes a cold-region adaptation and control module that utilizes the waste heat from pumped storage units to provide insulation for new energy storage. The system also achieves dynamic offsetting of waste heat and constant temperature protection unit energy consumption costs through a lower-level cost accounting model. Through the collaborative work of the above modules, this system achieves full-process revenue optimization for pumped storage and new energy storage to participate in the market, reasonable cross-regional capacity cost sharing, improved cold-region operation adaptability, and closed-loop adaptive dynamic optimization.

[0123] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A revenue optimization system for the synergistic participation of pumped hydro storage and novel energy storage in the market, characterized in that, It includes a data acquisition and preprocessing module for communication connection, a market information and policy analysis module, a collaborative scheduling and optimization module, a revenue calculation and intelligent allocation module, and a strategy execution and closed-loop optimization module; The data acquisition and preprocessing module is used to acquire multi-source heterogeneous data and perform preprocessing. The market information and policy analysis module is used to analyze the electricity market trading rules and electricity price policy requirements, and to construct dual participation constraints of market constraints and policy constraints. The collaborative scheduling optimization module is used to construct and solve a multi-objective collaborative optimization model based on the standardized data output by the data acquisition and preprocessing module and the dual constraints constructed by the market information and policy analysis module, with the objectives of maximizing comprehensive benefits, minimizing operating costs, and maximizing carbon emission reduction, and to generate a joint scheduling strategy for pumped storage and new energy storage. The revenue calculation and intelligent allocation module is used to perform refined calculation of multi-dimensional revenue based on the actual transaction data after the joint scheduling strategy is executed, and to allocate revenue in compliance with electricity price policy requirements. The strategy execution and closed-loop optimization module is used to distribute the joint scheduling strategy to the energy storage execution agency, collect actual operation data and revenue data, quantify and calculate the execution deviation and revenue deviation of the scheduling strategy, and trigger the online iterative update and parameter tuning of the multi-objective collaborative optimization model when the deviation value exceeds the preset threshold or when a change in the external environment is detected.

2. The revenue optimization system for the coordinated participation of pumped hydro storage and novel energy storage in the market according to claim 1, characterized in that, It also includes a pumped storage power station distribution module, which includes: The pumped storage power station digital unit is used to convert the available capacity and corresponding capacity electricity fee parameters of the pumped storage power station into standard data objects and broadcast them on the power trading network platform. The subscription instruction response unit is used to receive capacity subscription request data from nodes in different regions, update the occupancy status of the pumped storage power station according to the request timestamp and priority, and generate a pumped storage power station usage right allocation signal; the subscribed pumped storage power station only provides services to its subscriber. The remaining cost dynamic allocation unit is used to collect data on the remaining pumped storage power stations that have not been subscribed. The remaining pumped storage power stations serve various regions. Based on the load distribution weight data of each region's nodes, the allocation coefficient of the remaining pumped storage power stations is calculated and input as a cost item into the collaborative scheduling optimization module.

3. The revenue optimization system for the coordinated market participation of pumped hydro storage and novel energy storage as described in claim 1, characterized in that, The market information and policy analysis module includes: The market rule analysis unit is used to analyze the trading rules, price signals, and clearing mechanisms of the electricity market, capacity market, ancillary services market, and carbon market. The policy parameter dynamic adaptation unit is used to build in and update policy parameters such as the pumped storage revenue sharing ratio, the peak capacity conversion ratio of new energy storage, and the capacity electricity price reduction rules in real time. The market constraints are formed by the time-series market signals parsed by the market rule parsing unit; the policy constraints are formed by the real-time policy parameters provided by the policy parameter dynamic adaptation unit as boundaries.

4. The revenue optimization system for the coordinated market participation of pumped hydro storage and novel energy storage as described in claim 1, characterized in that, The revenue calculation and intelligent allocation module includes: The multi-dimensional revenue splitting unit is used to split and statistically analyze the comprehensive revenue according to market type, energy storage type, and scheduling time. It calculates the independent revenue generated by pumped storage and new energy storage in each market, and calculates the incremental revenue of collaboration by constructing two sets of parallel simulations of collaborative operation mode and independent operation mode and comparing the total revenue difference of the two sets of simulations under the same market conditions in the same period. The carbon asset and curtailment accounting unit is used to complete the linkage calculation of carbon asset accounting and the cost of new energy curtailment losses; The policy-based revenue distribution unit is used to distribute various revenues among pumped storage power station operators and new energy storage power station operators based on real-time policy parameters, and to offset the system operating costs with the policy-required revenue retention portion, while simultaneously calculating the electricity bill reduction revenue for power users and generating compliant revenue distribution ledgers and vouchers.

5. The revenue optimization system for the coordinated participation of pumped hydro storage and novel energy storage in the market according to claim 1, characterized in that, The strategy execution and closed-loop optimization module includes: The deviation quantization unit is used to collect actual operating condition data of the equipment and calculate the tracking deviation rate between the actual power and the commanded power. The profit deviation quantification unit is used to collect actual market transaction profit data and calculate the profit deviation rate between actual and expected profits. The model iterative optimization unit is used to automatically initiate online iterative updates of the multi-objective collaborative optimization model when the tracking deviation rate or revenue deviation rate exceeds a preset threshold and the duration exceeds a preset duration, or when external environmental abrupt signals such as power market rule adjustments, electricity price policy updates, or drastic changes in grid operation status are detected. The unit adjusts the objective function weights, core calculation parameters, and constraint thresholds of the model, and then solidifies the new parameters into the model after simulation verification.

6. The revenue optimization system for the coordinated participation of pumped hydro storage and novel energy storage in the market according to claim 1, characterized in that, It also includes a cold-region adaptation and control module, which includes an environmental sensing unit, a waste heat recovery module installed in the pumped storage unit, and a constant temperature protection unit installed in the new energy storage unit. The cold-region adaptation and control module is used to adjust the constant temperature protection unit in real time based on the low-temperature environment data collected by the environmental sensing unit and the waste heat parameters of the pumped storage unit collected by the waste heat recovery module, so as to provide heat preservation for the new energy storage unit using the waste heat of the pumped storage unit.

7. The revenue optimization system for the coordinated market participation of pumped hydro storage and novel energy storage as described in claim 6, characterized in that, The collaborative scheduling optimization module adopts a two-layer model architecture, including: The upper-level scheduling strategy model is used to determine the joint scheduling strategy of pumped storage and new energy storage, with the core of coordinated peak shaving of pumped storage and new energy storage, combined with grid load demand, new energy output forecast, cold region environmental constraints, and unit operation limitations, with the goal of optimizing the overall system operating efficiency and the best peak shaving effect. The lower-level cost accounting model is used to calculate the actual operating costs of pumped storage units and new energy storage units under the current scheduling mode. The lower-level cost accounting model correlates the operating energy consumption cost of the constant temperature protection unit with the operating status of the corresponding pumped storage unit. The correlation calculation includes dynamically deducting the energy consumption cost of the constant temperature protection unit based on the available waste heat of the waste heat recovery module of the pumped storage unit. The upper-level scheduling strategy model and the lower-level cost accounting model are solved collaboratively using a genetic algorithm to output the optimal joint scheduling strategy.

8. The revenue optimization system for the coordinated market participation of pumped hydro storage and novel energy storage as described in claim 7, characterized in that, The joint dispatch strategy of pumped storage and new energy storage includes: When the adjustment demand is a short-term frequency adjustment at the millisecond or second level, a coordinated response strategy is generated, which is independently responded by the power-type energy storage module in the new energy storage unit and monitored by the pumped storage unit in standby mode. When the regulation demand is minute-level peak shaving, a coordinated response strategy is generated, with the energy-type energy storage module in the new energy storage unit as the main response and the pumped storage unit as the auxiliary response, and the pumped storage unit gradually taking over the regulation load. When the regulation demand is hourly long-term peak shaving, a collaborative response strategy is generated, with the pumped storage unit as the main responder and the new energy storage unit as the auxiliary responder. The new energy storage unit compensates for the regulation delay of the pumped storage unit and smooths out the high-frequency fluctuations in the output of new energy sources.

9. The revenue optimization system for the coordinated market participation of pumped hydro storage and novel energy storage as described in claim 8, characterized in that, In the objective function of the multi-objective collaborative optimization model: The objective function for maximizing overall benefits includes revenue from the electricity market, capacity market, ancillary services market, and carbon trading. The objective function for minimizing operating costs includes pumped storage operating costs and new energy storage operating costs. The pumped storage operating costs include pumping electricity costs, start-up and shutdown costs, and head loss costs. The new energy storage operating costs include charging and discharging loss costs, cycle life decay costs, and constant temperature protection unit costs. The objective function for maximizing carbon emission reduction is calculated by substituting the carbon emission reduction from thermal power generation units.

Images