Balanced unit quotation decision-making method considering uncertainty

Abstract

The invention discloses a balance unit quotation decision-making method considering uncertainty, and relates to the technical field of power systems, and the method comprises the steps: describing the output of renewable energy sources based on a scene method, carrying out the new energy modeling of the uncertainty of the output of the renewable energy sources, outputting a green power distribution proportion, and collecting market data and the data of a balance unit; building a balance unit quotation decision model based on new energy modeling and input balance unit data; a combined clearing model of the spot market and the green electricity market is constructed through new energy modeling and input market data and balance unit data; and optimizing the balance unit quotation decision model and the joint clearing model, and outputting an optimal quotation strategy of the balance unit. The method has the beneficial effects that the uncertainty of renewable energy sources can be effectively dealt with, the operation risk of the balance unit is reduced, and the operation stability of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method for balancing unit bidding decision-making that takes uncertainty into account. Background Technology

[0002] With the large-scale grid connection of renewable energy, the electricity market trading environment is becoming increasingly complex, posing significant challenges to the coordinated bidding decisions of balancing units (such as energy storage and adjustable loads) in both the spot and green electricity markets. Existing research largely focuses on bidding strategies in single-market scenarios or neglects the impact of multi-dimensional stochastic factors such as wind and solar power output fluctuations and electricity price uncertainties on decision-making, resulting in insufficient cross-market arbitrage capabilities and risk-return imbalances for balancing units. Traditional bidding methods (such as deterministic optimization or simple stochastic programming) struggle to accurately characterize the temporal correlation of market prices and the constraints of green electricity trading, easily leading to conservative or aggressive strategies. Furthermore, current technologies lack modeling of the dynamic game mechanism of balancing units in multi-market coupled trading, failing to maximize returns under risk constraints. Therefore, a coordinated bidding decision-making method that considers multiple uncertainties is urgently needed to improve the economy and adaptability of balancing units in multi-market environments. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by this invention is that existing pricing decision-making methods have insufficient consideration of the fluctuations in renewable resource output, low cross-market collaborative optimization capabilities, low economic efficiency and adaptability when the balancing unit participates in multi-market transactions, and the problem of how to achieve optimal resource allocation in an uncertain environment.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a balancing unit pricing decision-making method considering uncertainty, comprising: describing renewable energy output based on a scenario-based approach; performing new energy modeling to address the uncertainty of renewable energy output and outputting a green electricity allocation ratio; collecting market data and balancing unit data; constructing a balancing unit pricing decision-making model based on the new energy modeling and input balancing unit data; constructing a joint clearing model for the spot market and the green electricity market through the new energy modeling and input market and balancing unit data; optimizing the balancing unit pricing decision-making model and the joint clearing model to output the optimal pricing strategy for the balancing unit; wherein the balancing unit pricing decision-making model includes determining the objective function of the pricing decision-making model and the constraints on the operation and pricing of each device within the balancing unit; wherein the joint clearing model includes the objective function of the joint clearing model and the constraints on the spot market and the green electricity market.

[0006] As a preferred embodiment of the balancing unit pricing decision method considering uncertainty described in this invention, the green electricity allocation ratio output by the new energy modeling includes generating different output scenarios based on the prediction curve obtained from historical data and the standard deviation of renewable energy output. Each scenario corresponds to a different renewable energy output value and probability. Based on the probability distribution of the output scenarios, the green electricity allocation ratio is determined.

[0007] As a preferred embodiment of the balancing unit pricing decision-making method considering uncertainty described in this invention, the construction of the balancing unit pricing decision-making model includes: determining the objective function and constraints of the balancing unit pricing decision-making model; the objective function of the balancing unit pricing decision-making model aims to optimize the comprehensive cost of the balancing unit in the spot market and the green electricity market; it takes into account the data elements of the balancing unit, and simultaneously incorporates the fixed costs of green electricity transactions and the price difference revenue in the spot market; based on probabilistic scenario analysis, it enables coordinated strategies for unit combination, energy storage scheduling, and load management under different market environments.

[0008] As a preferred embodiment of the balancing unit pricing decision method considering uncertainty described in this invention, the constraints of the balancing unit pricing decision model include: distributed resource operation constraints within the balancing unit; power upper and lower limit constraints, segmented cost limits, and ramp-up capability constraints of distributed gas turbine units; charging and discharging efficiency constraints, capacity storage limits, and initial and final SOC boundary constraints of energy storage units; balance of controllable load adjustment amounts; maximum reducible capacity limit of interruptible loads; and electrical load balance constraints of the balancing unit and bidding constraints in the spot market.

[0009] As a preferred embodiment of the balancing unit pricing decision method considering uncertainty described in this invention, the construction of the joint clearing model of the spot market and the green electricity market includes: constructing an objective function with minimizing operating costs as the optimization objective, setting constraints on the spot market and the green electricity market, optimizing the output of thermal power units and the energy of each segment of the balancing unit based on the power generation cost of thermal power units and the supply and demand matching of the balancing unit, adopting a bilateral negotiation mechanism for the green electricity market, and not including the cost of the green electricity market in the optimization objective function.

[0010] As a preferred embodiment of the balancing unit bidding decision method considering uncertainty described in this invention, the constraints of the spot market and the green electricity market include: allocation constraints of green electricity contracts proportionally decomposed into each time period, node active power balance constraints, line power flow safety restrictions, upper and lower limits of balancing unit bidding power constraints, aggregated winning bid constraints, and power generation enterprise winning bid constraints.

[0011] As a preferred embodiment of the balancing unit pricing decision-making method considering uncertainty described in this invention, the optimal pricing strategy of the balancing unit includes: generating the optimal pricing strategy through a multi-scenario stochastic optimization method; dynamically adjusting the trading volume in the spot market and the trading ratio in the green electricity market based on the fluctuations in renewable energy output, changes in spot market prices, and constraints of green electricity contracts, and based on the operating characteristics of each device within the balancing unit, while satisfying power balance and operating constraints.

[0012] Another objective of this invention is to provide a balancing unit pricing decision system that considers uncertainty. This system can construct a joint clearing model for the spot market and the green electricity market by modeling new energy sources and inputting market data and balancing unit data, thus solving the problem of low cross-market collaborative optimization in current pricing decision methods.

[0013] As a preferred embodiment of the balancing unit pricing decision system considering uncertainty described in this invention, the system includes: a data management module, a pricing decision module, a joint clearing module, and an optimization solution module. The data management module stores and integrates data required for the operation of the balancing unit, including network structure parameters, spot market transaction data, green electricity market contract data, and data from various devices within the balancing unit. This data comprises basic data on distributed gas turbine units, renewable energy units, energy storage units, controllable loads, and interruptible loads. It records information on market pricing costs and green electricity contract transaction volume limits for each time period, providing standardized data for modeling. The pricing decision module utilizes the operating parameters of the devices within the balancing unit and market transaction data to construct an optimizable balancing unit pricing decision model. Through multi-scenario analysis to handle the uncertainty of renewable energy output, and combined with spot market power trading limits and green electricity contract allocation constraints, it generates a model that meets equipment operating constraints and ionization balance requirements. The optimal bidding scheme outputs segmented bidding power and corresponding bidding strategies for balancing units in the spot market for each time period, determining the optimal allocation ratio for green electricity trading. The joint clearing module integrates the trading rules of the spot market and the green electricity market to construct an optimizable joint clearing model, coordinating the operating power of thermal power units with the bidding power of balancing units. Considering line transmission capacity limitations and phase angle relationships of each node, it outputs market clearing results for multiple scenarios, including generator scheduling schemes for each time period, the winning bid power of balancing units, and the marginal electricity price of nodes. The optimization solution module uses mathematical programming methods to process the balancing unit bidding decision model and the joint clearing model, transforming constraints into optimizable problems. It iteratively outputs the constructed mixed-integer linear programming problem through a commercial solver, outputting results in parallel across multiple scenarios. It handles random variables caused by uncertainties in renewable energy processing and outputs balancing unit bidding strategies and market clearing schemes that meet the constraints.

[0014] Another object of the present invention is to provide a balancing unit pricing decision device that takes into account uncertainty, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement a balancing unit pricing decision method that takes into account uncertainty.

[0015] Another object of the present invention is to provide a storage medium for balancing unit pricing decisions that takes into account uncertainty, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the steps of a balancing unit pricing decision method that takes into account uncertainty are implemented.

[0016] The beneficial effects of this invention are as follows: This invention provides a balancing unit pricing decision-making method that considers uncertainty. It describes the output of renewable energy based on a scenario-based approach, models the uncertainty of renewable energy output to output green electricity allocation ratios, collects market data and balancing unit data to improve the system's economy and operational stability, constructs a balancing unit pricing decision-making model based on renewable energy modeling and input balancing unit data, and achieves multi-device coordination to make the pricing strategy both economical and reliable. Through renewable energy modeling and input market and balancing unit data, a joint clearing model of the spot market and green electricity market is constructed to ensure the executability of the clearing results. Simultaneously, it explores cross-market synergistic benefits, optimizes the balancing unit pricing decision-making model and the joint clearing model, and outputs the optimal pricing strategy for the balancing unit. The two-layer optimization architecture ensures short-term market response agility while considering operational economy. This invention achieves better results in terms of economy, reliability, and cross-market synergistic benefits. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 The first embodiment of the present invention provides an overall flowchart of a balancing unit pricing decision method that takes into account uncertainty.

[0019] Figure 2 A typical output curve of a new energy source is provided as a method for making a pricing decision for a balancing unit that considers uncertainty, according to the second embodiment of the present invention.

[0020] Figure 3 The second embodiment of the present invention provides a load diagram of the internal load of a balancing unit for a balancing unit pricing decision method that takes into account uncertainty.

[0021] Figure 4The second embodiment of the present invention provides an internal power generation plan diagram of a balancing unit that considers uncertainty in a balancing unit bidding decision method. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for balancing unit pricing decision considering uncertainty is provided, comprising: S1: Describe the output of renewable energy based on the scenario method, model the uncertainty of renewable energy output, output the green electricity allocation ratio, and collect market data and balance unit data.

[0024] Furthermore, the green electricity allocation ratio output by new energy modeling includes generating different output scenarios based on the prediction curves obtained from historical data and the standard deviation of renewable energy output. Each scenario corresponds to different renewable energy output values ​​and probabilities. Based on the probability distribution of the output scenarios, the green electricity allocation ratio is determined.

[0025] It should be noted that the renewable energy forecast curve at a given time was obtained based on historical data. Standard deviation of renewable energy output Obtained based on scenario method There are several uncertain scenarios, each with a probability of 1 / 2. The green electricity allocation ratio can be obtained, and the green electricity allocation ratio is expressed as: , in, For the allocation ratio of green electricity, For uncertain scenarios, For a moment, For time, This represents the output value of renewable energy.

[0026] It should also be noted that the scenario-based approach is used to model the uncertainty of renewable energy output. By using the prediction curves and standard deviations obtained from historical data, multiple output scenarios and probability distributions are generated to obtain the green electricity allocation ratio. This solves the problem of power dispatch difficulties caused by the volatility of renewable energy, improves the grid's adaptability to uncertainty, and enhances the stability of the power system.

[0027] S2: Based on new energy modeling and input balance unit data, construct a balance unit pricing decision model.

[0028] Furthermore, constructing a balancing unit pricing decision model includes determining the objective function and constraints of the balancing unit pricing decision model. The objective function of the balancing unit pricing decision model aims to optimize the overall cost of the balancing unit in the spot market and the green electricity market. It takes into account the data elements of the balancing unit, the fixed costs of green electricity transactions and the price difference revenue in the spot market, and the coordinated strategies of unit combination, energy storage scheduling and load management under different market environments based on probabilistic scenario analysis.

[0029] Based on data from new energy modeling and balancing units, a model is constructed that includes defining the objective function of the model and the constraints on the operation and pricing of each device within the balancing unit. The objective is to minimize the expected energy cost of the balancing unit participating in the spot market and the green electricity market. The objective function is expressed as follows: , , , , , , , , , in, To balance the minimum energy cost for the unit to participate in the spot market and the green electricity market, The probability of each scenario, For the scene The operating costs of the lower balancing unit participating in the spot market and the green electricity market, For the scene The cost of lower-level balancing units participating in the green electricity market, For the scene The cost of the lower balancing unit participating in the spot market, for Time-based environmental unit price For the scene exist The power output of the balancing unit at any given time. For the scene exist The power consumption of the current balancing unit. For time intervals, The monthly contract price for green electricity. This refers to the monthly contracted purchase volume of green electricity. for Moment Scene Operating costs of distributed gas turbine units within the lower balancing unit. for Moment Scene Operating costs of energy storage units within the lower balancing unit. for Moment Scene The operating cost of controllable loads within the lower balancing unit. for Moment Scene The operating cost of interruptible loads within the lower balancing unit. Number the gas equipment. For the scene exist The first moment The power generation cost of a gas-fired power plant For the scene exist Distributed gas turbine units at all times Total contracted output For the first segment cost, For the scene exist Distributed gas turbine units at all times The Duan Zhongbiao contributed his efforts. For the first The charging and operating costs of an energy storage device For the scene exist The first moment Charging power of individual energy storage devices For the first Charging efficiency of individual energy storage devices For the first The discharge operation cost of an energy storage device For the scene exist The first moment Discharge power of an energy storage device For the first Discharge efficiency of individual energy storage devices The cost of transferring a unit amount of charge under controllable load. For the scene exist The amount of load reduction at any given time. For the scene exist The increase in load at any given time The unit interruption compensation price for interruptible loads, For the scene exist The capacity that can be reduced for interruptible loads at any time.

[0030] It should be noted that the constraints of the balancing unit bidding decision model include: the operation constraints of distributed resources within the balancing unit; the upper and lower limits of power, segmented cost limits, and ramp-up capability constraints of distributed gas turbine units; the charging and discharging efficiency constraints, capacity storage limits, and initial and final SOC boundary constraints of energy storage units; the balance of the upper and lower adjustment amounts of controllable loads; the maximum reducible capacity limit of interruptible loads; and the electrical load balance constraints of the balancing unit and the bidding constraints of the spot market.

[0031] The operating constraints of distributed gas turbine units are expressed as follows: , , , , in, For distributed gas turbine units The The maximum output of the winning bid in the section For the scene exist Distributed gas turbine units at all times The The total effort contributed by the winning bidder. In order to be in Distributed gas turbine units at all times The minimum winning bid output, In order to be in Distributed gas turbine units at all times Maximum output of the winning bid This represents the climbing ability in adjacent time periods.

[0032] The operating constraints of energy storage units are expressed as follows: , , , , , , in, For the scene exist Energy storage units at all times The discharge power, For the scene exist Energy storage units at all times Maximum discharge power, For the scene exist Energy storage units at all times The charging power, For the scene exist Energy storage units at all times Maximum charging power, For the scene exist Energy storage units at all times SOC, For the scene exist Energy storage units at all times The minimum SOC, For the scene exist Energy storage units at all times Maximum SOC For the scene exist Energy storage units at all times Initial SOC period For the scene exist Energy storage units at all times The end segment SOC.

[0033] The operating constraints of controllable loads are expressed as follows: , , , in, For the scene exist The amount of controllable load adjustment at any given time. For the scene exist The amount of controllable load adjustment at any given time. To minimize the load transfer adjustment ratio, To maximize the load transfer adjustment ratio, For the scene exist The internal electrical load of the unit is constantly balanced.

[0034] Interruptible load operation constraints are expressed as follows: , , in, For the scene exist The maximum slashable capacity of interruptible load at any given time.

[0035] The constraints of the balancing unit bidding decision model also include balancing unit operation constraints, electrical load balancing constraints, and bidding constraints. The electrical load balancing constraints are expressed as follows: , in, In order to be in Time of the first Uncontrollable load power, For the scene exist The power output of the balancing unit at any given time. For the scene exist The power consumption of the balancing unit at any given time.

[0036] Bidding constraints are expressed as follows: , , , , in, for time, The power quote for the first stage of the balancing unit in this scenario. for time, The first balancing unit in the scene The power quote for the purchase of the segment, for time, The first balancing unit in the scene The power quote for the purchase of the segment, for time, The first balancing unit in the scene The power quote for the segment for sale, for time, The first balancing unit in the scene The power quote for the segment for sale, This represents the maximum monthly purchase volume for green electricity contracts.

[0037] It should also be noted that by integrating new energy modeling with balance unit data, a balance unit pricing decision model is constructed. With the comprehensive cost of the spot market and the green electricity market as the optimal objective, the objective function and constraints are used to solve the problem of difficult coordinated scheduling of multiple types of resources under high-proportion renewable energy access, so as to balance the cost of green electricity with the dynamic revenue of the spot market, reduce the operating cost of the system, and enhance the flexibility and economy of the power grid.

[0038] S3: Construct a joint clearing model for the spot market and green electricity market by modeling new energy sources and inputting market data and balance unit data.

[0039] Furthermore, the joint clearing model for the spot market and the green electricity market includes constructing an objective function with the goal of minimizing operating costs, setting constraints for the spot market and the green electricity market, optimizing the output of thermal power units and the energy of each segment of the balance unit based on the power generation cost of thermal power units and the supply and demand matching of the balance unit, adopting a bilateral negotiation mechanism for the green electricity market, and not including the cost of the green electricity market in the optimization objective function.

[0040] Based on renewable energy output scenarios and market trading rules, this paper integrates unit pricing parameters from the spot market, network power flow constraints, and node power balance conditions, while also incorporating allocation restrictions from green electricity contracts. An objective function is established to minimize system operating costs, expressed as follows: , in, For operating costs, for Periodic thermal power units The runtime cost parameters, for Time Period Balance Unit The energy supply cost parameter for the segment. for Time Period Balance Unit The energy demand cost parameter for each segment. for Time period Thermal power units in the scenario Operating power for Time period The first balance unit in the scene Sectional power supply for Time period The first balancing unit in the scene Power required for the segment.

[0041] It should be noted that the constraints of the spot market and the green electricity market include the allocation constraints of green electricity contracts proportionally allocated to each time period, the active power balance constraints at nodes, the power flow safety restrictions of the line, the upper and lower limits of the bidding power of the balancing unit, the aggregation constraints of the winning bids, and the constraints of the power generation companies winning bids.

[0042] The allocation constraints of green electricity contracts are expressed as follows: , in, For green electricity contracts time The allocation amount in the scenario In order to be in time The proportion of green electricity allocated in the scenario.

[0043] It should also be noted that by integrating new energy output scenarios and market trading rules, a joint clearing model of the spot market and green electricity market is constructed. With the goal of minimizing system operating costs, the output of thermal power units and the segmented energy allocation of balancing units are optimized. The power generation costs of the spot market and the supply and demand transactions of the balancing units are coordinated. Green electricity contract allocation constraints are set to solve the problem of high multi-market collaborative clearing ratio under renewable energy access, realize the complementary optimization of thermal power and green electricity resources, and reduce system operating costs.

[0044] S4: Optimize the balancing unit's pricing decision model and joint clearing model, and output the optimal pricing strategy for the balancing unit.

[0045] Furthermore, the optimal pricing strategy for the balancing unit includes generating the strategy through a multi-scenario stochastic optimization method. Based on the fluctuations in renewable energy output, changes in spot market prices, and constraints of green electricity contracts, and based on the operating characteristics of each device within the balancing unit, the strategy dynamically adjusts the trading volume in the spot market and the trading ratio in the green electricity market while meeting power balance and operational constraints.

[0046] It should be noted that, based on the scenario-based method, multiple probabilistic scenarios for renewable energy output are generated. The operating parameters and constraints of each device within the balance unit are integrated to establish a balance unit bidding decision model with the goal of minimizing expected energy costs. A joint clearing model of the spot market and the green electricity market is constructed. The optimization problem is mathematically described using Matlab and solved using the gurobi commercial solver. Under the premise of satisfying all device operating constraints and market clearing conditions, the segmented bidding power, corresponding bidding strategies, and optimal allocation ratio of green electricity transactions for the balance unit in the spot market are output.

[0047] It should also be noted that by using a multi-scenario stochastic optimization method, a bidding decision model and a joint clearing model are constructed to dynamically optimize the segmented bidding power, bidding strategy, and green electricity trading allocation ratio in the spot market. This solves the difficult problem of multi-market collaborative optimization and risk management under a high proportion of renewable energy access, improves the economic efficiency of the balancing unit operation, and ensures the safety of grid operation.

[0048] Example 2, refer to Figures 2-4 As an embodiment of the present invention, a pricing decision method for a balanced unit considering uncertainty is provided. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0049] First, taking a certain region's actual power system as an example, where G represents thermal power units, PV represents photovoltaic units, and WT represents wind power units, such as... Figure 2 It showcases typical daily power output fluctuation curves for wind turbines and photovoltaic units, intuitively demonstrating the intermittent and uncertain characteristics of renewable energy, and providing a reference for scenario generation, such as... Figure 3 The display shows the changes in electricity demand of the balancing unit at different times, reflecting the timing and peak-valley differences of the load, and providing a load benchmark for the establishment of the pricing strategy model. The unit pricing parameter information is shown in Table 1.

[0050] Table 1 System Unit Parameters

[0051] Table 1 provides key data on the capacity, power generation cost, and ramp rate of different generating units, offering complete input parameters required for the model. Standardized parameter display ensures experimental repeatability, and data comparison across multiple unit types highlights the unique characteristics of the balancing unit. For example... Figure 4 When the load demand within the balancing unit is high and the predicted output of renewable energy is low, the balancing unit coordinates with the main grid to purchase electricity, thereby preventing power shortages and maintaining a stable supply. Based on a real-time assessment of the supply-demand gap, potential forecasting errors of renewable energy are considered, along with grid electricity prices to minimize costs. Conversely, when the internal load is low and the predicted output of renewable energy is high, renewable energy sources maximize economic benefits by selling surplus electricity back to the main grid. This not only improves the profitability of the balancing unit but also promotes the full utilization of green energy.

[0052] Example 3, an embodiment of the present invention, provides a balancing unit pricing decision system that considers uncertainty, including a data management module, a pricing decision module, a joint clearing module, and an optimization solution module.

[0053] The data management module is used to store and integrate the data required for the operation of the balancing unit, including network structure parameters, spot market transaction data, green electricity market contract data, and data of various devices within the balancing unit. It includes basic data of distributed gas turbine units, new energy units, energy storage units, controllable loads, and interruptible loads, and records information on market quotation costs and green electricity contract transaction volume limits for each period, providing standardized data for modeling.

[0054] The bidding decision module is used to construct an optimizable bidding decision model for the balancing unit by utilizing the internal equipment operating parameters and market transaction data. It analyzes and processes the uncertainty of renewable energy output through multi-scenario analysis, combines the power trading restrictions in the spot market and the allocation constraints of green electricity contracts, generates the optimal bidding scheme that meets the equipment operating restrictions and ionization balance requirements, and outputs the segmented bidding power and corresponding bidding strategies of the balancing unit in the spot market for each time period, thereby determining the optimal allocation ratio for green electricity transactions.

[0055] The joint clearing module is used to construct an optimizable joint clearing model by integrating the trading rules of the spot market and the green electricity market, coordinating the operating power of thermal power units with the bidding power of the balancing unit, taking into account the line transmission capacity limitations and the phase angle relationship of each node, and outputting the market clearing results under multiple scenarios, including the generator unit scheduling scheme, the winning bid power of the balancing unit and the marginal electricity price of the node for each time period.

[0056] The optimization solution module is used to process the balance unit bidding decision model and the joint clearing model using mathematical programming methods. It transforms the constraints into an optimizable problem, it iteratively outputs the constructed mixed integer linear programming problem through a commercial solver, outputs in parallel across multiple scenarios, handles the random variables brought about by the uncertainty of renewable energy processing, and outputs the balance unit bidding strategy and market clearing scheme that meet the constraints.

[0057] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements an emergency control system that takes into account the switching of grid-type energy storage parameters as proposed in the above embodiment.

[0058] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements an emergency control system that takes into account the switching of grid-type energy storage parameters as proposed in the above embodiment.

[0059] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0061] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0062] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0063] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A balancing unit offer decision method considering uncertainty, characterized in that, The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input.

2. The method of claim 1, wherein the method further comprises: determining a probability of each of the plurality of balance units being available; and determining the price of each of the plurality of balance units based on the probability of each of the plurality of balance units being available. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input.

3. The method of claim 1, wherein the method further comprises: determining a probability of each of the plurality of balance units being available; and determining the price of each of the plurality of balance units based on the probability of each of the plurality of balance units being available. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input.

4. The method of claim 3, wherein the method further comprises: The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input.

5. The method of claim 1, wherein the method further comprises: determining a probability of each of the plurality of balance units being available; and determining the price of each of the plurality of balance units based on the probability of each of the plurality of balance units being available. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input.

6. The method for balancing unit offer decision considering uncertainty according to claim 1 or 5, characterized in that: The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input.

7. The method of claim 1, wherein the method further comprises: determining a probability of each of the plurality of balance units being available; and determining the price of each of the plurality of balance units based on the probability of each of the plurality of balance units being available. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based on a renewable energy modeling and a market data input. The application relates to a method for optimizing a green electricity distribution ratio of a balancing unit based The method is generated by a multi-scenario random optimization method, is based on renewable energy output fluctuation, spot market price change and green electricity contract constraint, is based on the operation characteristics of each device in the balancing unit, dynamically adjusts the buying and selling electricity quantity of the spot market and the transaction proportion of the green electricity market under the conditions of satisfying power balance and operation limitation.

8. A balancing unit offer decision system considering uncertainty, employing a balancing unit offer decision method considering uncertainty according to any one of claims 1 to 7, characterized in that: The method comprises a data management module, a bidding decision module, a joint clearing module and an optimization solving module. The data management module is used for storing and integrating data required for balancing unit operation, recording information of market bidding cost, green electricity contract transaction volume limitation in each period and providing standardized data for modeling. The bidding decision module is used for constructing an optimizable balancing unit bidding decision model by using internal device operation parameters and market transaction data of the balancing unit and determining an optimal distribution proportion of green electricity transaction. The joint clearing module is used for constructing an optimizable joint clearing model by integrating transaction rules of the spot market and the green electricity market, coordinating the operation power of the thermal power unit and the bidding electricity quantity of the balancing unit, considering line transmission capacity limitation and phase angle relationship of each node and outputting market clearing under multiple scenarios. The optimization solving module is used for processing the balancing unit bidding decision model and the joint clearing model by using a mathematical programming method and outputting balancing unit bidding strategies and market clearing schemes meeting constraint conditions. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the balancing unit bidding decision method considering uncertainty in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the balancing unit bidding decision method considering uncertainty in any one of claims 1 to 7.

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