A method and system for quoting decision of multi-energy park participating in electricity-carbon-green electricity multi-market considering uncertainty, and a storage medium

Abstract

The application discloses a kind of considering uncertainty multi-energy park participation electric-car-green electricity multi-market quotation decision method, system, equipment and storage medium, it is related to electric power system technical field, including obtaining the internal equipment operation constraint parameter of multi-energy park, and operation rule and historical transaction data;Based on the preset electric-car mutual recognition mechanism, the green electricity consumption of green electricity market is mapped into equivalent carbon emission reduction index of carbon market using mutual recognition coefficient, to establish the cross-market coupling relationship of electric-car-green electricity;Combined with the probability distribution characteristics of renewable energy output, the operation cost under cross-market coupling relationship is calculated by scene weighting expectation;With the minimization of multi-energy park operation cost as target, overall performance constraint is jointly optimized to solve, and the joint quotation decision scheme is output.The method of the application realizes multi-market collaborative optimization, improves the comprehensive income of park, enhances the robustness of decision-making, effectively avoids market risk.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method, system, and storage medium for bidding decision-making in a multi-energy park participating in a multi-market of electricity, carbon, and green electricity, taking into account uncertainties. Background Technology

[0002] With the advancement of new power system construction, the coordinated operation and coupling of the electricity market, green electricity trading market, and carbon market are becoming increasingly close. On the one hand, at the multi-market coupling level of electricity-green electricity-carbon, electricity price signals, green electricity prices, and carbon prices influence and are interconnected. For example, green electricity consumption in industrial parks can simultaneously generate green electricity and reduce carbon footprint, thus creating a complex trade-off between electricity costs, green electricity revenue, and carbon quota settlement costs. This multi-market coupling mechanism, while providing market participants with diversified value realization channels, also significantly increases the dimensions and complexity of their decision-making. On the other hand, regarding the motivations for participation from industrial parks themselves, multi-energy parks, as integrated energy entities combining power generation, consumption, storage, and supply, often face multiple policy assessments simultaneously, such as carbon emission quotas and renewable energy consumption responsibility weights. To optimize energy costs, meet assessment targets, and maximize profits, industrial parks have a strong incentive to actively participate in the aforementioned multi-market transactions. However, the intertwining of multiple uncertainties, such as market clearing prices and renewable energy output, makes traditional single-market or deterministic pricing strategies difficult to adapt, easily leading to unstable decision-making returns and even assessment risks.

[0003] Therefore, under the dual background of the coordinated evolution of the electricity-carbon-green electricity market and the rigid assessment requirements of industrial parks, there is an urgent need for a pricing decision-making method for multi-energy industrial parks to participate in multiple electricity-carbon-green electricity markets that takes into account uncertainties, so as to support the parks to carry out integrated collaborative optimization and risk avoidance in a multi-market, multi-policy, and multi-uncertain environment. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that the existing multi-energy park participation in electricity market bidding decision-making methods have insufficient consideration of the multi-market coupling characteristics, low decision-making ability under output uncertainty, difficulty in avoiding revenue fluctuations, and the problem of how to convert green electricity consumption into carbon emission reduction.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a bidding decision-making method for multi-energy parks participating in multiple electricity-carbon-green electricity markets considering uncertainties, comprising: acquiring the internal equipment operation constraint parameters of the multi-energy park, as well as the operation rules and historical transaction data of the electricity market, carbon market, and green electricity market to which it belongs; based on a preset electricity-carbon mutual recognition mechanism, using the mutual recognition coefficient to map the green electricity consumption in the green electricity market to the equivalent carbon emission reduction index in the carbon market, so as to establish a cross-market coupling relationship of electricity-carbon-green electricity; combining the probability distribution characteristics of renewable energy output, performing scenario-weighted expectation calculation of operating costs under the cross-market coupling relationship; with the goal of minimizing the operating costs of the multi-energy park, jointly optimizing and solving the internal equipment operation constraints, market access constraints, and equivalent carbon emission reduction index compliance constraints, and outputting a joint bidding decision scheme.

[0007] As a preferred embodiment of the pricing decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, which takes into account uncertainties as described in this invention, the internal equipment operation constraint parameters include the output constraint of the cogeneration unit, the charging and discharging power constraint of the energy storage system, and the state of charge constraint; the output constraint of the cogeneration unit is achieved by obtaining the real-time heat load demand of the multi-energy park and dynamically adjusting the heat and electricity output ratio of the cogeneration unit, thereby physically matching and decoupling the heat flow and electrical flow within the park.

[0008] As a preferred embodiment of the multi-energy park participation in the electricity-carbon-green electricity multi-market bidding decision method that considers uncertainty as described in this invention, the energy storage system charging and discharging power constraints and state of charge constraints in the internal equipment operation constraints parameters utilize the energy time shift characteristics of the energy storage system to physically smooth out the random fluctuations in renewable energy output, and adjust the park market bidding timing in conjunction with the internal equipment operation constraints.

[0009] As a preferred embodiment of the bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, which takes into account uncertainty as described in this invention, the mutual recognition coefficient is taken as the average carbon emission intensity of the power grid in the region where the multi-energy park is located. The average carbon emission intensity is determined as the mutual recognition coefficient to characterize the carbon emission quota weight that is equivalently offset by a unit of green electricity consumption in the corresponding trading cycle.

[0010] As a preferred embodiment of the pricing decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, which takes into account uncertainty as described in this invention, the compliance constraints of the equivalent carbon emission reduction indicators include: real-time calculation of the deviation between the renewable energy power generation in the park and the preset consumption weight indicator; when the deviation is positive, the surplus is converted into tradable green electricity assets and included in the revenue; when the deviation is negative, the procurement scale of external green electricity or the amount of carbon quota replenishment is determined through joint optimization solution.

[0011] As a preferred embodiment of the pricing decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, which takes into account the uncertainty of the present invention, the method includes: combining the probability distribution characteristics of renewable energy output, generating several representative output scenarios through scenario sampling technology; calculating the corresponding market revenue and deviation penalty cost for each output scenario, and converting the operating cost into the expected value of the total cost after scenario weighting based on the probability of scenario occurrence.

[0012] As a preferred embodiment of the bidding decision-making method for multi-energy parks participating in multiple electricity-carbon-green electricity markets, which considers uncertainties as described in this invention, the joint optimization solution adopts a two-layer iterative optimization architecture, namely an upper-layer decision model and a lower-layer clearing model. The upper-layer decision model performs the objective of minimizing the operating cost of the multi-energy park and is used to generate initial values ​​of bidding parameters covering multiple dimensions of electricity, carbon, and green electricity. The lower-layer clearing model predicts the market clearing price based on the bidding status of each market participant by simulating the electricity market clearing rules and feeds it back to the upper-layer decision model. The upper-layer decision model corrects the bidding parameters based on the predicted price and iterates until the preset equilibrium convergence condition is reached.

[0013] As a preferred embodiment of the bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, which takes into account uncertainties as described in this invention, the lower-level clearing model includes node power balance constraints and line power flow security constraints; the lower-level clearing model guides the upper-level decision-making model to adjust the electricity declaration range of the multi-energy park by solving the marginal electricity price of each node.

[0014] Another objective of this invention is to provide a bidding decision system for multi-energy parks participating in the electricity-carbon-green electricity multi-market that takes into account uncertainties. This system can combine the probability distribution characteristics of renewable energy output to perform scenario-weighted expectation calculation of operating costs under cross-market coupling relationships, thus solving the problem that current multi-energy parks participating in electricity market bidding decision-making methods do not adequately consider multi-market coupling.

[0015] As a preferred embodiment of the multi-energy park participation in the electricity-carbon-green electricity multi-market pricing decision-making system considering uncertainty as described in this invention, the system includes: a multi-source information integration and scenario quantification module, an electricity-carbon-green electricity coupling decision-making module, a cross-market value transmission and joint clearing module, a price expectation correction and evaluation module, and a two-layer system optimization and strategy output module; the multi-source information integration and scenario quantification module is used to acquire the physical parameters of heterogeneous equipment within the multi-energy park and the multi-market trading rules, and to construct a set of power output uncertainty scenarios based on historical data using a normal distribution; the electricity-carbon-green electricity coupling decision-making module is used to construct and solve the upper-level decision-making model, which pre-sets mutual recognition of green electricity and carbon quotas. The coefficient maps the green electricity consumption to an equivalent carbon emission reduction index, incorporates it into the electricity carbon quota compliance assessment constraint, and generates a multi-market joint bidding model. The cross-market value transmission and joint clearing module receives the joint bidding model and constructs a lower-level clearing model to simulate the coordinated operation of the electricity-carbon-green electricity multi-market and outputs the clearing price. The price expectation correction and evaluation module performs weighted expectation processing on the clearing price based on the probability of occurrence of the power output uncertainty scenario set and feeds it back to the upper-level decision model for objective function correction. The two-layer system optimization and strategy output module executes an iterative loop and outputs the optimal bidding decision scheme under the condition that the upper-level decision model and the lower-level clearing model meet the constraints.

[0016] Another object of the present invention is to provide a storage medium for bidding decision-making in a multi-energy park participating in an electricity-carbon-green electricity multi-market, taking into account uncertainty, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the steps of a method for bidding decision-making in a multi-energy park participating in an electricity-carbon-green electricity multi-market, taking into account uncertainty, are implemented.

[0017] The beneficial effects of this invention are: This invention provides a multi-energy park participation pricing decision-making method that considers uncertainties in the electricity-carbon-green electricity multi-market. It constructs an integrated pricing decision-making model for multi-energy parks encompassing the electricity market, green electricity market, and carbon market. By quantitatively analyzing the linkage between electricity price, green electricity price, and carbon price, it can comprehensively optimize the park's bidding strategy in multiple markets, fully exploring the synergistic potential of electricity value, green environment value, and carbon asset value. This significantly improves the overall operational economy of the park while meeting performance constraints. Addressing the typical uncertainties of renewable energy output and multi-market prices, it employs advanced modeling techniques such as robust optimization or stochastic programming to generate pricing schemes that maintain stable performance even when facing fluctuations in uncertain parameters. This allows parks to proactively avoid revenue risks and performance default risks caused by drastic price fluctuations or new energy forecast deviations, thus supporting the development of safer and more reliable market participation strategies. This invention achieves better results in multi-market collaborative optimization, improving the overall revenue of parks, and mitigating market risks. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is an overall flowchart of a pricing decision-making method for multi-energy parks participating in multiple markets of electricity, carbon, and green electricity, which takes into account uncertainty, provided in Embodiment 1 of the present invention.

[0020] Figure 2 The flowchart of the two-layer iterative optimization architecture of a bidding decision method for multi-energy parks participating in multiple markets of electricity, carbon and green electricity that takes into account uncertainty, provided in Embodiment 1 of the present invention.

[0021] Figure 3 This is an overall schematic diagram of a pricing decision-making method for multi-energy parks participating in multiple markets of electricity, carbon, and green electricity, which takes into account uncertainty, provided in Embodiment 2 of the present invention. 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 Figures 1-2 As an embodiment of the present invention, a bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market considering uncertainties is provided, including: Specifically, the system collects operational constraint parameters of equipment within the multi-energy park in real time through communication interfaces. The multi-energy equipment within the park mainly consists of combined heat and power (CHP) units, thermal energy storage units, electric energy storage units, and interruptible loads. The system acquires physical parameters of the heterogeneous equipment within the multi-energy park, including the electrothermal elasticity coefficient of the CHP units, the capacity and charging / discharging efficiency of the energy storage system, and the power reduction of interruptible loads. Simultaneously, it introduces preset parameters such as the green electricity and carbon quota mutual recognition coefficient C, the consumption responsibility weight index, and the regional carbon emission intensity. The system connects to the electricity spot trading center, the carbon emission rights trading system, and the green electricity trading platform to obtain the current time-of-use electricity price, carbon quota price, green electricity market price, and other market operation rules R, and retrieves historical transaction data D from the multi-energy park over the past quarter as a benchmark reference.

[0024] Based on the preset mutual recognition coefficient C as the park's annual average carbon emission intensity, the system multiplies the park's green electricity consumption by the mutual recognition coefficient C, directly mapping it to the equivalent carbon emission reduction indicator I recognized by the carbon market. In this way, the originally isolated electricity consumption indicator is transformed into a carbon offset asset with financial attributes, thereby establishing a unified value accounting dimension covering the weights of electricity, carbon, and green electricity.

[0025] Considering the strong randomness of wind and solar power output, the system uses the normal distribution function of historical prediction errors to generate multiple typical output scenarios. Combining the probability distribution characteristics, the system calculates the scenario-weighted expectation of the operating cost O under the cross-market coupling relationship 100. The operating cost O includes not only electricity procurement costs, but also the balance assessment penalty caused by the output prediction deviation, as well as the purchase expenditure caused by the excess carbon emissions.

[0026] The system takes minimizing the operating cost O of the multi-functional park as its objective function, and enters a joint optimization solution 200. The system adopts a two-layer iterative optimization architecture: as follows: Figure 2 As shown, the upper-level decision-making model 201 simulates the optimal power output plan of the industrial park under the constraints of heat and power balance, energy storage charging and discharging, and equivalent carbon emission reduction indicator I compliance; the lower-level clearing model 202 simulates the market clearing feedback for this power output. After multiple rounds of iteration until convergence, the system finally outputs a joint bidding decision scheme 300. This scheme clarifies the power application volume and price of the industrial park in the next period, the expected carbon quota gap / surplus, and the green electricity trading strategy, effectively mitigating the economic risks brought about by the fluctuation of new energy sources and achieving the optimal comprehensive benefits of the industrial park in a multi-market environment.

[0027] Furthermore, a higher-level decision-making model 201 is constructed with the goal of minimizing the operating cost of the park. The objective function of the higher-level decision-making model 201 and the constraints of the operation, bidding, and market coupling of each device in the multi-energy park are determined to form the feasible domain for the multi-energy park to participate in the bidding decision of the electricity-carbon-green electricity multi-market. First, based on the energy relationship and safe operation conditions within the multi-energy park, the operation constraints of the multi-energy park are established, including the safe operation of internal equipment, the energy coupling relationship of the multi-energy park, and the market assessment constraints of the multi-energy park.

[0028] The safe operation of internal equipment includes cogeneration units, thermal energy storage units, electric energy storage units, and interruptible load constraints; the energy coupling relationship of multi-energy parks includes electrical load balance and thermal load balance; the market assessment constraints of multi-energy parks include market assessment constraints and electricity carbon assessment constraints.

[0029] Combined heat and power (CHP) units are represented as: , , , , , in, In the scene Next Taiwan thermal power unit Electricity output at all times For the first The elasticity coefficient between electrical and thermal power during condensing operation of a Taiwan thermal power unit. In the scene Next Taiwan thermal power unit Constant heat output, It is a constant. For the first The lower limit of the electrical output of a Taiwanese thermal power unit. For the first The reduction in power generation per unit of heat supplied by a Taiwanese thermal power unit. For the first The upper limit of the electrical output of a Taiwanese thermal power unit. In the scene Next Taiwan thermal power unit Electricity output at all times For the first The ramp rate of the thermal power unit. For the first The landslide rate of the Taiwan thermal power unit. For the first The lower limit of the thermal output of a Taiwanese thermal power unit. For the first The upper limit of the thermal output of a thermal power unit.

[0030] Thermal energy storage units are represented as: , , , in, In the scene Down The amount of heat stored in the thermal energy storage unit at all times. This is the maximum capacity of the thermal storage tank. In the scene Down The amount of heat stored in the thermal energy storage unit at all times. This represents the maximum heat storage rate of the thermal storage tank. This represents the maximum heat release rate of the thermal storage tank. The initial heat storage capacity of the thermal storage tank. This represents the final amount of heat stored in the thermal storage tank.

[0031] Electric energy storage units are represented as: , , , , , in, For energy storage units The charging power, For energy storage units The maximum charging power, For energy storage units The discharge power, For energy storage units The maximum discharge power, For energy storage units exist State of charge at time t, For energy storage units exist State of charge at time t, For energy storage units Energy storage charging efficiency, For energy storage units Energy storage and discharge efficiency, For energy storage units The lower limit of the energy state, For energy storage units The upper limit of energy states, For energy storage units The initial state of charge. For energy storage units The state of charge at the end of the period.

[0032] Interruptible load constraints are expressed as follows: , in, In the scene Down The load can be interrupted at any time to reduce power. The maximum power reduction for interruptible loads.

[0033] Electrical load balance is represented as: , in, For the number of combined heat and power units, The number of energy storage units. This represents the actual electrical load value of the multi-energy park. For the electricity sold in the multi-energy park, Electricity purchased for the multi-energy park.

[0034] Heat load balance is expressed as: , in, This represents the actual heat load value of the multi-energy park.

[0035] Market performance constraints are expressed as follows: , in, The total number of days in the assessment period. The total number of time periods per day. In the scene Down Time of the first Green electricity consumed under contract in the Tianyuan District Green Electricity Market In the scene Down Time of the first The electricity generated by Tianyuan District's self-generated renewable energy sources, In the scene Down Time of the first Tianyuan District purchases electricity from external sources. In the scene Down Time of the first Tianyuan sells electricity to external parties. As a performance indicator for the consumption responsibility weight, this constraint means that at the end of the assessment period of the consumption responsibility weight system, the proportion of the amount of green electricity held by the park in the total electricity consumption shall not be lower than the consumption responsibility weight indicator.

[0036] The carbon emission assessment constraints are expressed as follows: , , in, For the initial carbon allowance of the multi-energy park, For the carbon credit purchase quota of the multi-energy park, For certified emission reductions in the multi-energy park, For the carbon credits sold in the multi-energy park, The carbon emission intensity of the node where the equipment is located. The electricity purchased by the park from the external power grid, This refers to the electricity sold by the industrial park to the external power grid. This is the mutual recognition coefficient between green electricity and carbon allowances. This refers to the park's self-generated renewable energy power generation. This refers to the amount of green electricity consumed by the park through green electricity market contracts.

[0037] Furthermore, the objective function of the multi-functional park is constructed with the goal of minimizing operating costs, specifically expressed as: , , , , , .

[0038] in, In the scene Net cost during the assessment period in the lower industrial park In the scene Next Daily load service revenue, To reduce the cost for multi-energy parks to participate in the electricity spot market, The cost of multi-energy parks participating in the carbon market, To reduce the cost for multi-energy parks to participate in the green electricity market, The revenue generated per unit of electricity consumption in the multi-energy park. In the scene Down Time of the first Actual daily electric heating load In the scene Down Time of the first Tianjin Electricity spot market clearing price For the electricity purchased by the multi-energy park in the energy market, For the electricity sold by the multi-energy park in the energy market, In the scene Down Time of the first Clearing price in the carbon market For the multi-energy park to purchase electricity in the carbon market, For the sale of electricity in the carbon market by the multi-energy park, In the scene Green electricity contract price, In the scene The total amount of green electricity contracts signed in the industrial park. To minimize the expected total cost of the park, This represents the total number of scenes. For the scene The probability of.

[0039] It should be noted that establishing the lower-level clearing model 202 for the electricity spot market, carbon market, and green electricity market involves determining the objective function of the model and the constraints on the operation of the electricity spot market, carbon market, and green electricity market. This lower-level clearing model 202 does not treat the electricity spot market, carbon market, and green electricity market independently, but rather integrates the trading boundaries, constraints, and value transmission relationships of the three markets into a unified optimization framework for collaborative solution. The electricity spot market is used to determine the electricity volume purchased and sold between the industrial park and the system, as well as the clearing price. The carbon market is used to determine the scale of carbon quota trading and compliance costs for the industrial park, while the green electricity market determines the scale of green energy consumption and the value of green attributes of the industrial park through contracted electricity volume and contracted price. In the lower-level clearing model 202, the three markets are interconnected through variables such as the industrial park's electricity purchase and sale volume, green electricity consumption, and carbon quota demand, thereby achieving unified decision-making and joint clearing.

[0040] It should also be noted that the electricity spot market establishes market and network constraints, including constraints on ordinary generating units, line power constraints, and node power constraints, based on network information and safe operating conditions.

[0041] A standard generating unit is represented as: , , , in, In order to be in Time Node The power generation capacity of conventional generating units, This represents the maximum power output of a conventional generating unit. In order to be in Time Node The power generation capacity of conventional generating units, This represents the ramp rate for conventional generating units in adjacent time periods. This represents the landslide rate for adjacent time periods of conventional generating units.

[0042] Line power constraints are expressed as follows: , in, Let be the nodal admittance matrix of the line. For nodes phase angle, For nodes phase angle, This represents the maximum value of the power flow along the line.

[0043] The node power constraint is expressed as: , in, For the electricity sales capacity of the multi-energy park, For the power purchase capacity of the multi-energy park, For renewable energy units in scenarios Down Time Node of effort, for Time Node Fixed load, The dual variable is a constraint.

[0044] The objective function of the electricity spot market, with the goal of maximizing social welfare, is specifically expressed as follows: , in, The total system cost, The total number of power grid nodes. For the cost of generating electricity from conventional units, The electricity sales price declared for the multi-energy park The electricity purchase price declared for the multi-energy park.

[0045] It should also be noted that the carbon market constructs carbon market constraints based on carbon quota information, including carbon quota balance constraints and upper and lower limits constraints on carbon quota sales.

[0046] The carbon quota balance constraint is expressed as: , in, For the sale of carbon credits in the multi-energy park, For the total number of other participants, Carbon allowances provided to other generating units. Purchase carbon credits for multi-energy parks For conventional generator sets in the first sky The amount of carbon allowances that need to be purchased during a given period.

[0047] The upper and lower limits for carbon quota sales are expressed as follows: , in, The maximum amount of carbon allowances that can be sold to other participants.

[0048] The objective function of the carbon market, with the goal of maximizing social welfare, is specifically expressed as follows: , in, The total transaction value of the carbon market. The bid price for selling carbon allowances in the multi-energy park. The bid price for selling carbon allowances to other participants. The bid price for purchasing carbon allowances for the multi-energy park.

[0049] The green electricity market operates on a monthly basis, using a bilateral negotiation approach to determine electricity prices and the amount of green electricity purchased in advance.

[0050] Aligning the three markets involves three steps: First, the green electricity market determines the basic green electricity contract volume for the industrial park, which in turn affects the park's net electricity purchase or sale demand in the electricity spot market. The electricity spot market then determines the electricity price and volume clearing results based on supply and demand, network constraints, and renewable energy output. Finally, the carbon market determines carbon trading costs based on the park's actual electricity purchase and sale behavior, green electricity consumption levels, and carbon quota obligations. Because renewable energy output is uncertain, its changes will cause changes in the park's electricity purchase and sale scale, further affecting spot electricity prices and carbon quota demand. Therefore, there is a linkage between market prices and output uncertainty.

[0051] Furthermore, the impact of carbon prices on electricity pricing can be transmitted through the mutual recognition coefficient C between green electricity and carbon allowances. The mutual recognition coefficient C represents the carbon emission reduction value that can be converted from a unit of green electricity consumption. When carbon prices rise, the emission reduction benefits corresponding to a unit of green electricity increase simultaneously, thereby effectively reducing the overall electricity purchase cost of the industrial park and prompting the park to consider the value of green electricity consumption and the benefits of low-carbon energy use more in its electricity pricing. When carbon prices fall, this deduction effect weakens, and its corrective effect on electricity pricing also decreases accordingly. Therefore, the mutual recognition coefficient C constitutes a key link in the transmission of carbon prices to electricity pricing, enabling changes in carbon market prices to directly affect the pricing strategies of industrial parks in the electricity market.

[0052] It should also be noted that, based on the upper-level decision-making model 201 and the lower-level clearing model 202 of the multi-energy park participating in the joint clearing market of electricity-carbon-green electricity, as well as the constraints of each model, a two-level optimization was performed. This optimization problem was programmed in Matlab and solved using Gurobi. This yielded a joint bidding decision scheme 300 for multi-energy parks participating in the electricity-carbon-green electricity multi-market considering uncertainties.

[0053] First, in Matlab, the parameters, decision variables, objective functions, and constraint matrices of the upper-level decision model 201 and the lower-level clearing model 202 are defined. The upper-level decision model 201, as the upper-level model, aims to minimize the operating cost of the multi-energy park and solves the problems of unit output, energy storage charging and discharging power, interruptible load regulation, and bidding volume and parameters for participating in the electricity market, carbon market, and green electricity market at various time periods. Then, the above bidding volume and related application information are used as inputs to the lower-level clearing model 202. The lower-level model combines network constraints, market supply and demand constraints, and carbon quota constraints to clear the electricity spot market, carbon market transaction volume, and corresponding clearing price. The lower-level clearing results are then fed back to the upper-level model to correct the park's electricity purchase and sale costs, carbon trading costs, and green electricity trading revenue in the objective function, thus forming a solution chain of upper-level bidding decision-making, lower-level market clearing, and result feedback correction. Furthermore, the bi-level optimization problem can be transformed into a single-level mathematical programming model using KKT conditions and strong duality theory, and then the Gurobi solver can be called for unified solution, ultimately yielding the optimal pricing decision result for the multi-energy park considering uncertainties.

[0054] Example 2, refer to Figure 3 As an embodiment of the present invention, a pricing decision system for multi-energy parks participating in multiple markets of electricity, carbon and green electricity that takes into account uncertainty is provided. The system includes a multi-source information integration and scenario quantification module 400, an electricity-carbon-green electricity coupling decision module 500, a cross-market value transmission and joint clearing module 600, a price expectation correction and evaluation module 700, and a two-layer system optimization and strategy output module 800.

[0055] The multi-source information integration and scenario quantification module 400 is used to acquire the physical parameters of heterogeneous equipment and the transaction rules of multiple markets within the multi-energy park, and to construct a set of power uncertainty scenarios based on historical data using a normal distribution.

[0056] The electricity-carbon-green electricity coupling decision module 500 is used to construct and solve the upper-level decision model 201. The upper-level decision model 201 has a preset green electricity and carbon quota mutual recognition coefficient C, which maps the green electricity consumption to an equivalent carbon emission reduction index I, incorporates the electricity carbon quota compliance assessment constraint, and generates a multi-market joint bidding model.

[0057] The cross-market value transmission and joint clearing module 600 is used to receive the joint bidding model and construct the lower-level clearing model 202 to simulate the coordinated operation of multiple markets such as electricity, carbon and green electricity, and output the clearing price.

[0058] The price expectation correction and evaluation module 700 is used to perform weighted expectation processing on the clearing price based on the probability of occurrence of the output uncertainty scenario set, and feeds it back to the upper-level decision model 201 for objective function correction.

[0059] The two-layer system optimization and strategy output module 800 is used to execute iterative loops. Under the condition that the upper-layer decision model 201 and the lower-layer clearing model 202 meet the constraints, it outputs a joint bidding decision scheme 300.

[0060] 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 the bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, which takes into account uncertainties, as proposed in the above embodiment.

[0061] 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 the bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, which takes into account uncertainties, as proposed in the above embodiment.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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 bidding decision-making method for multi-energy industrial parks participating in a multi-market electricity-carbon-green electricity sector, considering uncertainty, characterized in that... include: Obtain the internal equipment operation constraints parameters of the multi-energy park, as well as the operating rules (R) and historical transaction data (D) of the electricity market, carbon market and green electricity market to which it belongs; Based on the pre-set electricity-carbon mutual recognition mechanism, the green electricity consumption in the green electricity market is mapped to the equivalent carbon emission reduction index (I) in the carbon market using the mutual recognition coefficient (C) in order to establish a cross-market coupling relationship between electricity, carbon and green electricity (100). Based on the probability distribution characteristics of renewable energy output, the scenario-weighted expectation of operating cost (O) under cross-market coupling relationship (100) is calculated; With the goal of minimizing the operating cost (O) of the multi-energy park, the joint optimization solution (200) is carried out by taking into account the internal equipment operation constraints, market access constraints and the compliance constraints of equivalent carbon emission reduction indicators (I), and outputs a joint bidding decision scheme (300).

2. The bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, considering uncertainty, as described in claim 1, is characterized in that: The internal equipment operation constraints include the output constraints of the cogeneration unit, the charging and discharging power constraints of the energy storage system, and the state of charge constraints. The output constraint of the cogeneration unit is achieved by acquiring the real-time heat load demand of the multi-energy park and dynamically adjusting the heat and electricity output ratio of the cogeneration unit, thereby physically matching and decoupling the heat flow and electrical flow within the park.

3. The bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, considering uncertainty, as described in claim 1 or 2, is characterized in that: The energy storage system charging and discharging power constraints and state of charge constraints in the internal equipment operation constraints utilize the energy time-shifting characteristics of the energy storage system to physically smooth out random fluctuations in renewable energy output, and adjust the timing of market quotations in the park in conjunction with the internal equipment operation constraints.

4. The bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, considering uncertainty, as described in claim 3, is characterized in that: The mutual recognition coefficient (C) is taken as the average carbon emission intensity of the power grid in the area where the multi-energy park is located. The average carbon emission intensity is determined as the mutual recognition coefficient to characterize the carbon emission quota weight that is equivalently offset by a unit of green electricity consumption in the corresponding trading cycle.

5. The bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, considering uncertainty, as described in claim 1, is characterized in that: The compliance constraints of the equivalent carbon emission reduction indicator (I) include, Real-time calculation of the deviation between renewable energy power generation in the park and the preset consumption weight index; When the deviation is positive, the surplus is converted into tradable green electricity assets and included in the income. When the deviation is negative, the procurement scale of external green electricity or the amount of carbon quota replenishment is determined by joint optimization solution (200).

6. The bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, considering uncertainty, as described in claim 1, is characterized in that: The probability distribution characteristics of renewable energy output are combined with scenario sampling technology to generate several representative output scenarios. For each output scenario, calculate the corresponding market revenue and deviation penalty cost, and combine the scenario occurrence probability to transform the operating cost (O) into the scenario-weighted expected total cost.

7. The bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, considering uncertainty, as described in claim 1 or 5, is characterized in that: The joint optimization solution (200) adopts a two-layer iterative optimization architecture, namely, an upper-layer decision model (201) and a lower-layer clearing model (202). The upper-level decision model (201) performs the objective of minimizing the operating cost (O) of the multi-energy park, which is used to generate initial values ​​of pricing parameters covering multiple dimensions of electricity, carbon and green electricity; The lower-level clearing model (202) simulates the electricity market clearing rules, predicts the market clearing price based on the bidding status of each market participant, and feeds it back to the upper-level decision model (201). The upper-level decision model (201) adjusts the quotation parameters based on the predicted price feedback and iterates until the preset equilibrium convergence condition is reached.

8. The bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, considering uncertainty, as described in claim 7, is characterized in that: The lower-level clearing model (202) includes node power balance constraints and line power flow security constraints; The lower-level clearing model (202) guides the upper-level decision-making model (201) to adjust the electricity declaration range of the multi-energy park by solving the marginal electricity price of each node.

9. A pricing decision-making system for multi-energy parks participating in the electricity-carbon-green electricity multi-market considering uncertainty, employing the pricing decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market considering uncertainty as described in any one of claims 1 to 8, characterized in that: It includes a multi-source information integration and scenario quantification module (400), an electricity-carbon-green electricity coupling decision-making module (500), a cross-market value transmission and joint clearing module (600), a price expectation correction and evaluation module (700), and a two-layer system optimization and strategy output module (800). The multi-source information integration and scenario quantification module (400) is used to obtain the physical parameters of heterogeneous equipment and the transaction rules of multiple markets within the multi-energy park, and to construct a set of power output uncertainty scenarios based on historical data using a normal distribution; The electricity-carbon-green electricity coupling decision module (500) is used to construct and solve the upper-level decision model (201). The upper-level decision model (201) presets the mutual recognition coefficient (C) between green electricity and carbon quotas, maps the green electricity consumption to the equivalent carbon emission reduction index (I), incorporates the electricity carbon quota compliance assessment constraint, and generates a joint bidding model for multiple markets. The cross-market value transmission and joint clearing module (600) is used to receive the joint bidding model and construct the lower-level clearing model (202), simulate the coordinated operation of multiple markets of electricity, carbon and green electricity, and output the clearing price; The price expectation correction and evaluation module (700) is used to perform weighted expectation processing on the clearing price based on the probability of occurrence of the power output uncertainty scenario set, and feed it back to the upper-level decision model (201) for objective function correction; The two-layer system optimization and strategy output module (800) is used to execute an iterative loop and output a joint bidding decision scheme (300) under the constraint conditions of the upper-layer decision model (201) and the lower-layer clearing model (202).

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the bidding decision-making method for multi-energy parks participating in the electricity-carbon-green electricity multi-market, as described in any one of claims 1 to 8, which takes into account uncertainty.

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