Electricity-carbon coupling day-ahead two-stage clearing optimization method considering novel main body

Abstract

The invention belongs to the technical field of electricity markets and low-carbon clearing, and discloses a novel subject participated electricity spot market day-ahead two-stage clearing method under electricity-carbon coupling. The method comprises the following steps: firstly, designing a two-stage clearing mechanism comprising preliminary clearing and multi-target robust optimization; secondly, a unit carbon quota accounting and transaction cost model and a multi-energy power generator model considering marginal cost and carbon emission are constructed based on regional power grid carbon emission factors; then the above models are fused, a non-parameterized uncertain set is adopted to describe new energy fluctuation, a day-ahead two-stage clearing model based on a min-max-min three-layer robust optimization framework is constructed, and the model fuses stepped quotation and dynamic carbon emission factors; finally, the model is converted into a mixed integer linear programming problem, and Camp is adopted; and solving by a CG algorithm. According to the method, the comprehensive clearing cost can be effectively reduced, energy conservation and carbon reduction are promoted, meanwhile, the robustness of the clearing plan to deal with extreme scenes is improved, and the system operation safety is guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and control technology, specifically involving a two-stage clearing optimization method for the day-ahead electricity-carbon coupling considering novel subjects, to achieve safe, low-carbon, and economical clearing of the day-ahead spot market of the power system, and is particularly suitable for new power systems with a high proportion of new energy access. Background Technology

[0002] Building a new power system dominated by new energy sources urgently requires the establishment of a power market mechanism that balances economic efficiency with low-carbon goals. However, the large-scale integration of fluctuating renewable energy sources such as wind and solar power, along with the emergence of diverse players such as energy storage and virtual power plants, brings significant complexity and uncertainty to the system. Traditional clearing mechanisms are insufficient to effectively promote carbon emission reduction while ensuring power security.

[0003] Currently, research integrating carbon targets into clearing models mainly falls into two categories: one is to directly convert carbon trading costs into unit bids, guiding low-carbon priority clearing by changing cost ranking; the other is to construct multi-objective optimization models that simultaneously consider electricity purchase costs and carbon emission costs to seek a balance between economics and environmental protection. However, these methods have significant limitations. First, most do not coordinate operating rules and low-carbon targets from the top-level system design of market mechanisms, leading to poor or distorted carbon cost signal transmission. Second, traditional models rely on deterministic predictions, and the electricity and ancillary service markets often operate independently, making it difficult to adapt to the strong uncertainty of high-proportion renewable energy. The clearing results may face difficulties in actual implementation, affecting the real-time balance of the system. At the same time, there have been preliminary explorations of clearing models for single entities such as energy storage and virtual power plants, but existing research generally focuses on a few entity types and lacks a unified clearing framework that can integrate multiple entities such as traditional units, renewable energy, energy storage, virtual power plants, and adjustable loads. The operational characteristics and market behavior of heterogeneous entities are difficult to accurately depict in a unified model, and their carbon attribute value cannot be fairly reflected, which limits the full realization of the system's flexibility potential and low-carbon benefits.

[0004] In summary, driven by energy transition, constructing a robust clearing mechanism that can organically coordinate the electricity market and carbon trading market, and efficiently accommodate the participation of diverse new market players, has become an urgent issue facing the current development of the electricity market. While existing research has made progress on individual technical aspects, it suffers from significant shortcomings in the systematic nature of mechanism design, robustness in dealing with uncertainties, and completeness in integrating multiple market players. Therefore, this invention, based on the global optimization perspective of the electricity trading center, aims to overcome existing limitations and propose a novel day-ahead market clearing mechanism and method that can simultaneously guarantee market efficiency, system security, and low-carbon goals. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention, based on the overall perspective of power trading centers, aims to solve key issues of coordinated clearing and multi-objective robust optimization in the electricity-carbon market. It proposes a day-ahead market clearing mechanism that considers the participation of new market participants under the electricity-carbon coupled market mechanism and includes two stages: preliminary clearing and multi-objective robust optimization. The goal is to construct a day-ahead clearing model that is economical, low-carbon, and robust, in order to provide practical theoretical and methodological support for the mechanism design and optimized operation of the electricity market under the new circumstances.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a two-stage clearing optimization method for an electro-carbon coupling front considering a novel host, characterized in that the method includes the following steps: Step 1: Analyze the joint operation framework of the electricity-carbon market and design a two-stage clearing mechanism for the day-ahead electricity spot market, which includes preliminary clearing and multi-objective robust optimization clearing, with the participation of new types of entities under the electricity-carbon coupled market mechanism. Step 2: Establish a carbon emission quota accounting and carbon trading cost calculation model for generating units based on the regional power grid carbon emission factor, and construct a multi-energy power generator model that takes into account marginal costs and carbon emissions, including traditional energy power generators, new energy power generators and new market entities; Step 3: Based on the description of the volatility characteristics of new energy based on the non-parametric uncertainty set, design a three-layer robust optimization framework of min-max-min, and construct a two-stage clearing model for the day-ahead electricity spot market that integrates tiered pricing and dynamic carbon emission factors and considers the participation of new entities under the electricity-carbon coupling. Step 4: Organize the objective function and constraints of the two-stage day-ahead clearing model of the electricity spot market into a matrix form, and use the C&CG algorithm to solve the clearing model to obtain the day-ahead clearing result of the electricity spot market, so as to realize the safe, low-carbon and economical operation of the new power system.

[0007] This technical solution is further optimized. The day-ahead two-stage clearing method for the electricity spot market, which considers the participation of new market players under the electricity-carbon coupled market mechanism, has an objective function in the initial clearing stage that minimizes the marginal energy cost of the day-ahead clearing scheme. The optimization decision variables include the start-up and shutdown plans of thermal power units of traditional energy generators. Power generation plan Upward rotation of standby capacity scheme and downward rotation spare capacity scheme It also includes load reduction plans for load aggregators among new market players. Boolean variables for load transfer Clearing plan for loads that can be shifted The initial period after the transferable load is moved. Independent energy storage charging and discharging state variables during time period t Independent energy storage charging and discharging power during time period t Charge and discharge plan for energy storage in a virtual power plant Charge and discharge capacity Load reduction plans The objective of the multi-objective robust optimization clearing phase is to maximize comprehensive social benefits. Specific optimization decision variables, besides the start-up and shutdown plans in the initial clearing phase, include... All other aspects are re-optimized in the new objective function, and this also includes the power adjustment plan for thermal power units in the second-stage lower-level model. wind curtailment Wasted light , Involuntary load shedding by load-side users Decision variables, etc.

[0008] This technical solution is further optimized, and step 1 specifically includes: Step 1.1: Analyze the coupling mechanism between the electricity and carbon markets: The carbon market directly influences the clearing order of the electricity spot market through carbon prices, altering the competitive landscape on the generation side. Rising carbon prices increase the marginal cost of fossil fuel units, causing them to fall behind in the cost-based clearing mechanism and reduce their output; while renewable energy units gain more power generation space due to their zero-carbon cost advantage, thereby promoting a cleaner power structure.

[0009] Meanwhile, the operation of the electricity spot market has a reverse impact on the carbon market. When electricity demand is high or renewable energy is insufficient, rising spot electricity prices will stimulate increased generation of fossil fuel units, leading to increased demand for carbon emission allowances and pushing up carbon prices. Conversely, a surge in renewable energy generation will suppress electricity prices and fossil fuel output, reducing allowance demand and putting downward pressure on carbon prices.

[0010] The two markets are closely coupled through price linkage and negative feedback mechanisms, forming a closed loop of "carbon price affecting electricity clearing → clearing affecting carbon emissions → carbon emissions affecting carbon price." The core link of this system is the "carbon intensity per kilowatt-hour," which quantitatively links electricity production and environmental costs. Through this dynamic linkage, the system can automatically seek an equilibrium point between economic and carbon costs amidst fluctuations, ultimately collaboratively achieving secure power supply and a low-carbon transformation for the power industry.

[0011] Step 1.2: Design a two-stage clearing mechanism for the electricity spot market, involving new participants and including preliminary clearing and multi-objective robust optimization clearing, under an electricity-carbon coupled market mechanism: To address the problem of low-carbon emission clean generating units facing obstacles in the electricity spot market due to their failure to highlight their low-carbon value, and to explore how to restructure the rules of the electricity spot market so that it can prioritize the output of low-carbon and environmentally friendly generating units while adhering to the principle of marginal clearing, this invention patents an improved two-stage clearing mechanism for the electricity spot market, involving new entities and including preliminary clearing and multi-objective robust optimization clearing, under an electricity-carbon coupled market mechanism. This clearing mechanism mainly includes the following two stages: (1) Preliminary clearing stage. With the goal of minimizing the marginal energy cost of the system, preliminary market clearing is carried out under the premise of meeting constraints such as unit operation, load balance, and grid safety. (2) Multi-objective robust optimization stage. With the goal of maximizing comprehensive social benefits, formal market clearing is carried out under the premise of meeting the two-stage coupling constraints of thermal power units and system safety constraints.

[0012] The two-stage clearing process of the electricity-carbon coupled market is as follows: after the trading center releases the market information for the next day, each participant submits an application; the trading center conducts preliminary clearing and multi-objective robust optimization clearing in sequence to form the electricity market results; then carbon emission trading is carried out, and the electricity-carbon market results are fed back to the next round of clearing to update the pricing and constraints, forming a closed-loop linkage.

[0013] This technical solution is further optimized, and step 2 specifically includes: Step 2.1: Establish a model for calculating unit carbon emission quotas and carbon trading costs. Based on the overall carbon emission levels and emission reduction expectations of the power generation industry, a baseline allocation of free initial carbon emission allowances is established. The free carbon allowances available up to the system date meet the following requirements: In the formula: For traditional energy power generators Free carbon emission allowances currently available in the market; T represents the free quota per unit of power; T is the number of periods in the entire clearing cycle. The clearing time interval is T = 24h / One decision-making period; Let be the output power during time period t; This refers to the number of all traditional energy power generators.

[0014] To accurately reflect the supply and demand law of carbon allowances in the carbon trading market, this invention employs a tiered pricing strategy to set the trading price of carbon emission allowances. The tiered carbon emission allowance trading price model is as follows.

[0015]

[0016] In the formula: The carbon trading price is tiered. As the benchmark price for the carbon market; The total carbon emissions of traditional energy power generator i under the recent cleanup scheme; d represents the tiered price growth rate; d represents the range length of the carbon emission credit trading price.

[0017] Step 2.2: Construct a model for traditional energy power generators, including operating constraints of thermal power units, a pricing and cost model for traditional energy power generators, and a dynamic carbon emission intensity model for traditional energy power generators based on the principle of equal area. (1) Operating constraints of thermal power units

[0018] In the formula: , These represent the minimum and maximum power of thermal power unit i, respectively. , These represent the upper and lower limits of the ramp power for thermal power unit i, respectively. It is a Boolean variable. This indicates that thermal power unit i is in a shutdown state during time period t. This indicates that thermal power unit i is in the operating state during time period t; , These represent the minimum number of time periods during which thermal power unit i is in the off or on state, respectively. , The upper and lower reserve capacity provided by thermal power unit i during time period t.

[0019] (2) Pricing and cost model of traditional energy power generators Traditional energy power generators include coal-fired and gas-fired power units. Based on the marginal cost of power generation model, a pricing model conforming to market rules was constructed. The fuel cost curve is represented by the following quadratic function:

[0020] In the formula: This refers to the fuel consumption cost of a traditional energy generator i during time period t. The unit price of fuel required for generating electricity by thermal power unit i; , and It is a traditional energy power generator The fitting coefficients and parameters of the coal consumption curve can be obtained by fitting historical operating data of the unit.

[0021] By analyzing traditional energy power generation thermal power units Differentiating the power generation cost curve yields its marginal power generation cost as:

[0022] Traditional energy power generators The price for segment n is:

[0023] In the formula: Indicates traditional energy power generators The quote in the nth segment; Indicates the generator The initial power generation capacity in the nth segment. Due to traditional energy generators The operating costs mainly include: fuel costs Start-up and shutdown costs Backup costs Therefore, the electricity purchase cost of the power grid from traditional energy generators satisfy: The components satisfy the following:

[0024] In the formula: , The cost of a single start-up and shutdown of thermal power unit i; , Provide cost coefficients for unit upper and lower standby capacity of thermal power unit i.

[0025] (3) Dynamic carbon emission intensity model of traditional energy power generators based on the principle of equal area The carbon emissions of traditional generator sets are related to their power generation capacity. The dynamic carbon emission intensity of the unit can be derived from the coal consumption cost formula above. The calculation formula is as follows:

[0026] In the formula: The carbon content of the coal used in conventional thermal power generating unit i; Here is the molar mass of carbon dioxide; denoted as , where is the molar mass of carbon.

[0027] Therefore, traditional energy power generators The carbon emission intensity in stage n is:

[0028] Therefore, the e-commerce platform The total carbon emissions under the current cleanup plan are:

[0029] Step 2.3: Construct a new energy power generation model: New energy power generators mainly include two types: wind farms and photovoltaic power plants. In the day-ahead declaration of the electricity spot market, new energy power generators declare their new energy power generation forecast curves. Therefore, wind farms (w) satisfy the following constraints:

[0030] Photovoltaic power station The following constraints must be met:

[0031] In the formula: The first among new energy power generators The predicted power output declared by each wind farm during time period t; This represents the maximum installed capacity of the wind farm. The number of wind farms; The first among new energy power generators The predicted power output of a photovoltaic power station during time period t; This represents the maximum installed capacity of the photovoltaic power station. This refers to the number of photovoltaic power plants.

[0032] Step 2.4: Construct a new market entity model, which includes load aggregators, independent energy storage, virtual power plants, and other entities. (1) The load aggregator mainly aggregates two types of load-side resources: loads that can be reduced and loads that can be shifted.

[0033] 1) Load can be reduced Reduceable loads refer to loads in a regional power grid whose operating time remains unchanged, but whose operating power can be reduced to a certain extent. The model satisfies the following constraints:

[0034]

[0035] In the formula: Indicates load aggregator The amount of load that can be reduced during period t; This indicates the maximum load reduction that can be declared; To reduce the price declared for the unit load power demand; The number of load aggregators; This indicates that the declared amount can reduce the growth rate of load prices; In order to reduce the initial load compensation price, the power grid calls load aggregators in the day-ahead spot market. Cost of reducing load for:

[0036] 2) Loads that can be moved The shiftable load is constrained by the production process; only the start time of the entire power consumption process can be planned, and its continuous operating time and power consumption cannot be changed. It must meet the following constraints:

[0037] In the formula: For load aggregator The initial period after the transfer of transferable loads in the day-ahead market clearing scheme; This refers to the original start time period of operation; , Price per unit power subject to application for transfer The earliest and latest start times affected; , After translation, in Power consumption during the time period compared to before the shift Power consumption during a given time period; For continuous runtime; This represents the electrical power consumed during time period t after translation. Where:

[0038] In the formula: This represents the maximum number of elastically translatable time periods. To match the unit power price for the application transfer The elastic expansion coefficient of the relevant acceptable translation time interval satisfies the following constraint:

[0039] In the formula: and These refer to the minimum price at which users are willing to change their electricity usage time to participate in the day-ahead spot market bidding and the price at which users can accept the upper limit of the shifted time period. To compensate for the sensitivity coefficient, the load aggregator is therefore... Application for transferable load Price per unit power of translation Therefore, the cost of shifting the load is... satisfy:

[0040] In the formula: It is a Boolean variable. Indicates a load that can be moved. Translation occurs. This indicates that no translation occurs.

[0041] (2) Independent energy storage 1) Independent energy storage operation constraints

[0042] In the formula: and These represent the charging and discharging state variables of the independent energy storage s during time period t, respectively, with values ​​of 0 or 1, and simultaneous charging and discharging are prohibited. This represents the total number of independent energy storage units in the system. and These are the charging and discharging power, respectively. and These represent the upper limits of the charging and discharging power of the energy storage, respectively.

[0043] 2) Considering the independent energy storage pricing and cost model over the entire life cycle. The operating costs of independent energy storage mainly include: charging costs. With discharge benefits Price difference and battery aging costs Therefore, the electricity purchase cost from the grid to independent energy storage s satisfy:

[0044] The components satisfy the following:

[0045] In the formula: The price per unit of discharge power; Cost per unit of charging power; This refers to the battery aging cost per unit of charge / discharge power. Therefore, independent energy storage... Declaring unit charging price when participating in the electricity spot market unit discharge price And the cost of battery aging compensation per unit charge / discharge power Its specific expression is as follows:

[0046] In the formula: , and These are the declared charging price elasticity coefficient, discharging price elasticity coefficient, and battery aging compensation cost coefficient per unit of charging and discharging power.

[0047] (3) Virtual power plant Virtual power plants integrate distributed energy resources, energy storage systems, and controllable loads to form flexible and controllable capabilities similar to traditional power plants, enabling them to participate in the electricity spot market. This invention considers virtual power plants that aggregate distributed resources such as photovoltaics, load shedding, and energy storage. Therefore, virtual power plants... The predicted power output of the aforementioned photovoltaic power plant during time period t is to be declared. Charging price for independent energy storage Discharge price Battery degradation compensation cost ; and segmented pricing that can reduce load. Relevant parameters participate in the clearing of the electricity spot market. The related constraints will not be elaborated here. Therefore, the response cost of the virtual power plant... For energy storage costs and can reduce load costs sum. Right now:

[0048]

[0049] In the formula: The day-ahead response cost of virtual power plant x; For the benefit of energy storage discharge; The price per unit of discharge power of energy storage; The discharge power of the energy storage during time period t; Cost of charging energy storage; The cost of charging a unit of energy storage power; The charging power of energy storage during time period t; The aging cost of energy storage batteries; Battery aging cost per unit charge / discharge power of energy storage; The price per unit of load reduction that can be reduced during time period t; This represents the amount of load reduction that can be achieved during time period t. This represents the number of virtual power plants.

[0050] This technical solution is further optimized, and step 3 specifically includes: Step 3.1: Establish the objective function of the preliminary clearing phase model: In the initial clearing phase, the power trading center optimizes the system with the goal of minimizing marginal energy costs, obtaining preliminary output plans for traditional units such as thermal power plants, providing an initial starting point for the second phase of optimization. Energy costs mainly include: generation costs, start-up and shutdown costs, and reserve costs for traditional energy generators; charging and discharging costs for independent energy storage; and response costs for load aggregators and virtual power plants.

[0051]

[0052] Step 3.2: Establish constraints for the preliminary clearing phase model: The initial clearing phase constraints include those related to individual thermal power units among traditional energy providers, constraints related to renewable energy generators, constraints related to load aggregators that can be reduced or shifted, constraints related to independent energy storage, constraints related to virtual power plants, power system power balance constraints, line transmission capacity constraints, and system reserve capacity constraints. Additionally, it also includes: Power balance constraints of the power system at any time interval t:

[0053] In the formula: For the initial clearing phase of traditional energy power generators, thermal power units The magnitude of the output during time period t; This is the day-ahead load demand forecast for the electricity spot market at time period t. The reserve capacity constraint of the power system at any time period t is:

[0054] In the formula: and These represent the minimum total safe upward and downward reserve capacity required by the system during time period t, respectively.

[0055] Step 3.3: Establish the objective function of the multi-objective robust optimization clearing stage model: The second-stage clearing model aims to maximize overall social welfare, balancing safety, economic, and low-carbon requirements while considering the uncertainties of new energy sources. Its objective function comprises four parts: day-ahead market operating costs (economic benefits), carbon trading costs (low-carbon benefits), unit output deviation penalties, and the risk control costs of the lower-level model. The lower-level model, based on the clearing results of the upper-level model, aims to minimize the risk control costs under extreme scenarios.

[0056] (1) Objective function of the upper-level model 1) Economic benefits – power system operating costs The goal of the second-stage upper-level model is to minimize the operating costs of the power system given the fixed start-up and shutdown status of traditional energy power generation units. The specific expression is as follows:

[0057] 2) Low-carbon benefits – Costs for power generators participating in carbon market trading

[0058] 3) Unit output deviation penalty item

[0059] In the formula: The coefficient for the penalty of deviation in unit output; The objective function for the second stage is to optimize the output of thermal power units in the clearing results of the second stage.

[0060] (2) Objective function of the lower-level model Lower-level risk penalty costs Including wind curtailment penalty costs The cost of abandoning light , and the cost of loss of load , can be represented as:

[0061] The components satisfy the following:

[0062] In the formula: , , The cost factor for the risk penalty of wind curtailment, solar curtailment, and load loss per unit of abandoned electricity; , , and These represent the power of wind curtailment, solar curtailment, and load shedding under extreme scenarios during time period t.

[0063] In summary, the objective function for the clearing phase of multi-objective robust optimization is:

[0064] In the formula: variables For the final required day-to-day clearing plan, The clearing scheme for the electricity spot market during period t; U represents a set of uncertain scenarios for the output and load demand of renewable energy generators; For the optimization decision quantity of the lower-level model, where For the optimization decision variables of the lower-level model in time period t, This represents the decision made on the optimization variable Y of the lower-level model, given an uncertain scenario u and a day-ahead scheduling scheme X.

[0065] Step 3.4: Establish constraints for the multi-objective robust optimization clearing phase model: (1) Constraints of the upper-level model In addition to the constraints in the first-stage model, the upper-level model constraints also include any line in the power system. The transmission capacity constraint, namely:

[0066] In the formula: , , , , and Assign a power transfer factor; This represents the total number of nodes in the system. This represents the maximum power flow value of line r in the system. The load size of node k in the next time period t is determined by the current plan.

[0067] (2) Lower-level model constraints The lower-level optimization model constraints include system power balance constraints considering the uncertainties of wind and solar power output and load demand, constraints related to traditional energy generators, and system transmission capacity constraints. The system power balance constraints are as follows:

[0068] In the formula: For traditional energy generators in the lower-level model The power generation adjustment during time period t; , , and These are the new energy output and load demand scenarios that take into account historical adverse scenario errors during simulation.

[0069] The relevant output and ramp-up constraints for traditional energy generators in the lower-level model are as follows:

[0070] Furthermore, based on historical power system datasets, the uncertainty constraints (set) for wind farm output among renewable energy power generators are as follows:

[0071] In the formula: , and Let represent the magnitude, lower limit, and upper limit of power fluctuation of the w-th wind farm during time period t under extreme scenarios.

[0072] The uncertainty constraints (set) of photovoltaic power plant output in renewable energy power generators are as follows:

[0073] In the formula: , and Let represent the magnitude, lower limit, and upper limit of power fluctuation of the v-th photovoltaic power station during time period t under extreme scenarios.

[0074] The load demand uncertainty constraint (set) is:

[0075] In the formula: , and This represents the magnitude, lower limit, and upper limit of the system load demand power fluctuation during time period t in extreme scenarios.

[0076] Any line in the lower-level model The transmission capacity constraint is:

[0077] In the formula: This represents the load demand of node k in time period t in the lower-level model.

[0078] This technical solution is further optimized, and step 4 specifically includes: Step 4.1: Second-stage model processing and solution Based on the objective function and constraints of the second stage of the constructed day-ahead spot market clearing optimization model, they are presented in matrix form. The compact form of the proposed multi-objective robust optimization clearing model can be expressed as follows:

[0079] In the formula: These are the equality constraints for the upper-level model in the second stage of the model; These are the inequality constraints for the upper-level model in the second stage of the model. This represents the equality constraints of the lower-level model in the second stage of the model; Represents the inequality constraints of the lower-level model in the second stage of the model; The C&CG algorithm is applied to solve the model. This method decomposes the practical problem into a main problem (MP) and a subproblem (SP), and solves these two problems alternately to gradually approach the optimal solution. The mathematical expressions for the main problem MP and the subproblem SP are as follows:

[0080]

[0081] In the formula: The auxiliary variables introduced to replace the subproblems are used to directly obtain a temporary solution. Then, the structure of the subproblems is transformed through duality theory, and the max-min problem is transformed into a max single-layer optimization problem. Step 4.2: Solving the C&CG algorithm flow Step 1: Initialize the upper bound of the objective function and lower boundary Number of iterations Let the convergence gap between the upper and lower bounds be . , Set to a small positive number; Step Two: Let The initial solution obtained Then, substitute the subproblems to solve for the initial extreme scenario. ; Step 3: Extreme scenarios Substituting into the main problem, we obtain the optimal solution. Update the Nether equal ; Step 4: Apply the optimal solution from Step 3 Substituting into the subproblem, we obtain the optimal solution. ;make Update the upper boundary for and sum; Step 5: Judgment If the condition is met, the process ends; otherwise, it will... Return to step three.

[0082] Unlike existing technologies, the beneficial effects of this invention are mainly reflected in the following aspects: Addressing the core bottlenecks of existing methods, such as the difficulty in systematically coordinating market rules and low-carbon goals, the inability to effectively cope with the uncertainties of high-proportion renewable energy, and the lack of a unified market framework integrating diverse and heterogeneous new entities, this invention proposes a robust clearing mechanism that organically coordinates the electricity and carbon trading markets and accommodates the participation of multiple entities. Compared to traditional clearing methods, this invention achieves three major breakthroughs: First, in the top-level design of the mechanism, it achieves endogenous coupling and signal connection between the electricity market and the carbon trading market, enabling carbon costs to directly and efficiently guide power generation sequence and investment decisions through market mechanisms, fundamentally strengthening emission reduction incentives; second, in dealing with uncertainty, it constructs a robust optimization model capable of uniformly handling strong uncertainties in source and load, and collaboratively optimizes electricity and ancillary services, significantly improving the feasibility and system balance capability of the clearing results in actual operation; third, it establishes for the first time a unified clearing framework that can accurately characterize and fairly aggregate the operating characteristics and carbon attributes of diverse entities such as traditional units, renewable energy, energy storage, virtual power plants, and adjustable loads, fully releasing the potential for system flexibility and synergistic low-carbon benefits. Attached Figure Description

[0083] Figure 1 The two-stage clearing optimization method process is designed to take into account the new type of electro-carbon coupling. Figure 2 A diagram illustrating the main structure of the electricity spot market under the new power system; Figure 3A two-stage clearing model framework for the electricity spot market with the participation of new entities under the electricity-carbon coupling; Figure 4 Here is the flowchart for the C&CG algorithm. Detailed Implementation

[0084] To explain in detail the technical content, structural features, objectives, and effects of the technical solution, the following description is provided in conjunction with specific embodiments and accompanying drawings.

[0085] This invention discloses a two-stage clearing optimization method for the electricity-carbon coupling day-ahead market that takes into account new market participants. Addressing key issues such as insufficient coordination in the current electricity-carbon market, difficulty in efficiently accommodating diverse participants, and challenges in handling strong uncertainties, this invention proposes a two-stage clearing mechanism for the electricity-carbon coupling day-ahead market with the participation of new market participants. This mechanism innovatively constructs a two-stage architecture comprising preliminary clearing and multi-objective robust optimization: In the preliminary clearing stage, a preliminary clearing scheme is generated based on typical scenarios to provide the market with clear price signals; in the multi-objective robust optimization stage, a power generator model considering marginal costs and carbon emissions is established, and a three-layer robust optimization framework based on non-parametric uncertainty sets is designed to accurately characterize the uncertainty of renewable energy output and market boundaries. Simultaneously, the model deeply integrates a tiered pricing mechanism and a dynamic carbon emission factor, thereby synergistically achieving the system's economic efficiency, low carbon footprint, and robustness within a unified framework, providing solid theoretical and technical support for the design of new electricity market mechanisms.

[0086] Please see Figure 1 The diagram shows the two-stage clearing mechanism of the electricity spot market with the participation of new entities under the electricity-carbon coupling model, including the following steps: Step 1: Analyze the joint operation framework of the electricity-carbon market, and design a two-stage clearing mechanism for the electricity spot market under the electricity-carbon coupled market mechanism, involving new types of participants and including preliminary clearing and multi-objective robust optimization clearing, as shown below: Step 1.1: Analyze the coupling mechanism between the electricity and carbon markets: The carbon market directly influences the clearing order of the electricity spot market through carbon prices, altering the competitive landscape on the generation side. Rising carbon prices increase the marginal cost of fossil fuel units, causing them to fall behind in the cost-based clearing mechanism and reduce their output; while renewable energy units gain more power generation space due to their zero-carbon cost advantage, thereby promoting a cleaner power structure.

[0087] Meanwhile, the operation of the electricity spot market has a reverse impact on the carbon market. When electricity demand is high or renewable energy is insufficient, rising spot electricity prices will stimulate increased generation of fossil fuel units, leading to increased demand for carbon emission allowances and pushing up carbon prices. Conversely, a surge in renewable energy generation will suppress electricity prices and fossil fuel output, reducing allowance demand and putting downward pressure on carbon prices.

[0088] The two markets are closely coupled through price linkage and negative feedback mechanisms, forming a closed loop of "carbon price affecting electricity clearing → clearing affecting carbon emissions → carbon emissions affecting carbon price." The core link of this system is the "carbon intensity per kilowatt-hour," which quantitatively links electricity production and environmental costs. Through this dynamic linkage, the system can automatically seek an equilibrium point between economic and carbon costs amidst fluctuations, ultimately collaboratively achieving secure power supply and a low-carbon transformation for the power industry.

[0089] Step 1.2: Design a two-stage clearing mechanism for the electricity spot market, involving new participants and including preliminary clearing and multi-objective robust optimization clearing, under an electricity-carbon coupled market mechanism: To address the problem of low-carbon emission clean generating units facing obstacles in the electricity spot market due to their failure to highlight their low-carbon value, and to explore how to restructure the rules of the electricity spot market so that it can prioritize the output of low-carbon and environmentally friendly generating units while adhering to the principle of marginal clearing, this invention patents an improved two-stage clearing mechanism for the electricity spot market, involving new entities and including preliminary clearing and multi-objective robust optimization clearing, under an electricity-carbon coupled market mechanism. This clearing mechanism mainly includes the following two stages: (1) Preliminary clearing stage. With the goal of minimizing the marginal energy cost of the system, preliminary market clearing is carried out under the premise of meeting constraints such as unit operation, load balance, and grid safety. (2) Multi-objective robust optimization stage. With the goal of maximizing comprehensive social benefits, formal market clearing is carried out under the premise of meeting the two-stage coupling constraints of thermal power units and system safety constraints.

[0090] The two-stage clearing process of the electricity-carbon coupled market is as follows: after the trading center releases the market information for the next day, each participant submits an application; the trading center conducts preliminary clearing and multi-objective robust optimization clearing in sequence to form the electricity market results; then carbon emission trading is carried out, and the results of the electricity-carbon market are fed back to the next round of clearing for updating bids and constraints, forming a closed-loop linkage.

[0091] Please see Figure 2 The diagram shown illustrates the main structure of the electricity spot market under the new power system.

[0092] Step 2: Establish a model for calculating unit carbon emission quotas and carbon trading costs based on regional power grid carbon emission factors. Construct a multi-energy power generator model that considers marginal costs and carbon emissions, including traditional energy power generators, new energy power generators, and new market entities. The specific steps are as follows: Step 2.1: Establish a model for calculating unit carbon emission quotas and carbon trading costs. Based on the overall carbon emission levels and emission reduction expectations of the power generation industry, a baseline allocation of free initial carbon emission allowances is established. The free carbon allowances available up to the system date meet the following requirements:

[0093] In the formula: For traditional energy power generators Free carbon emission allowances currently available in the market; Free quota per unit power; This represents the number of time periods in the entire clearing cycle. The clearing time interval is T = 24h / One decision-making period; Let be the output power during time period t; This refers to the number of all traditional energy power generators.

[0094] To accurately reflect the supply and demand law of carbon allowances in the carbon trading market, this invention employs a tiered pricing strategy to set the trading price of carbon emission allowances. The tiered carbon emission allowance trading price model is as follows.

[0095]

[0096] In the formula: The carbon trading price is tiered. As the benchmark price for the carbon market; For traditional energy power generators Total carbon emissions under the recent cleanup plan; The price growth rate is tiered. The range of prices for carbon emission credit trading.

[0097] Step 2.2: Construct a model for traditional energy power generators, including operating constraints of thermal power units, a pricing and cost model for traditional energy power generators, and a dynamic carbon emission intensity model for traditional energy power generators based on the principle of equal area. (1) Operating constraints of thermal power units

[0098] In the formula: , These represent the minimum and maximum power of thermal power unit i, respectively; , These are the upper and lower limits of ramp power, respectively. It is a Boolean variable. This indicates that the system was in a shutdown state during time period t. This indicates that the device is in an on-the-go running state during time period t; , These represent the minimum number of time periods during which the device is in a powered-off or powered-on state, respectively. , The upper and lower reserve capacity provided during time period t.

[0099] (2) Pricing and cost model of traditional energy power generators Traditional energy power generators include coal-fired and gas-fired power units. Based on the marginal cost of power generation model, a pricing model conforming to market rules was constructed. The fuel cost curve is represented by the following quadratic function:

[0100] In the formula: This refers to the fuel consumption cost of a traditional energy generator i during time period t. The unit price of fuel required for power generation; , and These are the fitting coefficients for the coal consumption curve, and the parameters can be obtained by fitting historical operating data of the unit.

[0101] By analyzing traditional energy power generation thermal power units Differentiating the power generation cost curve yields its marginal power generation cost as:

[0102] Traditional energy power generators The price for segment n is:

[0103] In the formula: This represents the quote from traditional energy generator i in the nth segment; This represents the initial power generation capacity of generator i in segment n. Since the operating costs of traditional energy generator i mainly include: fuel costs... Start-up and shutdown costs Backup costs Therefore, the electricity purchase cost of the power grid from traditional energy generators satisfy: The components satisfy the following:

[0104] In the formula: , The cost of a single start-up and shutdown of thermal power unit i; , Provide cost coefficients for unit upper and lower standby capacity of thermal power unit i.

[0105] (3) Dynamic carbon emission intensity model of traditional energy power generators based on the principle of equal area The carbon emissions of traditional generator sets are related to their power generation capacity. The dynamic carbon emission intensity of the unit can be derived from the coal consumption cost formula above. The calculation formula is as follows:

[0106] In the formula: The carbon content of the coal used in conventional thermal power generating unit i; Here is the molar mass of carbon dioxide; This refers to the molar mass of carbon. Therefore, traditional energy generators... The carbon emission intensity in stage n is:

[0107] Therefore, the e-commerce platform The total carbon emissions under the current cleanup plan are:

[0108] Step 2.3: Construct a new energy power generation model: New energy power generators mainly include two types: wind farms and photovoltaic power plants. In the day-ahead declaration of the electricity spot market, new energy power generators declare their new energy power generation forecast curves. Therefore, wind farms (w) satisfy the following constraints:

[0109] Photovoltaic power station The following constraints must be met:

[0110] In the formula: The first among new energy power generators The predicted power output declared by each wind farm during time period t; For the first Maximum installed capacity of wind farm; The number of wind farms; The first among new energy power generators The predicted power output of a photovoltaic power station during time period t; For the first The maximum installed capacity of a photovoltaic power station; This refers to the number of photovoltaic power plants.

[0111] Step 2.4: Construct a new market entity model, which includes load aggregators, independent energy storage, virtual power plants, and other entities. (1) The load aggregator mainly aggregates two types of load-side resources: loads that can be reduced and loads that can be shifted.

[0112] 1) Load can be reduced Reduceable loads refer to loads in a regional power grid whose operating time remains unchanged, but whose operating power can be reduced to a certain extent. The model satisfies the following constraints:

[0113]

[0114] In the formula: Indicates load aggregator The amount of load that can be reduced during period t; This indicates the maximum load reduction that can be declared; To reduce the price declared for the unit load power demand; The number of load aggregators; This indicates the growth rate of the price that can be reduced; In order to reduce the initial load compensation price, the power grid calls load aggregators in the day-ahead spot market. Cost of reducing load for:

[0115] 2) Loads that can be moved The shiftable load is constrained by the production process; only the start time of the entire power consumption process can be planned, and its continuous operating time and power consumption cannot be changed. It must meet the following constraints:

[0116] In the formula: For load aggregator The initial period after the transfer of transferable loads in the day-ahead market clearing scheme; For load aggregator The initial operating period of the load that can be shifted in the middle; , For load-bearing aggregators Price per unit power for the application of load transferable loads The earliest and latest start times affected; , respectively load aggregator After the load can be moved, it is in Power consumption during the time period compared to before the shift Power consumption during a given time period; For load aggregator The continuous operating time of the transferable load; For load aggregator The power consumption of the shiftable load during time period t after shifting. Wherein:

[0117] In the formula: For transferable loads The maximum number of elastically translatable time periods. To accommodate movable loads Price per unit power for application translation The elastic expansion coefficient of the relevant acceptable translation time interval satisfies the following constraint:

[0118] In the formula: and These refer to loads that can be moved. Users are willing to change their electricity usage time to participate in the day-ahead spot market at the minimum price and at the upper limit of the user's acceptable shift time period; For transferable loads Compensation sensitivity coefficient. Therefore, the load aggregation quotient. Similarly, applications can be submitted to transfer loads. Price per unit power of translation Therefore, the cost of shifting the load is... satisfy:

[0119] In the formula: For transferable loads Boolean variable, Indicates a load that can be moved. Translation occurs. Indicates a load that can be moved. No translation occurs.

[0120] (2) Independent energy storage 1) Independent energy storage operation constraints

[0121] In the formula: and These represent the charging and discharging state variables of the independent energy storage s during time period t, respectively, with values ​​of 0 or 1, and simultaneous charging and discharging are prohibited. This represents the total number of independent energy storage units in the system. and These represent the charging and discharging power of the independent energy storage s during time period t; and These represent the upper limits of the charging and discharging power of the energy storage, respectively.

[0122] 2) Considering the independent energy storage pricing and cost model over the entire life cycle. The operating costs of independent energy storage mainly include: charging costs. With discharge benefits Price difference and battery aging costs Therefore, the electricity purchase cost from the grid to independent energy storage s satisfy:

[0123] The components satisfy the following:

[0124] In the formula: The price per unit discharge power of independent energy storage; The cost per unit charging power for independent energy storage; This refers to the battery aging cost per unit charge / discharge power for independent energy storage devices (s). Therefore, when independent energy storage devices participate in the electricity spot market, they should declare the unit charging price. unit discharge price And the cost of battery aging compensation per unit charge / discharge power Its specific expression is as follows:

[0125] In the formula: , and These are the charging price elasticity coefficient, discharging price elasticity coefficient, and battery aging compensation cost coefficient per unit charging and discharging power declared by independent energy storage (s).

[0126] (3) Virtual power plant Virtual power plants integrate distributed energy resources, energy storage systems, and controllable loads to form flexible and controllable capabilities similar to traditional power plants, enabling them to participate in the electricity spot market. This invention considers virtual power plants that aggregate distributed resources such as photovoltaics, load shedding, and energy storage. Therefore, virtual power plants... The predicted power output of the aforementioned photovoltaic power plant during time period t is to be declared. Charging price for independent energy storage Discharge price Battery degradation compensation cost ; and segmented pricing that can reduce load. Relevant parameters participate in the clearing of the electricity spot market. The related constraints will not be elaborated here. Therefore, the response cost of the virtual power plant... For energy storage costs and can reduce load costs sum. Right now:

[0127]

[0128] In the formula: The day-ahead response cost of virtual power plant x; The revenue from energy storage discharge in virtual power plant x; The price per unit discharge power of energy storage in the virtual power plant x; Let x be the discharge power of the energy stored in the virtual power plant x during time period t; Cost of charging energy storage in a virtual power plant x; The cost per unit charging power of energy storage in virtual power plant x; Let x be the charging power of the energy storage in the virtual power plant during time period t; The aging cost of the batteries used for energy storage in the virtual power plant x; Battery aging cost per unit charge / discharge power of energy storage in virtual power plant x; The price per unit of load reduction in virtual power plant x during time period t; Let x be the load reduction amount that can be reduced in virtual power plant x during time period t. This represents the number of virtual power plants.

[0129] Please see Figure 3 The diagram shown is a framework diagram of a two-stage clearing model for the electricity spot market involving new entities under the electricity-carbon coupling.

[0130] Step 3: Based on the description of the volatility characteristics of new energy sources using non-parametric uncertainty sets, a three-layer robust optimization framework of min-max-min is designed. A two-stage clearing model for the day-ahead electricity spot market, considering the participation of new entities under electricity-carbon coupling, is constructed by integrating tiered pricing and dynamic carbon emission factors. The specific steps are as follows: Step 3.1: Establish the objective function of the preliminary clearing phase model:

[0131] Step 3.2: Establish constraints for the preliminary clearing phase model: The initial clearing phase constraints include those related to individual thermal power units among traditional energy providers, constraints related to renewable energy generators, constraints related to load aggregators that can be reduced or shifted, constraints related to independent energy storage, constraints related to virtual power plants, power system power balance constraints, line transmission capacity constraints, and system reserve capacity constraints. Additionally, it also includes: Power balance constraints of the power system at any time interval t:

[0132] In the formula: The output of thermal power unit i in traditional energy power generators during the initial clearing phase in time period t; This is the day-ahead load demand forecast for the electricity spot market at time period t. The reserve capacity constraint of the power system at any time period t is:

[0133] In the formula: and These represent the minimum total safe upward and downward reserve capacity required by the system during time period t, respectively.

[0134] Step 3.3: Establish the objective function of the multi-objective robust optimization clearing stage model: (1) Objective function of the upper-level model 1) Economic benefits – power system operating costs

[0135] 2) Low-carbon benefits – Costs for power generators participating in carbon market trading

[0136] 3) Unit output deviation penalty item

[0137] In the formula: The coefficient for the penalty of deviation in unit output; The objective function for the second stage is to optimize the output of thermal power units in the clearing results of the second stage.

[0138] (2) Objective function of the lower-level model Lower-level risk penalty costs Including wind curtailment penalty costs The cost of abandoning light , and the cost of loss of load , can be represented as:

[0139] The components satisfy the following:

[0140] In the formula: , , The cost factor for the risk penalty of wind curtailment, solar curtailment, and load loss per unit of abandoned electricity; , , and This represents the power curtailment of wind and solar power and the power loss of the power system under extreme scenarios during time period t.

[0141] In summary, the objective function for the clearing phase of multi-objective robust optimization is:

[0142] In the formula: variables For the final required day-to-day clearing plan, The clearing scheme for the electricity spot market during period t; U represents a set of uncertain scenarios for the output and load demand of renewable energy generators; For the optimization decision quantity of the lower-level model, where For the optimization decision variables of the lower-level model in time period t, This represents the decision made on the optimization variable Y of the lower-level model, given an uncertain scenario u and a day-ahead scheduling scheme X.

[0143] Step 3.4: Establish constraints for the multi-objective robust optimization clearing phase model: (1) Constraints of the upper-level model In addition to the constraints in the first-stage model, the upper-level model constraints also include any line in the power system. The transmission capacity constraint, namely:

[0144] In the formula: , , , , and These are the power transfer allocation factors; This represents the total number of nodes in the system. This represents the maximum power flow value of line r in the system. The load size of node k in the next time period t is determined by the current plan.

[0145] (2) Lower-level model constraints The lower-level optimization model constraints include system power balance constraints considering the uncertainties of wind and solar power output and load demand, constraints related to traditional energy generators, and system transmission capacity constraints. The system power balance constraints are as follows:

[0146] In the formula: This represents the power generation adjustment of traditional energy generator i in the lower-level model during time period t. , , and These are the new energy output and load demand scenarios that take into account historical adverse scenario errors during simulation.

[0147] The relevant output and ramp-up constraints for traditional energy generators in the lower-level model are as follows:

[0148] Furthermore, based on historical power system datasets, the uncertainty constraints (set) for wind farm output among renewable energy power generators are as follows:

[0149] In the formula: , and Let represent the magnitude, lower limit, and upper limit of power fluctuation of the w-th wind farm during time period t under extreme scenarios.

[0150] The uncertainty constraints (set) of photovoltaic power plant output in renewable energy power generators are as follows:

[0151] In the formula: , and Let represent the magnitude, lower limit, and upper limit of power fluctuation of the v-th photovoltaic power station during time period t under extreme scenarios.

[0152] The load demand uncertainty constraint (set) is:

[0153] In the formula: , and This represents the magnitude, lower limit, and upper limit of the system load demand power fluctuation during time period t in extreme scenarios.

[0154] The transmission capacity constraint for any line r in the lower-level model is:

[0155] In the formula: This represents the load demand of node k in time period t in the lower-level model.

[0156] Please see Figure 4 The diagram shown is a flowchart of the C&CG algorithm.

[0157] Step 4: Organize the objective function and constraints of the constructed two-stage day-ahead clearing model for the electricity spot market into a matrix form, and use the C&CG algorithm to solve the clearing model to obtain the day-ahead clearing results of the electricity spot market, thereby realizing the safe, low-carbon, and economical operation of the new power system. The specific steps are as follows: Step 4.1: Second-stage model processing and solution Based on the objective function and constraints of the constructed day-ahead spot market clearing optimization model, it is described in matrix form. The compact form of the proposed multi-objective robust optimization clearing model can be expressed as follows:

[0158] In the formula: These are the equality constraints for the upper-level model in the second stage of the model; These are the inequality constraints for the upper-level model in the second stage of the model. This represents the equality constraints of the lower-level model in the second stage of the model; Represents the inequality constraints of the lower-level model in the second stage of the model; The C&CG algorithm is applied to solve the model. This method decomposes the practical problem into a main problem (MP) and a subproblem (SP), and solves these two problems alternately to gradually approach the optimal solution. The mathematical expressions for the main problem MP and the subproblem SP are as follows:

[0159]

[0160] In the formula: The auxiliary variables introduced to replace the subproblems are used to directly obtain a temporary solution. Then, the structure of the subproblems is transformed through duality theory, and the max-min problem is transformed into a max single-layer optimization problem. Step 4.2: Solving the C&CG algorithm flow Step 1: Initialize the upper bound of the objective function and lower boundary Number of iterations Let the convergence gap between the upper and lower bounds be . , Set to a small positive number; Step Two: Let The initial solution obtained Then, substitute the subproblems to solve for the initial extreme scenario. ; Step 3: Extreme scenarios Substituting into the main problem, we obtain the optimal solution. Update the Nether equal ; Step 4: Apply the optimal solution from Step 3 Substituting into the subproblem, we obtain the optimal solution. ;make Update the upper boundary for and sum; Step 5: Judgment If the condition is met, the process ends; otherwise, it will... Return to step three.

[0161] This invention addresses the challenge of balancing economic efficiency, low-carbon emissions, and security in electricity market clearing under high-proportion renewable energy integration. It proposes a two-stage robust optimization clearing method that considers electricity-carbon coupling and the collaborative participation of new stakeholders. This method constructs a unified pricing model integrating marginal generation costs and dynamic carbon trading costs, embedding carbon costs into the price formation mechanism. It designs standardized participation interfaces and a collaborative clearing framework for diverse new stakeholders, fairly characterizing their regulatory capabilities and carbon attributes without disclosing their internal models. Based on a non-parametric uncertainty set describing renewable energy fluctuations, a three-layer robust optimization model (min-max-min) is established and efficiently solved using the C&CG algorithm, transforming it into a mixed-integer linear programming problem. This method significantly reduces overall clearing costs, promotes energy conservation and carbon reduction, and enhances the robustness of the clearing plan to extreme scenarios and the safety of system operation.

[0162] It should be noted that in this invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising..." or "including..." does not exclude the presence of additional elements in the process, method, article, or terminal device that includes said element. Additionally, in this invention, "greater than," "less than," "exceeding," etc., are understood to exclude the stated number; "above," "below," "within," etc., are understood to include the stated number.

[0163] Although the above embodiments have been described, those skilled in the art, once they understand the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the above descriptions are merely embodiments of the present invention and do not limit the scope of patent protection of the present invention. Any equivalent structural or procedural transformations made using the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A day-ahead two-stage dispatch optimization method that accounts for the electro-carbon coupling of new types of bodies, characterized in that, The clearing optimization method includes the following steps: Step 1: Analyze the joint operation framework of the electricity-carbon market and design a two-stage clearing mechanism for the day-ahead electricity spot market, which includes preliminary clearing and multi-objective robust optimization clearing, with the participation of new types of entities under the electricity-carbon coupled market mechanism. Step 2: Establish a carbon emission quota accounting and carbon trading cost calculation model for generating units based on the regional power grid carbon emission factor, and construct a multi-energy power generator model that takes into account marginal costs and carbon emissions, including traditional energy power generators, new energy power generators and new market entities; Step 3: Based on the description of the volatility characteristics of new energy based on the non-parametric uncertainty set, design a three-layer robust optimization framework of min-max-min, and construct a two-stage clearing model for the day-ahead electricity spot market that integrates tiered pricing and dynamic carbon emission factors and considers the participation of new entities under the electricity-carbon coupling. Step 4: Organize the objective function and constraints of the two-stage day-ahead clearing model of the electricity spot market into a matrix form, and use the C&CG algorithm to solve the clearing model to obtain the day-ahead clearing result of the electricity spot market, so as to realize the safe, low-carbon and economical operation of the new power system.

2. The day-ahead two-stage dispatch optimization method considering new-type units' electro- carbon coupling as claimed in claim 1, wherein, Step 1 analyzes the joint operation framework of the electricity-carbon market and designs a two-stage clearing mechanism for the electricity spot market, involving new entities and including preliminary clearing and multi-objective robust optimization clearing, under the electricity-carbon coupled market mechanism. The specific design is as follows: Step 1.1: Analyze the coupling mechanism between the electricity and carbon markets: The electricity and carbon markets are closely coupled through price linkage and negative feedback mechanisms, establishing a closed-loop negative feedback system in which "carbon price affects electricity clearing, electricity clearing affects carbon emissions, and carbon emissions affect carbon price". The core coupling link of this system is "carbon emission intensity per kilowatt-hour", which quantitatively links the physical attributes of electricity commodities with their environmental costs. Through the linkage and transmission of price signals, the system automatically seeks and tends towards a dynamic equilibrium point that balances economic costs and carbon emission costs amidst continuous fluctuations, ultimately achieving the synergistic optimization goal of the power industry's transformation towards low-carbonization while ensuring supply security. Step 1.2: Design a two-stage clearing mechanism for the electricity spot market, involving new participants and including preliminary clearing and multi-objective robust optimization clearing, under an electricity-carbon coupled market mechanism: The two-stage clearing mechanism of the electricity spot market includes the following two stages: (1) preliminary clearing stage, with the goal of minimizing the marginal cost of electricity in the system, and the market is initially cleared under the premise of meeting the constraints of unit operation, load balance and grid safety; (2) multi-objective robust optimization stage, with the goal of maximizing comprehensive social benefits, and the market is formally cleared under the premise of meeting the two-stage coupling constraints of thermal power units and system safety constraints.

3. The day-ahead two-stage dispatch optimization method considering new-type units' electro- carbon coupling as claimed in claim 2, wherein, The specific process for the two-stage clearing of the electricity spot market is as follows: After the trading center releases the market information for the following day, each participant submits an application; the trading center conducts preliminary clearing and multi-objective robust optimization clearing in sequence to form the electricity market results; then carbon emission trading is carried out, and the electricity-carbon market results are fed back to the next round of clearing to update pricing and constraints, forming a closed-loop linkage.

4. The day-ahead two-stage dispatch optimization method considering novel principal components of electro-carbon coupling as claimed in claim 1, characterized in that, In step 2, the model for calculating unit carbon emission quotas and carbon trading costs based on the regional power grid carbon emission factor is as follows: Step 2.1: Establish a model for calculating unit carbon emission quotas and carbon trading costs. The baseline allocation of free initial carbon emission quota is determined according to the carbon emission level of the entire power generation industry and the emission reduction expectation, and the free carbon quota of the system before the day meets: In the formula: is the number of traditional energy power generators is the free carbon emission quota in the day-ahead market; is the free quota per unit power, which is determined by the average emission factor calculated by the state, and the unit is tCO2 / MWh; is the number of time periods in the entire clearing cycle, is the clearing time interval, so there are T = 24h / decision periods in a day; is the thermal power unit is the output power size of the thermal power unit in the t period, and the unit is MW; is the number of all traditional energy power generators; A tiered pricing strategy is used to set the trading price of carbon emission credits in the carbon market. The tiered carbon emission credit trading price model is as follows: where: is a stepped carbon trading price; is a carbon market benchmark price; is a conventional energy generator total carbon emissions under the day-ahead clearing scheme; is a stepped price growth rate; is an interval length of the carbon emission trading price.

5. The day-ahead two-stage dispatch optimization method considering novel principal bodies' electro-carbon coupling of claim 4, wherein, In step S2, a multi-energy power generator model that takes into account marginal cost and carbon emissions is constructed, as follows: Step 2.2: Construct a model for traditional energy power generators, including operating constraints of thermal power units, a pricing and cost model for traditional energy power generators, and a dynamic carbon emission intensity model for traditional energy power generators based on the principle of equal area. (1) Operating constraints of thermal power units 1) Upper and lower limit constraints for thermal power units: wherein: , Pmin i, Pmax i represent the minimum and maximum power of the thermal power unit i, respectively; 2) Gradient constraints for thermal power units: wherein: , Pi, and Pj are the upper and lower limits of the ramping power of the thermal power unit i, respectively. 3) Start-up and shutdown constraints for thermal power units: wherein: is a Boolean variable, represents that the thermal power unit i is in a shutdown state at the time period t, represents that the thermal power unit i is in a startup operation state at the time period t; , respectively represent the minimum time period number of the thermal power unit i in the shutdown and startup states. 4) Reserve capacity constraints for thermal power units: In the formula: , is the upper and lower standby capacity provided by the thermal power unit i at the t period. (2) Pricing and cost model of traditional energy power generators Traditional energy power generators include coal-fired and gas-fired power units. Since the power generation cost of these units can typically be expressed as a quadratic function, and the electricity spot market allows them to submit monotonically increasing segmented price curves, a pricing model conforming to market rules is constructed based on the marginal generation cost model: the price curve is non-decreasing, and the length of each segment is no less than 10% of the difference between the unit's rated capacity and its minimum technical output. The fuel cost curve is represented by the following quadratic function: wherein: is a traditional energy generator is the fuel consumption cost at time t; is the unit price of fuel required for power generation by thermal power unit i; , and is a traditional energy generator is the fitting coefficient of the coal consumption curve, which can be obtained by fitting the historical operation data of the unit. By differentiating the power generation cost curve of the traditional energy power generator thermal power unit , the marginal power generation cost is obtained as , Assuming that the conventional energy generator quotes based on marginal cost without considering strategic pricing behavior, the pricing curve is constructed as a monotonic non-decreasing piecewise function to reflect its rising characteristics with the increase of output, so based on the principle of equal area, the marginal generation cost curve is divided into N segments on average, and the price of each segment is set to make the area surrounded by the marginal generation cost curve of each segment equal to the area surrounded by the stepped pricing line segment, therefore, the conventional energy generator The The price of the nth segment is: Wherein: Pi,n represents the price of the conventional energy generator i in the nth segment; Pstart,i,n represents the starting power size of the generator i in the nth segment; Since the operation cost of traditional energy power generator mainly includes: fuel consumption cost , start-stop cost , standby cost , the electricity purchasing cost of power grid to traditional energy power generator satisfies: The components satisfy the following: wherein: , is the single start-up, shut-down cost of thermal power unit i; , is the cost coefficient of providing one unit of up, down reserve capacity for thermal power unit i; (3) Dynamic carbon emission intensity model of traditional energy power generators based on the principle of equal area The carbon emissions of traditional generator sets are related to their power generation capacity. The dynamic carbon emission intensity of the unit can be derived from the coal consumption cost formula above, and its calculation formula is as follows: where: Cci is the carbon content of the coal used by the conventional thermal power plant unit i; Mco2is the molar mass of carbon dioxide; Mco is the molar mass of carbon. Thus, the carbon intensity of the traditional energy generator in the nth stage is: , The total carbon emission of the power producer i under the current clearing solution is: , Step 2.3 Constructing a new energy power generator model: New energy power generators mainly include two types: wind farms and photovoltaic power plants. In the day-ahead declaration of the electricity spot market, new energy power generators declare the new energy power generation forecast curve. Therefore, the wind farm w satisfies the following constraints: Photovoltaic power plant Satisfy the following constraints: wherein: is the predicted power size declared by the wth wind farm of the new energy power producer at period t; is the maximum installed capacity of the wth wind farm; is the number of wind farms; is the predicted power size declared by the vth photovoltaic power station of the new energy power producer at period t; is the maximum installed capacity of the vth photovoltaic power station; is the number of photovoltaic power stations; Step 2.4: Construct a new market entity model, in which the new market entities include load aggregators, independent energy storage, and virtual power plants: (1) Load aggregators mainly aggregate two types of load-side resources: loads that can be reduced and loads that can be shifted. 1) Load can be reduced Load aggregators predict users' adjustable load for the next 24 hours based on historical data, weather, user behavior, and other factors. This includes loads that can be reduced or shifted. Reduceable loads refer to loads in the regional power grid whose operating time remains unchanged, but whose operating power can be reduced to a certain extent. The model satisfies the following constraints: wherein: represents the amount of curable load of the load aggregator l at the time period t; represents the maximum amount of curable load of the load aggregator l declared at the time period t; is the price of the curable unit load demand power declared by the load aggregator l at the time period t; is the number of load aggregators; represents the growth rate of the curable load price declared by the load aggregator l; is the initial compensation price of the curable load of the load aggregator l; Therefore, the power grid can reduce load costs by calling load aggregators in the day-to-day spot market. for: , 2) Loads that can be moved The shiftable load is constrained by the production process; only the start time of the entire power consumption process can be planned, and its continuous operating time and power consumption cannot be changed. It must meet the following constraints: In the formula: For load aggregator The initial period after the transfer of transferable loads in the day-ahead market clearing scheme; For load aggregator The initial operating period of the load that can be shifted in the middle; , For load-bearing aggregators Price per unit power for the application of load transferable loads The earliest and latest start times affected; , respectively load aggregator After the load can be moved, it is in Power consumption during the time period compared to before the shift Power consumption during a given time period; For load aggregator The continuous operating time of the transferable load; For load aggregator The power consumption of the transferable load during time period t after the transfer is: In the formula: The maximum number of elastically movable time periods for the movable load l, where To submit a price per unit power for the transferable load. The elastic expansion coefficient of the relevant acceptable translation time interval satisfies the following constraint: In the formula: and These refer to the minimum price at which a user willing to change their electricity usage time can participate in the day-ahead spot market bid, and the price at which the user's acceptable shifting time period reaches its upper limit. The sensitivity coefficient for compensating for the transferable load; Therefore, when load aggregators participate in the day-ahead electricity spot market, the price per unit power shifted by the shiftable load is declared similarly to that of loads that can be reduced. Therefore, the cost of shifting the load is... satisfy: In the formula: Let l be a Boolean variable that can be shifted. This indicates that the movable load l has been translated. This indicates that the transferable load l does not undergo translation; (2) Independent energy storage 1) Independent energy storage operation constraints In the formula: and These represent the charging and discharging state variables of the independent energy storage s during time period t, respectively, with values ​​of 0 or 1, and simultaneous charging and discharging are prohibited. This represents the total number of independent energy storage units in the system. and These represent the charging and discharging power of the independent energy storage s during time period t; and These represent the upper limits of the charging and discharging power of the energy storage device, respectively. 2) Considering the independent energy storage pricing and cost model over the entire life cycle. Independent energy storage systems declare the price per unit of electricity in the spot market when the energy storage power station operates in different charge and discharge ranges. The discharge price must be greater than the charging price. Therefore, under the constructed model, independent energy storage system s only needs to declare the charging price. Discharge price and the initial state of charge of the energy storage power station When the market electricity price is higher than the discharge price of the energy storage, the energy storage will choose to discharge; when it is lower than the charging price, it will charge. If the market electricity price is within the charging and discharging price range, the energy storage will not be used, that is, it will not be cleared. In addition, considering that the operating life of independent energy storage is limited by the number of charge and discharge cycles throughout the entire life cycle, the battery aging cost can be converted into the cost of a single cycle and included. The operating costs of independent energy storage mainly include: charging costs. With discharge benefits Price difference and battery aging costs Therefore, the electricity purchase cost from the grid to independent energy storage s satisfy: The components satisfy the following: In the formula: The price per unit discharge power of independent energy storage; The cost per unit charging power for independent energy storage; Battery aging cost per unit charge / discharge power for independent energy storage; Therefore, when independent energy storage devices participate in the electricity spot market, they should declare the unit charging price. unit discharge price And the cost of battery aging compensation per unit charge / discharge power Its specific expression is as follows: In the formula: , and These are the charging price elasticity coefficient, discharging price elasticity coefficient, and battery aging compensation cost coefficient per unit charging and discharging power declared by independent energy storage (s). (3) Virtual power plant Virtual power plants integrate distributed energy resources, energy storage systems, and controllable loads to form flexible and controllable capabilities similar to traditional power plants, enabling them to participate in the electricity spot market. This virtual power plant, considering the aggregation of photovoltaic, load-cutting, and energy storage distributed resources, therefore, the virtual power plant x declares the predicted power of the aforementioned photovoltaic power station in time period t. Charging price for independent energy storage Discharge price Battery degradation compensation cost ; and segmented pricing that can reduce load. Relevant parameters participate in the clearing of the electricity spot market; Therefore, the response cost of a virtual power plant For energy storage costs and can reduce load costs The sum is: The components satisfy the following: In the formula: The day-ahead response cost of virtual power plant x; The revenue from energy storage discharge in virtual power plant x; The price per unit discharge power of energy storage in the virtual power plant x; Let x be the discharge power of the energy stored in the virtual power plant x during time period t; Cost of charging energy storage in a virtual power plant x; The cost per unit charging power of energy storage in virtual power plant x; Let x be the charging power of the energy storage in the virtual power plant during time period t; The aging cost of the batteries used for energy storage in the virtual power plant x; Battery aging cost per unit charge / discharge power of energy storage in virtual power plant x; The price per unit of load reduction in virtual power plant x during time period t; Let x be the load reduction amount that can be reduced in virtual power plant x during time period t. This represents the number of virtual power plants.

6. The two-stage clearing optimization method for the electro-carbon coupling of novel subjects as described in claim 5, characterized in that, Step 3, which constructs the preliminary clearing stage model of the two-stage day-ahead clearing model for the electricity spot market, is as follows: Step 3.1: Establish the objective function of the preliminary clearing phase model: In the designed market mechanism, the power trading center, in the initial clearing phase, aims to minimize the marginal cost of electricity in the system through preliminary optimization, obtaining preliminary output plans for traditional units such as thermal power plants. This serves as the initial point for the second phase, anchoring the optimized output of traditional energy generators. The electricity cost includes the generation cost, start-up and shutdown costs, and positive and negative spinning reserve capacity costs of traditional energy generators; the cost of independent energy storage due to the price difference between charging and discharging; and the response costs provided by load aggregators and virtual power plants. ， Step 3.2: Establish constraints for the preliminary clearing phase model: The initial clearing phase constraints include those related to each thermal power unit among traditional energy providers, constraints related to renewable energy generators, constraints related to load aggregators that can be reduced or shifted, constraints related to independent energy storage, constraints related to virtual power plants, power system power balance constraints, line transmission capacity constraints, and system reserve capacity constraints. Additionally, they also include: Power balance constraints of the power system at any time interval t: In the formula: The output of thermal power unit i in traditional energy power generators during the initial clearing phase in time period t; Forecast of day-ahead load demand in the electricity spot market during period t; The reserve capacity constraint of the power system at any time period t is: In the formula: and These represent the minimum total safe upward and downward reserve capacity required by the system during time period t, respectively.

7. The two-stage clearing optimization method for the electro-carbon coupling of novel subjects as described in claim 6, characterized in that, Step 3, which constructs the multi-objective robust optimization clearing stage model in the two-stage clearing model for the day-ahead clearing of the electricity spot market, is as follows: Step 3.3: Establish the objective function of the multi-objective robust optimization clearing stage model: In the second stage, the power trading center takes into account safety, economy and low carbon objectives, and handles the uncertainty of new energy output. With the goal of maximizing comprehensive social welfare, the final clearing optimization scheme is obtained through robust optimization method. Therefore, the upper objective function of the clearing optimization model in this stage consists of four parts: economic benefits, low carbon benefits, unit output deviation penalty term and risk control cost of the lower model. Among them, the economic benefit objective is represented by the day-ahead market operating cost. Low-carbon benefits are represented by the carbon trading costs incurred by power generators participating in carbon market transactions; The unit output deviation penalty term is described using squared error. The lower-level objective function is based on the day-ahead clearing result of the upper level, with the optimization objective being to minimize the system risk control cost under simulated extreme scenarios. (1) Objective function of the upper-level model 1) Economic benefits – power system operating costs The goal of the second-stage upper-level model is to minimize the operating costs of the power system given the fixed start-up and shutdown status of traditional energy power generation units. The specific expression is as follows: ， 2) Low-carbon benefits – Costs for power generators participating in carbon market trading ， 3) Unit output deviation penalty item To ensure the enforceability and stability of the electricity market clearing results, a penalty mechanism for output deviations during the initial clearing phase is introduced in the second phase of optimization. This mechanism is based on the thermal power unit output plan determined during the initial clearing phase. As a benchmark, a penalty term for unit output deviation is added to the objective function. To constrain excessive fluctuations in unit output during the optimization process, the squared error is used to describe it. In the formula: The coefficient for the penalty of deviation in unit output; The objective function for the second stage is to optimize the output of thermal power units in the clearing results of the second stage. (2) Objective function of the lower-level model Risk penalty cost Including wind curtailment penalty costs The cost of abandoning light , and the cost of loss of load , represented as: The components satisfy the following: In the formula: , , The cost factor for the risk penalty of wind curtailment, solar curtailment, and load loss per unit of abandoned electricity; , , and These represent the wind curtailment power of the w-th wind farm, the solar curtailment power of the v-th photovoltaic power station, the solar curtailment power of the photovoltaic power station in the x-th virtual power plant, and the power system load shedding power under extreme scenarios during time period t. In summary, the objective function for the clearing phase of multi-objective robust optimization is: In the formula: variables For the final required day-to-day clearing plan, A clearing scheme for the electricity spot market during period t; U represents a set of uncertain scenarios involving the power output and load demand of new energy power generators; For the optimization decision quantity of the lower-level model, where For the optimization decision variables of the lower-level model in time period t, This represents the decision-making process for the optimization variable Y of the lower-level model, given an uncertain scenario u and a day-ahead scheduling scheme X. Step 3.4: Establish constraints for the multi-objective robust optimization clearing phase model: (1) Constraints of the upper-level model In addition to the constraints in the first-stage model, the upper-level model also includes the transmission capacity constraint for any line r in the power system, namely: In the formula: , , , , and These are the power transmission allocation factors from the load of thermal power unit i, wind farm w, photovoltaic power station v, independent energy storage s, virtual power plant x, and node k to line r, respectively. This represents the total number of nodes in the system. This represents the maximum power flow value of line r in the system. The plan is to determine the load size of node k in the next time period t; (2) Lower-level model constraints The constraints of the lower-level optimization model include system power balance constraints considering the uncertainties of wind and solar power output and load demand, constraints related to traditional energy generators, and system transmission capacity constraints. Among them, the system power balance constraints are: In the formula: This represents the power generation adjustment of traditional energy generator i in the lower-level model during time period t. , , and These are the power generation forecast data and system load demand forecast data submitted by new energy power generators based on the upper-level model, respectively. Considering the historical severe scenario error simulation, the power generation of the w-th wind farm, the power generation of the v-th photovoltaic power station, the power generation of the photovoltaic power station in the x-th virtual power plant among new market entities, and the load demand in the power system are also included. The relevant output and ramp-up constraints for traditional energy generators in the lower-level model are as follows: , The uncertainty constraint of wind farm output in renewable energy power generators is: , The uncertainty constraint of photovoltaic power plant output in renewable energy power generators is: , The load demand uncertainty constraint is: , The transmission capacity constraint for any line r in the lower-level model is: In the formula: This represents the load demand of node k in time period t in the lower-level model.

8. The two-stage clearing optimization method for the electro-carbon coupling of novel subjects as described in claim 1, characterized in that, In step 4, the C&CG algorithm is used to solve the clearing model, as detailed below: Step 4.1: Second-stage model processing and solution Based on the objective function and constraints of the second stage of the constructed day-ahead spot market clearing optimization model, they are presented in matrix form. The compact form of the proposed multi-objective robust optimization clearing model can be expressed as follows: In the formula: These are the equality constraints for the upper-level model in the second stage of the model; These are the inequality constraints for the upper-level model in the second stage of the model. This represents the equality constraints of the lower-level model in the second stage of the model; Represents the inequality constraints of the lower-level model in the second stage of the model; The C&CG algorithm is applied to solve the model. This method decomposes the practical problem into a main problem (MP) and a subproblem (SP), and solves these two problems alternately to gradually approach the optimal solution. The mathematical expressions for the main problem (MP) and the subproblem (SP) are as follows: In the formula: The auxiliary variables introduced to replace the subproblems are used to directly obtain a temporary solution. Then, the structure of the subproblems is transformed through duality theory, and the max-min problem is transformed into a max single-layer optimization problem. Step 4.2: Solving the C&CG algorithm flow Step 1: Initialize the upper bound of the objective function and lower boundary Number of iterations Let the convergence gap between the upper and lower bounds be . , Set to a small positive number; Step Two: Let The initial solution obtained Then, substitute the subproblems to solve for the initial extreme scenario. ; Step 3: Extreme scenarios Substituting into the main problem, we obtain the optimal solution. Update the Nether equal ; Step 4: Apply the optimal solution from Step 3 Substituting into the subproblem, we obtain the optimal solution. ;make Update the upper boundary for and sum; Step 5: Judgment If the condition is met, the process ends; otherwise, it will... Return to step three.

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