A load aggregator operation adjustment method considering environmental right increment game

Abstract

The present application relates to a kind of load aggregator operation adjustment method considering environmental rights increment game, comprising: based on the coupling relationship of environmental rights value and electric energy value in green electricity trading market, environmental rights increment game model is built;Combining the technical characteristics and operating constraints of flexible resource, flexible resource operation model is built, and the flexible resource operation model includes equipment operation model and energy coupling model;Based on environmental rights increment game model and the flexible resource operation model, non-cooperative game and collaborative game method are fused, multi-agent game equilibrium is solved, and load aggregator initial operation adjustment strategy is obtained;According to the dynamic change of green electricity trading market scene, environmental rights weight coefficient and electric energy value weight coefficient are dynamically adjusted, and load aggregator optimal operation adjustment strategy is obtained by optimization.Compared with prior art, the present application has realized the collaborative maximization of load aggregator environmental rights increment and economic benefits and other advantages.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a load aggregator operation regulation method that considers incremental environmental rights game. Background Technology

[0002] As the global energy transition deepens, the green electricity trading market, centered on low-carbon and clean energy, is developing rapidly. The value of green electricity (referred to as green power) is simply differentiated into two attributes: environmental rights value (quantitatively reflected in green certificates, carbon allowances, etc.) and electrical energy value. The coupling and competition between these two attributes have become important factors affecting market efficiency. Against this backdrop, load aggregators, as the core entities integrating flexible resources (energy storage systems, interruptible loads, distributed photovoltaics, demand response resources, etc.) to participate in green power trading, play an increasingly important role in the electricity market because their operation and regulation strategies directly affect the green power absorption rate, the efficiency of environmental rights realization, and their own economic benefits.

[0003] Currently, the green electricity trading market faces the real challenges of conflicting interests among multiple stakeholders and complex value competition: on the one hand, the strengthening of renewable energy quota systems and carbon peaking and carbon neutrality targets at the policy level has promoted the prominence of environmental rights value; on the other hand, competition between traditional energy and green electricity at the market level, as well as price competition within green electricity, means that the economic constraints on the value of electricity remain constant. However, existing load aggregator operation and regulation methods have significant shortcomings: First, most methods fail to fully consider the game-theoretic characteristics of incremental environmental rights, severing the coupling relationship between environmental rights value and electricity value. They lack systematic modeling of the strategic interactions among multiple stakeholders (renewable energy generators, traditional energy generators, electricity users, and grid operators), resulting in inaccurate revenue quantification and a lack of a holistic perspective in strategy formulation. Second, flexible resource operation models are disconnected from game-theoretic mechanisms, considering only technical constraints or economic costs without combining the scheduling potential of flexible resources with the equilibrium results of the incremental environmental rights game, making it difficult to fully leverage the synergistic role of flexible resources in green electricity consumption and value optimization. Third, they lack dynamic adaptability to market scenarios. Facing different scenarios such as high carbon prices and strong policies, drastic electricity price fluctuations, and high renewable energy penetration rates, they have failed to establish flexible weight coefficient adjustment and parameter optimization mechanisms, making it difficult for operation and regulation strategies to balance incremental environmental rights and economic benefits, easily leading to insufficient realization of environmental rights or excessively high economic costs.

[0004] Furthermore, existing technologies lack specificity for solving multi-stakeholder game equilibria, failing to effectively integrate the advantages of non-cooperative game theory (Nash equilibrium) and cooperative game theory (Shapley value allocation). This makes it difficult to resolve the dilemma of "conflict between individual rationality and collective rationality," and cannot provide load aggregators with operational solutions that balance their own interests with overall market benefits. Therefore, how to construct an operational regulation system that considers incremental environmental rights game theory, and achieves flexible resource scheduling and synergistic optimization of multi-stakeholder interests, has become a core technical challenge faced by load aggregators in the current green electricity trading market, urgently requiring a scientific and systematic operational regulation method to solve.

[0005] A search revealed Chinese invention patent application publication number CN116432862A, which discloses a multi-agent game optimization method for renewable energy microgrids. The method includes: acquiring interaction data between microgrid operators, energy storage operators, and users; constructing microgrid operator models, energy storage operator models, and load-side models based on the interaction data; constructing a one-master-many-slave multi-agent game model based on the microgrid operator model, energy storage operator model, and load-side model; solving the one-master-many-slave multi-agent game model to generate a multi-agent game optimization strategy; wherein the multi-agent game optimization strategy is used to provide energy for the operation of the renewable energy microgrid. This existing patent application has the following problems: it only adopts a single game mode, fails to reduce environmental rights objectives to equipment-level hard constraints, cannot dynamically adapt to adjusting weights in multiple scenarios, and therefore cannot achieve dynamic synergistic optimization of environmental rights and electrical energy value, exhibiting weak adaptability to complex market scenarios.

[0006] How to achieve flexible resource scheduling and synergistic optimization of the interests of multiple stakeholders has become a technical problem that needs to be solved. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a load aggregator operation adjustment method that considers the incremental game of environmental rights.

[0008] The objective of this invention can be achieved through the following technical solutions: According to one aspect of the present invention, a load aggregator operation adjustment method considering incremental environmental interest game is provided, the method comprising: Based on the coupling relationship between environmental rights value and electricity value in the green electricity trading market, an incremental game model of environmental rights is constructed, which includes game subjects, strategy space, and payoff function. Combining the technical characteristics and operational constraints of flexible resources, a flexible resource operation model is constructed. The flexible resource operation model includes an equipment operation model that includes physical operation constraints of equipment and environmental target constraints, and an energy coupling model that includes electrical energy balance constraints and environmental rights coordination constraints. Based on the aforementioned environmental rights incremental game model and the aforementioned flexible resource operation model, and by integrating non-cooperative game and cooperative game methods, the multi-agent game equilibrium is solved to obtain the initial operation adjustment strategy of the load aggregator. Based on the load aggregator's initial operation adjustment strategy, and according to the dynamic changes in the green electricity trading market scenario, the environmental rights weight coefficient and the electricity value weight coefficient are dynamically adjusted to optimize the load aggregator's optimal operation adjustment strategy, thereby maximizing the synergistic effect between the increase in environmental rights and economic benefits.

[0009] As a preferred technical solution, the game participants include load aggregators, renewable energy generators, traditional energy generators, power users, and grid operators; The various game players interact around the incremental environmental rights and electricity revenue, and the strategy space includes: Flexible resource scheduling strategies, green electricity procurement ratio strategies, and green certificate trading strategies of load aggregators; The renewable energy power generation operator's renewable energy output declaration strategy, namely, the declaration of output scale and time period distribution; Fossil energy output adjustment strategies and carbon emission control strategies of traditional energy power generators; Strategies for adjusting the proportion of green electricity consumption by electricity users; And the power grid operators' transmission channel scheduling strategies and market trading rule execution strategies.

[0010] As a preferred technical solution, the payoff function is guided by maximizing the overall benefits of each game participant, and the payoff function is specifically as follows: The overall benefit for each participant is equal to the net benefit after deducting operating costs from the sum of incremental environmental rights revenue and electricity trading revenue; wherein the incremental environmental rights revenue is quantified through green certificate premium and carbon quota surplus; and the electricity trading revenue is calculated based on time-of-use electricity prices and electricity trading volume. As a preferred technical solution, the operating costs include flexible resource operation and maintenance costs and carbon emission compliance costs for traditional energy power generators. The flexible resource operation and maintenance costs include the charge and discharge cycle loss costs of the energy storage system, interruptible load compensation costs, distributed photovoltaic operation and maintenance costs, and demand response resource organization costs.

[0011] As a preferred technical solution, the equipment operation model includes the energy conversion relationship and operation constraints of various flexible resources, wherein the flexible resources include energy storage systems, interruptible loads, distributed photovoltaics, and demand response resources; The charging and discharging power of energy conversion in energy storage systems is dynamically correlated with the amount of energy stored. Operational constraints include energy storage system charging and discharging power constraints, state of charge constraints, and emission reduction target constraints. The constraints on interruptible loads include constraints on response time and cutoff capacity. The energy conversion of the distributed photovoltaic system is based on the correlation between light intensity, photovoltaic panel temperature and output power, and the operating constraints include output prediction deviation constraints. The constraints on the demand response resources include constraints on load transfer time periods and capacity.

[0012] As a preferred technical solution, the energy coupling model includes electrical energy balance constraints and environmental rights coordination constraints. The electrical energy balance constraints characterize the matching relationship between the total output of flexible resources and the load demand, while the environmental rights coordination constraints characterize the alignment relationship between the total carbon quota surplus and the emission reduction target.

[0013] As a preferred technical solution, solving for multi-agent game equilibrium includes: First, the equilibrium strategy for the non-cooperative game is solved using the Nash equilibrium algorithm; Then, the Shapley value allocation method is used to calculate the marginal contribution of each agent in the game alliance, allocate the cooperative benefits of the alliance, and output the cooperative game equilibrium result. Finally, by integrating the two game equilibrium results with the operational constraints of flexible resources, the load aggregator's flexible resource scheduling scheme, green electricity procurement ratio, and green certificate trading strategy are output, forming the aggregator's initial operation adjustment strategy.

[0014] As a preferred technical solution, the Shapley value allocation method is specifically calculated as follows: , In the formula, For the first i The value of the collaborative revenue distribution among the game's participants; S For a subset of the game alliance, U ( S ) is a subset of the alliance S Total revenue, The number of entities in the game alliance; N The total set of all game participants. The total number of players in the game; after the i-th player is added to subset S, For a subset of the alliance S Join the i After the individual players form a new alliance Total revenue.

[0015] As a preferred technical solution, the green electricity trading market scenarios include scenarios with high carbon prices and strong policy drivers, scenarios with drastic electricity price fluctuations and power shortages, and scenarios with high renewable energy penetration rates; the weighting coefficient adjustment rules for each scenario include: For scenarios with high carbon prices and strong policy drivers, priority should be given to increasing the incremental environmental rights, specifically: the environmental rights weighting coefficient α≥0.8, and the electrical energy value weighting coefficient β=1-α; In scenarios involving drastic fluctuations in electricity prices and power shortages, priority should be given to ensuring economic benefits, specifically: the weighting coefficient for the value of electricity β ≥ 0.7, and the weighting coefficient for environmental rights α = 1 - β. For scenarios with high renewable energy penetration, the environmental rights and economic benefits are optimized in a coordinated manner, specifically: the environmental rights weighting coefficient α=0.5, and the electrical energy value weighting coefficient β=0.5.

[0016] As a preferred technical solution, the objective function of the optimal operation and adjustment strategy of the load aggregator is to maximize the overall benefits of the load aggregator, specifically: , In the formula, U To maximize the overall benefits for load aggregators; α This refers to the environmental rights weighting coefficient. β The electrical energy value weighting coefficient; Δ V e For the incremental environmental equity of load aggregators, V p The value of electrical energy for load aggregators, C op To reduce the operating and maintenance costs of flexible resources; Flexible resource operation and maintenance costs C op The calculation formula is: , In the formula, k The charge-discharge cycle loss coefficient of the energy storage system; P charge , P discharge These are the total charging power and total discharging power of the energy storage system, respectively. c IL Compensation cost per unit of interruptible load; P IL Total interruptible load cut-off capacity; c PV The unit operation and maintenance cost of distributed photovoltaic power generation; P PV For the total output of distributed photovoltaic power; c DR Organizational cost per unit of demand response resources; PDR This represents the total load transfer for demand response resources.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1) This invention introduces for the first time the coupled game theory of incremental environmental rights and the value of electricity into the operation and regulation of load aggregators. It integrates a dual-game solution mechanism of non-cooperative and cooperative games, and combines dynamic weight adjustment in multiple scenarios to construct a full-link closed-loop technical solution that includes coupled modeling, resource constraints, dual-game solution, dynamic optimization, and target output. This effectively solves the problem of imbalance between environmental benefits and economic gains caused by focusing only on the value of electricity or a single game mode in existing technologies. It achieves the synergistic maximization of incremental environmental rights and economic gains for load aggregators, and significantly improves the efficiency and stability of multi-entity collaborative operation in the green electricity trading market.

[0018] 2) This invention clarifies the participants and strategy space of multi-party games, as well as the definition of a payoff function oriented towards comprehensive benefits. It refines the quantification of incremental environmental rights payoffs and electricity trading payoffs, making the boundaries of the game model clearer and the payoff calculation more accurate. This not only protects the interests of each game participant, but also strengthens the core position of environmental rights value in payoff distribution, providing a solid quantitative foundation for subsequent game equilibrium solutions.

[0019] 3) By qualitatively classifying operating costs and refining equipment operating constraints, this invention clarifies the dual composition of flexible resource operation and maintenance costs and traditional energy carbon emission costs, avoiding the problem of incomplete cost accounting in existing technologies. On the other hand, it decentralizes equipment-level emission reduction target constraints to the operating rules of flexible resources such as energy storage, breaking through the limitations of traditional system-level emission reduction constraints. This ensures that every scheduling decision of flexible resources directly serves the emission reduction target, effectively improving the efficiency of environmental benefit implementation.

[0020] 4) By constructing a dual coupling constraint of power energy balance and environmental rights coordination, as well as a dual game solution mechanism of non-cooperative game and cooperative game, the supply and demand balance of power grid operation and the coordinated achievement of environmental goals are guaranteed. Furthermore, the fair distribution of alliance cooperative benefits is realized through the Shapley value allocation method. This solves the problems of uneven distribution of benefits in multi-entity game and the disconnect between environmental goals and operational constraints in existing technologies, and significantly improves the stability and cooperative efficiency of multi-entity alliances.

[0021] 5) This invention designs a multi-scenario dynamic weight adjustment strategy, which dynamically adjusts the weights of environmental rights and electrical energy value for different market scenarios, enabling the operation adjustment strategy to adapt to complex scenarios such as high carbon prices, fluctuating electricity prices, and high proportion of new energy sources, significantly improving the practicality and scenario adaptability of the method. Attached Figure Description

[0022] Figure 1 This is a schematic flowchart of the load aggregator operation adjustment method in this invention; Figure 2 This is a comparison chart of peak-valley electricity price differences in three scenarios according to embodiments of the present invention; Figure 3 This is a comparison chart of carbon prices in three scenarios according to embodiments of the present invention; Figure 4 This is a comparison chart of the weight coefficients of three types of scenarios in an embodiment of the present invention; Figure 5 This is a schematic diagram of the load aggregator operation regulation system in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] This invention aims to address the technical problems in existing technologies, such as the insufficient integration of incremental environmental rights game theory in load aggregator operation and regulation, inadequate flexible resource scheduling and multi-stakeholder interest coordination, and lack of dynamic scenario adaptation capabilities. It seeks to maximize the synergy between incremental environmental rights and economic benefits, and provide scientific support for the operational decisions of load aggregators in the green electricity trading market.

[0025] This embodiment relates to a load aggregator operation adjustment method that considers incremental environmental rights game theory, such as... Figure 1 This includes the following steps: Step 1: Construct an incremental environmental rights game model; Step 2: Construct a flexible resource operation model; Step 3: Based on the incremental environmental rights game model and the flexible resource operation model, solve the multi-stakeholder game equilibrium to obtain the initial operation adjustment strategy; Step 4: Dynamically adjust parameters to obtain the optimal operation and adjustment strategy; In step 1, based on the coupling relationship between environmental rights value and electricity value in the green electricity trading market, and referring to the basic framework of game theory, the core elements of the game are identified, and an incremental game model of environmental rights covering the interaction of multiple game subjects is constructed to provide a theoretical basis for subsequent decision-making.

[0026] The incremental environmental rights game model includes game subjects, strategy space, payoff function, and information structure. Specifically, based on the game subjects, the core strategy space of each game subject is defined, payoffs and costs are quantified through formulas, and then public market data and private information of the subjects are integrated to complete the construction of the incremental environmental rights game model.

[0027] The game involves five types of participants: load aggregators, renewable energy generators, traditional energy generators, electricity users, and grid operators. These participants interact around the incremental environmental rights and electricity revenue, aligning with the multi-participant nature of the green electricity trading market. The functions and interests of these participants are clearly defined, consistent with the actual participation structure of the green electricity trading market, as described below: Among them, load aggregators are the core coordinating entities that integrate flexible resources to participate in green electricity consumption and market transactions, aiming to balance environmental rights and economic benefits; renewable energy generators influence the supply of green electricity and the allocation of environmental rights through power output declarations, aiming to increase the volume of green electricity transactions and premium revenue; traditional energy generators balance power generation revenue and compliance costs through power output adjustments and carbon emission control, aiming to reduce carbon emission penalties; electricity users are the main energy consumers who influence the market demand structure through adjustments to the proportion of green electricity consumption, aiming to control electricity costs under low-carbon demand; and grid operators are the main market regulators and dispatchers who ensure the safe and stable operation of the power system, aiming to maintain supply and demand balance and market fairness.

[0028] The strategy space defines the core action set of each game player, and the core strategy content differs for each player. Specifically, the strategy space for load aggregators includes flexible resource dispatch strategies (energy storage charging and discharging, interruptible load shedding, etc.), green electricity procurement ratio strategies (time-of-use procurement and allocation), and green certificate trading strategies (holding / selling timing); the strategy for renewable energy generators is renewable energy output declaration strategies (including output scale and time-of-use distribution declarations, such as photovoltaic midday peak output declarations); the strategy for traditional energy generators includes fossil energy output adjustment strategies (peak-valley time-of-use output allocation) and carbon emission control strategies (emission reduction technology investment, carbon quota trading); the strategy for electricity users is green electricity consumption ratio adjustment strategies (peak / valley green electricity consumption ratio allocation), comprehensively covering the key decision-making directions of market players; and the strategy space for grid operators includes transmission channel dispatch strategies (prioritizing green electricity transmission) and market trading rule execution strategies (time-of-use pricing announcements).

[0029] The payoff function, guided by maximizing the overall benefits for all game participants, quantifies the incremental environmental rights benefits, electricity trading revenue, and operating costs. The core of the payoff function is "Overall Benefit = Incremental Environmental Rights Benefit + Electricity Trading Revenue - Operating Costs," where the incremental environmental rights Δ... V e The formula for quantifying carbon allowance surpluses and green certificate trading revenue is as follows: , (1) In the formula, Δ V eIt is the incremental environmental rights (in yuan) of the game participants, that is, the total additional benefits obtained through carbon quota trading and green certificate trading; P c This is the carbon allowance trading price (yuan / ton), which is the trading price per unit of carbon emission rights in the carbon market, taken as the real-time listed price in the green electricity trading market (such as P in this embodiment). c =80 yuan / ton); E 基准 It is the carbon allowance benchmark (in tons) allocated by the government to the participating entities, i.e., the maximum allowable carbon emission limit, determined based on the entity's annual power generation (e.g., load aggregator E). 基准 = 500 tons); E 实际 It is the actual carbon emissions (tons) of the game participants, calculated by multiplying energy consumption by the carbon emission coefficient (e.g., the carbon emission coefficient for coal-fired power generation is 0.98 tons / MWh). P REC The green certificate trading price is (yuan / certificate), and each green certificate corresponds to the environmental rights value of 1 megawatt-hour of green electricity (P in this embodiment). REC =50 yuan / piece); P RE It is the green electricity trading volume (certificates), which corresponds one-to-one with the actual consumption of green electricity. That is, every 100 megawatt-hours of green electricity consumed corresponds to a green electricity trading volume of 100 green certificates.

[0030] Value of electrical energy V p Based on time-of-use pricing and transaction volume, the formula is as follows: , (2) In the formula, V p It is the total value of electricity trading (in yuan), that is, the profit obtained by the game participants through electricity trading; For the first t Time-of-use electricity pricing (RMB / kWh) follows market peak-valley pricing (as in this embodiment λ). 峰 = 1.5 yuan / kWh, λ 平 = 0.8 yuan / kWh, λ 谷 = 0.3 yuan / kWh); Q t For the first t Electricity trading volume during a given period (kWh) refers to the total amount of electricity bought or sold during that period. Buying is positive and selling is negative (e.g., if 5000 kWh is bought during peak load aggregation, then Q...). t =5000); T This represents the total number of time periods in the scheduling cycle (in this example, T=96, i.e., 15 minutes / time period, covering the entire 24 hours).

[0031] Operating costs include flexible resource operation and maintenance costs (C op The carbon emission compliance costs for traditional energy power generators, including the detailed calculation of flexible resource operation and maintenance costs, will be discussed later.

[0032] The overall benefit of each game player (U) i The final return for each entity is the net value of "revenue minus cost," which is the final return after deducting operating costs from the sum of the incremental environmental equity and the value of electrical energy. The specific calculation formula is as follows: In the formula, the subscript i Indicates the first i Game-like subjects ( i =1~5, corresponding to load aggregators, renewable energy generators, traditional energy generators, electricity users, and grid operators.

[0033] The information structure is the sum of information regarding time-of-use electricity prices, carbon quota trading prices, green certificate trading prices, renewable energy output forecasts, and the strategies executed by various stakeholders in the green electricity trading market. It provides data support for multi-stakeholder strategy interactions, aligning with the core concepts of information structure in game theory. It categorizes information into three types: The first category is publicly available market information, including time-of-use electricity price curves (λt) and carbon allowance prices (P). c ), Green Certificate Price (P) REC ), renewable energy output forecast curve (based on meteorological data fitting); The second category is the subject's private information, including equipment technical parameters (such as the maximum charge and discharge power P of energy storage). max =500kW), operating cost parameters (such as c) IL c PV ), strategy implementation progress (such as the completion rate of green electricity procurement); The third category is shared scheduling information, including real-time system load data and carbon quota benchmark values ​​(E). 基准 ), emission reduction target ratio (γ=80%).

[0034] This invention is based on the coupling relationship between environmental rights value and electrical energy value in the green electricity trading market to construct an incremental game model of environmental rights. It systematically portrays the strategic interaction logic of multiple subjects, solves the problem of existing technologies separating the two types of value, and makes the operating decisions of load aggregators more in line with market reality.

[0035] In step 2, a flexible resource operation model is constructed: based on the technical characteristics and operational constraints of flexible resources, a flexible resource operation system including an equipment operation model and an energy coupling model is constructed to achieve the connection between technical constraints and market value objectives.

[0036] The equipment operation model includes the energy conversion relationships and operational constraints of various flexible resources (energy storage system, interruptible load, distributed photovoltaic, and demand response resources) (taking the energy storage system as an example). In the energy storage system, the charging and discharging power and the stored capacity are dynamically related. The operational constraints include charging and discharging power constraints (Formula 3), stored capacity constraints (Formulas 4-5), and the capacity constraints of the energy storage system environment (Formula 6), as shown below: The charging and discharging power constraints of the energy storage system are: , (3) The state of charge constraints of the energy storage system are: , (4) State of charge (SOC) is dynamically updated during charge and discharge: , (5) The emission reduction targets for energy storage systems are constrained as follows: , (6) In the formula, for t Time-of-use energy storage charging power, for t Time-of-use energy storage discharge power, For maximum charge and discharge power of energy storage, for t State of charge of energy storage during time period , These are the minimum and maximum values ​​of the energy storage state of charge (20% and 90% respectively in this implementation). and These are the charging and discharging efficiencies, respectively. The proportion of emission reduction targets (dimensionless). for t The amount of electricity reduced in terms of emissions during a given period (or the increase in environmental rights). The reference electricity volume (or total annual emission reduction quota) is 500 tons in this example.

[0037] The constraints on interruptible loads include response time (the load must be cut off or restored within a preset time) and cut-off capacity (the single cut-off capacity shall not exceed a preset proportion of the rated load, such as to avoid interruption of user production, such as a maximum single cut-off of 300kW when the rated load is 1000kW). The stability requirements after load restoration and the duration limit of a single interruption must be met.

[0038] The energy conversion of distributed photovoltaics is based on the relationship between light intensity, photovoltaic panel temperature and output power. Operational constraints include output prediction deviation constraints (the deviation between actual output and predicted output shall not exceed a preset threshold, for example, the threshold is set to ±10%, and when the predicted output is 800kW, the actual output must be within the range of 720~880kW). The constraints on demand response resources are the load transfer range, including constraints on load transfer time and capacity. The first is the load transfer time constraint, which only allows load transfer during peak hours (8:00-10:00, 18:00-22:00) to the off-peak hours (0:00-6:00); the second is the load transfer capacity constraint, which stipulates that the capacity of a single transfer is ≤20% of the total load (e.g., when the total load is 600kW, the maximum capacity of a single transfer is 120kW).

[0039] The energy coupling model includes electrical energy balance constraints and environmental rights coordination constraints. The electrical energy balance constraint characterizes the matching relationship between the total output of flexible resources and load demand (the total output of flexible resources must meet the load demand ± a preset fluctuation range). The environmental rights coordination constraint characterizes the alignment between the total carbon quota surplus and the emission reduction target (total carbon quota surplus ≥ emission reduction target ratio × total carbon quota). These two types of constraints ensure that flexible resource scheduling balances supply and demand with emission reduction targets. The core constraint of environmental rights coordination, as shown in the following formula, ensures that the carbon quota surplus meets the emission reduction target: , (7) in, The carbon allowance benchmark (the maximum permissible carbon emissions allocated by the government to the participants in the game, in tons of CO2). The actual carbon emissions of the game participants (calculated from the type and amount of energy consumption, unit: tons of CO2); This refers to the carbon quota surplus; To achieve the target emission reduction ratio; This represents the total carbon allowance.

[0040] In step 3, solving the multi-agent game equilibrium to obtain the initial operational adjustment strategy involves: based on the environmental equity incremental game model constructed in step 1 and the flexible resource operation model in step 2, integrating non-cooperative and cooperative game methods to solve the multi-agent game equilibrium, forming the initial operational adjustment strategy for the load aggregator, and achieving a preliminary balance between the interests of multiple agents and technical constraints. Specifically, the non-cooperative game equilibrium is solved using the Nash equilibrium algorithm, then the Shapley value allocation method is used to allocate cooperative benefits, and finally, the game results and the operational constraints of flexible resources are integrated to output the initial operational adjustment strategy for the load aggregator.

[0041] The equilibrium solution for a multi-agent non-cooperative game satisfies the equilibrium condition that "no single agent can improve their own utility by unilaterally changing their strategy," such that the utility at equilibrium is greater than or equal to the effect of unilaterally changing the strategy. The mathematical expression is: , (8) In the formula: For the first i The utility function of a game player is used to quantify the player's payoff / satisfaction level under different strategies; To achieve Nash equilibrium, the first i The optimal strategy of each player in the game; To achieve the strategy combination of other players in the Nash equilibrium, For the first i When a player unilaterally deviates from equilibrium, any other strategy (non-equilibrium strategy) can be chosen, and S is the set of players (S={1~5}).

[0042] The solution is obtained iteratively using the particle swarm optimization algorithm, with 100 iterations and a convergence accuracy of 10. -6 When all entities are unable to improve their overall efficiency through unilateral strategy adjustments; U i When the time comes, output the non-cooperative game equilibrium strategy.

[0043] The equilibrium solution for multi-agent cooperative game theory employs the Shapley value allocation method. By calculating the marginal contribution of each agent in the alliance, the cooperative payoff is allocated, thus resolving the conflict between individual and collective rationality. An alliance of multiple agents can achieve a higher total payoff than acting individually. The mathematical expression for this is: , (9) In the formula, For the first i The value of the collaborative revenue distribution among the game's participants. S For a subset of the game alliance, U ( S ) is a subset of the alliance S Total revenue, The number of entities in the game alliance. N The total set of all game participants. Given the total number of players (N is 5 in this example), after adding the i-th player to subset S, For a subset of the alliance S Join the i After the individual players form a new alliance The total profit is represented by !, which indicates all permutations.

[0044] The solution first constructs 31 non-empty coalitions (25 -1), calculate U(S) for each alliance, then calculate the marginal contribution of each subject according to formula (9), allocate the collaborative benefits, and finally output the collaborative game equilibrium strategy that takes into account both individual and collective interests.

[0045] Marginal contribution of each subject Each factor is weighted according to its probability of appearing in all possible alliance formation sequences. To weight the final collaborative benefit distribution value It is the weighted average of all marginal contributions, thus ensuring the fairness of the distribution of benefits.

[0046] The initial strategy is formulated as follows: Combining the constraints of flexible resource operation, integrating the equilibrium results of multi-stakeholder game theory (both non-cooperative and cooperative), the load aggregator's flexible resource scheduling scheme, green electricity procurement ratio, and green certificate trading strategy are determined to form the initial operation adjustment strategy. This strategy ensures that the strategy simultaneously satisfies the interests of multiple stakeholders and is technically feasible. The initial operation adjustment strategy is as follows: Flexible resource scheduling: Energy storage valley period (0:00-6:00) charging (P charge =500kW), peak discharge (18:00-22:00) (P discharge =500kW); 200kW can be cut off during peak load periods; Green electricity procurement ratio: 30% during peak hours, 40% during off-peak hours, and 30% during valley hours (total procurement volume 100MWh); Green certificate trading: Hold 80 green certificates (corresponding to 80MWh of green electricity consumption), and sell the remaining 20 certificates when the carbon price is ≥100 yuan / ton.

[0047] This invention integrates solution methods from non-cooperative and cooperative game theory, ensuring individual rationality while achieving fair distribution of alliance benefits through Shapley value allocation. This resolves the dilemma of "conflict between individual and collective rationality," aligning with the application logic of game theory. By deeply integrating a flexible resource operation model with game theory mechanisms, it fully considers the synergy between technological constraints and value optimization, maximizing the scheduling potential of resources such as energy storage and interruptible loads, improving green electricity consumption efficiency, and achieving the core objective of flexible resources participating in green electricity trading.

[0048] In step 4, the parameters are dynamically adjusted to optimize the optimal operation and regulation strategy. Specifically, the current scenario type is identified based on real-time market data, the environmental rights weight coefficient and the electrical energy value weight coefficient are dynamically adjusted, the initial strategy is optimized, and the optimal operation and regulation strategy of the load aggregator is obtained by solving the current function, so as to maximize the synergistic effect between the increase in environmental rights and economic benefits.

[0049] The dynamic adjustment of the environmental rights weighting coefficient is based on the core objective of three clearly defined typical green electricity trading market scenarios: high carbon prices and strong policy-driven scenarios, scenarios with drastic electricity price fluctuations and power shortages, and scenarios with high renewable energy penetration. The peak-valley electricity price difference for these three scenarios is compared below. Figure 2 As shown, the peak-valley price difference is largest in scenarios with drastic electricity price fluctuations and power shortages (approximately 1.6 yuan / kWh), followed by scenarios with high carbon prices and strong policy-driven demand, while the peak-valley price difference is smallest in scenarios with high renewable energy penetration. Carbon price comparisons... Figure 3 As shown, carbon prices are highest in scenarios with high carbon prices and strong policy-driven growth, followed by scenarios with high renewable energy penetration, and lowest in scenarios with drastic electricity price fluctuations and power shortages. Figure 4 Differentiated weighting coefficients are set for different scenarios (α is the environmental rights weight, β=1-α is the electrical energy value weight).

[0050] Among them, the characteristics of high carbon price and strong policy-driven scenarios are: high carbon price (above the carbon price threshold, such as carbon price P) c ≥100 yuan / ton), and with the introduction of emission reduction subsidy policies, the adjustment rules for its environmental rights weight coefficient are as follows: environmental rights weight coefficient α≥0.8, electrical energy value weight coefficient β=1-α, and priority is given to increasing the incremental environmental rights; The characteristics of scenarios with drastic electricity price fluctuations and power shortages are: large peak-valley electricity price differences (e.g., ≥3 yuan / kWh) and large power supply gaps (e.g., >5%). The adjustment rules for the environmental rights weight coefficient are: the weight coefficient of electricity value β ≥ 0.7, the environmental rights weight coefficient α = 1 - β, and priority is given to ensuring economic benefits. The characteristics of a high renewable energy penetration scenario are: a high proportion of renewable energy power generation (e.g., ≥50%), and the adjustment rule for its environmental rights weight coefficient is: environmental rights weight coefficient α=0.5, and electrical energy value weight coefficient β=0.5, so as to achieve synergistic optimization of environmental rights and economic benefits; The dynamic adjustment of the energy value weighting coefficient combines scenario characteristics with flexible resource operation strategies to dynamically optimize scheduling parameters: In high carbon price scenarios, energy storage systems are prioritized to charge during peak renewable energy output periods (e.g., 12:00-14:00) to improve green electricity consumption; In scenarios with drastic electricity price fluctuations, energy storage systems charge during off-peak hours and discharge during peak hours, and interruptible loads are prioritized to trigger response during peak electricity price periods, with a single disconnection of 300kW; In scenarios with high renewable energy penetration, the proportion of distributed photovoltaic local consumption is increased (e.g., to 85%), and demand response loads are shifted to midday (12:00-14:00).

[0051] The optimal operation and regulation strategy is solved with the goal of maximizing the overall benefits of the load aggregator. The objective function is: , (10) In the formula, U The comprehensive benefit (in yuan) of the load aggregator is the net benefit after deducting operating costs from the incremental environmental rights revenue and the electricity revenue. α This refers to the environmental rights weighting coefficient. β The electrical energy value weighting coefficient; Δ V e For the incremental environmental equity of load aggregators, V p The value of electrical energy for load aggregators, C op To reduce the operating and maintenance costs of flexible resources; Flexible resource operation and maintenance costs C op The calculation formula is: (11) In the formula, k The charge-discharge cycle loss factor of the energy storage system (yuan / kWh, in this embodiment) k =0.05 yuan / kWh); P charge , P discharge These represent the total charging power and total discharging power of the energy storage system, respectively, in kWh; c IL For flexible resources; c PV The unit operation and maintenance cost of distributed photovoltaic (PV) is (yuan / kWh), and in this embodiment, cPV = 0.03 yuan / kWh; P PV For the total output of distributed photovoltaic power (kWh), c DR The organizational cost per unit of demand response resources (RMB / kWh) is calculated in this embodiment as cDR = RMB 0.1 / kWh; P DR Total demand response resource transfer load (kWh).

[0052] Solving the objective function yields the optimal operation and adjustment strategy for the load aggregator, ensuring that the strategy always achieves optimal overall benefits in dynamic scenarios.

[0053] The solution method employs mixed-integer linear programming, using the Gurobi solver with the following parameters as input: a. Market parameters: P c =90 yuan / ton (high carbon price scenario), λ 峰 = 1.8 yuan / kWh, λ 谷 = 0.3 yuan / kWh; b. Weighting coefficients: α=0.8, β=0.2; c. Flexible resource parameters: Same as step 2.

[0054] The optimal strategy output includes: 11) Flexible resource scheduling: 500kW of energy storage charging during midday (12:00-14:00) and 500kW of peak-hour discharging; 250kW of interruptible load shelving; 12) Green electricity procurement: 20% during peak hours, 50% during off-peak hours, and 30% during valley hours (total procurement volume 120MWh); 13) Green Certificate Trading: Sell 20 green certificates and earn 1,000 yuan (PREC = 50 yuan / certificate); 14) Overall benefits: U = 82,600 yuan (5.1% improvement over the initial strategy).

[0055] Specifically, its simulation verification and effect analysis; The simulation parameters are set as follows: 21) Scheduling cycle: 24 hours (T=96 time period); 22) Equipment parameters: Energy storage P max =500kW, photovoltaic installed capacity 1000kW, maximum interruptible load cut-off 300kW; 23) Load parameters: The average total load of users is 600kW (700-800kW during peak hours and 400-500kW during off-peak hours). 24) Market parameter: P c =90 yuan / ton, λ 峰 =1.8 yuan / kWh, λ 平 = 0.9 yuan / kWh, λ 谷 = 0.3 yuan / kWh, P REC =50 yuan / ticket.

[0056] The comparison group settings are as follows: a. Comparison group: Traditional method (not considering environmental rights game, only aiming to maximize economic benefits); b. Comparison group: Single game method (non-cooperative game only, no collaborative benefit distribution); c. Experimental group: The method of this invention (integrating two types of game theory + dynamic parameter adjustment).

[0057] The simulation results are shown in Table 1. In terms of environmental benefits, the carbon emission reduction of the experimental group increased by 72.7% and the green electricity consumption rate increased by 32.7% compared with the control group 1, verifying the effectiveness of the environmental rights game model. In terms of economic benefits, the overall benefits increased by 18.0% and the operating costs decreased by 7.2%, indicating that dynamic parameter adjustment and collaborative game can balance the "environment-economy" objectives. In terms of adaptability, under scenarios such as high carbon prices and fluctuating electricity prices, the strategy can adapt to the needs through weight adjustment, solving the problem of insufficient scenario adaptability of existing methods.

[0058] Table 1 This embodiment constructs an environmental rights increment game model and a flexible resource operation model, integrating non-cooperative and cooperative game methods to achieve dynamic optimization of load aggregator operation strategies. Simulation results show that the method in this embodiment can significantly improve environmental rights increment (+72.7%) and comprehensive benefits (+18.0%), while ensuring green electricity consumption rate (86.5%), providing effective support for the scientific decision-making of load aggregators in the green electricity trading market.

[0059] This invention establishes a dynamic adjustment mechanism adapted to three typical market scenarios. By flexibly adjusting the weight coefficients and scheduling parameters, it achieves a dynamic balance between the increase in environmental rights and economic benefits, thereby enhancing the practicality and adaptability of the strategy.

[0060] This embodiment also relates to a load aggregator operation and regulation system that considers the incremental game of environmental rights. This embodiment uses a provincial-level green electricity trading pilot park as the application scenario. This park includes distributed photovoltaic, energy storage systems, industrial interruptible load users, and commercial demand response users, facing a mixed market scenario with high carbon prices, a peak-valley electricity price difference of 3.2 yuan / kWh, and a renewable energy penetration rate of 55%. Figure 5 The load aggregator operation regulation system is deployed according to a four-layer architecture of perception layer, network layer, platform layer and resource layer, and combines four core modules to realize operation regulation: game model construction, resource model construction, initial strategy solution and optimal strategy optimization.

[0061] (I) Perception Layer: Data Acquisition and Terminal Interaction. The perception layer deploys four types of terminal devices to complete the real-time acquisition of all operational data. Market and Policy Data Acquisition Terminal: Simultaneously acquires market data such as carbon prices, time-of-use electricity prices, and green certificate trading premiums from the carbon trading market, as well as emission reduction subsidy policy documents. Flexible Resource Status Monitoring Terminal: Collects equipment status data such as distributed photovoltaic output, energy storage SOC, interruptible load capacity, and demand response resource load transfer potential. Equipment Operation Compliance Monitoring Terminal: Monitors compliance indicators such as energy storage charging and discharging power, photovoltaic output deviation, and interruptible load response time. Multi-Entity Strategy Interaction Terminal: Receives data on the output declaration strategies of renewable energy generators, the carbon emission control strategies of traditional energy generators, and the green electricity consumption ratio adjustment strategies of electricity users.

[0062] (ii) Network Layer: Data Transmission and Command Interaction. A low-latency transmission network is constructed using IoT cards, 5G private networks, and fiber optic leased lines to upload real-time data collected by the sensing layer to the platform layer and send scheduling commands generated by the platform layer to resource layer devices, ensuring data transmission latency ≤50ms to meet real-time scheduling requirements.

[0063] (III) Platform Layer: Core Modules Operate Collaboratively. The platform layer includes a game model construction module (environmental equity incremental game module), a resource model construction module (flexible resource collaborative scheduling module), an initial strategy solution module (core model library of the environmental equity incremental game module), and an optimal strategy optimization module (flexible resource collaborative scheduling module, scenario adaptation engine), specifically including: The game model construction module is used to construct an incremental game model of environmental rights based on the coupling relationship between environmental rights value and electricity value in the green electricity trading market, and to clarify the game subjects, strategy space, payoff function and information structure. The resource model construction module is used to combine the technical characteristics and operational constraints of flexible resources to construct a flexible resource operation model that includes an equipment operation model and an energy coupling model. The initial strategy solution module is used to solve the multi-agent game equilibrium based on the environmental equity incremental game model and the flexible resource operation model, and integrates the Nash equilibrium and Shapley value allocation method to obtain the initial operation adjustment strategy of the load aggregator. The optimal strategy optimization module is used to dynamically adjust the environmental rights weight coefficient and the electricity value weight coefficient according to the dynamic changes in the green electricity trading market scenario. By solving the comprehensive benefit objective function, the optimal operation and regulation strategy of the load aggregator is obtained.

[0064] (iv) Resource Layer: Command Execution and Effect Feedback. The resource layer receives scheduling commands from the platform layer to achieve coordinated operation of power generation, grid, load, storage, and consumption (power source resources, grid resources, load resources, energy storage systems, and consumption-side optimization). The game players for power source resources are renewable energy generators and traditional energy generators; the game players for grid resources are grid operators; the game players for load resources are electricity users; and the game players for energy storage systems are load aggregators. The game players for consumption-side optimization are electricity users.

[0065] Energy storage systems execute charging and discharging plans to improve peak-valley regulation capabilities; The proportion of distributed photovoltaic power consumption has increased to 98%, reducing curtailment of solar power. Industrial users implement interruptible load shedding plans, while commercial users implement load shifting plans (peak-to-valley shifting). The proportion of green electricity procurement increased to 50%, 120 green certificates were traded, and a carbon quota surplus of 120 tons was achieved, exceeding the emission reduction target.

[0066] The electronic device of this invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0067] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0068] The processing unit performs the various methods and processes described above. For example, in some embodiments, the methods may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute the methods by any other suitable means (e.g., by means of firmware).

[0069] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0070] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0071] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0072] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A load aggregator operation adjustment method considering incremental environmental rights game theory, characterized in that, The method includes: Based on the coupling relationship between environmental rights value and electricity value in the green electricity trading market, an incremental game model of environmental rights is constructed, which includes game subjects, strategy space, and payoff function. Combining the technical characteristics and operational constraints of flexible resources, a flexible resource operation model is constructed. The flexible resource operation model includes an equipment operation model that includes physical operation constraints of equipment and environmental target constraints, and an energy coupling model that includes electrical energy balance constraints and environmental rights coordination constraints. Based on the aforementioned incremental environmental rights game model and the aforementioned flexible resource operation model, and by integrating non-cooperative game and cooperative game methods, the multi-agent game equilibrium is solved to obtain the initial operation adjustment strategy of the load aggregator. Based on the load aggregator's initial operation adjustment strategy, and according to the dynamic changes in the green electricity trading market scenario, the environmental rights weight coefficient and the electricity value weight coefficient are dynamically adjusted to optimize the load aggregator's optimal operation adjustment strategy, thereby maximizing the synergistic effect between the increase in environmental rights and economic benefits.

2. The load aggregator operation adjustment method considering incremental environmental rights game theory as described in claim 1, characterized in that, The main players in the game include load aggregators, renewable energy generators, traditional energy generators, electricity users, and grid operators. The various game players interact around the incremental environmental rights and electricity revenue, and the strategy space includes: Flexible resource scheduling strategies, green electricity procurement ratio strategies, and green certificate trading strategies of load aggregators; The renewable energy power generation operator's renewable energy output declaration strategy, namely, the declaration of output scale and time period distribution; Fossil energy output adjustment strategies and carbon emission control strategies of traditional energy power generators; Strategies for adjusting the proportion of green electricity consumption by electricity users; And the power grid operators' transmission channel scheduling strategies and market trading rule execution strategies.

3. The load aggregator operation adjustment method considering incremental environmental rights game theory as described in claim 1, characterized in that, The payoff function is guided by maximizing the overall benefit of each game participant, and the payoff function is as follows: The overall benefit for each participant is equal to the net benefit after deducting operating costs from the sum of incremental environmental rights revenue and electricity trading revenue; wherein the incremental environmental rights revenue is quantified through green certificate premium and carbon quota surplus; and the electricity trading revenue is calculated based on time-of-use electricity prices and electricity trading volume.

4. The load aggregator operation adjustment method considering incremental environmental rights game theory according to claim 3, characterized in that, The operating costs include flexible resource operation and maintenance costs and carbon emission compliance costs for traditional energy power generators. Flexible resource operation and maintenance costs include energy storage system charge and discharge cycle loss costs, interruptible load compensation costs, distributed photovoltaic operation and maintenance costs, and demand response resource organization costs.

5. The load aggregator operation adjustment method considering incremental environmental rights game theory as described in claim 1, characterized in that, The equipment operation model includes the energy conversion relationship and operation constraints of various flexible resources, wherein the flexible resources include energy storage systems, interruptible loads, distributed photovoltaics, and demand response resources. The charging and discharging power of energy conversion in energy storage systems is dynamically correlated with the amount of energy stored. Operational constraints include energy storage system charging and discharging power constraints, state of charge constraints, and emission reduction target constraints. The constraints on interruptible loads include constraints on response time and cutoff capacity. The energy conversion of the distributed photovoltaic system is based on the correlation between light intensity, photovoltaic panel temperature and output power, and the operating constraints include output prediction deviation constraints. The constraints on the demand response resources include constraints on load transfer time periods and capacity.

6. The load aggregator operation adjustment method considering incremental environmental rights game theory according to claim 1, characterized in that, The energy coupling model includes electrical energy balance constraints and environmental rights coordination constraints. The electrical energy balance constraints characterize the matching relationship between the total output of flexible resources and the load demand, while the environmental rights coordination constraints characterize the alignment relationship between the total carbon quota surplus and the emission reduction target.

7. The load aggregator operation adjustment method considering incremental environmental rights game theory as described in claim 1, characterized in that, Solving for multi-agent game equilibrium includes: First, the equilibrium strategy for the non-cooperative game is solved using the Nash equilibrium algorithm; Then, the Shapley value allocation method is used to calculate the marginal contribution of each agent in the game alliance, allocate the cooperative benefits of the alliance, and output the cooperative game equilibrium result. Finally, by integrating the two game equilibrium results with the operational constraints of flexible resources, the load aggregator's flexible resource scheduling scheme, green electricity procurement ratio, and green certificate trading strategy are output, forming the aggregator's initial operation adjustment strategy.

8. The load aggregator operation adjustment method considering incremental environmental rights game theory according to claim 7, characterized in that, The Shapley value allocation method is specifically calculated as follows: ， In the formula, For the first i The value of the collaborative revenue distribution among the game's participants; S For a subset of the game alliance, U ( S ) is a subset of the alliance S Total revenue, The number of entities in the game alliance; N The total set of all game participants. The total number of players in the game; after the i-th player is added to subset S, For a subset of the alliance S Join the i After the individual players form a new alliance Total revenue.

9. The load aggregator operation adjustment method considering incremental environmental rights game theory according to claim 1, characterized in that, The green electricity trading market scenarios include scenarios with high carbon prices and strong policy drivers, scenarios with drastic electricity price fluctuations and power shortages, and scenarios with high penetration rates of renewable energy. The weighting adjustment rules for each scenario include: For scenarios with high carbon prices and strong policy drivers, priority should be given to increasing the incremental environmental rights, specifically: the environmental rights weighting coefficient α≥0.8, and the electrical energy value weighting coefficient β=1-α; In scenarios involving drastic fluctuations in electricity prices and power shortages, priority should be given to ensuring economic benefits, specifically: the weighting coefficient for the value of electricity β ≥ 0.7, and the weighting coefficient for environmental rights α = 1 - β. For scenarios with high renewable energy penetration, the environmental rights and economic benefits are optimized in a coordinated manner, specifically: the environmental rights weighting coefficient α=0.5, and the electrical energy value weighting coefficient β=0.

5.

10. The load aggregator operation adjustment method considering incremental environmental rights game theory according to claim 9, wherein the objective function of the optimal operation adjustment strategy of the load aggregator is to maximize the comprehensive benefits of the load aggregator, specifically: ， In the formula, U To maximize the overall benefits for load aggregators; α This refers to the environmental rights weighting coefficient. β The electrical energy value weighting coefficient; Δ V e For the incremental environmental equity of load aggregators, V p The value of electrical energy for load aggregators, C op To reduce the operating and maintenance costs of flexible resources; Flexible resource operation and maintenance costs C op The calculation formula is: ， In the formula, k The charge-discharge cycle loss coefficient of the energy storage system; P charge , P discharge These are the total charging power and total discharging power of the energy storage system, respectively. c IL Compensation cost per unit of interruptible load; P IL Total interruptible load cut-off capacity; c PV The unit operation and maintenance cost of distributed photovoltaic power generation; P PV For the total output of distributed photovoltaic power; c DR Organizational cost per unit of demand response resources; P DR This represents the total load transfer for demand response resources.

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