A power system dispatch optimization operation method and system considering source-load interaction

Abstract

The present application relates to a kind of power system scheduling optimization operation method and system considering source load interaction, belong to electric power technical field, solve the complex situation such as the existing power system generation capacity not considering two-stage electric power, green certificate and carbon market problem etc..Method includes: based on each energy unit output, carbon emission and carbon emission quota, build carbon responsibility apportionment source load interaction model;Build the power purchase optimization model of load side electric power user, build the power purchase cost model of load side electric power user and first constraint condition and build the electric energy cost model of inter-provincial market and second constraint condition;Build source side generator unit output optimization model;The above model is converted to process to predict the out-clear result of thermal power generator unit and green power generator unit;According to the out-clear result of predicted thermal power generator unit and green power generator unit, the power generation of thermal power generator unit and green power generator unit is optimized and scheduled.The generation capacity of power system is scheduled and optimized considering two-stage electric power, green certificate and carbon market.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and system for optimizing the operation of power system dispatch considering source-load interaction. Background Technology

[0002] Under the "dual carbon" goal, a new power system is being built with the electricity market at its core, and the carbon market and green certificate market developing in synergy. The power sector is continuously developing towards a cleaner and more efficient direction in the complex scenario of the electricity-carbon-green certificate market. In the electricity market, electricity supply and demand exhibit an inverse distribution. To achieve optimal allocation of electricity resources over a wide area, large-scale "inter-provincial and inter-regional" electricity trading has been carried out, forming a two-tiered electricity market model of "unified market, two-level operation" between provinces and within provinces. In the carbon market, carbon quotas are set and allocated to thermal power generating units, which fulfill their emission reduction obligations by buying and selling carbon quotas or paying CCERs (China Certified Emission Reductions). In the green certificate market, green certificates are issued to green electricity generating units, which sell green certificates to obtain the environmental value of green electricity. Green certificate buyers purchase green certificates to meet their corporate green electricity consumption targets or fulfill renewable energy quota obligations. In the complex scenario of multiple markets, the dispatching of the new power system needs to fully consider the impact of the complex scenario on power sources and loads, as well as the interaction between power sources and loads. On the one hand, the complex scenario of multiple markets will promote the low-carbon and clean development of the new power system. The production of thermal power generating units is constrained by carbon emission costs, while green electricity generating units generate green certificates that yield environmental benefits in the green certificate market and CCERs (China Certification of Energy) that offset carbon allowances in the carbon market, thereby promoting the production and consumption of green electricity. On the other hand, the interaction between power sources and loads facilitates flexible and efficient dispatching of the new power system. Under the two-tiered power market model, the relationship between power sources and loads is shifting from a traditional vertically integrated structure to an interactive and competitive structure. Power generation units on the source side can choose to sell electricity between or within provinces, and power users on the load side can also choose to purchase electricity between or within provinces. Therefore, the dispatching of the new power system must consider not only the impact of complex scenarios on the output of generating units but also the competition between power sales and purchases on both the source and load sides.

[0003] In complex scenarios, new power system dispatching considering source-load interaction will generate the following problems: First, the two-tiered power market will squeeze the trading space of the intra-provincial power market. The results of inter-provincial power transactions will serve as the boundary conditions for each province to conduct intra-provincial power transactions. Load-side power users will reconsider their strategies for allocating power purchase demand in the source-side intra-provincial and inter-provincial markets. This will cause the cleared power volume in inter-provincial transactions to squeeze the trading space of their respective province's generating units in the intra-provincial power market, affecting the overall profitability of generating units in the power market. Second, the environmental value of green electricity will be double-counted in the carbon market and the green certificate market. Both the carbon market and the green certificate market can reflect the environmental value of green electricity, but they have not yet achieved effective integration, resulting in double-counting of the environmental value of green electricity. Finally, after the two-tiered power market, the carbon market, and the green certificate carbon market form a coupled market, the strategic behaviors of load-side power users and source-side generating units will become more complex. Electricity users occupy a leading position in decision-making, and need to consider the allocation of electricity purchase demand in the provincial and inter-provincial markets on the source side. Generating units need to determine their trading decisions in the provincial and inter-provincial markets based on their own power generation capacity. At the same time, they need to consider the environmental costs in the carbon market and the environmental value benefits in the green certificate market. In addition, the behavior of both the source and load sides will also affect each other through electricity prices.

[0004] Research on novel power system dispatching considering source-load interaction in complex scenarios mainly focuses on two aspects: optimizing the operation of two-tier power markets and the impact of carbon-green certificate markets on the power industry. Regarding the optimization of two-tier power market operation, studies aiming at the construction of a unified national power market have explored the optimization relationship between provincial-level trading and carbon market trading. Based on market equilibrium theory and system dynamics models, the dual effects of green certificate trading and carbon market trading on the power market have been simulated. However, these studies primarily consider the impact of green certificate trading and carbon trading mechanisms on the power market and the effectiveness of the power system's green and low-carbon transformation, without clarifying how the green certificate market and carbon market trading mechanisms should be integrated. Currently, the coupling and interaction between power sources and loads in my country are becoming increasingly evident. The trading interaction between the green certificate market and the carbon market needs further clarification, and how power users and generating units can make scientific decisions in the coupled trading of multiple markets (electricity, carbon, and green certificates) also requires further research. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a method and system for optimizing the operation of power system dispatch considering source-load interaction, in order to solve the problems of how to reduce the operating cost of the power system and reduce carbon emissions under the conditions of source-load interaction technology considering carbon responsibility sharing and joint dispatch clearing of two-level power market, carbon market and green certificate market considering non-cooperative game behavior of generator units, and thus how to achieve the optimized operation of the new power system dispatch.

[0006] On one hand, embodiments of the present invention provide a power system dispatch optimization operation method considering source-load interaction, including: constructing a source-load interaction model for carbon responsibility sharing based on the output, carbon emissions, and carbon emission quotas of each energy unit to optimize the electricity price of thermal power generating units using historical data; constructing an optimized electricity purchase model for load-side power users, wherein a load-side power user electricity purchase cost model and a first constraint are constructed based on the number, output, and electricity price of green power generating units, thermal power generating units, and conventional energy generating units; and an inter-provincial market electricity cost model and a second constraint are constructed based on the cleared electricity volume of green power generating units, thermal power generating units, and conventional energy generating units; and constructing a source-side generator output... The optimization model includes: constructing a green power generation unit revenue model and a third constraint with the goal of maximizing the revenue of the green power generation units; constructing a green certificate market clearing model with the goal of clearing the green certificate market; constructing a carbon market clearing model and a fourth constraint with the goal of clearing the carbon market; constructing a thermal power generation unit revenue model and a fifth constraint with the goal of maximizing the revenue of the thermal power generation units; converting the load-side power user electricity purchase optimization model and the source-side generator output optimization model to predict the clearing results of thermal power generation units and green power generation units; and scheduling and optimizing the power generation of the thermal power generation units and the green power generation units based on the predicted clearing results of the thermal power generation units and the green power generation units.

[0007] The beneficial effects of the above technical solutions are as follows: The source-load interaction technology, considering carbon responsibility sharing, further explores carbon emission reduction mechanisms on both the source and load sides. Furthermore, it establishes a complex scenario trading model considering source-load interaction to analyze the strategic behavior of load-side electricity users and source-side generating units, aiming to minimize the sum of intra-provincial and inter-provincial electricity purchase costs for electricity users and maximize the benefits of green generating units participating in the electricity-green certificate-carbon market. It fully considers the complex operation of the two-tiered electricity market, green certificate market, and carbon market, optimizing unit dispatching and operation schemes to better guide the safe and stable operation of the new power system.

[0008] Further improvements to the above method, based on the output, carbon emissions, and carbon emission quotas of each energy unit, construct a source-load interaction model for carbon responsibility sharing, which further includes: expressing the carbon emission cost through the following formula:

[0009] (1) in, f CET For carbon emission costs, l CET The price of carbon allowances. N The number of conventional energy generating units, D i , Q i The units iActual carbon emissions and allocated carbon allowances. P i For the unit i Actual output s i For the unit i Carbon emissions per unit of output l i For the unit i Carbon emission allowance per unit output; the source-load shared carbon responsibility model for the carbon emissions of the thermal power units is expressed by the following formula: (2) (3) in, t For time integration variables, The power vector formed by the generator units. P i For the unit i Actual output This is a function for calculating the carbon trading costs of generating units. U i Unit power i Marginal carbon cost per unit power, allocated x i For the unit i The allocated marginal carbon cost; N The number of conventional energy generating units, T The total trading period is divisible. For the unit i The slight increase in power For the unit i During the period t The unit contribution of carbon emission costs; the environmental value benefit model of green electricity is expressed by the following formula: (4) in, For the price of green certificates, The price of carbon allowances. The number of green certificates converted into carbon allowances. To address the remaining number of green certificates, a source-load interaction model for carbon responsibility sharing is constructed, including a carbon emission cost model, a source-load joint carbon responsibility sharing model for carbon emissions from thermal power units, and a green electricity environmental value benefit model.

[0010] Based on further improvements to the above method, when the carbon responsibility sharing is that the generation side and the load side each bear half of the system carbon responsibility, the renewal price of the source-side unit under the shared carbon responsibility sharing is the sum of the initial price and the marginal carbon cost; and the renewal price of the load side is the difference between the initial price and the marginal carbon cost.

[0011] Further improvements to the above method, based on the number, power output, and electricity price of green power generating units, thermal power generating units, and conventional energy generating units, construct a load-side electricity user purchase cost model and its first constraint condition, which further includes: the load-side electricity user purchase cost model is expressed by the following formula: (5) in, For the cost of electricity to load-side users, , , These are the electricity energy in the provincial market on the source side, reserves, and inter-provincial electricity purchase costs, respectively. M , N , U These are the numbers of green energy generator sets, thermal power generator sets, and other conventional energy generator sets within the province of Heze. , , These are green power generator sets within the province on the load side. m conventional coal-fired power generating units n Other conventional energy generator sets u Electricity price quote; , , Generator sets m , n , u The power generation output; , Generator sets n , u Alternative quotes; , Separate generator sets n , u Backup output; To take into account the inter-provincial market during the time period after carbon responsibility allocation t Clearing electricity price, To take into account the electricity purchased by the load-side provinces from other provinces after carbon responsibility sharing; T Total trading session; The first constraint includes power balance constraint, reserve capacity constraint, unit ramping constraint, and unit output constraint, wherein the power balance constraint is expressed by the following formula: (6) in, For the Dutch side province j generator set During the period t Cleared electricity volume participating in the inter-provincial electricity market J The number of provinces on the Dutch side; For source side usersl During the period t The load demand, L The number of users with load demand; The reserve capacity constraint is expressed by the following formula: (7) in, This is the reserve factor for the provincial power grid on the source side; The unit's ramp-up constraint is expressed by the following formula: (8) in, , , , , , Green electricity generator sets m Thermal power generator sets n Other conventional energy generator sets u The upper and lower limits of the unit's output during ramping are expressed by the following formula: (9) in, , , , , , The green power generator sets are respectively m The thermal power generator set n Other conventional energy generator sets u The upper and lower limits of effort output.

[0012] Based on further improvements to the above method, the electricity cost model for the inter-provincial market, constructed based on the cleared electricity volume of green power generating units, thermal power generating units, and conventional energy generating units, and the second constraint further include: The electricity clearing price model for the inter-provincial market is expressed by the following formula: (10) in, For the cost of electricity in the inter-provincial market, J The number of provinces on the Dutch side; , , respectively the Dutch side province j The number of green energy generator sets, thermal power generator sets, and other conventional energy generator sets in the province; , , Dutch Province j Green electricity generator set conventional coal-fired power generating units Other conventional energy generator sets Electricity prices for inter-provincial market transactions after considering carbon responsibility allocation; For the Dutch side province To the source side of the province user l Inter-provincial power transmission prices; , , For generator sets , , The cleared electricity volume participating in the inter-provincial electricity market after considering carbon responsibility sharing.

[0013] The second constraint includes the balance constraint between load-side electricity purchase demand and inter-provincial electricity supply, the inter-provincial transmission line capacity constraint, and the cleared electricity volume constraint for load-side provinces participating in the inter-provincial market. The balance constraint between the source-side electricity purchase demand and the inter-provincial power supply is expressed by the following formula: (11) in, Line losses for inter-provincial power transmission lines; The transmission capacity constraint of the inter-provincial transmission line is expressed by the following formula: (12) , These are the upper and lower limits of the transmission capacity of inter-provincial power transmission lines; The clearing electricity constraint for the province participating in the inter-provincial market is expressed by the following formula: (13) in, , , , , , For generator sets , , Upper and lower limits on the capacity for participating in inter-provincial transactions.

[0014] Based on further improvements to the above method, the revenue optimization model of the green electricity generator set is expressed by the following formula: (14) in, Green electricity generator sets Benefits of participating in the electricity-green certificate-carbon market To clear out electricity prices in the Dutch province market, To generate electricity for the generator units in the Dutch provincial market; The third constraint includes: price quotation constraints, output constraints for green electricity generators allocated within and between provinces, and constraints on the issuance of green certificates and carbon quotas; among which... The pricing constraint is expressed by the following formula. (15) in, , Green electricity generator sets The upper and lower limits of the price; The output constraints of the green power generator sets distributed within and between provinces are expressed by the following formula. (16) in, This is the maximum power output of the unit; The constraints on the issuance of green certificates and carbon allowances are expressed by the following formula: (17) (18) in, The number of green certificates issued per unit of generator set; The green certificate market clearing model is expressed by the following formula: (19) (20) (twenty one) (twenty two) (twenty three) in, , These are the two parameters of the green certificate market price-production model. The number of green certificates issued for each green generator unit generating electricity; This indicates the price of green certificates when production reaches its maximum. This indicates the minimum amount of renewable energy consumption mandated by the Renewable Energy Consumption Scheme (RPS). This indicates that the calculation was performed using historical data. The consumption responsibility weight specified in the Renewable Energy Quota System (RPS); For users During the period t Real-time load demand; L For the number of users.

[0015] Based on the above method, a carbon market clearing model and a fourth constraint are further constructed with the goal of carbon market clearing, including: The carbon market clearing model is expressed by the following formula: (twenty four) in, , For thermal power at time t The purchase and sale prices of carbon allowances , Thermal power at time t The volume of carbon allowances purchased and sold. for t The price of CCER sold by Shike Green Electricity For a moment t Number of CCERs sold by green electricity The carbon market clearing model includes: carbon market quota and CCER supply and demand balance constraints, thermal power quota bidding constraints, and the number of CCERs obtained by green power units. The carbon market quotas and CCER supply and demand balance constraints are expressed by the following formula: (25) in, , which is the dual variable of the carbon quota supply and demand balance constraint, and represents the trading price of the carbon quota; The following formula represents the time period of thermal power. t The relationship between the amount of carbon allowances that domestic companies need to participate in market bidding and their carbon allowance purchase and sale volumes: (26) in, For the thermal power generator set n During the period t The cumulative carbon allowance trading volume within the period; The relationship between the amount of coal-fired power plant carbon market bidding and actual carbon emissions and carbon quota allocation is as follows: (27) (28) (29) (30) (31) (32) in, For the thermal power generator set n During trading hours t Cumulative carbon emissions within the country. For the thermal power generator set n The carbon allowances initially allocated during the compliance period For the thermal power generator set n In the t Initial carbon allowance decomposition factor for each trading session; This represents the total electricity generated by thermal power plants in the previous year. The approved carbon emission baseline; For the thermal power generator set n carbon emission coefficient, S The number of scenarios contributing to the real-time market for new energy. For the scene s The probability of occurrence For the thermal power generator set n In the scene s Down t Real-time market clearing volume; The number of CCERs obtained by the green motor unit is expressed by the following formula: (33) (34) in, The number of CCERs certified for green electricity The carbon dioxide emission reduction per unit of new energy power generation. New energy in the real-time market In the scene s Next moment t The winning bid volume; As the marginal emission factor of electricity, It is the capacity marginal emission factor; The net CCER value of the green electricity generator set is expressed by the following formula: (35) in, , Green electricity At any moment t Net CCER value owned, and CCERs sold in the carbon market.

[0016] Based on the above method, a further improvement is made to the revenue model of the thermal power generating unit, with the revenue of the thermal power generating unit as the objective, and the fifth constraint condition further includes: The revenue calculation model for thermal power generating units is expressed by the following formula: (36) in, , , The thermal power generating units are respectively n The day-ahead market return, real-time market return, and reserve return. For the thermal power generator set n The cost of electricity generation, For the thermal power generator set n Net carbon allowance revenue.

[0017] The corresponding constraints of the revenue calculation model for thermal power generating units are expressed by the following formula: (37) (38) (39) (40) (41) (42) (43) in, , These are the day-to-day and real-time market times, respectively. t Clearing electricity price, For the thermal power generator set After considering carbon responsibility sharing at time t The amount of electricity cleared from the market the day before yesterday, For the scene s The probability of occurrence S For the number of scenes, For the thermal power generator set After considering carbon responsibility sharing in the scenario s Next moment t Real-time market clearing volume, , , For the thermal power generator set The cost coefficient, For the carbon market t carbon price, , The thermal power generating units at time respectively t The volume of carbon allowances sold and purchased. For the thermal power generator set Electricity pricing after considering carbon responsibility allocation For the thermal power generator set Participate in carbon market bidding.

[0018] Further improvements to the above method involve transforming the load-side power user electricity purchase optimization model and the source-side generator output optimization model to predict the clearing results of thermal power generators and green power generators. This further includes Lagrange function transformation processing and corresponding optimality conditions, wherein... The Lagrange function transformation process is as follows: The Lagrange function of the lower-level model in the game subject model is defined as: (46) in, The objective function for optimizing the inter-provincial electricity market; , These are the equality constraints and inequality constraints in the corresponding constraints of the inter-provincial market electricity clearing price model, respectively. l , µ They are respectively , Lagrange multipliers; Construct the partial differential equation of the Lagrange function based on the Lagrange function, and calculate the Lagrange function with respect to the variable. , , The gradient is used to determine the optimality condition of the Lagrange function: (47) in, , , Generator sets , , Cleared electricity volume participating in the inter-provincial electricity market; The complementary relaxation condition is expressed by the following formula to ensure the correctness of the optimal solution: (48) For the form of Complementary relaxation conditions, using large The law performs equivalent processing; (49) In the formula: It is a binary variable, taking the value 0 or 1. It is a sufficiently large constant.

[0019] Using the KKT conditions, the two-layer model is transformed into a single-layer model: (50).

[0020] On one hand, embodiments of the present invention provide a power system dispatch optimization operation system considering source-load interaction, characterized by comprising: a source-load interaction model construction module, used to construct a source-load interaction model for carbon responsibility sharing to optimize the electricity price of thermal power generating units using historical data; a load-side electricity user purchase optimization model construction module, used to construct a load-side electricity user purchase optimization model, wherein a load-side electricity user purchase cost model and a first constraint are constructed based on the electricity purchase cost in the provincial market, the reserve electricity purchase cost, and the inter-provincial electricity purchase cost in the source-side market, and an inter-provincial electricity cost model and a second constraint are constructed based on the electricity costs of green power generating units, thermal power generating units, and conventional energy generating units; and a source-side generating unit output optimization model construction module, used to construct a source-load interaction model for optimizing the electricity price of thermal power generating units using historical data; a load-side electricity user purchase optimization model construction module, used to construct a load-side electricity user purchase cost model and a first constraint based on the electricity costs of green power generating units, thermal power generating units, and conventional energy generating units; and a source-side generating unit output optimization model construction module, used to construct a source-load interaction model for optimizing the electricity price of thermal power generating units. The system includes: a load-side generator output optimization model, comprising: a green power generator revenue model and a third constraint condition with the goal of maximizing the revenue of the green power generators; a green certificate market clearing model with the goal of clearing the green certificate market; a carbon market clearing model and a fourth constraint condition with the goal of clearing the carbon market; and a thermal power generator revenue model and a fifth constraint condition with the goal of clearing the revenue of the thermal power generators. A conversion module is used to convert the load-side power user purchase optimization model and the source-side generator output optimization model to predict the clearing results of the thermal power generators and green power generators. A scheduling module is used to schedule and optimize the power generation of the thermal power generators and green power generators based on the predicted clearing results.

[0021] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. This application proposes a source-load interaction technology for carbon responsibility sharing, fully exploring the carbon reduction potential on the load side. Traditional carbon emission cost calculation techniques focus on carbon emissions on the source side, while giving less consideration to the network structure and transmission characteristics of carbon flow in the power system, and providing insufficient incentive for demand-side carbon emission reduction in the power system. In the power system, the source and load sides are interdependent in terms of system carbon emission reduction responsibility, playing equally important roles and bearing equal responsibility for system carbon emission reduction. This application constructs a source-load joint carbon responsibility sharing technology based on the Aumann-Shapley method, considering the fairness of carbon emission reduction responsibility sharing between the source and load sides, and fully exploring the carbon reduction potential on the load side.

[0022] 2. Load-side power user electricity purchase optimization model: This application constructs a load-side power user electricity purchase optimization model, considering the bidding behavior of power users in the intra-provincial and inter-provincial two-tier power markets. Load-side power users will reconsider their power purchase demand allocation strategies in the source-side intra-provincial and inter-provincial markets in these two-tier markets. This results in the cleared electricity volume in inter-provincial transactions squeezing the trading space of their respective provincial generating units in the intra-provincial power market, affecting the overall profitability of generating units in the power market. Traditional load-side power user electricity purchase models rarely consider the complex trading behavior in these two-tier power markets. Based on intra-provincial power market transactions, this application extends the inter-provincial power market transaction model, constructing an inter-provincial power clearing price model with the optimization objective of minimizing the operating cost of the inter-provincial power market. This model considers the power energy in the source-side intra-provincial market, the inter-provincial power purchase cost of reserve loads, and the power purchase costs of load-side power users. Accordingly, a load-side power user electricity purchase optimization model is constructed with load-side provincial power users as the game players and the optimization objective of minimizing the sum of load-side power user electricity purchase costs, considering the mutual competition between power sales and purchases on both the source and load sides.

[0023] 3. This invention constructs a source-side generator output optimization model, considering the bidding behavior of generator units in multiple markets including electricity, carbon, and green certificates. The production of source-side thermal power generators is constrained by carbon emission costs in the carbon market, while green electricity generators generate environmental benefits in the green certificate market. Both the carbon market and the green certificate market reflect the environmental value of green electricity, but they are not yet effectively linked, leading to double-counting of the environmental value of green electricity. This invention constructs a generator output model considering both the value of electrical energy and environmental value in multiple markets including electricity, carbon, and green certificates, and centrally optimizes and clears the supply and demand of thermal power carbon quotas and the supply of green electricity CCERs in the carbon market. Accordingly, this invention constructs a source-side generator output optimization model with source-side green electricity generators as the game participants and the optimization objective being to maximize the output benefits of source-side green electricity generators, considering the bidding behavior of generator units in multiple markets including electricity, carbon, and green certificates.

[0024] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0025] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0026] Figure 1 is a flowchart of a power system dispatch optimization operation method considering source-load interaction according to an embodiment of this application; Figure 2 This is a block diagram of the green certificate and carbon joint market trading mechanism according to an embodiment of the present invention; Figure 3 This is a block diagram of a power system dispatch optimization operation system considering source-load interaction, as described in an embodiment of the present invention. Detailed Implementation

[0027] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0028] like Figure 1 As shown, a specific embodiment of the present invention discloses a power system dispatch optimization operation method considering source-load interaction. The method includes, in step S101, constructing a source-load interaction model for carbon responsibility sharing based on the output, carbon emissions, and carbon emission quotas of each energy unit to optimize the electricity price of thermal power generating units using historical data. The source-load interaction model for carbon responsibility sharing includes a carbon emission cost model, a source-load joint carbon responsibility sharing model for carbon emissions of thermal power units, and a green electricity environmental value benefit model. In step S102, constructing a load-side electricity user purchase optimization model, wherein a load-side electricity user purchase cost model and a first constraint are constructed based on the number, output, and electricity price of green electricity generating units, thermal power generating units, and conventional energy generating units, and a clearing electricity volume model is constructed based on the clearing electricity volume of green electricity generating units, thermal power generating units, and conventional energy generating units. The following steps are implemented: First, an inter-provincial electricity cost model and a second constraint are established. Second, in step S103, a source-side generator output optimization model is constructed, including a green power generator revenue model and a third constraint with the goal of maximizing the revenue of green power generators. Third, a green certificate market clearing model is constructed with the goal of clearing the green certificate market. Fourth, a carbon market clearing model and a fourth constraint are constructed with the goal of clearing the carbon market. Fifth, a thermal power generator revenue model and a fifth constraint are constructed with the goal of clearing the thermal power generators. In step S104, the load-side electricity user purchase optimization model and the source-side generator output optimization model are converted to predict the clearing results of thermal power generators and green power generators. Finally, in step S105, the power generation of thermal power generators and green power generators is scheduled and optimized based on the predicted clearing results.

[0029] Compared with existing technologies, the power system dispatch optimization operation method considering source-load interaction provided in this embodiment further explores carbon emission reduction mechanisms on both the source and load sides by incorporating source-load interaction technology that considers carbon responsibility sharing. Furthermore, a complex scenario trading model considering source-load interaction is established to analyze the strategic behavior of load-side power users and source-side generating units, aiming to minimize the sum of intra-provincial and inter-provincial electricity purchase costs for power users and maximize the benefits of green generating units participating in the electricity-green certificate-carbon market. This fully considers the complex operating conditions of the two-tiered electricity market, green certificate market, and carbon market, optimizes unit dispatch operation schemes, and better guides the safe and stable operation of the new power system.

[0030] In the following text, refer to Figure 1 and Figure 2 The present invention provides a detailed description of each step of the power system dispatch optimization operation method considering source-load interaction in embodiments of the present invention.

[0031] In step S101, a source-load interaction model for carbon responsibility sharing is constructed based on the output, carbon emissions, and carbon emission quotas of each energy unit to optimize the electricity price quotation of thermal power generating units using historical data. This construction of the source-load interaction model for carbon responsibility sharing includes building a carbon emission cost model, a source-load joint carbon responsibility sharing model for carbon emissions from thermal power units, and a green electricity environmental value benefit model. Historical data can be obtained from a power system database or through online big data methods.

[0032] (1) Carbon emission calculation: The baseline method is used to allocate carbon emission quotas to thermal power plants free of charge. The carbon trading cost calculation model for thermal power plants is shown in Equation (1): (1) In the formula: f CET For carbon emission costs, l CET The price of carbon allowances. N The number of conventional energy generating units, D i , Q i The units i Actual carbon emissions and allocated carbon allowances. P i For the unit i Actual output s i For the unit i Carbon emissions per unit of output l i For the unit i Carbon emission quota per unit of output.

[0033] (2) Source-Load Shared Carbon Responsibility Allocation Based on Aumann-Shapley Method: The Aumann-Shapley method is applied to the shared carbon responsibility allocation between the source and load sides. In power systems, the source and load sides are interdependent in terms of system carbon emission reduction responsibility. Both sides play equally important roles in the power system and should bear equal responsibility for power system carbon emission reduction. Therefore, this invention allocates the carbon emissions of thermal power units equally between the source and load sides. The Aumann-Shapley method uses an integral form for calculation. The carbon responsibility allocated to participating members based on the Aumann-Shapley method is the carbon emission from their actual power output from 0 to... The integral is shown in equation (2): (2) In the formula: For time integration variables, The power vector formed by the generator units. P i For the unit Actual output The function for calculating the carbon trading cost of the generating unit has been explained in detail in equation (1). For the unit Marginal carbon cost per unit power, allocated For the unit Marginal carbon cost allocated.

[0034] Considering that the calculation of the unit's carbon emission cost is not a continuously differentiable function, the calculation and solution process needs to be discretized, as shown in equation (3): (3) In the formula: The number of conventional energy generating units, The total trading period is divisible. For the unit The slight increase in power For the unit During the period The unit contribution of carbon emission cost.

[0035] This application adopts the principle that the generation side and the load side each bear half of the system carbon responsibility. Therefore, the new bid price for the load side unit under the shared carbon responsibility sharing is the initial bid price plus the marginal carbon cost, and the new bid price for the load is the initial bid price minus the marginal carbon cost.

[0036] (3) Calculation of the environmental value benefits of green electricity: The environmental value benefits of green electricity need to take into account the price signals of the green certificate market and the carbon market, so as to maximize the environmental value benefits, as shown in equation (4): (4) In the formula: For the price of green certificates, The price of carbon allowances. The number of green certificates converted into carbon allowances. This represents the remaining number of green certificates. The total number of green certificates obtained for green electricity.

[0037] In step S102, an optimization model for electricity purchase by load-side power users is constructed. This model includes a load-side power user electricity purchase cost model and a first constraint condition based on the number, power output, and electricity price of green power generators, thermal power generators, and conventional energy generators. Additionally, an inter-provincial market electricity cost model and a second constraint condition are constructed based on the cleared electricity volume of green power generators, thermal power generators, and conventional energy generators.

[0038] Load-side power purchase optimization model for electricity users: 1. Electricity Purchase Cost Model for Dutch Electricity Users: The game involves Dutch electricity users, and the optimization objective for Dutch electricity users is to minimize the sum of their electricity purchase costs. The objective function is set as follows: (5) in, For the cost of electricity to load-side users, , , These are the electricity energy in the provincial market on the source side, reserves, and inter-provincial electricity purchase costs, respectively. M , N , U These are the numbers of green energy generator sets, thermal power generator sets, and other conventional energy generator sets within the province of Heze. , , These are green power generator sets within the province on the load side. m conventional coal-fired power generating units n Other conventional energy generator sets u Electricity price quote; , , Generator sets m , n , u The power generation output; , Generator sets n , u Alternative quotes; , Separate generator sets n , u Backup output; To take into account the inter-provincial market during the time period after carbon responsibility allocation tClearing electricity price, To take into account the electricity purchased by the load-side provinces from other provinces after carbon responsibility sharing; T This refers to the total trading session.

[0039] The corresponding constraints of the load-side electricity user purchase cost model include power balance constraints, reserve capacity constraints, unit ramp-up constraints, and unit output constraints.

[0040] (1) Power balance constraints: (6) In the formula: For the Dutch side province generator set During the period Cleared electricity volume participating in the inter-provincial electricity market The number of provinces on the Dutch side; For source side users During the period The load demand, The number of users with load demand.

[0041] (2) Reserve capacity constraints: (7) In the formula: This is the reserve factor for the provincial power grid on the source side.

[0042] (3) Unit ramping constraints: (8) In the formula: , , , , , Green electricity generator sets Thermal power generator sets Other conventional energy generator sets The upper and lower limits of effort required to climb a slope.

[0043] (4) Unit output constraints: (9) In the formula: , , , , , Green electricity generator sets Thermal power generator sets Other conventional energy generator sets The upper and lower limits of effort output.

[0044] 2. Electricity clearing price model for inter-provincial market: The optimization objective of the inter-provincial market is to minimize the operating cost of the inter-provincial electricity market.

[0045] (10) In the formula: For the cost of electricity in the inter-provincial market, The number of provinces on the Dutch side; , , respectively the Dutch side province The number of green energy generator sets, thermal power generator sets, and other conventional energy generator sets in the province; , , For the Dutch side province Green electricity generator set conventional coal-fired power generating units Other conventional energy generator sets Electricity prices for inter-provincial market transactions after considering carbon responsibility allocation; For the Dutch side province To the source side of the province user Inter-provincial power transmission prices; , , For generator sets , , The cleared electricity volume participating in the inter-provincial electricity market after considering carbon responsibility sharing.

[0046] The corresponding constraints of the inter-provincial market electricity clearing price model include the balance constraint between load-side electricity purchase demand and inter-provincial electricity supply, the inter-provincial transmission line transmission capacity constraint, and the clearing electricity volume constraint for load-side provinces participating in the inter-provincial market.

[0047] (1) Balance constraints between power purchase demand on the source side and inter-provincial power supply (11) In the formula: This refers to the line loss of inter-provincial power transmission lines.

[0048] (2) Transmission capacity constraints of inter-provincial transmission lines (12) In the formula: , These are the upper and lower limits of the transmission capacity of inter-provincial power transmission lines.

[0049] (3) Clearing electricity volume constraints for Dutch provinces participating in the inter-provincial market (13) In the formula: , , , , , For generator sets , , Upper and lower limits on the capacity for participating in inter-provincial transactions.

[0050] In step S103, a source-side generator output optimization model is constructed, wherein a green power generator revenue model and a third constraint are constructed with the goal of maximizing the revenue of green power generators; a green certificate market clearing model is constructed with the goal of clearing the green certificate market; a carbon market clearing model and a fourth constraint are constructed with the goal of clearing the carbon market; and a thermal power generator revenue model and a fifth constraint are constructed with the goal of clearing the revenue of thermal power generators.

[0051] The source-side generator output optimization model: The game theory module aims to maximize the output revenue of source-side green electricity generators. In the electricity-green certificate-carbon multi-market trading, the revenue of green electricity generators mainly comes from power generation in the provincial market on the load side and CCER trading in the green certificate market. The revenue of thermal power generators mainly comes from electricity sales revenue, net carbon quota revenue, and reserve revenue. The supply and demand of thermal power carbon quotas and the supply of green electricity CCERs are mutually influenced by offsetting mechanisms during carbon market clearing and green certificate market clearing. Accordingly, this invention decomposes the source-side generator output optimization model into a green electricity generator revenue optimization model, a green certificate market clearing model, a carbon market clearing model, and a thermal power generator revenue calculation model, which are solved sequentially.

[0052] 1. Green Electricity Generating Unit Revenue Optimization Model: The objective function for optimizing the revenue calculation model of green electricity generating units participating in the electricity-green certificate-carbon multi-market is: (14) In the formula: Green electricity generator sets Benefits of participating in the electricity-green certificate-carbon market To clear out electricity prices in the Dutch province market, To generate electricity for the generator sets in the provincial market on the Dutch side.

[0053] The corresponding constraints of the green electricity generator revenue optimization model include: pricing constraints, output constraints of green electricity generators allocated within and between provinces, and green certificate and carbon quota issuance constraints.

[0054] (1) Pricing constraints (15) In the formula: , Green electricity generator sets The upper and lower limits of the price quote.

[0055] (2) Output constraints of green power generating units distributed within and between provinces (16) In the formula: This represents the maximum power output of the generating unit.

[0056] (3) Constraints on the issuance of green certificates and carbon quotas (17) (18) In the formula: To determine the number of green certificates issued per generator unit, the entropy weight-CRITIC-improved TOPSIS method can be used to differentiate the green value of green electricity and thus determine the number of green certificates issued for green electricity generator units.

[0057] 2. Green Certificate Market Clearing Model: Assuming the green certificate market clearing price model is based on the Cournot model of output competition, the calculation formula is as follows: (19) In the formula: , The two parameters of the green certificate market price-output model can be calculated using the parameters of the green certificate market. According to (19), the green certificate price and output are linear functions that change in a decreasing manner.

[0058] When the production of green certificates is zero, the price of green certificates is the highest acceptable price, i.e., the penalty price for green certificates. When green certificate production reaches its maximum, the green certificate price represents consumers' willingness to pay, calculated using the following formula: (20) At this point, the green certificate output is the amount of consumption responsibility stipulated in the Renewable Energy Permit System (RPS), calculated using the following formula: (twenty one) In equation (20) It can be calculated from historical data, in equation (21) The consumption responsibility weight stipulated in the Renewable Energy Specification System (RPS) For users During the period Real-time load demand; For the number of users.

[0059] Based on the above analysis, we can calculate (19) and The values ​​are respectively (twenty two) (twenty three) 3. Carbon Market Clearing Model: This model involves the centralized optimization and clearing of the supply and demand of carbon allowances for thermal power and the supply of CCERs for green electricity within the carbon market. The goal of carbon market optimization is to maximize the social welfare of the carbon market, and the objective function is set as follows: (twenty four) In the formula: , These represent the purchase and sale prices of carbon allowances for thermal power plants at time t, respectively. , Thermal power The amount of carbon allowances purchased and sold at any given time. The CCER price of green electricity sold at time t. for The number of CCERs sold by Green Electricity at any given time.

[0060] The carbon market clearing model includes: carbon market quota and CCER supply and demand balance constraints, thermal power quota bidding constraints, and the number of CCERs obtained by green power units.

[0061] (1) Carbon market quotas and CCER supply and demand balance constraints (25) In the formula: is the dual variable of the carbon quota supply and demand balance constraint, and represents the trading price of the carbon quota.

[0062] (2) Constraints on thermal power quota bidding: Thermal power in The relationship between the amount of carbon allowances required to participate in market bidding during the specified period and their purchase and sale volumes is as follows: (26) In the formula: For thermal power exist The cumulative carbon allowance trading volume during the period.

[0063] Furthermore, the relationship between the volume of bids in the thermal power carbon market and the actual carbon emissions and carbon quota allocations is as follows: (27) In the formula: For thermal power During trading hours Cumulative carbon emissions within the country. For generator sets The carbon allowances initially allocated during the compliance period For thermal power In the The initial carbon quota decomposition coefficient for each trading period needs to satisfy the constraints of equations (28) and (29).

[0064] (28) (29) Carbon allowance allocation is divided into two stages: pre-allocation and final verification. First, at the beginning of the year, carbon allowances are pre-allocated based on 70% of the unit's electricity supply from the previous year. At the end of the year, after verification of annual carbon emission data, the allowances are finally verified based on the unit's actual electricity supply. Initial carbon allowances are allocated free of charge using a baseline method. The pre-allocated carbon allowance is... (30) In the formula: This represents the total electricity generated by thermal power plants in the previous year. This is the approved carbon emission baseline.

[0065] Going further, It can be calculated based on the functional relationship between real-time market clearing electricity and carbon emissions, as shown in equation (31).

[0066] (31) In the formula: For thermal power units carbon emission coefficient, The number of scenarios contributing to the real-time market for new energy. For the scene The probability of occurrence For thermal power In the scene Down Real-time market clearing volume.

[0067] Carbon allowance settlement is a rigid constraint in the carbon trading process. On the deadline of the carbon market compliance period, thermal power generating units must settle their carbon allowances with the competent authority. The sum of the initially obtained carbon emission allowances and the carbon emission allowances purchased through the carbon market should be greater than or equal to the actual total carbon emissions during the compliance period.

[0068] (32) (3) The number of CCERs obtained by green generator sets (33) In the formula: The number of CCERs certified for green electricity The carbon dioxide emission reduction per unit of new energy power can be calculated using equation (34). New energy in the real-time market In the scene Down The amount of electricity won in the bid at any given moment.

[0069] (34) In the formula: As the marginal emission factor of electricity, This is the capacity marginal emission factor.

[0070] (4) Net CCER value of green electricity generator sets (35) In the formula: , Green electricity exist The net value of CCERs that you always own, and the CCERs that you sell in the carbon market.

[0071] It should be noted that this invention sets the trading interval for the electricity-carbon market at 1 hour. However, the carbon market clearing model constructed in this invention is not limited to hourly trading intervals; the carbon market can clear on daily, monthly, or quarterly trading cycles. (The text then assumes a carbon market clearing interval of 1 hour.) t For monthly, T The period is 1 year (12 months), during which green electricity needs to consider monthly CCER sales decisions. In the carbon market clearing model, formula (31) This refers to the cumulative carbon allowance for wind and solar power units within one month.

[0072] 4. Revenue Calculation Model for Thermal Power Generating Units: Thermal power revenue mainly comes from electricity sales revenue, net carbon quota revenue, and reserve revenue. Thermal power costs primarily consist of coal consumption costs. Revenue from providing reserve capacity by thermal power plants comprises two parts: one is the revenue from reserve capacity reserved in the day-ahead market under the day-ahead clearing plan; the other is the revenue from the actual use of reserve capacity in the real-time market. Therefore, the revenue settlement for providing reserve capacity by thermal power generating units consists of two parts: capacity price and electricity price. The profit maximization model for thermal power generating units is as follows: (36) Relevant constraints: (37) (38) (39) (40) (41) (42) (43) In the formula: , , thermal power The day-ahead market return, real-time market return, and reserve return. For thermal power The cost of generating electricity is generally a quadratic function of the power output. For thermal power Net carbon allowance revenue; , Daily and real-time markets respectively Real-time clearing electricity price For thermal power After considering carbon responsibility sharing The amount of electricity cleared in the market at the specified time. For the scene The probability of occurrence For the number of scenes, For thermal power After considering carbon responsibility sharing in the scenario Down Real-time market clearing volume. , , For thermal power The cost coefficient, For the carbon market Carbon price at any given moment , Thermal power The amount of carbon allowances sold and purchased at any given time. for Electricity pricing after considering carbon responsibility allocation for Participate in carbon market bidding.

[0073] In step S104, the load-side power user electricity purchase optimization model and the source-side generator set output optimization model are converted to predict the clearing results of thermal power generator sets and green power generator sets.

[0074] The generator set game model: The master-slave game consists of four elements: game participants, strategy set, payoff, and equilibrium strategy. Load-side green energy generator sets and source-side power users jointly constitute the master-slave game model. Load-side green energy generator sets use inter-provincial clearing tariffs as their optimization strategy, while source-side power users use inter-provincial electricity purchases as their decision strategy. In actual decision-making, power users and green energy generator sets are constrained by unit characteristics, load demand, and grid constraints. According to the definition of Stackelberg-Nash equilibrium, the game model has a Stackelberg-Nash equilibrium solution. Equations (44) and (45) should be satisfied.

[0075] (44) (45) In the formula: and The revenue is for electricity users and green electricity generators, respectively.

[0076] Model Equivalent Transformation: Common methods for finding equilibrium in master-slave game models include the optimal stationary point method and the fixed-point iterative search method. The core idea is to use the KKT conditions to transform the master-slave game problem into an equivalent MILP problem, and then use integer programming theory to obtain the Stackelberg-Nash equilibrium solution. Based on the above solution approach, the solution approach for the two-layer transaction model is as follows: First, the source-side provincial electricity market clearing model and the inter-provincial electricity market in the game theory model are regarded as a two-layer model. Since the lower-level model is a linear programming problem, the KKT conditions are the necessary and sufficient conditions for satisfying the optimality of the model. Based on the substitution of the KKT optimality conditions, the two-layer problem can be transformed into a single-layer problem.

[0077] Secondly, the transformed single-layer model and the game subject module are regarded as another two-layer model. The upper-layer model is the transformed single-layer model, and the lower-layer model is the non-cooperative game optimization model of the game subject.

[0078] Finally, since the lower-level model of the new bi-level model is also a linear programming problem, the KKT conditions can be used to transform the new bi-level model into a single-level model for solution.

[0079] (1) Lagrange function: The Lagrange function of the lower-level model in the game theory model is defined as: (46) In the formula: The objective function of the lower-level model is the optimization objective function of the inter-provincial electricity market (10); , These are the equality constraints and inequality constraints of the lower-level model, namely the equality constraints and inequality constraints of equations (10) to (13); l , µ Divided into , Lagrange multipliers.

[0080] (2) Optimality condition of Lagrange function: Construct the partial differential equation of Lagrange function and calculate the Lagrange function with respect to variables. , , The gradient of the Lagrange function is used to derive the optimality condition. Considering the KKT conditions, the gradient of the Lagrange function is set to zero, and the partial differential equations are solved: (47) In the formula: , , For generator sets , , Cleared electricity volume participating in the inter-provincial electricity market.

[0081] (3) Complementary Slackness Condition: The complementary slackness condition is part of the KKT conditions, ensuring that at the optimal solution, each inequality constraint is either active (equal to the condition) or its corresponding Lagrange multiplier is zero, thus ensuring the correctness of the optimal solution. (48) For the form of The complementary relaxation conditions can be further used to... The law performs equivalent processing.

[0082] (49) In the formula: It is a binary variable, taking the value 0 or 1. It is a sufficiently large constant.

[0083] Using the KKT conditions, a two-layer model can be transformed into a single-layer model, specifically in the following form: (50) Linearization: In the objective function of model (51) Since the model cannot be directly solved by multiplying two decision variables, it is necessary to linearize the nonlinear problem. Utilizing the strong duality property, the dual problem of the lower-level model is: (51) According to the properties of strongly dual problems, the optimal solution to the primal problem is equal to the optimal solution to the dual problem, that is... (52) Further rewriting the above equation: (53) Substituting equation (53) into equation (50), the nonlinear terms in the optimization model are transformed into linear terms that can be easily solved. Combining equation (50) and the non-cooperative game model of the game players into a new two-layer model, according to the optimality condition of (46) that can transform the game players into the upper-layer model, the new nonlinear terms can be linearized using (51).

[0084] In step S105, the power generation of thermal power generating units and green power generating units is optimized based on the predicted clearing results of thermal power generating units and green power generating units.

[0085] This invention explores carbon emission reduction mechanisms on both the source and load sides by considering source-load interaction technology for carbon responsibility sharing. Furthermore, it establishes a complex scenario trading model considering source-load interaction to analyze the strategic behavior of load-side electricity users and source-side generating units, aiming to minimize the sum of intra-provincial and inter-provincial electricity purchase costs for electricity users and maximize the benefits of green generating units participating in the electricity-green certificate-carbon market. This invention fully considers the complex operation of the two-tiered electricity market, green certificate market, and carbon market, optimizing unit dispatching and operation schemes to better guide the safe and stable operation of the new power system.

[0086] like Figure 3 As shown, a specific embodiment of the present invention discloses a power system dispatch optimization operation system considering source-load interaction, comprising: a source-load interaction model construction module 301, used to construct a source-load interaction model for carbon responsibility sharing based on the output, carbon emissions, and carbon emission quotas of each energy unit, so as to optimize the electricity price of thermal power generating units using historical data, wherein the source-load interaction model for carbon responsibility sharing includes carbon emission cost, a source-load joint carbon responsibility sharing model for carbon emissions of thermal power units, and a green electricity environmental value benefit model; and a load-side electricity user purchase optimization model construction module 302, used to construct a load-side electricity user purchase optimization model, wherein a load-side electricity user purchase cost model and a first constraint are constructed based on the number, output, and electricity price of green electricity generating units, thermal power generating units, and conventional energy generating units, and a clearing electricity model based on green electricity generating units, thermal power generating units, and conventional energy generating units. The system comprises the following modules: a power cost model for the inter-provincial market and a second constraint; a source-side generator output optimization model construction module 303, used to construct a source-side generator output optimization model, wherein a green power generator revenue model and a third constraint are constructed with the goal of maximizing the revenue of the green power generators; a green certificate market clearing model is constructed with the goal of green certificate market clearing; a carbon market clearing model and a fourth constraint are constructed with the goal of carbon market clearing; and a thermal power generator revenue model and a fifth constraint are constructed with the goal of thermal power generator revenue. A conversion module 304 is used to convert the load-side power user purchase optimization model and the source-side generator output optimization model to predict the clearing results of thermal power generators and green power generators. A scheduling module 305 is used to schedule and optimize the power generation of thermal power generators and green power generators based on the predicted clearing results of thermal power generators and green power generators. This ensures that the power system generates electricity according to the optimized power generation of thermal power generators and green power generators. For example, the optimization models for power purchase by load-side electricity users and power output of source-side generator units after equivalence and linearization processes are solved.

[0087] This invention aims to propose a source-load interaction technology that considers carbon responsibility sharing, and a joint dispatch and clearing optimization method considering the non-cooperative game behavior of generating units, involving a two-tiered electricity market, carbon market, and green certificate market. The goal is to reduce the operating costs of the power system, decrease carbon emissions, and achieve optimized operation of a new type of power system. The main improvements and technical effects of the method are as follows: (1) Source-Load Interaction Technology Considering Carbon Responsibility Sharing: This invention constructs a source-load interaction technology considering carbon responsibility sharing to fully explore the carbon emission reduction potential of the load side. Traditional carbon emission cost calculation techniques focus on carbon emissions on the source side, while giving less consideration to the network structure and transmission characteristics of carbon flow in the power system, and have insufficient incentive effect on the demand side of the power system to respond to carbon emission reduction. In the power system, the source side and the load side are interdependent in terms of system carbon emission reduction responsibility. The source side and the load side play equally important roles in the power system and should bear equal responsibility for carbon emission reduction. Accordingly, this invention constructs a source-load joint carbon responsibility sharing technology based on the Aumann-Shapley method, which considers the fairness of carbon emission reduction responsibility sharing on the source side and the load side, and fully explores the carbon emission reduction potential of the load side.

[0088] (2) Load-side power user power purchase optimization model: This invention constructs a load-side power user power purchase optimization model, considering the bidding behavior of power users in the provincial and inter-provincial two-tier power markets. Load-side power users will reconsider their power purchase demand allocation strategies in the source-side provincial and inter-provincial markets in the two-tier power markets, resulting in the clearing of electricity in inter-provincial transactions squeezing the trading space of their respective provincial generating units in the provincial power market, affecting the overall revenue of generating units in the power market. Traditional load-side power user power purchase models rarely consider the complex trading behavior of the two-tier power markets. This invention expands the inter-provincial power market trading model based on the intra-provincial power market transaction, constructing an inter-provincial market power clearing price model with the optimization objective of minimizing the operating cost of the inter-provincial power market, considering the power energy in the source-side provincial market, the inter-provincial power purchase cost of the reserve load, and the power purchase cost of load-side power users. Accordingly, this invention constructs a load-side power user power purchase optimization model with the game subject being the power users of the load province, and the optimization objective being to minimize the sum of the power purchase costs of the load-side power users, considering the mutual competition between the source and load sides in power sales and purchases.

[0089] (3) Source-side generator output optimization model: This invention constructs a source-side generator output optimization model, considering the bidding behavior of generators in the electricity-carbon-green certificate multi-market. The production of source-side thermal power generators is constrained by carbon emission costs in the carbon market, while the green certificates generated by green power generators will obtain environmental benefits realized in the green certificate market. Both the carbon market and the green certificate market can reflect the environmental value of green electricity, but the two have not yet achieved effective connection, and the environmental value of green electricity will be measured repeatedly. This invention constructs a generator output model in the electricity-carbon-green certificate multi-market that considers the value of electrical energy and environmental value, and centrally optimizes and clears the supply and demand of thermal power carbon quotas and the supply of green electricity CCERs in the carbon market. Accordingly, this invention constructs a source-side generator output optimization model with the source-side green power generator as the game subject and the optimization objective being to maximize the output benefits of the source-side green power generator, considering the bidding behavior of generators in the electricity-carbon-green certificate multi-market.

[0090] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0091] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A power system dispatch optimization operation method considering source-load interaction, characterized in that, include: A source-load interaction model for carbon responsibility sharing is constructed based on the output, carbon emissions, and carbon emission quotas of each energy unit to optimize the electricity price of thermal power generating units using historical data. An optimization model for electricity purchase by load-side users is constructed. The model includes a load-side electricity purchase cost model and a first constraint based on the number, power output, and electricity price of green power generators, thermal power generators, and conventional energy generators. The model also includes an inter-provincial market electricity cost model and a second constraint based on the cleared electricity volume of green power generators, thermal power generators, and conventional energy generators. An optimization model for the output of source-side generator units is constructed, wherein a green power generator unit revenue model and a third constraint are constructed with the goal of maximizing the revenue of the green power generator units; a green certificate market clearing model is constructed with the goal of clearing the green certificate market; a carbon market clearing model and a fourth constraint are constructed with the goal of clearing the carbon market; and a thermal power generator unit revenue model and a fifth constraint are constructed with the goal of maximizing the revenue of the thermal power generator units. The load-side power user electricity purchase optimization model and the source-side generator output optimization model are transformed to predict the clearing results of thermal power generators and green power generators; and Based on the predicted clearing results of thermal power generating units and green power generating units, the power generation of the thermal power generating units and the green power generating units is scheduled and optimized.

2. The power system dispatch optimization operation method considering source-load interaction according to claim 1, characterized in that, The source-load interaction model for carbon responsibility sharing further includes constructing a carbon emission cost model, a source-load joint carbon responsibility sharing model for carbon emissions from thermal power units, and a green electricity environmental value benefit model. The source-load interaction model for carbon responsibility sharing further includes: The carbon emission cost is expressed by the following formula: Official (1) in, f CET For carbon emission costs, λ CET The price of carbon allowances. N The number of conventional energy generating units, D i , Q i The units i Actual carbon emissions and allocated carbon allowances. P i For the unit i Actual output σ i For the unit i Carbon emissions per unit of output λ i For the unit i Carbon emission quotas per unit of output; The source-load shared carbon responsibility model for the carbon emissions of thermal power units is expressed by the following formula: Official (2) Official (3) in, t For time integration variables, The power vector formed by the generator units. P i For the unit i Actual output This is a function for calculating the carbon trading costs of generating units. U i Unit power i Marginal carbon cost per unit power, allocated x i For the unit i The allocated marginal carbon cost; N The number of conventional energy generating units, T The total trading period is divisible. For the unit i The slight increase in power For the unit i During the period t The unit contribution of carbon emission costs; The green electricity environmental value benefit model is expressed by the following formula: Official (4) in, λ TGC For the price of green certificates, λ CET The price of carbon allowances. The number of green certificates converted into carbon allowances. This represents the remaining number of green certificates. The total number of green certificates obtained for green electricity.

3. The power system dispatch optimization operation method considering source-load interaction according to claim 2, characterized in that, When the carbon responsibility sharing is such that the generation side and the load side each bear half of the system carbon responsibility, the renewal price of the source-side units under the shared carbon responsibility sharing is the sum of the initial price and the marginal carbon cost; and the renewal price of the load side is the difference between the initial price and the marginal carbon cost.

4. The power system dispatch optimization operation method considering source-load interaction according to claim 3, characterized in that, Based on the number, power output, and electricity price of green power generating units, thermal power generating units, and conventional energy generating units, a load-side electricity user purchase cost model is constructed, and the first constraint condition further includes: The electricity purchase cost model for load-side electricity users is expressed by the following formula: Official (5) in, For the cost of electricity to load-side users, , , These are the electricity energy in the provincial market on the source side, reserves, and inter-provincial electricity purchase costs, respectively. M , N , U These are the numbers of green energy generator sets, thermal power generator sets, and other conventional energy generator sets within the province of Heze. , , These are green power generator sets within the province on the load side. m conventional coal-fired power generating units n Other conventional energy generator sets u Electricity price quote; , , Generator sets m , n , u During the period t The power generation output; , Generator sets n , u Alternative quotes; , Separate generator sets n , u Backup output; To take into account the inter-provincial market during the time period after carbon responsibility allocation t Clearing electricity price, To take into account the electricity purchased by the load-side provinces from other provinces after carbon responsibility sharing; T Total trading session; The first set of constraints includes power balance constraints, reserve capacity constraints, unit ramp-up constraints, and unit output constraints, among which, The power balance constraint is expressed by the following formula: Official (6) in, For the Dutch side province j generator set During the period t Cleared electricity volume participating in the inter-provincial electricity market J The number of provinces on the Dutch side; For source side users l During the period t The load demand, L The number of users with load demand; The reserve capacity constraint is expressed by the following formula: Official (7) in, This is the reserve factor for the provincial power grid on the source side; The unit's ramp-up constraint is expressed by the following formula: Official (8) in, , , , , , Green electricity generator sets m Thermal power generator sets n Other conventional energy generator sets u Upper and lower limits of effort required for climbing slopes; The unit output constraint is expressed by the following formula: Official (9) in, , , , , , The green power generator sets are respectively m The thermal power generator set n Other conventional energy generator sets u The upper and lower limits of effort output.

5. The power system dispatch optimization operation method considering source-load interaction according to claim 4, characterized in that, The electricity cost model for the inter-provincial market, based on the cleared electricity volume of green power generating units, thermal power generating units, and conventional energy generating units, and the second constraint further include: The electricity clearing price model for the inter-provincial market is expressed by the following formula: Official (10) in, For the cost of electricity in the inter-provincial market, J The number of provinces on the Dutch side; , , respectively the Dutch side province j The number of green energy generator sets, thermal power generator sets, and other conventional energy generator sets in the province; , , Dutch Province j Green electricity generator set conventional coal-fired power generating units Other conventional energy generator sets Electricity prices for inter-provincial market transactions after considering carbon responsibility allocation; For the Dutch side province To the source side of the province user l Inter-provincial power transmission prices; , , For generator sets , , The cleared electricity volume participating in the inter-provincial electricity market after considering carbon responsibility sharing; The second constraint includes the balance constraint between load-side electricity purchase demand and inter-provincial electricity supply, the inter-provincial transmission line capacity constraint, and the cleared electricity volume constraint for load-side provinces participating in the inter-provincial market. The balance constraint between the source-side electricity purchase demand and the inter-provincial power supply is expressed by the following formula: Official (11) in, Line losses for inter-provincial power transmission lines; The transmission capacity constraint of the inter-provincial transmission line is expressed by the following formula: Official (12) , These are the upper and lower limits of the transmission capacity of inter-provincial power transmission lines; The clearing electricity constraint for the province participating in the inter-provincial market is expressed by the following formula: Official (13) in, , , , , , For generator sets , , Upper and lower limits on the capacity for participating in inter-provincial transactions.

6. The power system dispatch optimization operation method considering source-load interaction according to claim 4, characterized in that, The green electricity generator set revenue optimization model is expressed by the following formula: Official (14) in, Green electricity generator sets Benefits from participating in the electricity-green certificate-carbon market To clear out electricity prices in the Dutch province market, To generate electricity for the generator units in the Dutch provincial market; The third constraint includes: price quotation constraints, output constraints for green electricity generators allocated within and between provinces, and constraints on the issuance of green certificates and carbon quotas; among which... The pricing constraint is expressed by the following formula. Official (15) in, , Green electricity generator sets The upper and lower limits of the price; The output constraints of the green power generator sets distributed within and between provinces are expressed by the following formula. Official (16) in, This is the maximum power output of the unit; The constraints on the issuance of green certificates and carbon quotas are expressed by the following formula. Official (17) Official (18) in, The number of green certificates issued per unit of generator set; The green certificate market clearing model is expressed by the following formula: Official (19) Official (20) Official (21) Official (22) Official (23) in, , These are the two parameters of the green certificate market price-production model. The number of green certificates issued for each green generator unit generating electricity; This represents the price of green certificates when production reaches its maximum. This indicates the minimum amount of renewable energy consumption mandated by the Renewable Energy Consumption Scheme (RPS). This indicates that the calculation was performed using historical data. The consumption responsibility weight specified in the Renewable Energy Quota System (RPS); For users During the period t Real-time load demand; L For the number of users.

7. The power system dispatch optimization operation method considering source-load interaction according to claim 6, characterized in that, The carbon market clearing model and the fourth constraint, aimed at achieving carbon market clearing, further include: The carbon market clearing model is expressed by the following formula: Official (24) in, , For thermal power at time t The purchase and sale prices of carbon allowances , For thermal power at time t The volume of carbon allowances purchased and sold. for t The CCER price of Shike Green Electricity For a moment t Number of CCERs sold by green electricity The carbon market clearing model includes: carbon market quota and CCER supply and demand balance constraints, thermal power quota bidding constraints, and the number of CCERs obtained by green power units. The carbon market quotas and CCER supply and demand balance constraints are expressed by the following formula: Official (25) in, , which is the dual variable of the carbon quota supply and demand balance constraint, and represents the trading price of the carbon quota; The following formula represents the time period of thermal power. t The relationship between the amount of carbon allowances that domestic companies need to participate in market bidding and their carbon allowance purchase and sale volumes: Official (26) in, For the thermal power generator set n During the period t The cumulative carbon allowance trading volume within the period; The relationship between the amount of coal-fired power plant carbon market bidding and actual carbon emissions and carbon quota allocation is as follows: Official (27) Official (28) Official (29) Official (30) Official (31) Official (32) in, For the thermal power generator set n During trading hours t Cumulative carbon emissions within the country. For the thermal power generator set n The carbon allowances initially allocated during the compliance period For the thermal power generator set n In the t Initial carbon allowance decomposition factor for each trading session; This represents the total electricity generated by thermal power plants in the previous year. The approved carbon emission baseline; For the thermal power generator set n carbon emission coefficient, S The number of scenarios contributing to the real-time market for new energy. For the scene s The probability of occurrence For the thermal power generator set n In the scene s Down t Real-time market clearing volume; The number of CCERs obtained by the green motor unit is expressed by the following formula: Official (33) Official (34) in, The number of CCERs certified for green electricity The carbon dioxide emission reduction per unit of new energy power generation. New energy in the real-time market In the scene s Next moment t The winning bid volume; As the marginal emission factor of electricity, It is the capacity marginal emission factor; The net CCER value of the green electricity generator set is expressed by the following formula: Official (35) in, , Green electricity At any moment t Net CCER value owned, and CCERs sold in the carbon market.

8. The power system dispatch optimization operation method considering source-load interaction according to claim 7, characterized in that, The revenue model for thermal power generating units, constructed with the revenue of the aforementioned thermal power generating units as the objective, and the fifth constraint further include: The revenue calculation model for thermal power generating units is expressed by the following formula: Official (36) in, , , The thermal power generating units are respectively n The day-ahead market return, real-time market return, and reserve return. For the thermal power generator set n The cost of electricity generation, For the thermal power generator set n Net carbon allowance revenue; The corresponding constraints of the revenue calculation model for thermal power generating units are expressed by the following formula: Official (37) Official (38) Official (39) Official (40) Official (41) Official (42) Official (43) in, , These are the day-to-day and real-time market times, respectively. t Clearing electricity price, For the thermal power generator set After considering carbon responsibility sharing at time t The amount of electricity cleared from the market the day before yesterday, For the scene s The probability of occurrence S For the number of scenes, For the thermal power generator set After considering carbon responsibility sharing in the scenario s Next moment t Real-time market clearing volume, , , For the thermal power generator set The cost coefficient, For the carbon market t carbon price, , The thermal power generating units at time respectively t The volume of carbon allowances sold and purchased. For the thermal power generator set Electricity pricing after considering carbon responsibility allocation For the thermal power generator set Participate in carbon market bidding.

9. The power system dispatch optimization operation method considering source-load interaction according to claim 8, characterized in that, The conversion processing of the load-side power user electricity purchase optimization model and the source-side generator output optimization model to predict the clearing results of thermal power generators and green power generators further includes Lagrange function conversion processing and corresponding optimality conditions, wherein, The Lagrange function transformation process is as follows: The Lagrange function of the lower-level model in the game subject model is defined as: Official (46) in, The objective function for optimizing the inter-provincial electricity market; , These are the equality constraints and inequality constraints in the corresponding constraints of the inter-provincial market electricity clearing price model, respectively. λ , µ They are respectively , Lagrange multipliers; Construct the partial differential equation of the Lagrange function based on the Lagrange function, and calculate the Lagrange function with respect to the variable. , , The gradient is used to determine the optimality condition of the Lagrange function: Official (47) in, , , Generator sets , , Cleared electricity volume participating in the inter-provincial electricity market; The complementary relaxation condition is expressed by the following formula to ensure the correctness of the optimal solution: Official (48) For the form of Complementary relaxation conditions, using large The law performs equivalent processing; Official (49) In the formula: It is a binary variable, taking the value 0 or 1. It is a constant; Using the KKT conditions, the two-layer model is transformed into a single-layer model: Official (50).

10. A power system dispatch optimization operation system considering source-load interaction, characterized in that, include: The source-load interaction model building module is used to build a source-load interaction model for carbon responsibility sharing based on the output, carbon emissions and carbon emission quotas of each energy unit, so as to optimize the electricity price of thermal power generating units by utilizing historical data. The load-side power user electricity purchase optimization model construction module is used to construct the load-side power user electricity purchase optimization model. It constructs the load-side power user electricity purchase cost model and the first constraint based on the number, power generation output and electricity price of green power generators, thermal power generators and conventional energy generators, and constructs the inter-provincial market electricity cost model and the second constraint based on the cleared electricity of green power generators, thermal power generators and conventional energy generators. The source-side generator output optimization model construction module is used to construct a source-side generator output optimization model, wherein: a green power generator revenue model and a third constraint are constructed with the goal of maximizing the revenue of the green power generator; a green certificate market clearing model is constructed with the goal of clearing the green certificate market; a carbon market clearing model and a fourth constraint are constructed with the goal of clearing the carbon market; and a thermal power generator revenue model and a fifth constraint are constructed with the goal of maximizing the revenue of the thermal power generator. The conversion module is used to convert the load-side power user electricity purchase optimization model and the source-side generator output optimization model to predict the clearing results of thermal power generators and green power generators; and The scheduling module is used to schedule and optimize the power generation of the thermal power generating units and the green power generating units based on the predicted clearing results of the thermal power generating units and the green power generating units.

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