A virtual power plant optimal scheduling method considering carbon trading and demand response

Abstract

The application discloses a kind of virtual power plant optimization scheduling method considering carbon trading and demand response, steps include: establishing considering carbon trading and demand response of virtual power plant and the master-slave framework of centralized photo-thermal power station;Formulate the step-by-step carbon trading mode of virtual power plant;Virtual power plant demand response model is constructed based on power load and heat load characteristics;The master-slave game model of distribution system operator-virtual power plant is constructed;Solve master-slave game model, the result value of decision variable is as virtual power plant optimization scheduling strategy, and the virtual power plant is controlled to execute optimization scheduling strategy.The present application can make system carbon emission and total cost be reduced, while increasing distribution system operator income, improve the low-carbon economic benefit of virtual power plant.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant power generation, and more specifically to an optimized scheduling method for virtual power plants. Background Technology

[0002] Currently, the scarcity of fossil fuels and environmental problems are becoming increasingly prominent. With the vigorous development of the low-carbon energy revolution, building a power system primarily based on energy sources and carrying out a multi-source, energy-saving, and low-carbon energy transition is an inevitable trend. To improve energy utilization efficiency, the integration of wind power (WT), photovoltaic (PV), flexible loads, and other resources into virtual power plants has been proposed to form multi-energy complementary energy systems. As the installed capacity of new energy sources gradually increases, how to improve the utilization rate of new energy sources by coordinating the internal resource output of virtual power plants and fully tapping their low-carbon potential has become a top priority.

[0003] To promote the consumption of renewable energy, resource optimization on both the source and load sides can be achieved by adjusting price levels or incentive mechanisms and utilizing virtual power plants to guide demand response. Some scholars have proposed an optimal virtual power plant scheduling method that considers demand response, coordinating the energy storage and demand response resources of virtual power plants to achieve peak shaving and valley filling while increasing renewable energy consumption. Furthermore, in the context of sustainable development, incorporating carbon trading mechanisms into virtual power plants containing renewable energy can reduce carbon emissions and improve low-carbon economic benefits. Some scholars have established a low-carbon economic optimal scheduling model combining virtual power plants and carbon trading mechanisms, adjusting unit output to improve wind power consumption capacity. However, few studies have focused on the operation and demand response of virtual power plants combining concentrated solar thermal power plants and combined heat and power (CHP) units.

[0004] Master-slave game theory is an effective model for analyzing the decision-making process between decision-makers and responders. Some scholars have established a two-level bidding model of master-slave game theory between virtual power plants and their members to determine the trading prices and scheduling plans of members in virtual power plants. However, this type of model ignores the flexible load resources of virtual power plants and does not incorporate carbon trading mechanisms to analyze the low-carbon characteristics of virtual power plants.

[0005] In conclusion, in order to achieve the operation of a low-carbon power system, it is crucial to address how to consider carbon trading and demand response, optimize the scheduling of virtual power plants, and thereby reduce carbon emissions and lower the total system cost while ensuring operator revenue. Summary of the Invention

[0006] This invention proposes a virtual power plant (VPS) optimization scheduling method that considers carbon trading and demand response. Its objectives are: 1. To consider carbon trading and demand response during the optimization scheduling of VPS, thereby reducing carbon emissions and lowering the total system cost while ensuring operator revenue. 2. To consider the impact of centralized solar thermal power plants participating in VPS, energy supply, and the tiered carbon trading mechanism during the scheduling process, thereby stimulating the enthusiasm of VPS to participate in demand response.

[0007] The technical solution of this invention is as follows:

[0008] A virtual power plant optimal scheduling method considering carbon trading and demand response, comprising the following steps:

[0009] Step S1: Establish a master-slave framework for virtual power plants and centralized solar thermal power plants that takes into account carbon trading and demand response;

[0010] Step S2: Develop a tiered carbon trading model for virtual power plants;

[0011] Step S3: Construct a virtual power plant demand response model based on the characteristics of electricity load and heat load;

[0012] Step S4: Based on the master-slave framework, tiered carbon trading model, and virtual power plant demand response model, construct a master-slave game model between distribution system operators and virtual power plants. The master-slave game model includes: a dynamic pricing game model for distribution system operators and an energy management game model for virtual power plants.

[0013] Step S5: Solve the master-slave game model, use the result value of the decision variable as the virtual power plant optimization scheduling strategy, and control the virtual power plant to execute the optimization scheduling strategy.

[0014] As a further improvement to the virtual power plant optimization scheduling method considering carbon trading and demand response, the master-slave framework of the virtual power plant and centralized solar thermal power plant considering carbon trading and demand response in step S1 includes: the virtual power plant includes wind power, photovoltaic, centralized solar thermal power plant, cogeneration unit, energy storage, heat pump, fixed load and flexible load; wherein, the centralized solar thermal power plant device consists of a heat collection part, a heat storage part and a power generation part, and the heat transfer medium is fluid; the centralized solar thermal power plant device and the cogeneration unit operate together.

[0015] As a further improvement to the virtual power plant optimal scheduling method that considers carbon trading and demand response, the tiered carbon trading mode for virtual power plants in step S2 includes:

[0016] Virtual power plants use a tiered carbon trading mechanism to conduct intra-carbon trading with external carbon markets;

[0017] A tiered carbon trading model for virtual power plants is constructed, which uses a baseline method to allocate initial carbon emission allowances for virtual power plants. When the actual carbon emissions of a virtual power plant are lower than the initial carbon allowance, the remaining carbon allowances can be sold to generate revenue.

[0018] The initial carbon emission allowance and actual carbon emission of the virtual power plant include the purchase of electricity and the output of the combined heat and power unit, respectively.

[0019]

[0020] Among them, D VPP D grid and D CHP These are the initial carbon emission allowances for virtual power plants, purchased electricity, and combined heat and power units, respectively; T is the number of time periods divided into the dispatch interval, t is the sequence number of the time period, and time t represents a period of time within the dispatch interval; N CHP This represents the total number of combined heat and power (CHP) units, where i is the unit number; λ e and λ h These are the carbon emission quota coefficients per unit of electricity and per unit of heat, respectively. The electricity purchased by the virtual power plant from the power distribution system operator at time t is the decision variable. It is the electrothermal conversion coefficient of the combined heat and power unit; and These are the power and heat output of the i-th cogeneration unit at time t, respectively, both of which are decision variables; and These represent the actual carbon emissions from virtual power plants, purchased electricity, and combined heat and power (CHP) units, respectively. and These are the actual carbon emission coefficients per unit of electrical energy and per unit of thermal energy, respectively.

[0021] The carbon trading costs for virtual power plants are calculated as follows:

[0022]

[0023] Among them, C c d is the carbon trading cost; c is the carbon trading price; d is the length of the carbon allowance purchase interval; k is the carbon price growth coefficient.

[0024] As a further improvement to the virtual power plant optimal scheduling method that considers carbon trading and demand response, the virtual power plant demand response model in step S3 includes:

[0025] The method for calculating the demand response cost of electrical load based on flexible electrical load is as follows:

[0026]

[0027] in, and These are the reducible and transferable electrical loads at time t, respectively, both of which are decision variables; and These are the upper limits for the electrical load that can be reduced and the electrical load that can be transferred, respectively. It is the total amount of power load transferred during the dispatch period; C P,DR It is the electrical load demand response cost; q P,tran It is the cost per unit of transferable electrical load; q P,cut This allows for cost reductions in electricity load per unit;

[0028] The method for calculating the cost of heat load demand response based on flexible heat load is as follows:

[0029] Flexible heat loads are mainly divided into reduceable heat loads and transferable heat loads:

[0030]

[0031] In the formula, and These are the reducible heat load and the transferable heat load at time t, respectively, both of which are decision variables; and These are the upper limits for the heat load that can be reduced and the heat load that can be transferred, respectively. It is the total amount of heat load transferred during the scheduling period; C H,DR It is the cost of heat load demand response; q H,tran It is the cost per unit of transferable heat load; q H,cut It is the cost per unit of transferable heat load;

[0032] The calculation method for electric vehicle discharge compensation fee is as follows:

[0033]

[0034] In the formula, C evd For electric vehicle emission compensation fees; N ev It represents the number of electric vehicles; q evd It is the compensation coefficient for electric vehicle discharge; It is the discharge power of the nth electric vehicle at time t.

[0035] As a further improvement to the virtual power plant optimal scheduling method that considers carbon trading and demand response, the dynamic pricing game model in step S4 includes:

[0036] Maximizing the profits of power distribution system operators is used as the objective function of the dynamic pricing game model.

[0037]

[0038] Among them, FDSO It is in the interest of the power distribution system operator; and These are the electricity sales price and purchase price of the power market by the power distribution system operator at time t; and These are the electricity sales price and purchase price of the virtual power plant to the power distribution system operator at time t; and It refers to the amount of electricity sold and purchased by the power distribution system operator to the electricity market within time t. and The electricity sold and purchased by the virtual power plant to the distribution system operator within time t are both decision variables.

[0039] As a further improvement to the virtual power plant optimal scheduling method that considers carbon trading and demand response, the energy management game model in step S4 includes:

[0040] Minimizing the total cost of the virtual power plant is used as the objective function of the energy management game model.

[0041]

[0042] Among them, F VPP This is the total cost of the virtual power plant; C buy C p C wv and C DR These are the energy purchase cost, operation and maintenance cost, wind and solar curtailment cost, and demand response cost of the virtual power plant. and These represent the electricity purchase price and the electricity sales price of the virtual power plant to the distribution system operator at time t, respectively. and The virtual power plant's purchase and sale of electricity to and from the distribution system operator within time t are both decision variables; q g It is the purchase price of natural gas. It is the gas consumption of the i-th cogeneration unit at time t; It is the operating and maintenance cost coefficient of equipment r, where R is the equipment in the virtual power plant, including photovoltaic, wind power, combined heat and power units, heat pumps and energy storage; Let be the operating power of device r at time t, and be the decision variable; and These are the cost coefficients for power generation and heating supply from a centralized solar thermal power plant; and These are the power generation of the centralized solar thermal power plant at time t and the heat load of the thermal storage device, both of which are decision variables; q w q v These are the penalty coefficients for curtailing wind and solar power, respectively. and These are the predicted outputs of wind power and solar power at time t, respectively; P w,t and P v,t These are the actual outputs of wind power and solar power at time t, respectively, and both are decision variables.

[0043] As a further improvement to the virtual power plant optimal scheduling method that considers carbon trading and demand response, the energy management game model in step S4 also includes the following constraints:

[0044] 1) Wind and solar power output constraints:

[0045]

[0046] 2) Heat balance relationship of centralized solar thermal power plants:

[0047]

[0048] in, It is the heat power from the heat collector to the heat transfer fluid at time t. The heat power η is the heat transfer fluid's output to the power generation link at time t. S η PB These are the photothermal conversion efficiency and the thermoelectric conversion efficiency, respectively; S S It is the area of ​​the sunglasses field; D t It is the direct solar radiation index. It is the unused heat in the heat collection link; H SU It is the start-up thermal power of the power generation link at time t; u t It is the startup state variable of the centralized solar thermal power plant at time t; and These are the upper and lower limits of the power generation capacity of a centralized solar thermal power plant;

[0049] 3) Constraints of electrical energy storage:

[0050] S SOC,min ≤S SOC (t)≤S SOC,max ;

[0051] In the formula: S SOC (t) represents the state of charge of the energy storage system at time t; S SOC,min S SOC,max These are the upper and lower limits of the state of charge of the energy storage system, respectively.

[0052] 4) Constraints of combined heat and power units:

[0053] The energy constraints and capacity constraints of a combined heat and power (CHP) unit are expressed as follows:

[0054]

[0055]

[0056] In the formula: These represent the power generation efficiency and heating efficiency of a combined heat and power (CHP) unit, respectively; F h (t) represents the gas consumption power of the combined heat and power unit h at time t;

[0057] 5) Constraints on charging and discharging electric vehicles:

[0058]

[0059] in, and These represent the charging and discharging states of the electric vehicle at time t: when the electric vehicle is charging, The value is 1; when the electric vehicle is discharging, =1; It is the charging / discharging power of the electric vehicle at time t; and These are the maximum charging power and maximum discharging power of an electric vehicle, respectively.

[0060] 6) Constraints of electric vehicle batteries:

[0061]

[0062] Among them, SOC max and SOC min These are the upper and lower limits of the electric vehicle charging SoC; It is the remaining power of an electric vehicle when it finishes driving; E es It is the rated capacity of the electric vehicle battery; η c and η d These are charging efficiency and discharging efficiency, respectively; t in This refers to the visit time of electric vehicles;

[0063] 7) Constraints on power balance, including virtual power plants meeting the balance constraints of electricity, heat, and gas:

[0064]

[0065]

[0066] In the formula, This represents the power consumption of the heat pump at time t; and These are the charging and discharging power of electrical energy storage over time t; Indicates the load electrical power in response to demand; This represents the heat energy power of the heat pump at time t; This represents the heat energy power of the load that can be transferred at time t; This represents the thermal power output of the combined heat and power unit at time t. This indicates the demand response load thermal power.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] This invention constructs a virtual power plant framework with centralized concentrated solar thermal power plants. Based on a tiered carbon trading model and an incentive-based demand response (DR) model for electricity and heat load, the operational flexibility of the virtual power plant is improved. Finally, with the goal of minimizing total cost, a low-carbon economic optimization scheduling model for the virtual power plant is established to optimize equipment output and load incentive-based demand response schemes. Examples demonstrate the effectiveness of this method in improving the low-carbon and economic feasibility of virtual power plants, including: 1) After considering electricity price optimization and master-slave game theory, the carbon emissions of the virtual power plant are reduced, and the revenue of the distribution system operator is significantly increased; 2) When the centralized concentrated solar thermal power plant units are included in the model, both system carbon emissions and total cost are reduced; 3) Considering the participation of centralized concentrated solar thermal power plants in the virtual power plant, as well as their participation in energy supply and cooperation with the tiered carbon trading mechanism, can further stimulate the virtual power plant's enthusiasm for participating in demand response and improve the low-carbon economic benefits of the virtual power plant. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating the method.

[0070] Figure 2 This method considers a master-slave framework for virtual power plants and centralized solar thermal power plants that take into account carbon trading and demand response.

[0071] Figure 3 This is a prediction curve of wind power and photovoltaic output, electricity and heat load in a specific implementation method. Detailed Implementation

[0072] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings.

[0073] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. The invention is described more specifically in the following paragraphs by way of example with reference to the accompanying drawings. The advantages and features of the invention will become clearer from the following description and claims. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the invention.

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0075] like Figure 1 and 2 A virtual power plant optimal scheduling method considering carbon trading and demand response, comprising the following steps:

[0076] Step S1: Establish a master-slave framework for virtual power plants and centralized solar thermal power plants that takes into account carbon trading and demand response.

[0077] The master-slave framework of virtual power plants and centralized solar thermal power plants considering carbon trading and demand response in step S1 includes: virtual power plants include wind power, photovoltaics, centralized solar thermal power plants, cogeneration units, energy storage, heat pumps, stationary loads, and flexible loads; wherein, the centralized solar thermal power plant consists of a heat collection section, a heat storage section, and a power generation section, and the heat transfer medium is fluid; the centralized solar thermal power plant and the cogeneration unit operate in conjunction, which can alleviate the degree of electrothermal coupling in cogeneration and achieve flexible and controllable coordinated operation of electrothermal coupling.

[0078] Step S2: Develop a tiered carbon trading model for virtual power plants. This specifically includes:

[0079] Virtual power plants use a tiered carbon trading mechanism to conduct intra-carbon trading with external carbon markets, thereby improving the overall low-carbon performance of the system through carbon emission constraints.

[0080] A tiered carbon trading model for virtual power plants is constructed, which uses a baseline method to allocate initial carbon emission allowances for virtual power plants. When the actual carbon emissions of a virtual power plant are lower than the initial carbon allowance, the remaining carbon allowances can be sold to generate revenue.

[0081] The initial carbon emission allowance and actual carbon emission of the virtual power plant include the purchase of electricity and the output of the combined heat and power unit, respectively.

[0082]

[0083] Among them, D VPP D grid and D CHP These are the initial carbon emission allowances for virtual power plants, purchased electricity, and combined heat and power units, respectively; T is the number of time periods divided into the dispatch interval, t is the sequence number of the time period, and time t represents a period of time within the dispatch interval; N CHP This represents the total number of combined heat and power (CHP) units, where i is the unit number; λ e and λ h These are the carbon emission quota coefficients per unit of electricity and per unit of heat, respectively. The electricity purchased by the virtual power plant from the power distribution system operator at time t is the decision variable. It is the electrothermal conversion coefficient of the combined heat and power unit; and These are the power and heat output of the i-th cogeneration unit at time t, respectively, both of which are decision variables; and These represent the actual carbon emissions from virtual power plants, purchased electricity, and combined heat and power (CHP) units, respectively. and These are the actual carbon emission coefficients per unit of electrical energy and per unit of thermal energy, respectively.

[0084] The tiered carbon trading mechanism divides carbon allowances into multiple purchase intervals. The more carbon allowances a virtual power plant purchases, the higher the purchase price for the corresponding carbon emission interval, and the higher the carbon trading cost. The carbon trading cost for virtual power plants is calculated as follows, combining the initial carbon emission allowances and actual carbon emissions:

[0085]

[0086] Among them, C c d is the carbon trading cost; c is the carbon trading price; d is the length of the carbon allowance purchase interval; k is the carbon price growth coefficient.

[0087] Step S3: Construct a virtual power plant demand response model based on the characteristics of electricity load and heat load. Specifically, this includes:

[0088] Based on the electrical and thermal load response characteristics of a virtual power plant, an incentive-driven demand response model for the virtual power plant was established, guiding the demand response of flexible electrical and thermal loads through incentive contracts. By adjusting the flexible load within appropriate time periods, it is possible to reduce equipment output and external energy procurement, thereby lowering economic and carbon trading costs. Flexible electrical loads are mainly categorized into reduceable loads, transferable loads, and electric vehicles. The method for calculating the demand response cost of electrical loads based on flexible electrical loads is as follows:

[0089]

[0090] in, and These are the reducible and transferable electrical loads at time t, respectively, both of which are decision variables; and These are the upper limits for the electrical load that can be reduced and the electrical load that can be transferred, respectively. It is the total amount of power load transferred during the dispatch period; C P,DR It is the electrical load demand response cost; q P,tran It is the cost per unit of transferable electrical load; q P,cut This allows for cost reductions in electricity load per unit.

[0091] Flexible heat loads are mainly divided into reduceable heat loads and transferable heat loads. The method for calculating the cost of heat load demand response based on flexible heat loads is as follows:

[0092] Flexible heat loads are mainly divided into reduceable heat loads and transferable heat loads:

[0093]

[0094] In the formula, and These are the reducible heat load and the transferable heat load at time t, respectively, both of which are decision variables; and These are the upper limits for the heat load that can be reduced and the heat load that can be transferred, respectively. It is the total amount of heat load transferred during the scheduling period; C H,DR It is the cost of heat load demand response; q H,tran It is the cost per unit of transferable heat load; q H,cut It is the cost per unit of transferable heat load.

[0095] Electric vehicles possess the dual characteristics of both power source and load. Based on research into electric vehicle travel patterns, a charging and discharging model for electric vehicles was established, and a method for electric vehicles to participate in incentive demand response was proposed.

[0096]

[0097] In the formula, C evd For electric vehicle emission compensation fees; N ev It represents the number of electric vehicles; q evd It is the compensation coefficient for electric vehicle discharge; It is the discharge power of the nth electric vehicle at time t.

[0098] Step S4: Based on the master-slave framework, tiered carbon trading model, and virtual power plant demand response model, construct a master-slave game model between distribution system operators and virtual power plants. The master-slave game model includes: a dynamic pricing game model for distribution system operators and an energy management game model for virtual power plants.

[0099] Dynamic pricing game models include:

[0100] Maximizing the profits of distribution system operators is used as the objective function of the dynamic pricing game model, including the costs / benefits of purchasing / selling electricity in virtual power plants and the electricity market:

[0101]

[0102] Among them, F DSO It is in the interest of the power distribution system operator; and These are the electricity sales price and purchase price of the power market by the power distribution system operator at time t; and These are the electricity sales price and purchase price of the virtual power plant to the power distribution system operator at time t; and It refers to the amount of electricity sold and purchased by the power distribution system operator to the electricity market within time t. and The electricity sold and purchased by the virtual power plant to the distribution system operator within time t are both decision variables.

[0103] Energy management game theory models include:

[0104] Under the premise of satisfying system constraints, the economic efficiency and low carbon emissions of virtual power plants are improved by using the minimum total cost of virtual power plants as the utility function, including energy purchase costs, carbon trading costs, operation and maintenance costs, wind and solar power waste disposal costs, and demand response costs.

[0105]

[0106] Among them, F VPP This is the total cost of the virtual power plant; C buy C p C wv and C DR These are the energy purchase cost, operation and maintenance cost, wind and solar curtailment cost, and demand response cost of the virtual power plant. and These represent the electricity purchase price and the electricity sales price of the virtual power plant to the distribution system operator at time t, respectively. and The virtual power plant's purchase and sale of electricity to and from the distribution system operator within time t are both decision variables; q g It is the purchase price of natural gas. It is the gas consumption of the i-th cogeneration unit at time t; It is the operating and maintenance cost coefficient of equipment r, where R is the equipment in the virtual power plant, including photovoltaic, wind power, combined heat and power units, heat pumps and energy storage; Let be the operating power of device r at time t, and be the decision variable; and These are the cost coefficients for power generation and heating supply from a centralized solar thermal power plant; and These are the power generation of the centralized solar thermal power plant at time t and the heat load of the thermal storage device, both of which are decision variables; q w q v These are the penalty coefficients for curtailing wind and solar power, respectively. and These are the predicted outputs of wind power and solar power at time t, respectively; P w,t and P v,t These are the actual outputs of wind power and solar power at time t, respectively, and both are decision variables.

[0107] The energy management game model also includes the following constraints:

[0108] 1) Wind and solar power output constraints:

[0109]

[0110] 2) Heat balance relationship of centralized solar thermal power plants:

[0111]

[0112] in, It is the heat power from the heat collector to the heat transfer fluid at time t. The heat power η is the heat transfer fluid's output to the power generation link at time t. S η PB These are the photothermal conversion efficiency and the thermoelectric conversion efficiency, respectively; S S It is the area of ​​the sunglasses field; D t It is the direct solar radiation index. It is the unused heat in the heat collection link; H SU It is the start-up thermal power of the power generation link at time t; u t It is the startup state variable of the centralized solar thermal power plant at time t; and These are the upper and lower limits of the power generation capacity of a centralized solar thermal power plant;

[0113] 3) Constraints of electrical energy storage:

[0114] The energy storage system stores energy from batteries. To prevent the energy storage system from being overcharged and discharged, thus reducing its lifespan, its remaining energy must be maintained within a certain range during dispatching.

[0115] S SOC,min ≤S SOC (t)≤S SOC,max ;

[0116] In the formula: S SOC (t) represents the state of charge of the energy storage system at time t; S SOC,min S SOC,max These are the upper and lower limits of the state of charge of the energy storage system, respectively.

[0117] 4) Constraints of combined heat and power units:

[0118] The energy constraints and capacity constraints of a combined heat and power (CHP) unit are expressed as follows:

[0119]

[0120]

[0121] In the formula: These represent the power generation efficiency and heating efficiency of a combined heat and power (CHP) unit, respectively; F h (t) represents the gas consumption power of the combined heat and power unit h at time t;

[0122] 5) Constraints on charging and discharging electric vehicles:

[0123]

[0124] in, and These represent the charging and discharging states of the electric vehicle at time t: when the electric vehicle is charging, The value is 1; when the electric vehicle is discharging, =1; It is the charging / discharging power of the electric vehicle at time t; and These are the maximum charging power and maximum discharging power of an electric vehicle, respectively.

[0125] 6) Constraints of electric vehicle batteries:

[0126]

[0127] Among them, SOC max and SOC min These are the upper and lower limits of the electric vehicle charging SoC; It is the remaining power of an electric vehicle when it finishes driving; E es It is the rated capacity of the electric vehicle battery; η c and η d These are charging efficiency and discharging efficiency, respectively; t in This refers to the visit time of electric vehicles;

[0128] 7) Constraints on power balance, including virtual power plants meeting the balance constraints of electricity, heat, and gas:

[0129]

[0130]

[0131] In the formula, This represents the power consumption of the heat pump at time t; and These are the charging and discharging power of electrical energy storage over time t; Indicates the load electrical power in response to demand; This represents the heat energy power of the heat pump at time t; This represents the heat energy power of the load that can be transferred at time t; This represents the thermal power output of the combined heat and power unit at time t. This indicates the demand response load thermal power.

[0132] Step S5: Use the Yalmip toolkit to solve the master-slave game model built on the MATLAB platform, use the result value of the decision variable as the virtual power plant optimization scheduling strategy, and control the virtual power plant to execute the optimization scheduling strategy.

[0133] To verify the correctness of the proposed method, the following case analysis was conducted:

[0134] The predicted values ​​for wind and solar power output, electrical load, and heat load are attached. Figure 3 As shown in Table 1, the carbon trading floor price is 52 yuan / ton, the carbon emission range is 80 tons, the carbon price growth coefficient is 0.25, and the number of electric vehicles is 100. The parameters for electric vehicles, transferable loads, and reducible loads are listed in Table 1. This paper uses the Yalmip toolkit to solve problems based on the MATLAB platform.

[0135] Table 1. Parameters of Incentive Contracts for Electric Vehicles, Transferable Loads, and Reduceable Loads

[0136] parameter value parameter value <![CDATA[E cap / (kW·h)]]> 57 <![CDATA[q evd / (yuan·(MW·h) -1 )]]> 40 <![CDATA[q P,tran / (Yuan·(MW·hy) -1 )]]> 20 <![CDATA[Q H,tran / (yuan·(MW) -1 )]]> 20 <![CDATA[q P,cut / (Yuan·(MW·hy) -1 )]]> 120 <![CDATA[Q H,cut / (yuan·(MW) -1 )]]> 160

[0137] To analyze the impact of master-slave game models, tiered carbon trading mechanisms, and demand response on virtual power plants, a comparative analysis was conducted on the following five scenarios:

[0138] Case 1: Using a master-slave game model, the electricity price is optimized through the power distribution system operator.

[0139] Case 2: Using the grid connection price and the grid price as the purchase and sale prices for the distribution system operator, the distribution system operator did not optimize the electricity price.

[0140] Case 3: There is no centralized solar thermal power plant in Case 1.

[0141] Case 4: There was no demand response in Case 1.

[0142] Case 5: Case 1 changed the tiered carbon trading system to the traditional carbon trading system.

[0143] Table 2. Cost comparison under different conditions.

[0144]

[0145] By comparing the five scenarios in Table 2, we can see that: Case 1, based on Case 2, optimizes the electricity price, significantly increasing the revenue of the distribution system operator and reducing the carbon emissions of the virtual power plant. Considering centralized solar thermal power plants based on Case 3, Case 1 reduces the total cost of the virtual power plant by 20.98 million yuan, reduces carbon emissions by 214.26 tons, and increases the revenue of the distribution system operator by 25.85 million yuan. This indicates that centralized solar thermal power plants can improve the overall economic efficiency and low-carbon performance of the system. Then, based on Case 4, Case 1 considers demand response. While demand response costs increase, the cost of wind and solar curtailment decreases, resulting in a reduction of the total cost of the virtual power plant by 21.75 million yuan, a reduction of carbon emissions by 339.11 tons, and an increase in the revenue of the distribution system operator by 37.25 million yuan. Furthermore, comparing Case 3 and Case 4 shows that although the carbon trading cost under the tiered carbon trading mechanism is higher than traditional carbon trading, it can incentivize virtual power plants to participate in demand response and improve their low-carbon economic benefits.

[0146] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A virtual power plant optimal scheduling method considering carbon trading and demand response, characterized in that... The steps are as follows: Step S1: Establish a master-slave framework for virtual power plants and centralized solar thermal power plants that takes into account carbon trading and demand response; The master-slave framework of virtual power plants and centralized solar thermal power plants considering carbon trading and demand response in step S1 includes: virtual power plants include wind power, photovoltaics, centralized solar thermal power plants, cogeneration units, energy storage, heat pumps, stationary loads, and flexible loads; wherein, the centralized solar thermal power plant consists of a heat collection section, a heat storage section, and a power generation section, and the heat transfer medium is fluid; the centralized solar thermal power plant and the cogeneration unit operate in conjunction; Step S2: Develop a tiered carbon trading model for virtual power plants; Step S3: Construct a virtual power plant demand response model based on the characteristics of electricity load and heat load; The virtual power plant demand response model mentioned in step S3 includes: The method for calculating the demand response cost of electrical load based on flexible electrical load is as follows: ； in, and They are in time Both the reducible and transferable electrical loads are decision variables; and These are the upper limits for the electrical load that can be reduced and the electrical load that can be transferred, respectively. It represents the total amount of power load transferred during the dispatching period; It is the cost of electrical load demand response; It is the cost per unit of transferable electrical load; This allows for cost reductions in electricity load per unit; The number of time periods into which the scheduling interval is divided. The sequence number of the time period, time Represents a period of time within the scheduling interval; The method for calculating the cost of heat load demand response based on flexible heat load is as follows: Flexible heat loads are divided into reduceable heat loads and transferable heat loads: ； In the formula, and In time Both the heat load that can be reduced and the heat load that can be transferred are decision variables; and These are the upper limits for the heat load that can be reduced and the heat load that can be transferred, respectively. It is the total amount of heat load transferred during the scheduling period; It is the cost of heat load demand response; It is the cost per unit of transferable heat load; It is the cost per unit of transferable heat load; The calculation method for electric vehicle discharge compensation fee is as follows: ； In the formula, For electric vehicle emission compensation fees; It refers to the number of electric vehicles; It is the compensation coefficient for electric vehicle discharge; It is the first The discharge power of an electric vehicle at time t; Step S4: Based on the master-slave framework, tiered carbon trading model, and virtual power plant demand response model, construct a master-slave game model between distribution system operators and virtual power plants. The master-slave game model includes: a dynamic pricing game model for distribution system operators and an energy management game model for virtual power plants. Step S5: Solve the master-slave game model, use the result value of the decision variable as the virtual power plant optimization scheduling strategy, and control the virtual power plant to execute the optimization scheduling strategy.

2. The virtual power plant optimal scheduling method considering carbon trading and demand response as described in claim 1, characterized in that: The tiered carbon trading model for virtual power plants in step S2 includes: Virtual power plants use a tiered carbon trading mechanism to trade carbon with external carbon markets; A tiered carbon trading model for virtual power plants is constructed, which uses a baseline method to allocate initial carbon emission allowances for virtual power plants. When the actual carbon emissions of a virtual power plant are lower than the initial carbon allowance, the remaining carbon allowances can be sold to generate revenue. The initial carbon emission allowance and actual carbon emission of the virtual power plant include the purchase of electricity and the output of the combined heat and power unit, respectively. ； in, , and These are the initial carbon emission allowances for virtual power plants, electricity purchases, and combined heat and power units; It is the total number of combined heat and power units. The designation of the combined heat and power (CHP) unit; and These are the carbon emission quota coefficients per unit of electricity and per unit of heat, respectively. It is a virtual power plant in time The electricity purchased from the power distribution system operator is a decision variable; It is the electrothermal conversion coefficient of the combined heat and power unit; and They are the first Taiwan cogeneration unit in time Power and heat output are both decision variables; , and These represent the actual carbon emissions from virtual power plants, purchased electricity, and combined heat and power (CHP) units, respectively. and These are the actual carbon emission coefficients per unit of electrical energy and per unit of thermal energy, respectively. The carbon trading costs for virtual power plants are calculated as follows: ； in, d is the carbon trading cost; c is the carbon trading price; d is the length of the carbon allowance purchase interval; k is the carbon price growth coefficient.

3. The virtual power plant optimal scheduling method considering carbon trading and demand response as described in claim 1, characterized in that, The dynamic pricing game model in step S4 includes: Maximizing the profits of power distribution system operators is used as the objective function of the dynamic pricing game model. ； in, It is in the interest of the power distribution system operator; and These are the time periods of the power distribution system operators. Electricity sales prices and electricity purchase prices in the electricity market; and These are virtual power plants in time The electricity sales price and purchase price to the power distribution system operator; and It is time The electricity sales and purchases by internal power distribution system operators to the electricity market. and It is time The electricity sold and purchased by the virtual power plant to the distribution system operator are both decision variables.

4. The virtual power plant optimal scheduling method considering carbon trading and demand response as described in claim 2, characterized in that, The energy management game model in step S4 includes: Minimizing the total cost of the virtual power plant is used as the objective function of the energy management game model. ； in, This is the total cost of the virtual power plant; , , and These are the energy purchase cost, operation and maintenance cost, wind and solar curtailment cost, and demand response cost of the virtual power plant. and The virtual power plant in time The purchase price and sale price of electricity from the power distribution system operator, and It is time The amount of electricity purchased and sold by the virtual power plant from the distribution system operator are both decision variables. It is the purchase price of natural gas. It is the first Taiwan's combined heat and power units in time Gas consumption; It is equipment The operating and maintenance cost coefficient, These are the devices in a virtual power plant, including photovoltaic, wind power, combined heat and power units, heat pumps, and electric energy storage. For equipment In time The operating power is the decision variable; and These are the cost coefficients for power generation and heating supply from a centralized solar thermal power plant; and These are the time periods of concentrated solar thermal power plants. The power generation capacity and the heat load of the thermal storage device are both decision variables; , These are the penalty coefficients for curtailing wind and solar power, respectively. and These are wind power and solar power in time. The predicted output; and These are wind power and solar power in time. The actual outputs are all decision variables.

5. The virtual power plant optimal scheduling method considering carbon trading and demand response as described in claim 4, characterized in that, The energy management game model in step S4 also includes the following constraints: 1) Wind and solar power output constraints: ； 2) Heat balance relationship of centralized solar thermal power plants: ； in, It is in time The heat power from the heat collector to the heat transfer fluid. It is in time The heat power from the heat transfer fluid to the power generation link. , These are the photothermal conversion efficiency and the thermoelectric conversion efficiency, respectively. It refers to the area of ​​the sunglasses display area; It is the direct solar radiation index. It is the unused heat in the heat collection link; It is the power generation link in time Start-up thermal power; It is a concentrated solar thermal power plant in time The startup state variables; and These are the upper and lower limits of the power generation capacity of a centralized solar thermal power plant; 3) Constraints of electrical energy storage: ； In the formula: For time State of charge of energy storage systems; , These are the upper and lower limits of the state of charge of the energy storage system, respectively. 4) Constraints of combined heat and power units: The energy constraints and capacity constraints of a combined heat and power (CHP) unit are expressed as follows: ； ； In the formula: , These represent the power generation efficiency and heating efficiency of the combined heat and power unit, respectively. Indicates the time of the combined heat and power unit h. The gas consumption power; 5) Constraints on charging and discharging electric vehicles: ； in, and These are electric vehicles in time Charging and discharging states: When an electric vehicle is charging, The value is 1; when the electric vehicle is discharging, =1; Electric vehicles in time The charging power; and These are the maximum charging power and maximum discharging power of an electric vehicle, respectively. 6) Constraints of electric vehicle batteries: ； in, and These are the upper and lower limits of the electric vehicle charging SoC; It is the remaining power of the electric vehicle when it finishes driving; It is the rated capacity of the electric vehicle battery; and These are charging efficiency and discharging efficiency, respectively. This refers to the visit time of electric vehicles; 7) Constraints on power balance, including virtual power plants meeting the balance constraints of electricity, heat, and gas: ； ； In the formula, Indicates the time of the heat pump Power consumption; and It is the time of energy storage The charging power and discharging power; Indicates the load electrical power in response to demand; Indicates time The thermal energy output of a heat pump; Indicates time Thermal power of transferable load; Indicates time Thermal power of combined heat and power units; This indicates the demand response load thermal power.

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