Virtual power plant low-carbon joint scheduling method based on multi-factor dynamic pricing mechanism

Abstract

The invention belongs to the technical field of virtual power plant scheduling, and discloses a virtual power plant low-carbon joint scheduling method based on a multi-factor dynamic pricing mechanism, which comprises the following steps: step 1, constructing a model of an energy service provider and a user aggregator in a virtual power plant; 2, constructing a stepped carbon transaction model and a green certificate transaction model; step 3, constructing a multi-factor dynamic pricing mechanism according to carbon emission, clean energy consumption and demand response; and 4, constructing a supply and demand combined scheduling model, establishing an intelligent combined scheduling center to execute dynamic pricing and combined scheduling of the virtual power plant, and solving the mixed integer linear programming model by using CPLEX to obtain an optimized scheduling result. According to the method, the carbon capture unit is integrated into the virtual power plant, and a stepped carbon transaction mechanism and a green certificate transaction mechanism are adopted, so that the low-carbon potential of the system is fully excavated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual power plant scheduling, in particular to a low-carbon joint scheduling method for virtual power plant based on a multi-factor dynamic pricing mechanism. BACKGROUND

[0002] Low-carbon optimal scheduling of virtual power plant is one of the key directions of current energy system research. Existing researches focus on improving the flexibility of virtual power plant in energy supply and demand sides to optimize system operation economy, improve energy utilization efficiency and promote renewable energy consumption. However, the current research still has shortcomings in depicting the interaction between users and energy service providers, usually only considering the demand side as controllable load resources.

[0003] Chinese invention patent: publication number "CN120377272A", named "intelligent load scheduling method and system for virtual power plant user end", discloses a method including: obtaining original load data of virtual power plant user end; performing feature extraction according to the original load data to obtain feature vector; performing load demand prediction according to the feature vector to obtain predicted load demand; adopting a load scheduling model based on game theory according to the predicted load demand to obtain a load scheduling scheme; wherein the load scheduling model includes a local game layer and a global game layer, and in each round of game process, the load scheduling model determines the load decision weight of the user end according to the fault tolerance factor, and adjusts the load decision of the user end based on the fault tolerance factor. The technical solution can effectively improve the flexibility and accuracy of virtual power plant user end load scheduling. However, the technical solution only considers the demand side as controllable load resources, and the interaction between users and energy service providers is insufficient. In the technical solution, the information interaction between the demand side users and the energy service providers relies on the coordination of the third party platform intelligent joint scheduling center, and lacks in-depth consideration of the two-way interaction mechanism between users and energy service providers. It is still necessary to conduct further research in fully tapping the potential of user-side active participation in scheduling. On the other hand, since the interaction between users and service providers is mainly guided by price signals, there is a lack of deep incentive strategies (such as personalized preferences, response willingness, etc.) for users to actively participate in scheduling, resulting in limited flexibility and autonomy of user response behavior. SUMMARY

[0004] In order to solve the above-mentioned problems of insufficient consideration of the interaction between users and energy service providers in the prior art, and the one-sidedness and insufficiency of the scheduling scheme, the present application proposes a low-carbon joint scheduling method for virtual power plant based on a multi-factor dynamic pricing mechanism, which releases price signals to guide demand side users to preferentially consume renewable energy and reduce dependence on non-clean energy; and fully utilizes the schedulable value of demand response and electric vehicles to promote the preferential consumption of renewable energy. The present application can effectively reduce the carbon emissions of the system and reasonably coordinate the conflicts of interest between the energy supply side and the demand side.

[0005] The application is realized by the following technical scheme: comprising the following steps:

[0006] Step 1, constructing a model of energy service providers and user aggregators in a virtual power plant;

[0007] Step 2, constructing a ladder-type carbon trading model and a green certificate trading model;

[0008] Step 3, constructing a multi-factor dynamic pricing mechanism according to carbon emissions, clean energy consumption and demand response;

[0009] Step 4, constructing a supply-demand joint scheduling model, establishing an intelligent joint scheduling center to execute dynamic pricing and joint scheduling of the virtual power plant, using CPLEX to solve a mixed integer linear programming model to obtain an optimized scheduling result.

[0010] Compared with the prior art, the application has the beneficial effects that:

[0011] 1. The application integrates a carbon capture unit into a virtual power plant (VPP) and adopts a ladder-type carbon trading mechanism and a green certificate trading mechanism, thereby fully tapping the low-carbon potential of the system.

[0012] 2. The application proposes a multi-factor differentiated dynamic pricing strategy, integrates the ladder-type carbon trading mechanism, the green certificate trading mechanism and the demand response mechanism into the pricing mechanism, so that the energy price has environmental and user demand attributes. The proposed pricing strategy can guide users to preferentially consume renewable energy through price signals and adjust their energy purchasing strategies according to the price, thereby making up for the low sensitivity of users to traditional time-of-use electricity prices.

[0013] 3. The application considers the demand response mechanism and the charging and discharging strategy of an electric vehicle charging station, thereby relieving the energy supply pressure of the system, stabilizing the load peak-valley curve and promoting the economic operation of the system

[0014] 4. The application constructs a joint optimization scheduling model, constructs a joint objective function to solve the benefit conflict between the source side and the load side in low-carbon scheduling under the premise of respectively establishing optimization models of energy service providers (ESPs) and user aggregators (UAs). DETAILED DESCRIPTION

[0015] Figure 1 It is a whole flowchart of the scheduling method of the application.

[0016] Figure 2 It is a structure diagram of the virtual power plant (VPP) of the application.

[0017] Figure 3 It is a ladder-type carbon trading and green certificate trading mechanism diagram of the application.

[0018] Figure 4Schematic diagram of the multi-factor dynamic pricing strategy of the present application.

[0019] Figure 5 Interaction diagram between internal stakeholders of the scheduling method of the present application.

[0020] Figure 6 Flowchart of the joint optimization model solution of the present application.

[0021] Figure 7 Initial load and renewable energy predicted power curve chart in the embodiment of the present application.

[0022] Figure 8 Energy pricing chart of each type in the virtual power plant VPP in the embodiment of the present application.

[0023] Figure 9 System electric power balance chart in the virtual power plant VPP in the embodiment of the present application. DETAILED DESCRIPTION

[0024] The advantages and features of the present application will be illustrated and explained by the following non-limiting description of preferred embodiments, which are given by way of example only, with reference to the accompanying drawings.

[0025] As Figure 1As shown, the application provides a virtual power plant low-carbon joint scheduling method based on a multi-factor dynamic pricing strategy, a virtual power plant (VPP) model is designed, carbon capture and storage technology is considered on the energy supply side to reduce carbon dioxide emissions, and the value of demand response (DR) and electric vehicles (EV) is considered on the energy demand side to promote stable operation of the entire system; A mathematical model of the ladder type carbon trading and green certificate trading is constructed to fully tap the low-carbon potential of the system; A multi-factor dynamic mechanism model is constructed, carbon emissions, renewable energy consumption and demand response are used as three factors affecting pricing, by introducing the concepts of non-clean unit carbon emission penalty price, green certificate correction price and demand response correction price, the sales prices of different sources of electricity and heat are set, and the users are given the power to choose different energy types; An intelligent joint scheduling center (IJSC) is established to be responsible for the differentiated dynamic pricing and joint scheduling of the virtual power plant VPP, and to coordinate the economic interests of the energy service providers (ESP) on the energy supply side and the user aggregators (UA) on the demand side; The CPLEX is used to solve the mixed integer linear programming (MILP) model. The dynamic pricing mechanism designed by the application can guide users to preferentially consume clean energy by releasing price signals to the demand side users, effectively reduce the carbon emissions of the system, and guide the virtual power plant VPP to develop in the direction of green and low carbon.

[0026] As Figure 2As shown, a framework diagram of the virtual power plant VPP constructed by the present application. The energy service provider ESP obtains benefits by selling electric energy and thermal energy to users. The virtual power plant framework constructed by the present application includes wind turbine units, photovoltaic units, combined heat and power (CHP) units, gas boiler (GB) units, carbon capture units, electric boiler (EB) units, heat storage tanks, batteries and a plurality of electric vehicles. In addition to the wind turbine, photovoltaic panel, gas boiler GB unit and combined heat and power CHP unit provided inside the energy service provider ESP, the energy service provider ESP can also purchase electric energy from the power grid to meet the load demand of the demand side users. In addition, the energy service provider ESP should fulfill the responsibility of low carbon and environmental protection. Therefore, in order to promote the consumption of new energy and reduce the carbon emissions generated by the gas unit, the system is equipped with a carbon capture unit to capture and store carbon dioxide. In addition to the user aggregator UA, the demand side is also equipped with an electric vehicle charging station (EVCS) and an electric boiler (EB) energy storage device. The electric boiler EB and the energy storage device are equipped to support the coupling and storage of electric energy and thermal energy on the load side.

[0027] The present application provides a virtual power plant low-carbon joint scheduling method based on a multi-factor dynamic pricing strategy, and the specific steps are as follows:

[0028] Step 1, constructing a model of the energy service provider ESP and the user aggregator UA in the virtual power plant.

[0029] In addition to the wind turbine belonging to the wind turbine unit, the photovoltaic panel belonging to the photovoltaic unit, the gas boiler belonging to the gas boiler GB unit and the combined heat and power CHP unit responsible for energy supply inside the energy service provider ESP of the present application, the energy service provider ESP can also purchase electric energy from the power grid to meet the load demand of the users, and further equipped with a carbon capture unit to capture and store carbon dioxide. The steps of constructing the model are as follows:

[0030] Step 11, according to the input natural gas power of the combined heat and power CHP unit, the electric output power and the heat output power of the combined heat and power CHP unit are obtained respectively, and the formula is as follows:

[0031]

[0032] In the formula, represents the input natural gas power of the combined heat and power CHP unit at time t; respectively represent the gas-to-electricity and gas-to-heat efficiencies of the combined heat and power CHP unit; and respectively represent the electric output power and the heat output power of the combined heat and power (CHP) unit at time t.

[0033] In the process of gas power generation of the combined heat and power (CHP) unit, the waste heat recovery device can be used to supply heat to the outside, so that the economic performance and efficiency of the whole system are improved.

[0034] Step 12: obtaining the heat output power of the gas-fired boiler (GB) unit according to the natural gas power input into the gas-fired boiler (GB) unit, and the formula is as follows:

[0035]

[0036] In the formula, η GB represents the energy conversion efficiency of the gas-fired boiler (GB) unit; Q GB represents the heat output power of the gas-fired boiler (GB) unit at time t; and P GB represents the natural gas power input into the gas-fired boiler (GB) unit at time t. In the formula, η GB represents the energy conversion efficiency of the gas-fired boiler (GB) unit; Q GB represents the heat output power of the gas-fired boiler (GB) unit at time t; and P GB represents the natural gas power input into the gas-fired boiler (GB) unit at time t. In the formula, η GB represents the energy conversion efficiency of the gas-fired boiler (GB) unit; Q GB represents the heat output power of the gas-fired boiler (GB) unit at time t; and P GB represents the natural gas power input into the gas-fired boiler (GB) unit at time t. In the formula, η GB represents the energy conversion efficiency of the gas-fired boiler (GB) unit; Q GB represents the heat output power of the gas-fired boiler (GB) unit at time t; and P GB represents the natural gas power input into the gas-fired boiler (GB) unit at time t.

[0037] Step 13: constructing a carbon capture model according to the natural gas power input into the combined heat and power (CHP) unit and the natural gas power input into the gas-fired boiler (GB) unit, and the formula is as follows:

[0038]

[0039] In the formula, C represents the carbon emission amount captured by the carbon capture unit at time t; e represents the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit; and f represents the carbon capture rate of the carbon capture unit at time t. In the formula, C represents the carbon emission amount captured by the carbon capture unit at time t; e represents the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit; and f represents the carbon capture rate of the carbon capture unit at time t. g In the formula, C represents the carbon emission amount captured by the carbon capture unit at time t; e represents the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit; and f represents the carbon capture rate of the carbon capture unit at time t. In the formula, C represents the carbon emission amount captured by the carbon capture unit at time t; e represents the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit; and f represents the carbon capture rate of the carbon capture unit at time t. In the formula, C represents the carbon emission amount captured by the carbon capture unit at time t; e represents the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit; and f represents the carbon capture rate of the carbon capture unit at time t. In the formula, C represents the carbon emission amount captured by the carbon capture unit at time t; e represents the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit; and f represents the carbon capture rate of the carbon capture unit at time t. In the formula, C represents the carbon emission amount captured by the carbon capture unit at time t; e represents the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit; and f represents the carbon capture rate of the carbon capture unit at time t. In the formula, C represents the carbon emission amount captured by the carbon capture unit at time t; e represents the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit; and f represents the carbon capture rate of the carbon capture unit at time t. In the formula, C represents the carbon emission amount captured by the carbon capture unit at time t; e represents the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit; and f represents the carbon capture rate of the carbon capture unit at time t.

[0040] The carbon capture energy consumption includes the fixed energy consumption and the operating energy consumption, and is related to the carbon capture amount, and a carbon capture model as shown in formula (3) can be constructed.

[0041] Step 14: constructing a load model, and the formula is as follows:

[0042]

[0043] In the formula, Q includes P and H, P represents the electric load, and H represents the heat load. respectively represent the load before and after the demand response at time t. is the time-shiftable load at time t, positive for shifting in and negative for shifting out; is the interruptible load at time t; is the upper and lower limit of the time-shiftable load at time t, respectively; T is the total number of time periods in a dispatch cycle, which is 24; is the maximum value of the interruptible load at time t.

[0044] The load includes electrical load and thermal load, which are further divided into fixed load and flexible load. The fixed load does not participate in demand response, and the electrical flexible load is divided into time-shiftable load and interruptible load according to the characteristics of demand response, thus constructing the load model shown in equation (4). The time-shiftable load is characterized by a constant total electricity consumption within a 24-hour dispatch cycle, and the electricity consumption time can be flexibly changed. The interruptible load refers to the part of the load that can be interrupted by the user during periods of load pressure or high energy prices, thereby reducing the energy supply pressure, thus constructing the time-shiftable load model shown in equation (5).

[0045] Step 15, according to the daily driving mileage of the electric vehicle EV, the electric vehicle EV load demand model P is constructed EV,t .

[0046] Step 151, according to the standard deviation and mean value of the daily driving mileage of the electric vehicle EV, the daily driving mileage of the electric vehicle EV is obtained, and the formula is as follows:

[0047]

[0048] In the formula, s i is the daily driving mileage of the electric vehicle; σ s , μ s are the standard deviation and mean value of the daily driving mileage of the electric vehicle, respectively.

[0049] Step 152, according to the daily driving mileage and the cruising range of the electric vehicle EV, the state of charge at the end of charging is obtained, and the formula is as follows:

[0050]

[0051] In the formula, S i is the cruising range; S OC,i is the state of charge at the end of charging;

[0052] Step 153, according to the charging power of the electric vehicle EV the rated capacity of the battery and the charging efficiency the charging time of the i-th electric vehicle EV is obtained, and the formula is as follows:

[0053]

[0054] Step 154, superimpose the charging time of each electric vehicle EV to obtain the total load demand P of the electric vehicle charging station EVCS in each period EV,t is:

[0055]

[0056] In the formula: z i is a judgment variable, z i is 1, indicating charging, and 0, indicating no charging; N represents the total number of electric vehicles in the system.

[0057] Electric vehicles EV have the dual characteristics of controllable load and energy storage equipment. Guiding them to participate in demand response can not only smooth the source-end fluctuations brought by high penetration rate of renewable energy, but also promote renewable energy grid connection and consumption and improve system operation economy. The Monte Carlo simulation method is used to simulate the unordered charging demand of electric vehicles, and the results are used as the initial input of the double-layer optimization model. In the iterative optimization process of the model, the electric vehicle charging station EVCS optimizes the charging and discharging plan according to the price signal. According to the statistics of the United States National Highway Traffic Safety Administration, the trip termination time of home electric vehicles should conform to the expression of segmented logarithmic normal distribution:

[0058]

[0059] In the formula: μ1, μ2 are the average values of the arrival and departure times of electric vehicles at the charging station; σ1, σ2 are the standard deviations of the arrival and departure times of electric vehicles at the charging station.

[0060] According to formula (10), the daily load demand of electric vehicle charging is related to the daily driving distance and charging time. Generally speaking, the daily driving distance of electric vehicles is considered to follow a normal distribution, and formula (6) can be obtained by derivation.

[0061] Step 16, according to the electric heating conversion efficiency of the electric boiler EB unit and the power consumption of the electric boiler EB unit, the model of the electric boiler EB unit is constructed, that is, the heat output power of the electric boiler EB unit, and the formula is as follows:

[0062]

[0063] In the formula, is the power consumption of the electric boiler EB unit at time t; H EB,t is the heat output power of the electric boiler EB unit at time t; H EB is the rated heating power of the electric boiler EB unit; η EB is the electric heating conversion efficiency of the electric boiler EB unit.

[0064] Step 17, construct the energy storage model, and the formula is as follows:

[0065]

[0066] S∈{ESS, HSS} is the set of electrical and thermal energy storage; is the energy storage amount of the energy storage device at time t; is the energy storage state at time t, 1 represents the energy storage state; is the energy release state at time t, 1 represents the energy release state; is the energy storage device charging and discharging power at time t, respectively; is the energy storage device charging and discharging efficiency, respectively; is the upper and lower limit of the energy storage capacity of the energy storage device, respectively; is the energy storage amount at the beginning and end of a scheduling period, respectively; is the upper limit of the energy storage device charging and discharging power, respectively.

[0067] Formulas (1), (2), and (3) are the ESP model of the energy service provider, and formulas (4) to (9) and (11) to (15) are the UA model of the user aggregator.

[0068] Step 2, construct a ladder-type carbon trading model and a green certificate trading model.

[0069] As shown in Figure 3 , it is a schematic diagram of the carbon trading and green certificate trading mechanism. The carbon trading mechanism is a market-oriented control tool established by the government to achieve emission reduction targets, and its core lies in the allocation and trading of carbon emission rights. In the traditional mode, carbon quotas are uniformly fixed at a price, resulting in poor emission reduction constraints. The innovative ladder-type carbon pricing mechanism divides multiple emission intervals and implements a ladder pricing policy for excess emissions, significantly improving the emission reduction incentive effect and market regulation ability. To coordinate with the renewable energy development policy, China has established a green certificate trading mechanism. Under this mechanism, new energy power generation enterprises can obtain corresponding green certificates based on their power generation capacity. If the amount of green certificates held by an enterprise exceeds the legal quota, the excess part can enter the trading market to obtain income; enterprises that do not meet the standard need to purchase the difference through the market, otherwise they will be subject to penalties. This system design not only guarantees the realization of renewable energy consumption targets, but also optimizes resource allocation efficiency through market-oriented means.

[0070] Carbon trading mechanism is a market-oriented control tool established by the government to achieve emission reduction targets, and its core is the allocation and trading of carbon emission rights. In the traditional mode, carbon quota adopts uniform fixed pricing, which leads to poor emission reduction constraint effect. The innovative step-by-step carbon pricing mechanism significantly improves the emission reduction incentive effect and market regulation ability by dividing multiple emission intervals and implementing the step-by-step premium policy for excess emissions. To coordinate with the renewable energy development policy, China has established a green certificate trading mechanism. Under this mechanism, new energy power generation enterprises can obtain corresponding green certificates according to the power generation capacity. If the green certificate holding amount of the enterprise exceeds the statutory quota, the excess part can enter the trading market to obtain income; enterprises that do not meet the standard need to purchase the difference through the market, otherwise they will bear the penalty. This system design not only guarantees the realization of renewable energy consumption target, but also optimizes the resource allocation efficiency through market-oriented means.

[0071] Step 21, constructing a step-by-step carbon trading model.

[0072] Step 211, obtaining the carbon emission quota of the energy service provider ESP according to the electric output power and the thermal output power of the combined heat and power CHP unit, the thermal output power of the gas boiler GB unit, and the electricity purchased from the power grid, and the formula is as follows:

[0073]

[0074] In the formula, E c,t is the carbon emission quota of the energy service provider ESP at time t; φ is the conversion factor of the electric power and the thermal power of the combined heat and power CHP unit; δ h is the carbon emission quota coefficient of the gas unit per unit; δ grid is the carbon emission quota coefficient of the unit electric energy purchased from the power grid; is the electric power purchased from the power grid by the ESP at time t; and are the electric output power and the thermal output power of the combined heat and power CHP unit, respectively, obtained from step 11; is the thermal output power of the gas boiler GB unit, obtained from step 12.

[0075] The baseline method is used to determine the free carbon emission quota.

[0076] Step 212, obtaining the actual carbon emission amount of the energy service provider ESP according to the carbon emission amount captured by the carbon capture unit, and the formula is as follows:

[0077]

[0078] In the formula, is the actual carbon emission amount of the energy service provider ESP at time t; e gThe carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas boiler (GB) unit is consistent with step 13;e grid The parameter of the carbon emission intensity per unit of electricity purchased from the power grid; The natural gas power input into the combined heat and power (CHP) unit at time t is consistent with step 11; The natural gas power input into the gas boiler (GB) unit at time t is consistent with step 12; The carbon emission amount captured by the carbon capture unit is obtained from step 13.

[0079] Step 213, subtract the actual carbon emission amount of the energy service provider (ESP) obtained from step 212 from the carbon emission quota E of the energy service provider (ESP) obtained from step 211 c,t to obtain the carbon quota E that needs to be traded at time t. VPP,t The formula is as follows:

[0080]

[0081] Step 214, superimpose the carbon quota E that needs to be traded at time t obtained from step 213 VPP,t to obtain the total carbon emission quota E that needs to be traded within the dispatching period T. VPP The formula is as follows:

[0082]

[0083] Step 215, according to the benchmark price, the step increase rate of the price, the upper limit of the carbon emission interval, and the price of selling the excess carbon emission quota, obtain the step-type carbon trading cost , that is, the step-type carbon trading model, the formula is as follows:

[0084]

[0085] In the formula, is the benchmark price; α is the step increase rate of the price; ν is the upper limit of the carbon emission interval; is the price of selling the excess carbon emission quota.

[0086] Step 22, construct a green certificate trading model.

[0087] Step 221, according to the actual consumption power of photovoltaic power generation and the actual consumption power of wind power generation obtain the number of green certificates N obtained by the renewable energy power generation unit at time t: own,t

[0088]

[0089] ​Step 222, according to the actual thermal power generation of the system The green certificate quota N required by the thermal power generator set at time t is derived gov,t The total green certificate quantity N required for trading is obtained by superimposing the green certificate quantity required for trading in each period green :

[0090]

[0091] The actual thermal power generation in this step is the electric output power of the combined heat and power CHP unit obtained in step 11, because the thermal power unit of the present application includes a combined heat and power CHP unit and a gas boiler GB unit, but the gas boiler GB unit does not generate electricity, i.e. does not generate power generation, so the actual thermal power generation only needs to consider the power generation of the combined heat and power CHP unit.

[0092] Step 223, according to the green certificate trading selling price purchase price and penalty price get the green certificate trading cost i.e. the green certificate trading model, the formula is as follows:

[0093]

[0094] Step 3, according to carbon emissions, clean energy consumption and demand response, a multi-factor dynamic pricing mechanism is constructed.

[0095] Step 31, according to the electric load demand response, the electric power consumed by the electric boiler EB unit at time t, and the charging and discharging of the electric vehicle charging station EVCS and the electric energy storage device, the equivalent electric load is obtained; according to the heat demand response, the charging and discharging of the heat storage device and the heat of the electric boiler EB unit at time t, the equivalent heat load is obtained, the formula is as follows:

[0096]

[0097] In the formula, is the equivalent electric load of the demand side; is the electric load after demand response at time t, which is obtained by constructing step 14; represents the power consumption of the electric boiler EB unit at time t; represents the charging power of the electric vehicle charging station EVCS at time t; represents the discharging power of the electric vehicle charging station EVCS at time t; represents the electric energy storage state at time t, which is obtained by step 17; represents the charging power of the electric energy storage unit at time t; represents the discharging state of the electric energy storage at time t, which is obtained by step 17; This represents the discharge power of the energy storage unit at time t; This is the equivalent heat load on the demand side; The heat load after the demand response at time t is obtained from step 14; The thermal energy storage state at time t is obtained from step 17; The charging power of the thermal energy storage device is obtained from step 17; This indicates the energy release state of the thermal energy storage device at time t; The energy release power of the thermal energy storage device at time t is obtained from step 17; H EB,t The heat output power of the electric boiler EB unit is obtained from step 16.

[0098] The Intelligent Joint Dispatch Center (IJSC) considers the capacity of energy service providers (ESPs), carbon trading mechanisms, green certificate trading mechanisms, demand response levels, and demand-side equivalent load requirements to set energy sales prices corresponding to each production mode. The demand-side equivalent load is calculated as shown in equations (25) and (26).

[0099] Step 32: Construct the carbon emission penalty price, using the following formula:

[0100]

[0101] In the formula: E represents the carbon emission penalty price for electricity generated by a combined heat and power (CHP) unit at time t. VPP,t The carbon allowance that needs to be traded at time t is obtained from step S213; The benchmark price is α, the step growth rate of the price is α, and the upper limit of the carbon emission interval is υ, consistent with step 215. The efficiency of the gas-to-electricity conversion of the combined heat and power (CHP) unit is consistent with step 11; e g The carbon emission intensity per unit energy for CHP combined heat and power units and GB gas-fired boiler units is consistent with step 13; φ and δ h These represent the conversion coefficients between electrical and thermal power of the CHP cogeneration unit and the carbon emission quota coefficient per unit of the gas turbine unit, respectively, consistent with step 211. The subscript 's' in the text includes CHP and GB, that is... The carbon emission penalty price for the heat energy generated by the combined heat and power (CHP) unit and the gas-fired boiler (GB) unit at time t, respectively. include and This indicates the gas-to-heat conversion efficiency of a combined heat and power (CHP) unit. χ represents the energy conversion efficiency of a gas-fired boiler GB unit. gind,t Let δ be the carbon emission penalty price of grid electricity at time t; gridThe carbon emission quota coefficient for the unit electric energy purchased from the power grid is consistent with step 211; e grid The parameter of carbon emission intensity for each unit of electric power purchased from the power grid is consistent with step 212.

[0102] Under the carbon emission influencing factor, the present application designs a carbon emission penalty price, which is calculated according to the total carbon emission level and the carbon emission intensity. This additional price will dynamically change with the actual carbon emission level of the ESP in each period. This means that the greater the amount of carbon emissions exceeding the carbon quota, the higher the additional price. Since renewable energy power generation has no carbon emissions, the carbon emission penalty price is zero.

[0103] Step 33, construct the green certificate correction price, the formula is as follows:

[0104] Step 331, according to the green certificate correction price coefficient δ g , the green certificate correction price at time t is obtained The formula is as follows:

[0105]

[0106] In the formula, is the green certificate correction price at time t; δ g is the green certificate correction price coefficient; N gov,t is the green certificate quota N required by the thermal power generator set at time t, which is obtained from step 222; N gov,t is the green certificate quota N required by the thermal power generator set at time t, which is obtained from step 222; N own,t is the green certificate quota N required by the thermal power generator set at time t, which is obtained from step 222.

[0107] Under the new energy consumption factor, the present application designs a green certificate correction price. This correction price will dynamically change with the quantity relationship between the green certificates obtained by the energy service provider ESP in each period and the quotas that need to be met. This means that the greater the consumption power of clean energy, the greater its green certificate correction price, and the green certificate correction price of non-clean energy is zero.

[0108] Step 332, according to the equivalent power load and the power of renewable energy at time t, the basic price χ RE,t of renewable energy at time t is obtained, the formula is as follows:

[0109]

[0110] In the formula, represents the equivalent power load of the demand side, which is obtained from step 31; is the valley price of the power grid; P WT,t , P PV,tThe wind power and the photovoltaic predicted power at the time t are respectively. The renewable energy in the application includes the wind energy in the wind power generation and the solar energy in the photovoltaic generation, that is, the power of the renewable energy includes the power provided by the wind power and the power provided by the photovoltaic.

[0111] In step 34, the demand response correction price is obtained according to the interruptible load and the load before the demand response, and the formula is as follows:

[0112]

[0113] In the formula, The correction price of the interruptible electric load at the time t is obtained by step 14. The correction price of the interruptible thermal load at the time t is obtained by step 14. The interruptible electric load at the time t is obtained by step 14. The interruptible thermal load at the time t is obtained by step 14. The electric load before the demand response at the time t is obtained by step 14. The thermal load before the demand response at the time t is obtained by step 14.

[0114] In the application, the demand response correction price is designed under the demand response factor. When the energy price is high or the system energy supply pressure is large, the demand side can choose to interrupt a part of the load. The third-party pricing platform reduces the energy price according to the interruptible load amount. When the interruptible load amount is larger, the demand response correction price is larger.

[0115] In step 35, the pricing of the sales price of each energy is obtained according to the energy cost price, the carbon emission penalty price, the green certificate correction price and the demand response correction price, and the formula is as follows:

[0116]

[0117] In the formula, p RE,t The new energy sales price at the time t is obtained by step 14. gird,t The sales price of the electric power transmitted by the IJSC to the demand side at the time t is obtained by step 14. The time-of-use electricity price of the electricity purchased by the ESP from the power grid at the time t is obtained by step 14. CHP,t The sales price of the electric power transmitted by the IJSC to the demand side at the time t is obtained by step 14. g The natural gas price is obtained by step 14. gas The low calorific value of the natural gas is obtained by step 14. CHP,t GB,t The sales prices of the heat transmitted by the IJSC to the demand side at the time t by the CHP unit and the GB unit are obtained by step 14.

[0118] As Figure 4 ​As shown, it is a schematic diagram of the multi-factor dynamic pricing strategy of the application, and the pricing of the sales price of each energy can be obtained according to the energy cost price, the carbon emission penalty price, the green certificate correction price and the demand response correction price.

[0119] Step 4, a supply-demand joint scheduling model is constructed, a smart joint scheduling center IJSC is established to execute dynamic pricing and joint scheduling of a virtual power plant VPP, a CPLEX is used to solve a mixed integer linear programming MILP model, and an optimized scheduling result is obtained.

[0120] As shown in Figure 5 As shown, it is an information interaction relationship between an energy service provider ESP and a demand-side user aggregator UA in a virtual power plant VPP and a smart joint scheduling center IJSC. Specifically, the smart joint scheduling center IJSC serves as an information hub and is connected with the energy service provider ESP and the user aggregator UA. The smart joint scheduling center IJSC calculates the sales price of various energies according to a pricing mechanism and publishes it to the user aggregator UA. The user aggregator UA participates in demand response DR to minimize energy procurement costs, adjusts the operation of an electric boiler EB unit and an energy storage device, optimizes the energy purchasing strategy and the charging and discharging strategy of an electric vehicle charging station EVCS, and feeds back the adjusted load demand and total cost to the smart joint scheduling center IJSC. The smart joint scheduling center IJSC then conveys the energy procurement demand to the energy service provider ESP, the energy service provider ESP formulates a production plan and returns it to the smart joint scheduling center IJSC, aiming to minimize the operation cost. Then, the smart joint scheduling center IJSC recalculates the new energy sales price and notifies the UA, and enters the next round of iteration until the end. Due to the dynamic interaction between the energy price and the load demand in each period, the economic benefits of the energy service provider ESP and the user aggregator UA are in conflict, so a reasonable joint optimization objective function needs to be set to coordinate the interests of all parties. Finally, the smart joint scheduling center IJSC determines the optimal strategy according to the objective function and issues it to the energy service provider ESP and the user aggregator UA for execution. The specific steps of step 4 are as follows:

[0121] Step 41, an energy service provider ESP optimization model is constructed according to the carbon trading cost and the green certificate trading cost.

[0122] Step 411, the compensation cost of interruptible load The cost of the energy service provider ESP to purchase electricity from the power grid The operation cost of a combined heat and power unit The operation cost of a gas boiler unit Carbon trading cost Green certificate trading cost The cost of carbon dioxide transportation and storage The penalty cost of renewable energy reduction The cost of purchasing electric energy by the user The cost of purchasing thermal energy The operation cost F1 of the energy service provider ESP is obtained, i.e., the energy service provider ESP optimizes the objective function, as follows:

[0123]

[0124] The carbon trading cost is obtained from step 215; the green certificate trading cost is obtained from step 223; and respectively represent the new energy, the grid power, the power generated by the combined heat and power unit, the heat generated by the combined heat and power unit and the heat generated by the gas boiler unit purchased by the demand side user at time t; σ TS is the transportation and storage price of unit carbon dioxide; is the penalty cost of unit abandoned wind power; is the penalty cost of unit abandoned light power; is the compensation coefficient of interruptible power load.

[0125] Step 412, the constraint condition of the operation cost F1 of the energy service provider ESP is constructed, as follows:

[0126]

[0127]

[0128] wherein, formula (36) is the electric-thermal power balance constraint; formula (37) is the combined heat and power unit constraint; formula (38) is the gas boiler unit constraint; formula (39) is the carbon capture unit constraint; formula (40) is the constraint of purchasing power from the grid; formula (41) is the new energy output constraint; respectively represent the upper and lower limits of the input power of the combined heat and power CHP unit; respectively represent the upper and lower limits of the climbing power of the combined heat and power CHP unit; respectively represent the upper and lower limits of the input power of the gas boiler GB unit; respectively represent the upper and lower limits of the climbing power of the gas boiler GB unit; η CHP,max is the maximum carbon capture rate; is the power purchased from the grid by the energy service provider ESP at time t; is the upper limit of the power purchased from the grid; P s,t , and The subscript s of the subscript s includes wind power WT and photovoltaic PV, is the curtailment power of s at time t; P s,t represents the power prediction value of s at time t; This represents the actual power consumed by time s at time t.

[0129] Step 42: Construct a User Aggregator (UA) optimization model.

[0130] Step 421: Based on the cost of electricity and heat purchased by user aggregator UA from energy service provider ESP and the interruptible power load compensation obtained from demand response, The total cost F2 of the user aggregator (UA) is derived, which is the optimization objective function of the user aggregator (UA), as shown in the following formula:

[0131]

[0132] Step 422: Construct the constraints for the total cost F2 of the user aggregator (UA), as follows:

[0133]

[0134]

[0135] Where, Equation (43) represents the user's electricity and heat energy purchase constraints; Upper limit of power output for CHP units; The upper limit of heating output for CHP units is calculated from the upper limit of electrical output. The upper limit of the heating output of GB units; DR constraints are shown in equation (5); electric boiler constraints are shown in equation (11); energy storage equipment constraints are shown in equations (12-15); equation (44) is the constraint for electric vehicle charging stations. These are the upper limits for the charging and discharging power of EVCS, respectively.

[0136] Step 43: Based on the operating cost F1 of the energy service provider ESP obtained in Step 41 and the total cost F2 of the user aggregator UA obtained in Step 42, construct the joint optimization objective function, as shown in the following formula:

[0137]

[0138] In the formula: F is the objective function of joint optimization.

[0139] Step 44: Use the CPLEX solver to solve the objective function of the joint optimization and obtain the optimal scheduling result.

[0140] like Figure 6 The diagram shows the flowchart for solving the joint optimization model using the CPLEX solver. The specific steps of step 44 are as follows:

[0141] Step 441: With the goal of minimizing the operating cost F1 of the energy service provider ESP, call CPLEX to solve the optimization model of the energy service provider ESP, and pass the result to the Intelligent Joint Dispatch Center (IJSC); the Intelligent Joint Dispatch Center (IJSC) calculates the new selling price according to the pricing mechanism and passes it to the user aggregator UA.

[0142] Step 442: With the goal of minimizing the total cost F2 of the user aggregator UA, call CPLEX to solve the optimization model of the user aggregator UA, obtain the electricity purchase plan and the total cost F2 of the user aggregator UA, and feed it back to the Intelligent Joint Dispatch Center (IJSC); the Intelligent Joint Dispatch Center (IJSC) publishes the total electricity purchase, heat purchase, and total energy purchase cost obtained from the demand-side optimization to the energy service provider ESP; the energy service provider ESP calls CPLEX to solve the optimization model with the goal of minimizing the operating cost, and feeds back the optimized production plan and the minimum operating cost F1 to the Intelligent Joint Dispatch Center (IJSC);

[0143] Step 443: The Intelligent Joint Scheduling Center (IJSC) calculates the joint optimization objective function value in this iteration based on the results of step 442;

[0144] Step 444: Determine if the iteration termination condition is met. The principle for termination is that the current iteration count exceeds the preset maximum iteration count. If this condition is met, the iteration process stops; otherwise, the Intelligent Joint Dispatch Center (IJSC) will calculate the new sales price to be passed to the user aggregator UA based on the optimization results of the energy service provider ESP and the user aggregator UA in the previous iteration, and then return to step 442 until the iteration terminates.

[0145] Step 445: Obtain the final joint optimal solution and output the scheduling plan.

[0146] Example: The following virtual power plant framework includes a wind turbine generator set, a photovoltaic generator set, a combined heat and power (CHP) unit, a gas-fired boiler (GB) unit, a carbon capture unit, an electric boiler (EB), a thermal storage tank, a battery, and 50 electric vehicles; the gas-to-electricity conversion efficiency of the CHP unit... Gas-to-heat efficiency The electrical power to heat power conversion coefficient φ of the CHP cogeneration unit is 1.67, and the gas-to-heat conversion efficiency of the GB gas boiler unit is 0.35 and 0.45 respectively. The carbon emission intensity per unit energy of the gas turbine unit is 0.45. g The energy consumption per unit of CO2 captured by the carbon capture unit is 0.39 kg / kWh. The electrothermal conversion efficiency η of the electric boiler EB unit is 0.269 kWh / kg. EB It is 0.99. The rated capacity of each electric vehicle. is 60 kWh, charging efficiency is 0.9, charging power is 15 kw, cruising range S i is 450 km. Charging and discharging efficiency of the battery and are both 0.8, charging and discharging efficiency of the heat storage tank and are both 0.8. Unit carbon emission quota coefficient δ of the gas turbine unit h is 0.367 kg / kWh, unit carbon emission quota coefficient δ of the power grid grid and carbon emission intensity e grid are 0.798 kg / kWh and 0.889 kg / kWh respectively, price of the sold excess carbon emission quota is 0.30 yuan / kg, carbon trading benchmark price is 0.20 yuan / kg, step-up rate α of the carbon trading price is 0.2, carbon emission interval v is 100 kg. Selling and buying price of the green certificate and are 100 yuan per book, penalty price is 200 yuan per book. Natural gas price λ g is 1.26 yuan / m 3 , heat value Q of the natural gas gas is 9.97 kWh / m 3 , and the steps of the example are further illustrated. Figure 7 is the original load and renewable energy day-ahead prediction power curve of the present application.

[0147] Step 1, constructing a model of the energy service provider ESP and the user aggregator UA in the virtual power plant;

[0148] Step 11, according to the input natural gas power of the combined heat and power CHP unit, the gas-to-electricity efficiency of the combined heat and power CHP unit is 0.35 and the gas-to-heat efficiency of the combined heat and power CHP unit is 0.45, the electric output power and the heat output power of the combined heat and power CHP unit are obtained by substituting into formula (1) respectively, and the formula is as follows:

[0149]

[0150] Step 12, according to the input natural gas power of the gas boiler GB unit, the gas-to-heat efficiency of the gas boiler GB unit is 0.45, the heat output power of the gas boiler GB unit is obtained by substituting into formula (2), and the formula is as follows:

[0151]

[0152] Step 13, according to the input natural gas power of the combined heat and power (CHP) unit and the input natural gas power of the gas boiler (GB) unit, the carbon emission intensity e of the gas unit is 0.39 kg / kWh g , the energy consumption of the carbon capture unit to capture unit CO2 is 0.269 kWh / kg , formula (3) is brought into, the carbon capture model is constructed,

[0153] Step 14, a load model is constructed:

[0154] Step 15, according to the daily driving mileage of the electric vehicle (EV), an electric vehicle (EV) load demand model P is constructed EV,t .

[0155] Step 151, according to the standard deviation and mean value of the daily driving mileage of the electric vehicle (EV), the daily driving mileage of the electric vehicle (EV) is obtained:

[0156] Step 152, according to the daily driving mileage and the cruising range S of the electric vehicle (EV) i 450km, formula (7) is brought into, the state of charge at the end of charging is obtained:

[0157] Step 153, according to the charging power of the electric vehicle (EV) 15kw, the rated capacity of the battery 60kWh and the charging efficiency 0.9, formula (8) is brought into, the charging time of the i-th electric vehicle (EV) is obtained:

[0158] Step 154, the charging time of each electric vehicle (EV) is superimposed to obtain the total load demand P of the electric vehicle charging station (EVCS) in each period EV,t :

[0159] Step 16, according to the electric-thermal conversion efficiency η of the electric boiler (EB) unit EB 0.99, the power consumption of the electric boiler (EB) unit at time t formula (11) is brought into to obtain the heat output power H of the electric boiler (EB) unit at time t EB,t :

[0160] Step 17, according to the charging and discharging efficiency of the battery and 0.8, the charging and discharging efficiency of the thermal storage tank and 0.8, formula (12) to (15) is brought into to construct an energy storage model, as follows:

[0161] Step 2, a step-type carbon trading model and a green certificate trading model are constructed.

[0162] Step 21: Construct a tiered carbon trading model.

[0163] Step 211: Based on the electrical and thermal output power of the CHP cogeneration unit, the thermal output power of the GB gas-fired boiler unit, the electricity purchased from the grid, the electrical-to-thermal power conversion coefficient φ of the CHP cogeneration unit being 1.67, and the unit carbon emission quota coefficient δ of the gas-fired unit... h The carbon emission allowance coefficient δ per unit of electricity in the power grid is 0.367 kg / kWh. grid The value is 0.798 kg / kWh. Substituting this into formula (16), we obtain the carbon emission allowance for energy service provider ESP:

[0164] Step 212: Based on the carbon emissions captured by the carbon capture unit and the carbon emission intensity per unit energy of the gas turbine unit, e g The carbon emission intensity per unit of electricity in the power grid is 0.39 kg / kWh. grid The value is 0.889 kg / kWh. Substituting this into formula (17), we obtain the actual carbon emissions of energy service provider ESP:

[0165] Step 213: Calculate the actual carbon emissions of energy service provider ESP obtained in step 212. And the carbon emission allowance E obtained from the energy service provider ESP in step 211 c,t Subtracting the two, we get the carbon allowance E that needs to be traded at time t. VPP,t :

[0166] Step 214: Take the carbon allowance E that needs to be traded at time t obtained in step 213. VPP,t By superimposing these values, we obtain the total carbon emission allowance E that needs to be traded within the scheduling period T. VPP :

[0167] Step 215: Based on the price of selling surplus carbon emission allowances The benchmark price for carbon trading is 0.3 yuan / kg. With a price of 0.2 yuan / kg, a tiered growth rate α for carbon trading prices of 0.2, and a carbon emission interval ν of 100 kg, substituting these values ​​into formula (20) yields the tiered carbon trading model.

[0168] Step 22: Construct a green certificate trading model.

[0169] Step 221: Based on the actual photovoltaic power consumption capacity Actual power consumption of wind power generation The number of green certificates N obtained by the renewable energy generating unit at time t is calculated. own,t :

[0170] Step 222: Based on the actual thermal power generation of the system The green certificate quota N required by the thermal power generating unit at time t is obtained gov,t The total green certificate quantity N required for trading is obtained by adding the green certificate quantity required for trading in each period green :

[0171] Step 223, according to the selling and buying price of the green certificate And 100 yuan per book, the penalty price 200 yuan per book, bring into formula (24), the green certificate quantity N required for trading green , calculate the green certificate transaction cost

[0172] Step 3, according to carbon emission, clean energy consumption and demand response, a multi-factor dynamic pricing mechanism is constructed.

[0173] Step 31, according to the electric load demand response, the electric power consumed by the electric boiler EB unit at time t, and the charging and discharging of the electric vehicle charging station EVCS and the electricity storage device, the equivalent electric load is obtained; according to the heat demand response, the charging and discharging of the heat storage device and the heat of the electric boiler EB unit at time t, the equivalent heat load is obtained:

[0174] Step 32, the carbon trading benchmark price 0.20 yuan / kg, the step increase rate α of the carbon trading price is 0.2, and the carbon emission interval v is 100 kg. Into formulas (27) to (29), the carbon emission penalty price is constructed:

[0175] Step 33, the green certificate correction price is constructed, and the formula is as follows:

[0176] Step 331, according to the green certificate correction price coefficient δ g , the green certificate correction price at time t is obtained

[0177] Step 332, according to the equivalent electric load and the power of renewable energy at time t, the basic price χ of renewable energy at time t is obtained: RE,t

[0178] Step 34, according to the interruptible load and the load before demand response, the demand response correction price is obtained:

[0179] Step 35, according to the energy cost price, the carbon emission penalty price, the green certificate correction price, the demand response correction price, the natural gas price λ g 1.26 yuan / m 3 , the natural gas heat value Q gas 9.97 kWh / m 3 , the gas-to-electricity efficiency of combined heat and power CHP unit and the gas-to-heat efficiency​ The corresponding figures are 0.35 and 0.45, respectively, for the gas-to-heat efficiency of GB units for gas-fired boilers. Substituting 0.45 into formula (33), we obtain the pricing for each type of energy source:

[0180] Step 4: Construct a supply and demand joint scheduling model, establish an Intelligent Joint Scheduling Center (IJSC) to execute dynamic pricing and joint scheduling of virtual power plants (VPPs), and use CPLEX to solve the mixed integer linear programming (MILP) model to obtain the optimized scheduling results.

[0181] Step 41: Based on carbon trading costs and green certificate trading costs, construct an ESP optimization model for energy service providers.

[0182] Step 411: Based on the compensation cost of interruptible loads The cost of ESP, an energy service provider, purchasing electricity from the grid Operating costs of combined heat and power units Operating costs of gas-fired boiler units Carbon trading costs Green certificate transaction costs Costs of transporting and storing carbon dioxide Penalty costs of renewable energy cuts UA's cost of purchasing electricity and the cost of purchasing heat energy The cost of transporting and storing a unit of carbon dioxide σ TS The penalty cost is 0.005 yuan / kg per unit of wind curtailment power. The penalty cost is 0.20 yuan / kWh per unit of curtailed solar power. The compensation coefficient for interruptible power load is 0.20 yuan / kWh. The cost is 0.35 yuan / kWh. Substituting this into formula (35), we obtain the operating cost F1 of the energy service provider ESP.

[0183] Step 412: Construct the constraints for the operating cost F1 of the energy service provider ESP:

[0184] η CHP,max The maximum carbon capture rate is 0.9.

[0185] Step 42: Construct a User Aggregator (UA) optimization model.

[0186] Step 421: Based on the cost of electricity and heat purchased by user aggregator UA from energy service provider ESP and the interruptible power load compensation obtained from demand response, The total cost F2 of the user aggregator (UA) is derived as follows:

[0187] Step 422: Construct the constraints for the total cost F2 of the user aggregator (UA):

[0188] Step 43, according to the operation cost F1 of the energy service provider ESP obtained in step 41 and the total cost F2 of the user aggregator UA obtained in step 42, a joint optimization objective function is constructed:

[0189] Step 44, the joint optimization objective function is solved by using a CPLEX solver to obtain an optimal scheduling result.

[0190] The final result of the embodiment is that the pricing results of each energy are as shown in Figure 8 The power balance diagram of the system is as shown in Figure 9 Under the multi-factor differentiated pricing strategy designed in the application, the user can influence the pricing of energy by optimizing his own energy purchasing strategy. As shown in the pricing result diagram, the prices of various types of electric energy and thermal energy are differentiated, and the sales price of renewable energy provided by the electric power is the lowest among the prices of three different sources of electric energy. As shown in Figure 9 It can be seen that the pricing strategy of the application gives the user the power to choose different types of energy, which is conducive to guiding the demand side users and electric vehicle charging stations to preferentially consume renewable energy. When renewable energy cannot meet the load demand, the energy with a lower price among other energy types is selected in turn. The introduction of the demand response mechanism enables the demand side to appropriately transfer the electric load and the thermal load to the period when the energy price is usually low, and interrupt part of the load to relieve the pressure when the energy supply is under pressure, stabilize the load peak-valley curve, and promote the economic operation of the system. On the other hand, the electric vehicle charging station not only has the load characteristics, but also has the energy storage characteristics. As shown in Figure 9 In the optimization model, the scheduling center guides the electric vehicle charging station to schedule the charging demand to the period when the energy price is low through the price signal, and releases the electric energy in the period when the energy supply is under pressure and the energy price is high, thereby relieving the energy supply pressure of the system. The application promotes the consumption of renewable energy, reduces the dependence on non-clean units, thereby reducing the carbon emissions and the energy purchasing cost of the demand side.

[0191] In addition to the above embodiment, the application can have other implementation manners, and any technical solution formed by equivalent substitution or equivalent transformation falls within the protection scope of the application.

Claims

1. A virtual power plant low-carbon joint scheduling method based on a multi-factor dynamic pricing strategy, characterized in that: It comprises the following steps: Step 1, constructing a model of energy service providers and user aggregators in a virtual power plant; Step 2, constructing a step-type carbon trading model and a green certificate trading model; Step 3, constructing a multi-factor dynamic pricing mechanism according to carbon emissions, clean energy consumption and demand response; Step 4, constructing a supply-demand joint scheduling model, establishing an intelligent joint scheduling center to execute dynamic pricing and joint scheduling of the virtual power plant, using CPLEX to solve the mixed integer linear programming model to obtain the optimized scheduling result.

2. The method of claim 1, wherein the multi-factor dynamic pricing strategy based virtual power plant low-carbon joint dispatching method is characterized in that: The specific steps of step 1 are as follows: Step 11, according to the input natural gas power of the combined heat and power unit, the electric output power and the heat output power of the combined heat and power unit are obtained respectively, and the formula is as follows: In the formula, Pgas(t) represents the natural gas power of the input of the combined heat and power CHP unit at time t; ηe(t) and ηh(t) respectively represent the gas-to-electricity and gas-to-heat efficiencies of the combined heat and power CHP unit; and PCHP(t) and QCHP(t) respectively represent the electric output power and the heat output power of the combined heat and power CHP unit at time t; Step 12, according to the input natural gas power of the gas boiler GB unit, the heat output power of the gas boiler GB unit is obtained, and the formula is as follows: wherein represents the energy conversion efficiency of the gas boiler GB unit; represents the thermal output power of the gas boiler GB unit at time t; represents the natural gas power input to the gas boiler GB unit at time t; Step 13, according to the input natural gas power of the combined heat and power CHP unit and the input natural gas power of the gas boiler GB unit, a carbon capture model is constructed, and the formula is as follows: wherein, is the carbon emission captured by the carbon capture unit at time t; e g is the carbon emission intensity per unit energy of the combined heat and power (CHP) unit and the gas boiler (GB) unit; is the carbon capture rate of the carbon capture unit at time t; is the operation energy consumption of the carbon capture unit at time t; is the energy consumption per unit of CO2 captured; is the fixed energy consumption of the carbon capture unit; is the total energy consumption of the carbon capture unit at time t; is the net output of the combined heat and power (CHP) unit at time t; Step 14, constructing a load model, the formula is as follows: In the formula, Q includes P and H, P represents electric load, and H represents heat load; respectively represent load before and after demand response at time t; is the time-shiftable load at time t; is the interruptible load at time t; respectively represent upper and lower limits of the time-shiftable load at time t; T is the total number of time periods in a scheduling period; is the maximum value of the interruptible load at time t; Step 15, constructing an electric vehicle EV load demand model P according to the daily driving mileage of the electric vehicle EV EV,t The formula is as follows: wherein z i is a judgment variable; T ch,i charging time of the i-th electric vehicle EV; Step 16, according to the electric heating conversion efficiency and power consumption of the electric boiler unit, the heat output power of the electric boiler unit is constructed, and the formula is as follows: In the formula, is the power consumption of the electric boiler EB unit at time t; H EB,t is the heat output of the electric boiler EB unit at time t; H EB is the rated heat supply power of the electric boiler EB unit; η EB is the electric-heat conversion efficiency of the electric boiler EB unit; Step 17, constructing an energy storage model, the formula is as follows: In the formula: S ∈ {ESS, HSS} is the set of electric and thermal energy storage; is the energy storage amount of the energy storage device at time t; is the energy storage state at time t; represents the energy release state at time t; are the charging and discharging power of the energy storage device at time t, respectively; are the charging and discharging efficiency of the energy storage device, respectively; are the upper and lower limits of the storage capacity of the energy storage device, respectively; are the energy storage amount at the beginning and end of a scheduling period, respectively; are the upper limits of the charging and discharging power of the energy storage device, respectively.

3. The method of claim 2, wherein the multi-factor dynamic pricing strategy based virtual power plant low-carbon joint dispatching method is characterized by: The specific steps of step 2 are as follows: Step 21, constructing a step-type carbon trading model; Step 211, according to the electric output power and the heat output power of the combined heat and power unit, the heat output power of the gas boiler unit and the electricity purchased in the power grid, the carbon emission quota of the energy service provider is obtained, and the formula is as follows: wherein E c,t is the carbon emission quota of the energy service provider (ESP) at time t; φ is the conversion factor of the electric power and the thermal power of the combined heat and power (CHP) unit; δ h is the carbon emission quota coefficient of the gas unit per unit; δ grid is the carbon emission quota coefficient of the purchased electric energy per unit from the power grid; is the electric power purchased from the power grid by the ESP at time t; Step 212, according to the amount of carbon emissions captured by the carbon capture unit, the actual carbon emissions of the energy service provider are obtained, and the formula is as follows: In the formula, is the actual carbon emission of the energy service provider ESP at time t; e grid is a parameter of the carbon emission intensity of each unit of electricity purchased from the power grid; Step 213, subtracting the carbon emission quota E of the energy service provider obtained in step 211 from the actual carbon emission amount of the energy service provider obtained in step 212, to obtain the carbon quota E that needs to be traded at time t c,t VPP,t , the formula is as follows:​​ Step 214, obtaining the carbon quota E needed for transaction at time t from step 213 VPP,t Superimposing, the total carbon emission quota E needed for transaction in the scheduling period T is obtained VPP The formula is as follows: Step 215, obtaining the ladder-type carbon trading cost according to the benchmark price, the ladder increasing rate of the price, the upper limit of the carbon emission interval, and the price of selling the excess carbon emission quota The formula is as follows: In the formula, c is the benchmark price, α is the step-up rate of the price, ν is the upper limit of the carbon emission interval, and c0 is the price of selling excess carbon emission quota; Step 22, constructing a green certificate trading model; Step 221, according to the actual consumption power of photovoltaic power generation and the actual consumption power of wind power generation The number N of green certificates obtained by the renewable energy generator set at time t is derived own,t : Step 222, according to the actual thermal power generation of the system The green certificate quota N required by the thermal power generator at time t is derived gov,t The total green certificate quantity N required for trading is derived by superimposing the green certificate quantities required for trading in each period green : Step 223, sell price according to green certificate transaction Purchase price And penalty price Get green certificate transaction cost That is, the green certificate transaction model, the formula is as follows:

4. The method of claim 3, wherein the multi-factor dynamic pricing strategy based virtual power plant low-carbon joint dispatching method is characterized by: The specific steps of step 3 are as follows: Step 31, according to the electric load demand response, the power consumed by the electric boiler EB unit at time t, and the charging and discharging of the electric vehicle charging station EVCS and the energy storage device, the equivalent electric load is obtained; according to the heat demand response, the charging and discharging of the heat storage device and the heat of the electric boiler EB unit at t, the equivalent heat load is obtained, and the formula is as follows: wherein is the equivalent electrical load on the demand side; is the equivalent thermal load on the demand side; Step 32, constructing a carbon emission penalty price, the formula is as follows: wherein: is the carbon emission penalty price for the electricity produced by the cogeneration unit at time t; The subscript s in includes CHP and GB, i.e. is the carbon emission penalty price for the heat produced by the cogeneration unit and the gas boiler unit at time t, respectively; includes and denotes the gas-to-heat efficiency of the cogeneration unit, denotes the energy conversion efficiency of the gas boiler GB unit; χ gind,t is the carbon emission penalty price for the electricity in the grid at time t; Step 33, constructing a green certificate correction price, the formula is as follows: Step 331, according to the green certificate correction price coefficient δ g , the green certificate correction price at time t is obtained The formula is as follows: In the formula, is the green certificate correction price at time t; δ g is the green certificate correction price coefficient; Step 332, according to the equivalent power load and the power of the renewable energy at time t, the basic price χ of the renewable energy at time t is obtained RE,t The formula is as follows: In the formula, is the valley price of the power grid; P WT,t , P PV,t are the wind power and photovoltaic predicted power at time t, respectively. Step 34, according to the interruptible load and the load before demand response, the demand response correction price is obtained, and the formula is as follows: wherein is the modified price of the interruptible electric load at time t; is the modified price of the interruptible thermal load at time t; Step 35, according to the energy cost price, the carbon emission penalty price, the green certificate correction price and the demand response correction price, the pricing of each energy sales price is obtained, and the formula is as follows: wherein, p RE,t is the new energy sales price at time t; p gird,t is the sales price of the power grid power transmitted by IJSC to the demand side at time t; is the time-of-use electricity price of ESP for purchasing power from the power grid at time t; p CHP,t is the sales price of the power transmitted by the CHP unit to the demand side by the intelligent joint dispatch center at time t; λ g is the natural gas price; Q gas is the low calorific value of natural gas; q CHP,t , q GB,t are the sales prices of the heat transmitted by the cogeneration unit and the gas boiler unit to the demand side by the intelligent joint dispatch center at time t, respectively.

5. The method of claim 4, wherein the multi-factor dynamic pricing strategy based virtual power plant low-carbon joint dispatching method is characterized by: The specific steps of step 4 are as follows: Step 41, according to the carbon trading cost and the green certificate trading cost, the operation cost F1 of the energy service provider is constructed; Step 42, constructing the total cost F2 of the user aggregator; Step 43, according to the operation cost F1 of the energy service provider obtained in step 41 and the total cost F2 of the user aggregator obtained in step 42, constructing the objective function of joint optimization, and the formula is as follows: In the formula, F is a joint optimization objective function; Step 44, using the CPLEX solver to solve the joint optimization objective function to obtain the optimal scheduling result.

6. The method of claim 5, wherein the multi-factor dynamic pricing strategy based virtual power plant low-carbon joint dispatching method is characterized by: The step 15 gives the electric vehicle load demand model P EV,t The specific steps are as follows: Step 151, obtaining the daily driving mileage of the electric vehicle EV according to the standard deviation and the average value of the daily driving mileage of the electric vehicle EV, and the formula is as follows: where s i is the daily driving range of the electric vehicle; σ s , μ s are the standard deviation and the mean of the daily driving range of the electric vehicle, respectively; Step 152, obtaining the state of charge at the end of charging according to the daily driving mileage of the electric vehicle EV and the cruising range, and the formula is as follows: wherein S i is the range; S OC,i is the state of charge at the end of charging; Step 153, determining the charging power of the electric vehicle EV according to the rated capacity of the battery rated capacity of the battery and charging efficiency determining the charging time of the ith electric vehicle EV, according to the following formula: Step 154, superimpose the charging time of each electric vehicle EV to obtain the total load demand P of the electric vehicle charging station EVCS in each period EV,t is:

7. The method of claim 5, wherein the multi-factor dynamic pricing strategy based virtual power plant low-carbon joint dispatching method is characterized by: The specific steps of constructing the operation cost F1 of the energy service provider in the step 41 are as follows: Step 411, the compensation cost of interruptible load Cost of electricity purchased by the energy service provider from the grid Operating cost of the combined heat and power unit Operating cost of the gas boiler unit Carbon trading cost Green certificate trading cost Cost of carbon dioxide transportation and storage Penalty cost of renewable energy reduction Cost of electricity purchased by the UA and the cost of purchasing thermal energy Get the operating cost F1 of the energy service provider ESP, as follows; In the formula, and respectively, are the new energy, grid power, power generated by a combined heat and power unit, heat generated by the combined heat and power unit, and heat generated by a gas boiler unit purchased by a demand-side user at time t; σ TS is the transportation and storage price of unit carbon dioxide; is the penalty cost of unit abandoned wind power; is the penalty cost of unit abandoned light power; is the compensation coefficient of interruptible power load; Step 412, constructing the constraint condition of the operation cost F1 of the energy service provider, and the formula is as follows: wherein, are, respectively, the upper and lower limits of the input power of the cogeneration CHP unit; are, respectively, the upper and lower limits of the ramping power of the cogeneration CHP unit; are, respectively, the upper and lower limits of the input power of the gas boiler GB unit; are, respectively, the upper and lower limits of the ramping power of the gas boiler GB unit; η CHP,max is the maximum carbon capture rate; is the electricity purchased from the grid by the energy service provider ESP at time t; is the upper limit of the electricity purchased from the grid; P s,t , and the subscript s refers, respectively, to the wind turbine WT and to the photovoltaic PV, is the curtailment power of s at time t; P s,t denotes the power forecast value of s at time t; denotes the power actually consumed by s at time t.

8. The method of claim 5, wherein the multi-factor dynamic pricing strategy based virtual power plant low-carbon joint dispatching method is characterized by: The steps of constructing the total cost F2 of the user aggregator in the step 42 are as follows: Step 421, obtaining compensation for interruptible power load from the energy service provider according to the cost of electricity and heat purchased by the user aggregator from the energy service provider and the demand response The total cost F2 of the user aggregator is obtained, and the formula is as follows: Step 422, constructing the constraint condition of the total cost F2 of the user aggregator UA, as follows: wherein, is the upper limit of the power output of the CHP unit; is the upper limit of the heat output of the CHP unit; is the upper limit of the heat output of the GB unit; are the upper limits of the charging and discharging power of the EVCS, respectively.

9. The method of claim 5, wherein the multi-factor dynamic pricing strategy based virtual power plant low-carbon joint dispatching method is characterized by: The steps of using the CPLEX solver to solve the joint optimization objective function in the step 44 are as follows: Step 441, calling the CPLEX to solve the energy service provider optimization model with the minimum operation cost F1 of the energy service provider as the target, and passing the result to the intelligent joint scheduling center; The intelligent joint scheduling center calculates a new selling price according to the pricing mechanism and passes it to the user aggregator; Step 442, calling the CPLEX to solve the user aggregator optimization model with the minimum total cost F2 of the user aggregator as the target, obtaining the electricity purchase plan and the total cost F2 of the user aggregator, and feeding back to the intelligent joint scheduling center; The intelligent joint scheduling center publishes the total electricity purchase amount, heat purchase amount and total energy purchase cost obtained by the demand side optimization to the energy service provider; the energy service provider calls the CPLEX to solve the optimization model with the minimum operation cost as the target, and feeds back the optimized production plan and the minimum operation cost F1 to the intelligent joint scheduling center; Step 443, the intelligent joint scheduling center calculates the joint optimization objective function value in this iteration based on the result of step 442; Step 444, if the current iteration number exceeds the preset maximum iteration number, the iteration process is stopped; Otherwise, the intelligent joint scheduling center will calculate the new selling price passed to the user aggregator according to the optimization results of the energy service provider and the user aggregator in the previous iteration, and then return to step 442 until the iteration is terminated; Step 445: obtaining the final joint optimal solution and outputting the scheduling plan.

10. The method of claim 2 to 9, wherein the method is based on a multi-factor dynamic pricing strategy. The value of the total period number T of a scheduling cycle is 24.

Images