An energy routing system optimization scheduling method considering the demand responsiveness model of charging pile electricity price incentives for building terminal clusters

Abstract

This invention provides an energy routing system optimization and scheduling method that considers a demand responsiveness model for electricity price incentives for cluster charging piles at building terminals. The building terminal energy routing management system centrally regulates indoor controllable and conventional loads in the terminal area. By formulating a reasonable incentive electricity price strategy for electric vehicles accessing cluster charging piles, a demand responsiveness model for electricity price incentives for cluster charging piles is constructed, with the optimization goal of minimizing the total electricity purchase cost for the building terminal and the total price incentive cost for cluster charging piles participating in system regulation. The present invention coordinates the optimized operation and solution of various indoor devices through the building terminal energy routing management system, ultimately achieving an optimized scheduling result. This invention can effectively reduce the comprehensive daily operating cost of the building terminal energy routing management system, encourage building terminal electric vehicle users to actively participate in the unified scheduling of cluster charging piles, and ensure the energy comfort of indoor users in the building.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation management, and in particular relates to an energy routing system optimization scheduling method considering a building terminal cluster charging pile electricity price incentive demand responsiveness model. Background Art

[0002] With the rapid development of the electric vehicle industry, the number of planned and constructed cluster charging piles for building terminals is increasing. However, when electric vehicles at building terminals are connected to cluster charging piles in a disorderly manner, it is easy to cause a surge in the electricity load at the building terminal, resulting in a significant increase in the load regulation cost of the building terminal. Therefore, under the premise of ensuring the comfort of user energy use, it is necessary to adopt some effective price incentive measures for disorderly charging cluster charging piles to ensure the unified management of the building terminal energy routing system.

[0003] Currently, with the increasing number of energy consumption types among building end users, the number of uncontrollable load types at the building end is also increasing. From the perspective of the building's indoor living environment, many users currently have increasing demands for flexible indoor temperature and hot water supply adjustment. At the same time, energy conservation at the building end is also receiving increasing attention. Therefore, while meeting the diverse energy needs, energy comfort, and energy conservation of building end users, how to ensure the orderly participation of electric vehicle users at the building end in cluster charging stations, and how to formulate reasonable electricity prices and electricity price incentive response strategies for electric vehicle users to ensure the unified, coordinated optimization of the building end energy management system and effectively reduce users' comprehensive energy costs are key issues that need to be addressed. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an energy routing system optimization scheduling method that considers the demand responsiveness model of the electricity price incentive of the building terminal cluster charging pile. It takes into account the joint optimization scheduling between the building terminal cluster charging piles and the indoor controllable / uncontrollable loads. It can not only effectively encourage the building terminal electric vehicle users to participate in the unified scheduling of cluster charging piles in an orderly manner, but also effectively reduce the comprehensive daily operating cost of the building terminal energy routing system.

[0005] The present invention achieves the above technical objectives through the following technical means.

[0006] An energy routing system optimization scheduling method considering a building terminal cluster charging pile electricity price incentive demand responsiveness model includes the following steps:

[0007] Step 1: Establish an optimization objective function that includes the total electricity purchase cost of building terminals and the total price incentive cost of cluster charging piles participating in system regulation;

[0008] Step 2: Establish a building terminal power supply and demand balance constraint model, a rooftop photovoltaic output prediction constraint model, an inequality constraint model for the transmission power of the tie line between the building terminal and the external distribution network, an equality and inequality constraint model for indoor flexible gas temperature-controlled loads, an equality and inequality constraint model for indoor flexible hot water temperature-controlled loads, and an equality and inequality constraint model for electric vehicle access to building terminal charging piles. Overall, the charging and discharging power of electric vehicles connected to charging piles, which is uniformly dispatched by the building terminal energy routing system, is used as the decision variable value.

[0009] Step 3: Establish a demand responsiveness model for charging pile electricity price incentives in building terminal clusters, and input the initial parameters and optimization algorithm convergence accuracy required for the unified coordination and optimization scheduling operation of the energy routing management system within the building terminal area;

[0010] Step 4: Solve the system optimization scheduling problem for the total electricity purchase cost of the energy routing management system in the building terminal area and the total price incentive cost of the cluster charging piles participating in the system regulation, output the optimized decision variables, and check whether the optimized scheduling result meets the convergence conditions; if the convergence conditions are met, the algorithm terminates and outputs the optimal scheduling result; if the convergence conditions are not met, update the initial parameters of the energy routing management system in the building terminal area, return to step 3 to check whether all the initial parameters are reasonable, and then solve again.

[0011] Furthermore, in step 3, the demand responsiveness model of the electricity price incentive for the charging piles of the building terminal cluster is as follows:

[0012]

[0013] Where i represents the number of the charging pile connected to the electric vehicle; N ev The number of charging piles for building terminal cluster; n ev The number of electric vehicles connected to charging piles for building terminals; λ is the price incentive demand responsiveness of building terminal electric vehicles connected to charging piles; k is the price demand response utility coefficient that encourages building terminal electric vehicle users to participate in charging at charging piles; λ b λ is the benchmark value of price incentive demand responsiveness for building terminal electric vehicles to access charging piles; max The price incentive demand responsiveness limit for building terminal electric vehicles to access charging piles; are the charging power and discharging power of electric vehicle i connected to the charging pile at the building terminal during period t; They represent the total charging power and total discharging power of the cluster charging piles at the building terminal in time period t respectively;

[0014] The unit cost parameter for the price incentive of charging piles in building terminal clusters participating in the unified regulation of the energy routing management system; δconstant,value1 The minimum expected price for electric vehicle users at the building terminal to access charging piles; constant,value2 The maximum expected price for connecting electric vehicle users to charging stations in buildings.

[0015] Furthermore, in step 1, the objective function for optimizing the scheduling of the terminal energy routing system management center is established with the goal of minimizing the total cost of the building terminal purchasing electricity from the distribution network and the total cost of the price incentive for the cluster charging piles to participate in the unified regulation of the energy routing system:

[0016] f=min{C grid,buy,c +C ev,incentive,c}

[0017] in:

[0018]

[0019]

[0020] Where, f is the target of the optimal scheduling of the building terminal energy routing system management center; C grid,buy,c The total cost of purchasing electricity from the distribution grid for the building terminal; C ev,incentive,c is the total price incentive cost for charging piles in the building terminal cluster to participate in the unified regulation of the energy routing system; T represents the optimized scheduling time of the building terminal energy routing system management center; Unit cost parameters for purchasing electrical energy from the distribution grid for building terminals; is the electric power purchased by the building terminal from the external distribution network during period t; i represents the number of the charging pile connected to the electric vehicle; N ev The number of charging piles for building terminal clusters; Unit cost parameters for price incentives for charging piles in clusters of building terminals to participate in the unified regulation of the energy routing system; are the charging power and discharging power of electric vehicle i at the building terminal connected to the charging pile during period t.

[0021] Furthermore, in step 2, the building terminal electric energy power supply and demand balance constraint model is as follows:

[0022]

[0023] Where, Predict the output power of the photovoltaic cells on the roof of the building during period t; is the electric power purchased by the building terminal from the external distribution network during period t; i represents the number of the charging pile connected to the electric vehicle; N ev The number of charging piles for building terminal clusters; are the charging power and discharging power of electric vehicle i connected to the charging pile at the building terminal during period t; is the power consumed by the building's indoor temperature-controlled load during period t; is the power consumed by the building's indoor flexible hot water supply load during period t; is the power consumed by the conventional load during period t.

[0024] Furthermore, in step 2, based on the photovoltaic cell parameters under the standard test conditions provided by the manufacturer, the following rooftop photovoltaic output prediction constraint model is established by introducing a compensation coefficient:

[0025]

[0026] Where, Predict the output power of the photovoltaic cells on the roof of the building during period t; is the maximum output power of the photovoltaic array on the roof of the building under standard conditions during period t; are the actual temperature and actual light intensity of the photovoltaic cells on the roof of the building during period t; are the temperature reference value and light intensity reference value of the photovoltaic cells on the roof of the building during period t respectively; are the difference between the actual temperature of the photovoltaic cells on the roof of the building during period t and the temperature reference value, and the difference between the actual light intensity and the light intensity reference value; a, b, and c are all compensation coefficients.

[0027] Furthermore, in step 2, the tie line transmission power inequality constraint model between the building terminal and the external distribution network is as follows:

[0028]

[0029] Where, is the electric power purchased by the building terminal from the external distribution network during period t;

[0030] are the maximum and minimum limits of electric power purchased by the building terminal from the external distribution network during period t;

[0031] The equality and inequality constraint models for indoor flexible gas temperature control loads are as follows:

[0032]

[0033] Where, is the power consumed by the building's indoor temperature-controlled load during period t; T represents the optimized scheduling time of the building's terminal energy routing system management center; It represents the demand power of the hot and cold gas temperature-controlled load predicted at the building terminal during period t; are the upper limit temperature fluctuation coefficient value and the lower limit temperature fluctuation coefficient value of the cold and hot gas temperature control load predicted at the building terminal during period t, respectively; are the upper limit temperature value and lower limit temperature value of the indoor temperature fluctuation predicted at the building terminal during period t, respectively; The indoor human comfort temperature value predicted for the building terminal during period t; The predicted outdoor temperature value of the building terminal during period t.

[0034] Furthermore, in step 2, the indoor flexible hot water temperature control load equation and inequality constraint model are as follows:

[0035] Where, is the power consumed by the building's indoor flexible hot water supply load during period t; T represents the optimized scheduling time of the building terminal energy routing system management center; It represents the demand power of indoor hot water temperature control load predicted at the building terminal during period t; are the upper and lower temperature fluctuation coefficients of the indoor hot water temperature control load predicted at the building terminal during period t; Vcold,water,storage is the total hot water storage volume at the building terminal; C water Terminal hot water parameters for buildings; are the upper limit and lower limit temperature values of the indoor hot water temperature fluctuation predicted at the building terminal during period t; is the indoor hot water comfort temperature value predicted for the building terminal during period t; It is the temperature value when cold water replaces hot water at the building terminal during period t.

[0036] Furthermore, it is characterized in that, in step 2, the equality and inequality constraint model for connecting electric vehicles to building terminal charging piles is as follows:

[0037]

[0038] Where Δt represents the time period when the i-th electric vehicle at the building terminal is connected to the charging pile; and They represent the state of charge of the i-th electric vehicle at the building terminal during period t and period t-1 respectively; Q i,ev Battery capacity of electric vehicles at the building terminals; are the charging power and discharging power of electric vehicle i connected to the charging pile at the building terminal during period t; η ev,charge ,η ev,discharge are the charging efficiency and discharging efficiency of electric vehicles at the building terminals respectively; and are the maximum and minimum limits of the state of charge of the i-th electric vehicle at the building terminal during period t, respectively; and are the rated charging limit and rated discharge power limit of the i-th electric vehicle connected to the charging pile at the building terminal during period t, respectively; is the charging and discharging status value of the i-th electric vehicle at the building terminal during period t, When it is 1, it means the electric vehicle is in charging state. When it is 0, it means the electric vehicle is in the discharge state; t i,ev,start and t i,ev,end They are the starting time period and the interruption time period of the electric vehicle connected to the charging pile at the building terminal. It means that the charging pile does not generate charging or discharging status when the i-th electric vehicle has not reached the building terminal.

[0039] Furthermore, in step 4, whether the internal power supply of the building terminal and the power required by the user are balanced during the optimization process is used as a convergence condition:

[0040]

[0041] Where ε represents the difference between the internal power supply of the building terminal and the power required by the user, that is, the convergence accuracy of the optimization algorithm; Predict the output power of the photovoltaic cells on the roof of the building during period t; is the electric power purchased by the building terminal from the external distribution network during period t; i represents the number of the charging pile connected to the electric vehicle; N ev The number of charging piles for building terminal clusters; are the charging power and discharging power of electric vehicle i connected to the charging pile at the building terminal during period t; is the power consumed by the building's indoor temperature-controlled load during period t; is the power consumed by the building's indoor flexible hot water supply load during period t; It is the power consumed by conventional loads in the building during period t.

[0042] Furthermore, in step 3, the initial parameters are basic parameters related to operation, including photovoltaic output power and electric energy transmission power; after inputting the initial parameters and the convergence accuracy of the optimization algorithm, it is also necessary to set the number parameters of electric vehicles connected to charging piles for charging at building terminals, the price incentive unit cost parameters for cluster charging piles to participate in the unified regulation of the energy routing system, the minimum expected price parameters for electric vehicle users to access charging piles, the demand response utility coefficient for incentivizing electric vehicle users to participate in charging at charging piles, and the price incentive demand responsiveness limit value parameters for electric vehicles to access charging piles.

[0043] The present invention has the following beneficial effects:

[0044] The present invention takes into account that building terminals can encourage electric vehicle users to actively participate in the optimized scheduling of demand-side regulation by formulating reasonable electricity prices. It can greatly reduce the total electricity purchase cost of building terminals and the total price incentive cost of cluster charging piles participating in system regulation. At the same time, it can also effectively encourage building terminal electric vehicle users to participate in the unified scheduling of cluster charging piles in an orderly manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of the energy routing system optimization scheduling method considering the building terminal cluster charging pile electricity price incentive demand responsiveness model described in the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0047] The energy routing system optimization scheduling method of the present invention considering the building terminal cluster charging pile electricity price incentive demand responsiveness model is as follows Figure 1 As shown, the specific steps include:

[0048] Step 1: Establish an optimization objective function that includes the total electricity purchase cost of building terminals and the total price incentive cost of cluster charging piles participating in system regulation;

[0049] Taking the total cost of purchasing electricity from the distribution network for building terminals and the total cost of price incentives for cluster charging piles to participate in the unified regulation of the energy routing system as the goal, the objective function of the terminal energy routing system management center for optimal scheduling is established:

[0050] f=min{C grid,buy,c +C ev,incentive,c}

[0051] in:

[0052]

[0053]

[0054] Where, f is the target of the optimal dispatch of the building terminal energy routing system management center, and the unit is ￥; C grid,buy,c The total cost of electricity purchased from the distribution network by the building terminal, in ￥; The unit cost parameter for the building terminal to purchase electricity from the distribution network, in RMB / kWh; is the electric power purchased by the building terminal from the external distribution network during period t, in kW; C ev,incentive,c The total price incentive cost for charging piles in building terminal clusters to participate in the unified regulation of the energy routing system, in ￥; The unit cost parameter for the price incentive for charging piles in the building terminal cluster to participate in the unified regulation of the energy routing system, in ￥ / kWh; are the charging power and discharging power of electric vehicle i connected to the charging pile at the building terminal during period t, both in kW; N ev is the number of charging piles in the building terminal cluster; i represents the number of the charging pile connected to the electric vehicle; T represents the optimized scheduling time of the building terminal energy routing system management center.

[0055] Step 2: Establish a building terminal electric energy power supply and demand balance constraint model, a rooftop photovoltaic output prediction constraint model, an inequality constraint model for the transmission power of the interconnection line between the building terminal and the external distribution network, an equality and inequality constraint model for the indoor flexible gas temperature-controlled load, an equality and inequality constraint model for the indoor flexible hot water temperature-controlled load, and an equality and inequality constraint model for the electric vehicle access charging pile at the building terminal; overall, the charging and discharging power of the electric vehicle access charging pile uniformly dispatched by the building terminal energy routing system is used as the decision variable value.

[0056] The building terminal power supply and demand balance constraint model is as follows:

[0057]

[0058] Where, is the predicted output power of the photovoltaic cells on the roof of the building during period t, in kW; is the power consumed by temperature-controlled loads such as indoor air conditioning in the building during period t, in kW; is the power consumed by the building's indoor flexible hot water supply load during period t, in kW; It is the power consumed by conventional loads such as indoor lighting and elevators in the building during period t, in kW.

[0059] Under standard test conditions, based on the photovoltaic cell parameters under standard test conditions provided by the manufacturer, the rooftop photovoltaic output prediction constraint model established by introducing the compensation coefficient is as follows:

[0060]

[0061] Where, is the maximum output power of the photovoltaic array on the roof of the building under standard conditions during period t, in kW; are the temperature reference value and light intensity reference value of the photovoltaic cells on the roof of the building during period t, respectively, and Set to 25℃, Set to 1000W / m 2 ; are the actual temperature and actual light intensity of the photovoltaic cells on the roof of the building during period t, in units of ℃ and W / m 2 ; They are the difference between the actual temperature of the photovoltaic cells on the roof of the building and the temperature reference value, and the difference between the actual light intensity and the light intensity reference value in period t, in units of ℃ and W / m 2 ; a, b, c are compensation coefficients, and are set to 0.0025℃ respectively -1 , 0.5m 2 / kW, 0.00288℃ -1 .

[0062] The inequality constraint model for the transmission power of the tie line between the building terminal and the external distribution network is as follows:

[0063]

[0064] Where, They are the maximum and minimum limits of electric power purchased by the building terminal from the external distribution network during period t, both in kW.

[0065] The indoor flexible gas temperature control load equation and inequality constraint model are as follows:

[0066]

[0067] Where, It represents the demand power of the hot and cold gas temperature-controlled load predicted at the building terminal during period t, in kW; are the upper limit temperature fluctuation coefficient value and the lower limit temperature fluctuation coefficient value of the cold and hot gas temperature control load predicted at the building terminal during period t, respectively; are the upper and lower limit temperature values of the indoor temperature fluctuation predicted at the building terminal during period t, both in °C; is the indoor human comfort temperature value predicted for the building terminal during period t, in °C; The outdoor temperature value predicted for the building terminal during period t, in °C.

[0068] The indoor flexible hot water temperature control load equation and inequality constraint model are as follows:

[0069]

[0070] Where, It represents the demand power of indoor hot water temperature control load predicted at the building terminal during period t, in kW; are the upper limit temperature fluctuation coefficient value and the lower limit temperature fluctuation coefficient value of the indoor hot water temperature control load predicted at the building terminal during period t; V cold,water,storageis the total hot water storage volume at the building terminal, in L; C water Terminal hot water parameters for buildings; is the indoor hot water comfort temperature value predicted at the building terminal during period t, in °C; The temperature value when cold water replaces hot water at the building terminal during period t, in °C;

[0071] are the upper and lower temperature limits of the indoor hot water temperature fluctuation predicted at the building terminal during period t, both in °C;

[0072] The equation and inequality constraint model for connecting electric vehicles to building terminal charging piles is as follows:

[0073]

[0074] Where Δt represents the time period when the i-th electric vehicle at the building terminal is connected to the charging pile; and They represent the state of charge of the i-th electric vehicle at the building terminal during period t and period t-1 respectively; Q i,ev is the battery capacity of the electric vehicle at the building terminal, in kW·h; η ev,charge ,η ev,discharge are the charging efficiency and discharging efficiency of electric vehicles at the building terminals respectively; and are the maximum and minimum limits of the state of charge of the i-th electric vehicle at the building terminal during period t, respectively; and are the rated charging limit and rated discharge power limit of the i-th electric vehicle connected to the charging pile at the building terminal during period t, both in kW; is the charging and discharging status value of the i-th electric vehicle at the building terminal during period t, When it is 1, it means the electric vehicle is in charging state. When it is 0, it means the electric vehicle is in the discharge state; t i,ev,start and t i,ev,end They are the starting time period and the interruption time period of the electric vehicle connected to the charging pile at the building terminal. It means that the charging pile does not generate charging or discharging status when the i-th electric vehicle has not reached the building terminal.

[0075] Step 3: Establish a demand responsiveness model for electricity price incentives for cluster charging piles at building terminals, and input the initial parameters and convergence accuracy of the optimization algorithm required for the unified coordination and optimization scheduling operation of the energy routing management system in the building terminal area. The initial parameters include basic parameters related to operation, such as photovoltaic output power and electric energy transmission power; then set the number parameters of electric vehicles connected to charging piles for charging at building terminals, the price incentive unit cost parameters for cluster charging piles to participate in the unified regulation of the energy routing system, the minimum expected price parameters for electric vehicle users to access charging piles, the demand response utility coefficient for incentivizing electric vehicle users to participate in charging at charging piles, and the price incentive demand responsiveness limit value for electric vehicles to access charging piles.

[0076] The demand responsiveness model of electricity price incentive for charging piles in building terminal clusters is as follows:

[0077]

[0078] Where n ev The number of electric vehicles connected to charging piles for building terminals; λ is the price incentive demand responsiveness of building terminal electric vehicles connected to charging piles; k is the price demand response utility coefficient for encouraging building terminal electric vehicle users to participate in charging at charging piles. The larger k is, the higher the price incentive degree of building terminals for electric vehicle users; λ b A benchmark value for price incentive demand responsiveness for building terminal electric vehicle charging pile access; and They represent the total charging power and total discharging power of the cluster charging piles at the building terminal in time period t, both in kW;

[0079] The unit cost parameter for the price incentive of charging piles in the building terminal cluster participating in the unified regulation of the energy routing management system, in RMB / kWh; δ constant,value1 The minimum expected price for electric vehicle users at the building terminal to access charging piles, in ￥ / kW·h; δ constant,value2 is the maximum expected price for electric vehicle users at the building to access charging piles, in ￥ / kW·h; λ max The price incentive demand responsiveness limit for building terminal electric vehicles to access charging piles;

[0080] When the price incentive effect is poor, it shows that the electric vehicle users at the building end are almost unwilling to connect to the charging pile for charging;

[0081] When , the price incentive effect is better, indicating that almost all electric vehicle users at the building terminal are willing to connect to the charging pile for charging;

[0082] This shows that despite the good price incentive effect, terminal electric vehicle users in buildings are still unwilling to connect to charging piles under special circumstances.

[0083] Step 4: Solve the optimization scheduling model of the energy routing management system within the building terminal area, that is, solve the optimization scheduling problem of the total electricity purchase cost of the energy routing management system within the building terminal area and the total price incentive cost of the cluster charging piles participating in the system regulation, output the optimized decision variables, and check whether the optimization scheduling results meet the convergence conditions. In this embodiment, whether the internal power supply power of the building terminal and the user demand power are balanced during the optimization process is used as the convergence condition:

[0084]

[0085] Where ε represents the difference between the internal power supply of the building terminal and the power required by the user, that is, the convergence accuracy of the optimization algorithm;

[0086] If the convergence condition is met, the algorithm terminates and outputs the optimal scheduling result;

[0087] If the convergence condition is not met, update the initial parameters of the energy routing management system in the building terminal area, return to step 3 to check whether all the initial parameters are reasonable, and then solve again.

[0088] The embodiments described are preferred implementations of the present invention, but the present invention is not limited to the above implementations. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essence of the present invention are within the scope of protection of the present invention.

Claims

1. An energy routing system optimization scheduling method considering the building terminal cluster charging pile electricity price incentive demand responsiveness model, characterized by: The steps include: Step 1: Establish an optimization objective function that includes the total electricity purchase cost of building terminals and the total price incentive cost of cluster charging piles participating in system regulation; Step 2: Establish a building terminal power supply and demand balance constraint model, a rooftop photovoltaic output prediction constraint model, an inequality constraint model for the transmission power of the tie line between the building terminal and the external distribution network, an equality and inequality constraint model for indoor flexible gas temperature-controlled loads, an equality and inequality constraint model for indoor flexible hot water temperature-controlled loads, and an equality and inequality constraint model for electric vehicle access to building terminal charging piles. Overall, the charging and discharging power of electric vehicles connected to charging piles, which is uniformly dispatched by the building terminal energy routing system, is used as the decision variable value. Step 3: Establish a demand responsiveness model for charging pile electricity price incentives in building terminal clusters, and input the initial parameters and optimization algorithm convergence accuracy required for the unified coordination and optimization scheduling operation of the energy routing management system within the building terminal area; Step 4: Solve the optimization scheduling problem for the total electricity purchase cost of the energy routing management system within the building terminal area and the total price incentive cost of the cluster charging piles participating in the system regulation. Output the optimized decision variables and check whether the optimized scheduling results meet the convergence conditions. If the convergence conditions are met, the algorithm terminates and outputs the optimal scheduling result; if the convergence conditions are not met, the initial parameters of the energy routing management system in the building terminal area are updated, and the algorithm returns to step 3 to check whether all the initial parameters are reasonable, and then the solution is repeated; In step 1, the objective function for optimizing the scheduling of the terminal energy routing system management center is established with the goal of minimizing the total cost of the building terminal purchasing electricity from the distribution network and the total cost of the price incentive for the cluster charging piles to participate in the unified regulation of the energy routing system: f=min{C grid,buy,c +C ev,incentive,c } in: Where, f is the target of the optimal scheduling of the building terminal energy routing system management center; C grid,buy,c The total cost of purchasing electricity from the distribution grid for the building terminal; C ev,incentive,c is the total price incentive cost for charging piles in the building terminal cluster to participate in the unified regulation of the energy routing system; T represents the optimized scheduling time of the building terminal energy routing system management center; Unit cost parameters for purchasing electrical energy from the distribution grid for building terminals; is the electric power purchased by the building terminal from the external distribution network during period t; i represents the number of the charging pile connected to the electric vehicle; N ev The number of charging piles for building terminal clusters; Unit cost parameters for price incentives for charging piles in clusters of building terminals to participate in the unified regulation of the energy routing system; are the charging power and discharging power of electric vehicle i connected to the charging pile at the building terminal during period t; In step 3, the demand responsiveness model of the electricity price incentive for the charging piles of the building terminal cluster is as follows: Where n ev The number of electric vehicles connected to charging piles for building terminals; λ is the price incentive demand responsiveness of building terminal electric vehicles connected to charging piles; k is the price demand response utility coefficient that encourages building terminal electric vehicle users to participate in charging at charging piles; λ b λ is the benchmark value of price incentive demand responsiveness for building terminal electric vehicles to access charging piles; max The price incentive demand responsiveness limit for building terminal electric vehicles to access charging piles; They represent the total charging power and total discharging power of the cluster charging piles at the building terminal in time period t respectively; δ constant,value1 The minimum expected price for building end-user electric vehicle users to access charging piles; δ constant,value2 The maximum expected price for connecting electric vehicle users to charging stations in buildings; In step 4, whether the internal power supply of the building terminal and the power required by the user are balanced during the optimization process is used as a convergence condition: Where ε represents the difference between the internal power supply of the building terminal and the power required by the user, that is, the convergence accuracy of the optimization algorithm; Predict the output power of the photovoltaic cells on the roof of the building during period t; is the power consumed by the building's indoor temperature-controlled load during period t; is the power consumed by the building's indoor flexible hot water supply load during period t; It is the power consumed by conventional loads in the building during period t.

2. The energy routing system optimization scheduling method considering the building terminal cluster charging pile electricity price incentive demand responsiveness model according to claim 1 is characterized in that: In step 2, the building terminal power supply and demand balance constraint model is as follows: Where, Predict the output power of the photovoltaic cells on the roof of the building during period t; is the electric power purchased by the building terminal from the external distribution network during period t; i represents the number of the charging pile connected to the electric vehicle; N ev The number of charging piles for building terminal clusters; are the charging power and discharging power of electric vehicle i connected to the charging pile at the building terminal during period t respectively; is the power consumed by the building's indoor temperature-controlled load during period t; is the power consumed by the building's indoor flexible hot water supply load during period t; is the power consumed by the conventional load during period t.

3. The energy routing system optimization scheduling method considering the building terminal cluster charging pile electricity price incentive demand responsiveness model according to claim 1 is characterized in that: In step 2, based on the photovoltaic cell parameters under the standard test conditions provided by the manufacturer, the following rooftop photovoltaic output prediction constraint model is established by introducing a compensation coefficient: Where, Predict the output power of the photovoltaic cells on the roof of the building during period t; is the maximum output power of the photovoltaic array on the roof of the building under standard conditions during period t; are the actual temperatures of the photovoltaic cells on the roof of the building during period t. 、 Actual light intensity; are the temperature reference value and light intensity reference value of the photovoltaic cells on the roof of the building during period t respectively; are the difference between the actual temperature of the photovoltaic cells on the roof of the building during period t and the temperature reference value, and the difference between the actual light intensity and the light intensity reference value; a, b, and c are all compensation coefficients.

4. The energy routing system optimization scheduling method considering the building terminal cluster charging pile electricity price incentive demand responsiveness model according to claim 1 is characterized in that: In step 2, the tie line transmission power inequality constraint model between the building terminal and the external distribution network is as follows: Where, is the electric power purchased by the building terminal from the external distribution network during period t; are the maximum and minimum limits of electric power purchased by the building terminal from the external distribution network during period t; The equality and inequality constraint models for indoor flexible gas temperature control loads are as follows: Where, is the power consumed by the building's indoor temperature-controlled load during period t; T represents the optimized scheduling time of the building's terminal energy routing system management center; It represents the demand power of the hot and cold gas temperature-controlled load predicted at the building terminal during period t; are the upper limit temperature fluctuation coefficient value and the lower limit temperature fluctuation coefficient value of the cold and hot gas temperature control load predicted at the building terminal during period t, respectively; are the upper limit temperature value and lower limit temperature value of the indoor temperature fluctuation predicted at the building terminal during period t, respectively; The indoor human comfort temperature value predicted for the building terminal during period t; The predicted outdoor temperature value of the building terminal during period t.

5. The energy routing system optimization scheduling method considering the building terminal cluster charging pile electricity price incentive demand responsiveness model according to claim 1 is characterized in that: In step 2, the indoor flexible hot water temperature control load equation and inequality constraint model are as follows: Where, is the power consumed by the building's indoor flexible hot water supply load during period t; T represents the optimized scheduling time of the building terminal energy routing system management center; It represents the demand power of indoor hot water temperature control load predicted at the building terminal during period t; are the upper limit temperature fluctuation coefficient value and the lower limit temperature fluctuation coefficient value of the indoor hot water temperature control load predicted at the building terminal during period t; V cold,water,storage is the total hot water storage volume at the building terminal; C water Terminal hot water parameters for buildings; are the upper limit and lower limit temperature values of the indoor hot water temperature fluctuation predicted at the building terminal during period t; is the indoor hot water comfort temperature value predicted for the building terminal during period t; It is the temperature value when cold water replaces hot water at the building terminal during period t.

6. The energy routing system optimization scheduling method considering the building terminal cluster charging pile electricity price incentive demand responsiveness model according to claim 1 is characterized in that: In step 2, the equation and inequality constraint model for connecting electric vehicles to building terminal charging piles is as follows: Where Δt represents the time period when the i-th electric vehicle at the building terminal is connected to the charging pile; and They represent the state of charge of the i-th electric vehicle at the building terminal during period t and period t-1 respectively; Q i,ev Battery capacity of electric vehicles at the building terminals; are the charging power and discharging power of electric vehicle i connected to the charging pile at the building terminal during period t; η ev,charge ,η ev,discharge are the charging efficiency and discharging efficiency of electric vehicles at the building terminals respectively; and are the maximum and minimum limits of the state of charge of the i-th electric vehicle at the building terminal during period t, respectively; and are the rated charging limit and rated discharge power limit of the i-th electric vehicle connected to the charging pile at the building terminal during period t, respectively; is the charging and discharging status value of the i-th electric vehicle at the building terminal during period t, When it is 1, it means the electric vehicle is in charging state. When it is 0, it means the electric vehicle is in the discharge state; t i,ev,start and t i,ev,end They are the starting time period and the interruption time period of the electric vehicle connected to the charging pile at the building terminal. It means that the charging pile does not generate charging or discharging status when the i-th electric vehicle has not reached the building terminal.

7. The energy routing system optimization scheduling method considering the building terminal cluster charging pile electricity price incentive demand responsiveness model according to claim 1 is characterized in that: In step 3, the initial parameters are basic parameters related to operation, including photovoltaic output power and electric energy transmission power. After inputting the initial parameters and the convergence accuracy of the optimization algorithm, it is also necessary to set the number parameters of electric vehicles connected to charging piles for charging at building terminals, the price incentive unit cost parameters for cluster charging piles to participate in the unified regulation of the energy routing system, the minimum expected price parameters for electric vehicle users to access charging piles, the demand response utility coefficient for incentivizing electric vehicle users to participate in charging at charging piles, and the price incentive demand responsiveness limit value parameters for electric vehicles to access charging piles.

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