Multi-type adjustment resource response cost calculation and economy analysis method

Abstract

The invention belongs to the technical field of power cost measurement and calculation, and particularly relates to a multi-type adjustment resource response cost measurement and calculation and economy analysis method. Comprising the following steps: S1, carrying out multi-type adjustment resource response cost calculation; s2, establishing a multi-type adjustment resource economy optimization configuration model considering the whole life cycle cost; s3, economic analysis of adjustment resource response in different scenes is carried out; according to the method, for typical adjustable electrical loads including an electric vehicle load, an air conditioner load and an electrolytic aluminum load, response characteristics of multiple types of adjustment resources are analyzed, and a response cost measuring and calculating method is provided; analyzing cost influence factors in the whole life cycle of the typical adjustable electrical load, designing an adjustment resource response model considering the whole life cycle cost, and proposing an economic optimization configuration model; and multi-scene economy analysis is carried out for typical scenes, supply-preserving scenes and consumption-preserving scenes of wind and light loads, and response cost and economic benefits of multi-type adjustment resources in multiple scenes are measured and calculated.

Description

Technical Field

[0001] The present invention belongs to the technical field of power cost calculation, and specifically relates to a method for calculating the response cost of multiple types of regulating resources and an economic analysis method. Background Art

[0002] With the steady growth of social economy, my country's electricity demand has increased, and problems such as the increase in power peak-valley difference have become increasingly prominent. At the same time, with the rapid development of smart grids, the power system has undergone earth-shaking changes in all aspects such as power generation, transmission, distribution and power consumption. On the user side, the proportion of flexible loads such as air conditioners, smart homes, and electric vehicles has continued to increase, making demand-side resources more adjustable, which has laid a solid foundation for using demand-side resources to alleviate the problem of tight power supply. Power demand side management has been carried out in developed countries such as North America and Europe to explore the potential of limited resources, guide users to change their electricity consumption behavior, and postpone the construction of new power plants as much as possible. Power demand side management mainly refers to the management method that encourages power users to change their electricity consumption behavior through the support of government policies and regulations and the effective guidance of incentives of power grid companies, thereby reducing power consumption, improving terminal power efficiency, and ultimately promoting energy conservation and environmental protection. Power demand side management has made a positive contribution to the sustainable growth of the world economy. Demand side response is one of the solutions for power demand side management. Demand response (DR) refers to the short-term behavior of users adjusting their original electricity consumption behavior and reducing or transferring the power load in the corresponding period after receiving the price change signal or direct compensation notice of load reduction issued by the implementer of demand response, so as to ensure the safe and stable operation of the system and achieve the balance of power supply and demand. Incorporating demand response into the power market and coordinating the planning of power generation side resources and demand side resources through effective incentives and guidance measures are the future development direction of the power market. In the process of demand-side response development, policies and energy resource conditions are the driving force for development, advanced equipment such as smart meters, monitoring and control are the physical basis, and the power market environment is the key to long-term implementation. User habits and load structure will also have a certain degree of impact on the implementation effect. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method for calculating the response cost of multiple types of regulation resources and analyzing the economic performance.

[0004] The object of the present invention is achieved by: a method for calculating the response cost of multiple types of regulation resources and analyzing the economic efficiency, comprising the following steps:

[0005] S1. Calculate the response cost of multiple types of regulation resources;

[0006] S2. Establish an economic optimization allocation model for multiple types of regulatory resources taking into account the full life cycle cost;

[0007] S3. Conduct economic analysis of adjusting resource responses under different scenarios.

[0008] Furthermore, the step S1 of calculating the response cost of multiple types of adjustment resources includes:

[0009] The economic calculation process of electric vehicles participating in demand response is as follows:

[0010] F t,sc =R dr ×(1-α)-C dr (1)

[0011]

[0012] Where: F t,sc is the net cash flow in the tth year of participating in demand response, RMB; R dr is the annual demand response revenue, RMB; α is the revenue share of the demand response organizer, set to 30%; C dr is the annual demand response operation and maintenance cost, with an initial investment of 2% each year; P is the unified clearing price, RMB / kW; Q is the user's single invitation response volume, kW; n is the number of annual demand-side response releases; k1 is the response volume coefficient, which is 1; k2 is the single response speed coefficient, which is 0.8 for private charging piles, 0.8 for dedicated charging piles, and 2 for battery swap stations; k3 is the response activity duration, which is 3 for private charging piles, 2 for dedicated charging piles, and 1 for battery swap stations; k4 is the response participation coefficient, which is 1;

[0013] When residential users participate in the demand response of air conditioning load, their requirements for living comfort cannot be ignored. Therefore, the temperature change range during air conditioning operation must be limited to a certain extent, as shown in the following formula:

[0014] T d -d+δ / 2≤T set ≤T d +d-δ / 2(3)

[0015] Where: the temperature deviation allowed by the user is set as d, δ represents the accuracy of air conditioning temperature control, T d To meet the desired temperature for residential user comfort;

[0016] When residential users participate in the process of demand response, the air conditioning temperature needs to be adjusted, resulting in a deviation between the indoor temperature and the expected temperature, which reduces the user's comfort and brings about the comfort loss cost. The calculation method is as follows:

[0017]

[0018] Where: k is the response cost coefficient.

[0019] Furthermore, the step S2 of establishing an economic optimization configuration model for multiple types of regulation resources taking into account the full life cycle cost includes:

[0020] (1) Electric vehicle load model

[0021] 1.1. Single electric vehicle charging and discharging model

[0022] The charging and discharging power range of a single electric vehicle should meet the following constraints:

[0023]

[0024] Where: t is the time period number, each hour is a time period, and there are 24 time periods in a day; r is the number of the electric vehicle; are the charging and discharging power of electric vehicle r in period t respectively; are the maximum charging and discharging power of electric vehicle r, respectively; are the time when the electric vehicle r connects to and leaves the charging pile, respectively;

[0025] In addition, the charging and discharging power of electric vehicles should also meet the following mutually exclusive constraints:

[0026]

[0027] The above formula indicates that the electric vehicle r cannot be charged and discharged simultaneously in a certain period of time;

[0028] The net charging capacity constraint of electric vehicles is:

[0029]

[0030] Where: are the start and end time of the controllable period of the electric vehicle r, respectively; Δt is the time interval; They are the battery power of the electric vehicle at the start and end of the controllable time period respectively;

[0031] 1.2 Model Linearization

[0032] The charging and discharging power of the electric vehicle group is expressed as a decision variable P rt Indicates that P rt <0 means that the electric vehicle group discharges to the grid, P rt >0 means that the electric vehicle group is charged from the power grid, and the binary parameter U is introduced. rt ; Make a reasonable assumption about the charging and discharging power range of electric vehicles: the maximum charging power is equal to the maximum discharging power, then the power range constraint of electric vehicles can be converted to:

[0033] -P r,max U rt ≤P rt ≤Pr,max U rt (9)

[0034]

[0035] Where: P rt is the power of electric vehicle r in period t; P r,max is the maximum power of electric vehicle r; U rt is the binary state variable of the electric vehicle r, U rt =1 means the electric vehicle has been connected to the charging pile, U rt =0 means the electric vehicle is not connected to a charging pile;

[0036] The battery power constraints of electric vehicles at different time periods are:

[0037] E rt =E rt-1 +P rt U rt Δt(11)

[0038]

[0039] E rt is the battery power value of electric vehicle r in period t; The initial power value of the electric vehicle when it is connected to the charging pile; are the maximum and minimum values ​​of the battery power of the electric vehicle r, respectively;

[0040] Each electric vehicle needs to meet the charging requirements set by the electric vehicle user when leaving the charging pile, which is:

[0041]

[0042] Where: The charging capacity requirement value set for electric vehicle users;

[0043] 1.3 Battery degradation model considering the entire life cycle

[0044] The service life of an electrochemical cell is determined by its cycle life T cycle and floating life T float The cycle life is calculated as follows:

[0045]

[0046] Where: It is the maximum number of charge and discharge cycles in the battery life at a specific charge and discharge depth d; is the average number of cycles of the battery at the charge and discharge depth d per day; D is the average number of operating days of the battery per year after taking into account the necessary operation and maintenance time; for different types of electrochemical batteries, Both can be expressed as a function of the charge and discharge depth d, as shown in the following formula:

[0047]

[0048] Where: The mathematical expression of f(d) can be obtained through fitting technology based on the detailed experimental data provided by the battery manufacturer;

[0049] In a charge and discharge cycle, if the battery is charged from the initial charge E start Discharge to end capacity E end , and then by E end Charge back to initial capacity E start , or by the initial charge E start Charge to end power E end , and then by E end Discharge back to initial charge E start The ratio of the difference between the starting and ending power of the battery in this cycle to the battery capacity is the depth of charge and discharge of the battery, as shown in the following formula:

[0050]

[0051] Where: E cap is the battery capacity;

[0052] Assume that the charge and discharge cycle depth of the battery in period t-1 is d t-1 , then the cycle depth d of the battery in period t t The discharge power P during the period from t-1 to t can be t calculate:

[0053]

[0054] Where: η dis is the discharge efficiency of the battery; since the charging process is ignored, P t is a non-negative value; the incremental battery aging caused by this cycle process can be expressed as θ(d t ), the marginal aging amount can be obtained by θ(d t ) t Take the derivative and substitute it into the calculation, as shown below:

[0055]

[0056] The battery discharge depth (0-100%) is evenly divided into L segments, and the discharge power corresponding to each segment is P l disAnd the battery replacement cost R ($) is proportionally distributed to each charge and discharge cycle depth, and a piecewise linear approximate function is obtained to construct the battery cycle aging cost function C, which consists of L segments, as shown in the following formula:

[0057]

[0058] in:

[0059]

[0060] Assign a discharge power to each cycle depth segment In order to track the energy level of each segment independently and identify the current cycle depth, the battery cycle degradation cost can be expressed as the sum of the costs of each segment:

[0061]

[0062] (2) Air conditioning load model

[0063] A simplified model of the thermal dynamics of the AC system is used to describe the thermodynamic and electrical models of the building and air conditioning, as shown below:

[0064]

[0065] In the formula, is the wall temperature, Indicates the indoor air temperature; Indicates the amount of heat gained internally; Indicates the heat gained from solar energy, C a Indicates the wall temperature coefficient; C m represents the indoor air temperature coefficient, and its relationship is as follows, where f AC , f s and f i is the fractional coefficient;

[0066]

[0067] For the electrical model of the AC system, the output power of the air conditioning load Cooling capacity and compressor on / off status The relationship between can be expressed as follows, where is the coefficient of performance of the AC system;

[0068]

[0069] The life cycle cost of air conditioning load C L Including equipment loss cost during operation C loss , Equipment maintenance cost Cre and equipment scrapping recovery cost C dis ; That is: C L =C loss +C re +C dis ;

[0070] (3) Electrolytic aluminum load model

[0071] The thermoelectric process is represented by a set of differential equations as follows:

[0072]

[0073] in:

[0074]

[0075]

[0076] In the above formula, τ is time, a is coefficient, T a is the chamber air temperature, T s is the solid layer temperature, T e is the liquid layer temperature, H s is the thermal conductivity between the chamber air and the solid layer, H e is the thermal conductivity between the solid layer and the liquid layer, C s is the thermal mass of the solid layer, C e is the thermal mass of the liquid layer, k e is the current coefficient, I e The intensity of the current used to generate heat;

[0077] The life cycle cost of electrolytic aluminum load C L Including raw material processing cost C pro , Equipment use cost C use , Recovery cost C re and equipment loss cost C loss ; That is: C L =C pro +C use +C re +C loss .

[0078] Furthermore, the economic analysis of adjusting resource response in different scenarios in step S3 includes:

[0079] (1) Grid timing production simulation model considering electric vehicles

[0080] 1) Objective function

[0081] For a regional power grid consisting of k subsystems, its sequential operation simulation can be modeled as a mixed integer linear programming model. The objective function of the mixed integer linear programming model is composed of the power generation cost of the generator. Start-up cost Downtime costs Demand response cost C DR (t), Electric vehicle battery cycle aging cost C L (t) and the power curtailment penalty for renewable energy, which shall be in the following form:

[0082]

[0083] Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalties for abandoned photovoltaic power and abandoned wind power are respectively corresponding to each MW; p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t, which is converted from meteorological data;

[0084] 2) Power balance constraints

[0085] For any subsystem k, there is a power balance constraint:

[0086]

[0087] where p G,i (t) is the power output of generator i at time t; p W,k (t) is the wind power output at time t in system k; p S,k (t) is the photovoltaic output at time t in system k; T I,k (t) and T O,k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L,k (t) is the load power at time t in system k; p EV (t) is the total power of the electric vehicle load at time t;

[0088] 3) Alternative constraints

[0089] For any subsystem k, the safe spare capacity of this subsystem must be ensured, and the spare constraint is:

[0090]

[0091] where u i(t) is the start / stop state of generator i, which is 1 when the generator is running and 0 when it is stopped; is the rated capacity of generator i; W,k and ε S,k are the maximum prediction error of wind power output and photovoltaic output of subsystem k respectively; η L,k is the maximum reserve demand factor of subsystem k, which is 5%;

[0092] 4) Power output range constraints

[0093]

[0094] in p G,i is the minimum technical output of generator i when it is running;

[0095] 5) Generator climbing constraints

[0096] The output power of unit i at time t needs to meet the unit's ramp constraint:

[0097]

[0098] where r U,i and r D,i are the maximum ramp-up speed rate and the maximum ramp-down speed rate of generator i, respectively. N is a large constant. The output constraints of the generator when starting and stopping are as follows:

[0099]

[0100] Among them, s U,i Indicates the starting state of generator i; s D,i The shutdown state of generator i; N is a large constant.

[0101] 6) Minimum start and stop time constraints

[0102]

[0103] Among them, u i (t) is the working state of generator i at time t; T U,i and T D,i They represent the time that generator i needs to continue to run and remain in shutdown state at time t, respectively. The time can be calculated by the following formula:

[0104] T U,i (t) = min{M U,i ,T-t+1}(40)

[0105] T D,i (t) = min{M D,i ,T-t+1}(41)

[0106] Where M U,i and M D,i are the minimum start-up time and minimum downtime of generator i, that is, the generator must keep running for M after each start-up. U,i After each shutdown of the generator, it needs at least M D,i In the process of rolling solution, the final state of the previous rolling period will be used as the initial state input of the current rolling period. Therefore, it is assumed that the U has been started for the final state of the previous rolling period. i0 Hours and Closed D i0 The initial start and stop constraints of the unit in the current rolling period are:

[0107]

[0108] (2) Power grid time-series production simulation model considering massive heterogeneous air conditioning loads

[0109] The life cycle cost of air conditioning load C L Including equipment loss cost during operation C loss , Equipment maintenance cost C re and equipment scrapping recovery cost C dis That is: C L =C loss +C re +C dis Therefore, its full life cycle cost should also be reflected in the objective function.

[0110] For a regional power grid consisting of k subsystems, its sequential operation simulation can be modeled as a mixed integer linear programming model. The objective function of the mixed integer linear programming model is composed of the power generation cost of the generator. Start-up cost Downtime costs Demand response cost C DR (t), life cycle cost C L (t) and the power curtailment penalty for renewable energy, which shall be in the following form:

[0111]

[0112] Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalties for abandoned photovoltaic power and abandoned wind power are respectively corresponding to each MW; p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t, which is converted from meteorological data;

[0113] For any subsystem k, there is a power balance constraint:

[0114]

[0115] where p G,i (t) is the power output of generator i at time t; p W,k (t) is the wind power output at time t in system k; p S,k (t) is the photovoltaic output of system k at time t; T I,k (t) and T O,k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L,k (t) is the load power at time t in system k; p AC (t) is the total power of the massive air conditioning load at time t;

[0116] Its backup constraints, power output range constraints, generator ramp constraints, and minimum start-stop time constraints are the same as those in the grid sequential production simulation model considering electric vehicles;

[0117] For air conditioning load, its power is related to the outside temperature and the set temperature T set,i Generally, the indoor temperature T a,i (t) is required to float within a certain range from the set temperature, and the floating difference is ΔT set,i , which is as follows:

[0118]

[0119] At the same time, the air conditioning load should also work within the set minimum and maximum power range, as shown in the following formula:

[0120]

[0121] The air conditioner is an adjustable load, and its upward power P ACu,i (t) and the reduced power P ACd,i (t) cannot be performed simultaneously, similar to the charging and discharging power of the energy storage device, and its upward and downward power constraints are:

[0122]

[0123] where u ac,i (t) is a 0-1 variable, bigM is P ACmax,i -P ACmin,i ;

[0124] (3) Grid timing production simulation model considering flexible industrial aluminum electrolytic load

[0125] The life cycle cost of electrolytic aluminum load C L Including raw material processing cost C pro , Equipment use cost C use , Recovery cost C re and equipment loss cost C loss , that is: C L =C pro +C use +C re +C loss ,Therefore, its life cycle cost should also be reflected in the objective function;

[0126] For a regional power grid consisting of k subsystems, its sequential operation simulation can be modeled as a mixed integer linear programming model. The objective function of the mixed integer linear programming model is composed of the power generation cost of the generator. Start-up cost Downtime costs Demand response cost C DR (t), life cycle cost C L (t) and the power curtailment penalty for renewable energy, which shall be in the following form:

[0127]

[0128] Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalties for abandoned photovoltaic power and abandoned wind power are p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t, which is converted from meteorological data;

[0129] For any subsystem k, there is a power balance constraint:

[0130]

[0131] where p G，i (t) is the power output of generator i at time t; p W，k (t) is the wind power output at time t in system k; p S，k (t) is the photovoltaic output at time t in system k; T I，k (t) and T O，k(t) are the power flowing into and out of subsystem k through the tie line at time t; pL，k (t) is the load power at time t in system k; p EA (t) is the total power of the flexible electrolytic aluminum load at time t;

[0132] Its backup constraints, power output range constraints, generator ramp constraints, and minimum start-stop time constraints are the same as those in the grid sequential production simulation model considering electric vehicles;

[0133] The production of electrolytic aluminum is mainly carried out in aluminum electrolytic cells. Direct current passes through molten cryolite containing alumina and other elements that are conducive to electrolysis, and generates metallic aluminum through electrochemical reactions. Therefore, in the working mode, the direct current should be within the minimum allowable current and maximum allowable current range of the electrolytic cell, as shown in the following formula,

[0134]

[0135] At the same time, as an industrial production product, electrolytic aluminum also has certain constraints on its output, as shown below:

[0136]

[0137] Where: I EA_base，i is the benchmark current size when electrolytic aluminum is working, and T is the total working period;

[0138] Flexible electrolytic aluminum is an adjustable load, which increases the current I EAu，i (t) and the lower current I EAd，i (t) cannot be performed simultaneously, similar to the charging and discharging power of the energy storage device, then the upward and downward current constraints are:

[0139]

[0140] where u EA，i (t) is a 0-1 variable, bigM is I EA max，i- I EA min，i .

[0141] Beneficial effects of the invention: The method for calculating the response cost and economic analysis of multiple types of regulating resources of the invention comprises the steps of S1, calculating the response cost of multiple types of regulating resources; S2, establishing an economic optimization configuration model for multiple types of regulating resources taking into account the full life cycle cost; S3, conducting an economic analysis of the response of regulating resources in different scenarios; The method for calculating the response cost and economic analysis of multiple types of regulating resources of the invention studies the response cost calculation and economic analysis of multiple types of regulating resources, analyzes the response characteristics of multiple types of regulating resources for typical adjustable electric loads, including electric vehicle loads, air-conditioning loads and electrolytic aluminum loads, and proposes a response cost calculation method; analyzes the cost influencing factors of typical adjustable electric loads over their full life cycle, designs a regulating resource response model taking into account the full life cycle cost, and proposes an economic optimization configuration model; conducts a multi-scenario economic analysis of typical scenarios, supply guarantee scenarios and consumption guarantee scenarios of wind and solar loads, calculates the response cost and economic benefits of multiple types of regulating resources in multiple scenarios, and solves the problem of "the cost and potential benefits required for power loads to participate in the "double guarantee"". BRIEF DESCRIPTION OF THE DRAWINGS

[0142] Figure 1 It is a schematic diagram of the segmented approximate fitting of the battery charge and discharge cycle depth.

[0143] Figure 2 Schematic diagram of the equivalent integrated thermal parameter model for flexible industrial aluminum electrolysis.

[0144] Figure 3 This is a comparison chart of load shedding before and after adding electric vehicles.

[0145] Figure 4 This is the charging situation of electric vehicles on a typical day from June 20 to June 22.

[0146] Figure 5 This is a comparison chart of wind power abandonment before and after adding electric vehicles.

[0147] Figure 6 This is a comparison chart of the abandoned light situation before and after adding electric vehicles.

[0148] Figure 7 This is a comparison chart of load shedding before and after adding air conditioning load.

[0149] Figure 8 This is a diagram of the air conditioning load operation on a typical day from June 20 to June 22.

[0150] Fig. 9 This is the actual power diagram of air conditioning load on typical days from June 20 to June 22.

[0151] Fig.10 This is a comparison chart of load shedding before and after adding electrolytic aluminum load.

[0152] Fig.11 This is a diagram of the typical electrolytic aluminum load action from June 20 to June 22.

[0153] Fig.12 This is a comparison chart of the total wind and solar power curtailment before and after the addition of electrolytic aluminum load. DETAILED DESCRIPTION

[0154] The present invention will be further described below in conjunction with the accompanying drawings.

[0155] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0156] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0157] The method for calculating the response cost of multi-type regulation resources and analyzing economic efficiency of the present invention comprises the following steps:

[0158] S1. Calculate the response cost of multiple types of regulation resources;

[0159] S2. Establish an economic optimization allocation model for multiple types of regulatory resources taking into account the full life cycle cost;

[0160] S3. Conduct economic analysis of adjusting resource responses under different scenarios.

[0161] Furthermore, the step S1 of calculating the response cost of multiple types of adjustment resources includes:

[0162] The economic calculation process of electric vehicles participating in demand response is as follows:

[0163] F t,sc =R dr ×(1-α)-C dr (1)

[0164]

[0165] Where: F t,sc is the net cash flow in the tth year of participating in demand response, RMB; R dr is the annual demand response revenue, RMB; α is the revenue share of the demand response organizer, set to 30%; C dris the annual demand response operation and maintenance cost, with an initial investment of 2% each year; P is the unified clearing price, RMB / kW; Q is the user's single invitation response volume, kW; n is the number of annual demand-side response releases; k1 is the response volume coefficient, which is 1; k2 is the single response speed coefficient, which is 0.8 for private charging piles, 0.8 for dedicated charging piles, and 2 for battery swap stations; k3 is the response activity duration, which is 3 for private charging piles, 2 for dedicated charging piles, and 1 for battery swap stations; k4 is the response participation coefficient, which is 1;

[0166] Since the real-time response capabilities of private piles, dedicated piles, and battery swap stations range from weak to strong, the response speeds of private piles and dedicated piles are set according to the previous day's notice, and the response speed of battery swap stations is set according to the 0.5h advance notice. Considering that electric vehicles have a high response accuracy, it is assumed that their average response ratio is between 80% and 120%. In addition, considering that the vehicle is connected to the private pile for the longest time, followed by the dedicated pile, and the battery swap station has the shortest continuous response time due to the intensity of battery swap operations, it is assumed that the k3 values ​​of the three types of charging facilities are 3, 2, and 1 respectively. Since electric vehicles do not participate in demand response bidding during the pilot period, the annual response coefficients of the three types of charging facilities are uniformly taken as 1.

[0167] When residential users participate in the demand response of air conditioning load, their requirements for living comfort cannot be ignored. Therefore, the temperature change range during air conditioning operation must be limited to a certain extent, as shown in the following formula:

[0168] T d -d+δ / 2≤T set ≤T d +d-δ / 2(3)

[0169] Where: the temperature deviation allowed by the user is set as d, δ represents the accuracy of air conditioning temperature control, T d To meet the desired temperature for residential user comfort;

[0170] When residential users participate in the process of demand response, the air conditioning temperature needs to be adjusted, resulting in a deviation between the indoor temperature and the expected temperature, which reduces the user's comfort and brings about the comfort loss cost. The calculation method is as follows:

[0171]

[0172] Where: k is the response cost coefficient.

[0173] Furthermore, the step S2 of establishing an economic optimization configuration model for multiple types of regulation resources taking into account the full life cycle cost includes:

[0174] (1) Electric vehicle load model

[0175] 1.1. Single electric vehicle charging and discharging model

[0176] The charging and discharging power range of a single electric vehicle should meet the following constraints:

[0177]

[0178]

[0179] Where: t is the time period number, each hour is a time period, and there are 24 time periods in a day; r is the number of the electric vehicle; are the charging and discharging power of electric vehicle r in period t respectively; are the maximum charging and discharging power of electric vehicle r, respectively; are the time when the electric vehicle r connects to and leaves the charging pile, respectively;

[0180] In addition, the charging and discharging power of electric vehicles should also meet the following mutually exclusive constraints:

[0181]

[0182] The above formula indicates that the electric vehicle r cannot be charged and discharged simultaneously in a certain period of time;

[0183] The net charging capacity constraint of electric vehicles is:

[0184]

[0185] Where: are the start and end time of the controllable period of the electric vehicle r, respectively; Δt is the time interval; They are the battery power of the electric vehicle at the start and end of the controllable time period respectively;

[0186] 1.2 Model Linearization

[0187] The charging and discharging power of the electric vehicle group is expressed as a decision variable P rt Indicates that P rt <0 means that the electric vehicle group discharges to the grid, P rt >0 means that the electric vehicle group is charged from the power grid, and the binary parameter U is introduced. rt ; Make a reasonable assumption about the charging and discharging power range of electric vehicles: the maximum charging power is equal to the maximum discharging power, then the power range constraint of electric vehicles can be converted to:

[0188] -P r,max U rt ≤P rt ≤P r,max U rt (9)

[0189]

[0190] Where: P rtis the power of electric vehicle r in period t; P r,max is the maximum power of electric vehicle r; U rt is the binary state variable of the electric vehicle r, U rt =1 means the electric vehicle has been connected to the charging pile, U rt =0 means the electric vehicle is not connected to a charging pile;

[0191] The battery power constraints of electric vehicles at different time periods are:

[0192] E rt =E rt-1 +P rt U rt Δt(11)

[0193]

[0194] E rt is the battery power value of electric vehicle r in period t; The initial power value of the electric vehicle when it is connected to the charging pile; are the maximum and minimum values ​​of the battery power of the electric vehicle r, respectively;

[0195] Each electric vehicle needs to meet the charging requirements set by the electric vehicle user when leaving the charging pile, which is:

[0196]

[0197] Where: The charging capacity requirement value set for electric vehicle users;

[0198] 1.3 Battery degradation model considering the entire life cycle

[0199] The service life of an electrochemical cell is determined by its cycle life T cycle and floating life T float The cycle life is calculated as follows:

[0200]

[0201] Where: It is the maximum number of charge and discharge cycles in the battery life at a specific charge and discharge depth d; is the average number of cycles of the battery at the charge and discharge depth d per day; D is the average number of operating days of the battery per year after taking into account the necessary operation and maintenance time; for different types of electrochemical batteries, Both can be expressed as a function of the charge and discharge depth d, as shown in the following formula:

[0202]

[0203] Where: The mathematical expression of f(d) can be obtained through fitting technology based on the detailed experimental data provided by the battery manufacturer;

[0204] In a charge and discharge cycle, if the battery is charged from the initial charge E start Discharge to end capacity E end , and then by E end Charge back to initial capacity E start , or by the initial charge E start Charge to end power E end , and then by E end Discharge back to initial charge E start The ratio of the difference between the starting and ending power of the battery in this cycle to the battery capacity is the depth of charge and discharge of the battery, as shown in the following formula:

[0205]

[0206] Where: E cap is the battery capacity;

[0207] Assume that the charge and discharge cycle depth of the battery in period t-1 is d t-1 , then the cycle depth d of the battery in period t t The discharge power P during the period from t-1 to t can be t calculate:

[0208]

[0209] Where: η dis is the discharge efficiency of the battery; since the charging process is ignored, P t is a non-negative value; the incremental battery aging caused by this cycle process can be expressed as θ(d t ), the marginal aging amount can be obtained by θ(d t ) t Take the derivative and substitute it into the calculation, as shown below:

[0210]

[0211] The battery discharge depth (0-100%) is evenly divided into L segments, and the discharge power corresponding to each segment is P l dis And the battery replacement cost R ($) is proportionally distributed to each charge and discharge cycle depth, and a piecewise linear approximate function is obtained to construct the battery cycle aging cost function C, which consists of L segments, as shown in the following formula:

[0212]

[0213] in:

[0214]

[0215] The number of segments into which the battery cycle discharge depth is evenly divided is different, and the corresponding battery life curve fitting effect is also different, such as Figure 1 It can be seen that the more average segments of the battery charge and discharge cycle depth, the better the fitting effect of the piecewise linear function compared with the original battery life loss curve. When the number of segments is 4, the approximate fitting accuracy has reached more than 85%.

[0216] Assign a discharge power to each cycle depth segment In order to track the energy level of each segment independently and identify the current cycle depth, the battery cycle degradation cost can be expressed as the sum of the costs of each segment:

[0217]

[0218] (2) Air conditioning load model

[0219] A simplified model of the thermal dynamics of the AC system is used to describe the thermodynamic and electrical models of the building and air conditioning, as shown below:

[0220]

[0221] In the formula, is the wall temperature, Indicates the indoor air temperature; Indicates the amount of heat gained internally; Indicates the heat gained from solar energy, C a Indicates the wall temperature coefficient; C m represents the indoor air temperature coefficient, and its relationship is as follows, where f AC , f s and f i is the fractional coefficient;

[0222]

[0223] For the electrical model of the AC system, the output power of the air conditioning load Cooling capacity and compressor on / off status The relationship between can be expressed as follows, where is the coefficient of performance of the AC system;

[0224]

[0225] The life cycle cost of air conditioning load C L Including equipment loss cost during operation C loss , Equipment maintenance cost C re and equipment scrapping recovery cost Cdis ; That is: C L =C loss +C re +C dis ;

[0226] (3) Electrolytic aluminum load model

[0227] The equivalent integrated thermal parameter model of flexible industrial aluminum electrolysis is as follows: Figure 2 As shown, the thermoelectric process is expressed by a set of differential equations as follows:

[0228]

[0229] in:

[0230]

[0231]

[0232] In the above formula, τ is time, a is coefficient, T a is the chamber air temperature, T s is the solid layer temperature, T e is the liquid layer temperature, H s is the thermal conductivity between the chamber air and the solid layer, H e is the thermal conductivity between the solid layer and the liquid layer, C s is the thermal mass of the solid layer, C e is the thermal mass of the liquid layer, k e is the current coefficient, I e The intensity of the current used to generate heat;

[0233] The life cycle cost of electrolytic aluminum load C L Including raw material processing cost C pro , Equipment use cost C use , Recovery cost C re and equipment loss cost C loss ; That is: C L =C pro +C use +C re +C loss .

[0234] Furthermore, the economic analysis of adjusting resource response in different scenarios in step S3 includes:

[0235] (1) Grid timing production simulation model considering electric vehicles

[0236] 1) Objective function

[0237] For a regional power grid consisting of k subsystems, its sequential operation simulation can be modeled as a mixed integer linear programming model. The objective function of the mixed integer linear programming model is composed of the power generation cost of the generator. Start-up cost Downtime costs Demand response cost C DR (t), Electric vehicle battery cycle aging cost C L (t) and the power curtailment penalty for renewable energy, which shall be in the following form:

[0238]

[0239] Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalties for abandoned photovoltaic power and abandoned wind power are respectively corresponding to each MW; p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t, which is converted from meteorological data;

[0240] 2) Power balance constraints

[0241] For any subsystem k, there is a power balance constraint:

[0242]

[0243] where p G,i (t) is the power output of generator i at time t; p W,k (t) is the wind power output at time t in system k; p S,k (t) is the photovoltaic output of system k at time t; T I,k (t) and T O,k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L,k (t) is the load power at time t in system k; p EV (t) is the total power of the electric vehicle load at time t;

[0244] 3) Alternative constraints

[0245] For any subsystem k, the safe spare capacity of this subsystem must be ensured, and the spare constraint is:

[0246]

[0247] where u i(t) is the start / stop state of generator i, which is 1 when the generator is running and 0 when it is stopped; is the rated capacity of generator i; W,k and ε S,k are the maximum prediction error of wind power output and photovoltaic output of subsystem k respectively; η L,k is the maximum reserve demand factor of subsystem k, which is 5%;

[0248] 4) Power output range constraints

[0249]

[0250] in p G,i is the minimum technical output of generator i when it is running;

[0251] 5) Generator climbing constraints

[0252] The output power of unit i at time t needs to meet the unit's ramp constraint:

[0253]

[0254] where r U,i and r D,i are the maximum ramp-up speed rate and the maximum ramp-down speed rate of generator i, respectively. N is a large constant. The output constraints of the generator when starting and stopping are as follows:

[0255]

[0256] Among them, s U,i Indicates the starting state of generator i; s D,i The shutdown state of generator i; N is a large constant.

[0257] 6) Minimum start and stop time constraints

[0258]

[0259] Among them, u i (t) is the working state of generator i at time t; T U,i and T D,i They represent the time that generator i needs to continue to run and remain in shutdown state at time t, respectively. The time can be calculated by the following formula:

[0260] T U,i (t) = min{M U,i ,T-t+1}(40)

[0261] T D,i (t) = min{M D,i ,T-t+1}(41)

[0262] Where M U,i and M D,i are the minimum start-up time and minimum downtime of generator i, that is, the generator must keep running M after each start-up. U,i After each shutdown of the generator, it needs at least M D,i In the process of rolling solution, the final state of the previous rolling period will be used as the initial state input of the current rolling period. Therefore, it is assumed that the U has been started for the final state of the previous rolling period. i0 Hours and Closed D i0 The initial start and stop constraints of the unit in the current rolling period are:

[0263]

[0264] Taking a certain province as an example, there are about 850,000 electric vehicles in the province, and the charging power of each vehicle is about 6kW, and each charging takes at least 4 hours. If electric vehicles are involved in load demand response to "guarantee supply", it is stipulated that electric vehicles can be charged from 18:00 to 6:00 the next day; if "guarantee consumption" is carried out, it is stipulated that electric vehicles can be charged from 6:00 to 18:00 the next day, and the original fixed load is replaced by 850,000*6k*4 / 12≈1,700 megawatts of electric vehicle load during the corresponding period. At this time, the power system contains adjustable loads, and then the time-series production simulation technology is used to analyze extreme scenarios of supply and consumption. After the addition of electric vehicles, there is some improvement in the original load shedding situation in mid-to-late June, mid-July, and mid-to-early August. The overall situation is as follows Figure 3 shown.

[0265] Since the load shedding amount shown in the figure is not obvious, it is converted into load shedding rate. It can be seen that after adding the electric vehicle load, the load shedding rate drops from the original 1.6446% to 1.1634%, and the load shedding situation has improved to a certain extent. Select the typical days from June 20 to June 22 to analyze the charging behavior of electric vehicles. Figure 4 As shown in the figure, for the "supply guarantee" scenario, electric vehicles start charging at 2:00 p.m. and end charging at around 6:00 a.m. It can be seen that electric vehicles choose not to charge during the evening peak load period, but charge during the early morning hours when the load level is relatively low, which has a certain load transfer effect, relieves the pressure of the peak load, and thus reduces the situation of load shedding.

[0266] After tripling the installed capacity of wind power and quadrupling the capacity of photovoltaic power, we used time-series production to simulate the wind and solar power abandonment in a province's power system throughout the summer after the capacity of new energy was increased. We found that most of the wind and solar power abandonment occurred in the afternoon. Since photovoltaic power generation was high at this time, it could not be absorbed. The amount of solar power abandoned reached its peak at 13:00 and 15:00, and the amount of wind power abandoned reached its peak at 14:00. In order to improve the absorption of new energy and reduce the occurrence of wind and solar power abandonment, we added electric vehicles, an adjustable load, and conducted time-series production simulation again to analyze the extreme scenario of "ensuring absorption". The overall situation results are as follows: Figure 5 , 6 As shown in the figure, the wind and solar power abandonment in the whole summer has been significantly improved after the addition of electric vehicles. The wind and solar power abandonment rates have been reduced by orders of magnitude after the addition of electric vehicles. For the "guaranteed consumption" scenario, electric vehicles start charging at around 10:00 in the morning and end charging at around 14:00. At this time, due to the charging action of electric vehicles, the new energy generated in the afternoon can be consumed, reducing the abandonment of wind and solar power.

[0267] (2) Power grid time-series production simulation model considering massive heterogeneous air conditioning loads

[0268] The life cycle cost of air conditioning load C L Including equipment loss cost during operation C loss , Equipment maintenance cost C re and equipment scrapping recovery cost C dis That is: C L =C loss +C re +C dis Therefore, its full life cycle cost should also be reflected in the objective function.

[0269] For a regional power grid consisting of k subsystems, its sequential operation simulation can be modeled as a mixed integer linear programming model. The objective function of the mixed integer linear programming model is composed of the power generation cost of the generator. Start-up cost Downtime costs Demand response cost C DR (t), life cycle cost C L (t) and the power curtailment penalty for renewable energy, which shall be in the following form:

[0270]

[0271] Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ WThe penalties for abandoned photovoltaic power and abandoned wind power are respectively corresponding to each MW; p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t, which is converted from meteorological data;

[0272] For any subsystem k, there is a power balance constraint:

[0273]

[0274] where p G,i (t) is the power output of generator i at time t; p W,k (t) is the wind power output at time t in system k; p S,k (t) is the photovoltaic output of system k at time t; T I,k (t) and T O,k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L,k (t) is the load power at time t in system k; p AC (t) is the total power of the massive air conditioning load at time t;

[0275] Its backup constraints, power output range constraints, generator ramp constraints, and minimum start-stop time constraints are the same as those in the grid sequential production simulation model considering electric vehicles;

[0276] For air conditioning load, its power is related to the outside temperature and the set temperature T set,i Generally, the indoor temperature T a,i (t) is required to float within a certain range from the set temperature, and the floating difference is ΔT set,i , which is as follows:

[0277]

[0278] At the same time, the air conditioning load should also work within the set minimum and maximum power range, as shown in the following formula:

[0279]

[0280] The air conditioner is an adjustable load, and its upward power P ACu,i (t) and the reduced power P ACd,i (t) cannot be performed simultaneously, similar to the charging and discharging power of the energy storage device, and its upward and downward power constraints are:

[0281]

[0282] where u ac,i(t) is a 0-1 variable, bigM is P ACmax,i -P ACmin,i ;

[0283] Take a certain province as an example. In summer, the cooling load of residents in the province is about 27,000 megawatts. Therefore, the original fixed load is replaced by 27,000 megawatts of air-conditioning load. At this time, the power system contains adjustable loads, and then the time-series production simulation technology is used to analyze the extreme scenarios of supply and consumption. After adding the air-conditioning load, the original load shedding in mid-to-late June, mid-July and mid-to-early August has been significantly improved. Load shedding only occurred when the load reached its peak on August 7. The overall situation is as follows Figure 7 As shown in the figure, the typical days from June 20 to June 22 are selected to analyze the operation of air conditioning load. Figure 8 , Fig. 9 As shown in the figure, the air conditioning load starts to increase from around 12:00 noon, the actual power is greater than the reference power, the indoor temperature is lowered to cope with high temperature weather, and the actual power peak is around 28,000 MW. Then it starts to decrease from around 15:00 in the afternoon, the actual power is less than the reference power, and when the fixed load reaches its peak around 18:00 and 24:00, the air conditioning load decreases most significantly, relieving the pressure of the load and reducing the load shedding.

[0284] (3) Grid timing production simulation model considering flexible industrial aluminum electrolytic load

[0285] The life cycle cost of electrolytic aluminum load C L Including raw material processing cost C pro , Equipment use cost C use , Recovery cost C re and equipment loss cost C loss , that is: C L =C pro +C use +C re +C loss ,Therefore, its life cycle cost should also be reflected in the objective function;

[0286] For a regional power grid consisting of k subsystems, its sequential operation simulation can be modeled as a mixed integer linear programming model. The objective function of the mixed integer linear programming model is composed of the power generation cost of the generator. Start-up cost Downtime costs Demand response cost C DR (t), life cycle cost C L (t) and the power curtailment penalty for renewable energy, which shall be in the following form:

[0287]

[0288] Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalties for abandoned photovoltaic power and abandoned wind power are p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t, which is converted from meteorological data;

[0289] For any subsystem k, there is a power balance constraint:

[0290]

[0291] where p G,i (t) is the power output of generator i at time t; p W,k (t) is the wind power output at time t in system k; p S,k (t) is the photovoltaic output of system k at time t; T I,k (t) and T O,k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L,k (t) is the load power at time t in system k; p EA (t) is the total power of the flexible electrolytic aluminum load at time t;

[0292] Its backup constraints, power output range constraints, generator ramp constraints, and minimum start-stop time constraints are the same as those in the grid sequential production simulation model considering electric vehicles;

[0293] The production of electrolytic aluminum is mainly carried out in aluminum electrolytic cells. Direct current passes through molten cryolite containing alumina and other elements that are conducive to electrolysis, and generates metallic aluminum through electrochemical reactions. Therefore, in the working mode, the direct current should be within the minimum allowable current and maximum allowable current range of the electrolytic cell, as shown in the following formula,

[0294]

[0295] At the same time, as an industrial production product, electrolytic aluminum also has certain constraints on its output, as shown below:

[0296]

[0297] Where: I EA_base,i is the benchmark current size when electrolytic aluminum is working, and T is the total working period;

[0298] Flexible electrolytic aluminum is an adjustable load, which increases the current I EAu,i (t) and the lower current I EAd,i (t) cannot be performed simultaneously, similar to the charging and discharging power of the energy storage device, then the upward and downward current constraints are:

[0299]

[0300] where u EA,i (t) is a 0-1 variable, bigM is I EAmax,i -I EAmin,i .

[0301] Taking a certain province as an example, among its eleven major industries, industrial electricity load accounts for about 71.09%, among which "non-ferrous metal smelting and rolling processing industry" has a large adjustable potential, and the typical operating load is about 4,800 megawatts. Therefore, the original fixed load is replaced by a 4,800-megawatt flexible industrial electrolytic aluminum load. At this time, the power system contains an adjustable load, and then the time-series production simulation technology is used to analyze extreme scenarios for supply and consumption. After adding the electrolytic aluminum load, the original load shedding situation in mid-to-late June, mid-July, and mid-to-early August did not improve significantly. The overall situation is as follows Fig.10 As shown in the figure, the load shedding amount is not obvious, so it is converted into load shedding rate. It can be seen that after adding the flexible industrial electrolytic aluminum load, the load shedding rate dropped from the original 6.3543% to 1.5198%, and the load shedding situation has been improved to a certain extent. The typical days from June 20 to June 22 were selected to analyze the operation of the electrolytic aluminum load, as shown in the figure. Fig.11 As shown, for the "supply guarantee" scenario, the focus is on the reduction of electrolytic aluminum load. As can be seen from the figure, the electrolytic aluminum load begins to be reduced from 18:00 in the evening to cope with the evening peak load. The reduction is most obvious around 22:00, and the actual power is reduced to 3,000 megawatts, alleviating the pressure on the load and reducing the load shedding.

[0302] After expanding wind power installed capacity threefold and photovoltaic capacity fourfold, we used time-series production to simulate the wind and solar power abandonment in a province's power system throughout the summer after the new energy capacity was increased. We found that most of the wind and solar power abandonment occurred in the afternoon. Since photovoltaic power generation was high at this time, it could not be absorbed. The amount of solar power abandoned reached its peak at 13:00 and 15:00, and the amount of wind power abandoned reached its peak at 14:00. In order to improve the absorption of new energy and reduce the abandonment of wind and solar power, we added an adjustable load such as flexible industrial electrolytic aluminum load, and conducted time-series production simulation again to analyze the extreme scenario of "ensuring absorption". The overall situation results are as follows: Fig.12 As shown in the figure, it can be observed that the improvement in the wind and solar power abandonment phenomenon is not obvious. When converted into the total wind and solar power abandonment rate, the total wind and solar power abandonment rate is reduced to a certain extent after adding the electrolytic aluminum load.

[0303] In summary, the method for calculating the response cost and economic analysis of multiple types of regulating resources of the present invention comprises the steps of S1, calculating the response cost of multiple types of regulating resources; S2, establishing an economic optimization configuration model of multiple types of regulating resources taking into account the full life cycle cost; S3, conducting an economic analysis of the response of regulating resources in different scenarios; the method for calculating the response cost and economic analysis of multiple types of regulating resources of the present invention studies the calculation and economic analysis of the response cost of multiple types of regulating resources, analyzes the response characteristics of multiple types of regulating resources for typical adjustable electric loads, including electric vehicle loads, air conditioning loads and electrolytic aluminum loads, and proposes a response cost calculation method; analyzes the cost influencing factors of typical adjustable electric loads in the full life cycle, designs a regulating resource response model taking into account the full life cycle cost, and proposes an economic optimization configuration model; conducts a multi-scenario economic analysis for typical scenarios, supply guarantee scenarios and consumption guarantee scenarios of wind and solar loads, calculates the response cost and economic benefits of multiple types of regulating resources in multiple scenarios, and solves the problem of "the cost and potential benefits required for power loads to participate in the "double guarantee"".

[0304] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for calculating the response cost of multi-type regulation resources and analyzing economic performance, characterized in that: The following steps are involved: S1. Calculate the response cost of multiple types of regulation resources; S2. Establish an economic optimization allocation model for multiple types of regulatory resources taking into account the full life cycle cost; S3. Conduct economic analysis of adjusting resource responses under different scenarios.

2. The method for calculating the response cost and analyzing the economic performance of multi-type regulation resources according to claim 1, characterized in that: The step S1 of calculating the response cost of multiple types of adjustment resources includes: The economic calculation process of electric vehicles participating in demand response is as follows: F t,sc =R dr ×(1-α)-C dr (1) Where: F t,sc is the net cash flow in the tth year of participating in demand response, RMB; R dr is the annual demand response revenue, RMB; α is the revenue share of the demand response organizer; C dr is the annual demand response operation and maintenance cost, with an initial investment of 2% each year; P is the unified clearing price, RMB / kW; Q is the number of users’ single invitation response, kW; n is the number of annual demand-side response releases; k1 is the response quantity coefficient; k2 is the single response speed coefficient; k3 is the response activity duration; k4 is the response participation coefficient; The temperature variation range during air conditioning operation must be limited to a certain extent, as shown in the following formula: T d -d+δ / 2≤T set ≤T d +d-d / 2 (3) Where: the temperature deviation allowed by the user is set as d, δ represents the accuracy of air conditioning temperature control, T d To meet the desired temperature for residential user comfort; The cost of comfort loss is calculated as follows: Where: k is the response cost coefficient.

3. The method for calculating the response cost and analyzing the economic performance of multi-type regulation resources according to claim 1, characterized in that: The step S2 of establishing a multi-type regulation resource economic optimization configuration model taking into account the full life cycle cost includes: (1) Electric vehicle load model 1.

1. Single electric vehicle charging and discharging model The charging and discharging power range of a single electric vehicle should meet the following constraints: Where: t is the time period number, each hour is a time period, and there are 24 time periods in a day; r is the number of the electric vehicle; are the charging and discharging power of electric vehicle r in period t respectively; are the maximum charging and discharging power of electric vehicle r, respectively; are the time when the electric vehicle r connects to and leaves the charging pile, respectively; In addition, the charging and discharging power of electric vehicles should also meet the following mutually exclusive constraints: The above formula indicates that the electric vehicle r cannot be charged and discharged simultaneously in a certain period of time; The net charging capacity constraint of electric vehicles is: Where: are the start and end time of the controllable period of the electric vehicle r, respectively; Δt is the time interval; They are the battery power of the electric vehicle at the start and end of the controllable time period respectively; 1.2 Model Linearization The charging and discharging power of the electric vehicle group is expressed as a decision variable P rt Indicates that P rt <0 means that the electric vehicle group discharges to the grid, P rt >0 means that the electric vehicle group is charged from the power grid, and the binary parameter U is introduced. rt ; Make a reasonable assumption about the charging and discharging power range of electric vehicles: the maximum charging power is equal to the maximum discharging power, then the power range constraint of electric vehicles can be converted to: -P r,max IN rt ≤P rt ≤P r,max IN rt (9) Where: P rt is the power of electric vehicle r in period t; P r,max is the maximum power of electric vehicle r; U rt is the binary state variable of the electric vehicle r, U rt =1 means the electric vehicle has been connected to the charging pile, U rt =0 means the electric vehicle is not connected to a charging pile; The battery power constraints of electric vehicles at different time periods are: HAVE BEEN rt =E rt-1 +P rt U rt Δt (11) E rt is the battery power value of electric vehicle r in period t; The initial power value of the electric vehicle when it is connected to the charging pile; are the maximum and minimum values ​​of the battery power of the electric vehicle r, respectively; Each electric vehicle needs to meet the charging requirements set by the electric vehicle user when leaving the charging pile, which is: Where: The charging capacity requirement value set for electric vehicle users; 1.3 Battery degradation model considering the entire life cycle The service life of an electrochemical cell is determined by its cycle life T cycle and floating life T float The cycle life is calculated as follows: Where: It is the maximum number of charge and discharge cycles in the battery life at a specific charge and discharge depth d; is the average number of cycles of the battery at the charge and discharge depth d per day; D is the average number of operating days of the battery per year after taking into account the necessary operation and maintenance time; for different types of electrochemical batteries, Both can be expressed as a function of the charge and discharge depth d, as shown in the following formula: Where: The mathematical expression of f(d) can be obtained through fitting technology based on the detailed experimental data provided by the battery manufacturer; In a charge and discharge cycle, if the battery is charged from the initial charge E start Discharge to end capacity E end , and then by E end Charge back to initial capacity E start , or by the initial charge E start Charge to end power E end , and then by E end Discharge back to initial charge E start The ratio of the difference between the starting and ending power of the battery in this cycle to the battery capacity is the depth of charge and discharge of the battery, as shown in the following formula: Where: E cap is the battery capacity; Assume that the charge and discharge cycle depth of the battery in period t-1 is d t-1 , then the cycle depth d of the battery in period t t The discharge power P during the period from t-1 to t can be t calculate: Where: η dis is the discharge efficiency of the battery; since the charging process is ignored, P t is a non-negative value; the incremental battery aging caused by this cycle process can be expressed as θ(d t ), the marginal aging amount can be obtained by θ(d t ) t Take the derivative and substitute it into the calculation, as shown below: The battery discharge depth (0-100%) is evenly divided into L segments, and the discharge power corresponding to each segment is P l dis And the battery replacement cost R ($) is proportionally distributed to each charge and discharge cycle depth, and a piecewise linear approximate function is obtained to construct the battery cycle aging cost function C, which consists of L segments, as shown in the following formula: in: Assign a discharge power to each cycle depth segment In order to track the energy level of each segment independently and identify the current cycle depth, the battery cycle degradation cost can be expressed as the sum of the costs of each segment: (2) Air conditioning load model A simplified model of the thermal dynamics of the AC system is used to describe the thermodynamic and electrical models of the building and air conditioning, as shown below: In the formula, is the wall temperature, Indicates the indoor air temperature; Indicates the amount of heat gained internally; Indicates the heat gained from solar energy, C a Indicates the wall temperature coefficient; C m represents the indoor air temperature coefficient, and its relationship is as follows, where f AC , f s and f i is the fractional coefficient; For the electrical model of the AC system, the output power of the air conditioning load Cooling capacity and compressor on / off status The relationship between can be expressed as follows, where is the coefficient of performance of the AC system; The life cycle cost of air conditioning load C L Including equipment loss cost during operation phase C loss , Equipment maintenance cost C re and equipment scrapping recovery cost C dis ; That is: C L =C loss +C re +C dis ; (3) Electrolytic aluminum load model The thermoelectric process is represented by a set of differential equations as follows: in: In the above formula, τ is time, a is coefficient, T a is the chamber air temperature, T s is the solid layer temperature, T e is the liquid layer temperature, H s is the thermal conductivity between the chamber air and the solid layer, H e is the thermal conductivity between the solid layer and the liquid layer, C s is the thermal mass of the solid layer, C e is the thermal mass of the liquid layer, k e is the current coefficient, I e The intensity of the current used to generate heat; The life cycle cost of electrolytic aluminum load C L Including raw material processing cost C pro , Equipment use cost C use , Recovery cost C re and equipment loss cost C loss ; That is: C L =C pro +C use +C re +C loss .

4. The method for calculating the response cost and analyzing the economic performance of multi-type regulation resources according to claim 1, characterized in that: The economic analysis of adjusting resource response in different scenarios in step S3 includes: (1) Grid timing production simulation model considering electric vehicles 1) Objective function For a regional power grid consisting of k subsystems, its sequential operation simulation can be modeled as a mixed integer linear programming model. The objective function of the mixed integer linear programming model is composed of the power generation cost of the generator. Start-up cost Downtime costs Demand response cost C DR (t), Electric vehicle battery cycle aging cost C L (t) and the power curtailment penalty for renewable energy, which shall be in the following form: Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalties for abandoned photovoltaic power and abandoned wind power are respectively corresponding to each MW; p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t, which is converted from meteorological data; 2) Power balance constraints For any subsystem k, there is a power balance constraint: where p G,i (t) is the power output of generator i at time t; p W,k (t) is the wind power output at time t in system k; p S,k (t) is the photovoltaic output of system k at time t; T I,k (t) and T O,k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L,k (t) is the load power at time t in system k; p EV (t) is the total power of the electric vehicle load at time t; 3) Alternative constraints For any subsystem k, the safe spare capacity of this subsystem must be ensured, and the spare constraint is: where u i (t) is the start / stop state of generator i, which is 1 when the generator is running and 0 when it is stopped; is the rated capacity of generator i; W,k and ε S,k are the maximum prediction error of wind power output and photovoltaic output of subsystem k respectively; η L,k is the maximum reserve demand factor of subsystem k, which is 5%; 4) Power output range constraints in p G,i is the minimum technical output of generator i when it is running; 5) Generator climbing constraints The output power of unit i at time t needs to meet the unit's ramp constraint: where r U,i and r D,i are the maximum ramp-up speed rate and the maximum ramp-down speed rate of generator i, respectively. N is a large constant. The output constraints of the generator when starting and stopping are as follows: Among them, s U,i Indicates the starting state of generator i; s D,i The shutdown state of generator i; N is a large constant. 6) Minimum start and stop time constraints Among them, u i (t) is the working state of generator i at time t; T U,i and T D,i They represent the time that generator i needs to continue to run and remain in shutdown state at time t, respectively. The time can be calculated by the following formula: T U,i (t)=min{M U,i ,T-t+1} (40) T D,i (t)=min{M D,i ,T-t+1} (41) Where M U,i and M D,i are the minimum start-up time and minimum downtime of generator i, that is, the generator must keep running for M after each start-up. U,i After each shutdown of the generator, it needs at least M D,i In the process of rolling solution, the final state of the previous rolling period will be used as the initial state input of the current rolling period. Therefore, it is assumed that the U has been started for the final state of the previous rolling period. i0 Hours and Closed D i0 The initial start and stop constraints of the unit in the current rolling period are: (2) Power grid time-series production simulation model considering massive heterogeneous air conditioning loads The life cycle cost of air conditioning load C L Including equipment loss cost during operation C loss , Equipment maintenance cost C re and equipment scrapping recovery cost C dis That is: C L =C loss +C re +C dis Therefore, its full life cycle cost should also be reflected in the objective function. For a regional power grid consisting of k subsystems, its sequential operation simulation can be modeled as a mixed integer linear programming model. The objective function of the mixed integer linear programming model is composed of the power generation cost C of the generator. i G (t), startup cost C i SU (t), downtime cost C i SD (t), demand response cost C DR (t), life cycle cost C L (t) and the power curtailment penalty for renewable energy, which shall be in the following form: Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalties for abandoned photovoltaic power and abandoned wind power are respectively corresponding to each MW; p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t, which is converted from meteorological data; For any subsystem k, there is a power balance constraint: where p G,i (t) is the power output of generator i at time t; p W,k (t) is the wind power output at time t in system k; p S,k (t) is the photovoltaic output of system k at time t; T I,k (t) and T O,k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L,k (t) is the load power at time t in system k; p AC (t) is the total power of the massive air conditioning load at time t; Its backup constraints, power output range constraints, generator ramp constraints, and minimum start-stop time constraints are the same as those in the grid sequential production simulation model considering electric vehicles; For air conditioning load, its power is related to the outside temperature and the set temperature T set,i Generally, the indoor temperature T a,i (t) is required to float within a certain range from the set temperature, and the floating difference is ΔT set,i , which is as follows: At the same time, the air conditioning load should also work within the set minimum and maximum power range, as shown in the following formula: The air conditioner is an adjustable load, and its upward power P ACu,i (t) and the reduced power P ACd,i (t) cannot be performed simultaneously, similar to the charging and discharging power of the energy storage device, and its upward and downward power constraints are: where u ac,i (t) is a 0-1 variable, bigM is P ACmax,i -P ACmin,i ; (3) Grid timing production simulation model considering flexible industrial aluminum electrolytic load The life cycle cost of electrolytic aluminum load C L Including raw material processing cost C pro , Equipment use cost C use , Recovery cost C re and equipment loss cost C loss , that is: C L =C pro +C use +C re +C loss ,Therefore, its life cycle cost should also be reflected in the objective function; For a regional power grid consisting of k subsystems, its sequential operation simulation can be modeled as a mixed integer linear programming model. The objective function of the mixed integer linear programming model is composed of the power generation cost of the generator. Start-up cost Downtime costs Demand response cost C DR (t), life cycle cost C L (t) and the power curtailment penalty for renewable energy, which shall be in the following form: Among them, G k is the total number of generators in subsystem k, H is the total operating time, for the annual hourly operation simulation, H = 8760, θ S and θ W The penalties for abandoned photovoltaic power and abandoned wind power are p S,k (t) and p W,k (t) are the photovoltaic and wind power received by subsystem k at time t, and is the maximum power that can be generated by photovoltaic and wind power at time t, which is converted from meteorological data; For any subsystem k, there is a power balance constraint: where p G,i (t) is the power output of generator i at time t; p W,k (t) is the wind power output at time t in system k; p S,k (t) is the photovoltaic output at time t in system k; T I,k (t) and T O,k (t) are the power flowing into and out of subsystem k through the tie line at time t; p L,k (t) is the load power at time t in system k; p EA (t) is the total power of the flexible electrolytic aluminum load at time t; Its backup constraints, power output range constraints, generator ramp constraints, and minimum start-stop time constraints are the same as those in the grid sequential production simulation model considering electric vehicles; The production of electrolytic aluminum is mainly carried out in aluminum electrolytic cells. Direct current passes through molten cryolite containing alumina and other elements that are conducive to electrolysis, and generates metallic aluminum through electrochemical reactions. Therefore, in the working mode, the direct current should be within the minimum allowable current and maximum allowable current range of the electrolytic cell, as shown in the following formula, At the same time, as an industrial production product, electrolytic aluminum also has certain constraints on its output, as shown below: Where: I EA_base,i is the benchmark current size when electrolytic aluminum is working, and T is the total working period; Flexible electrolytic aluminum is an adjustable load, which increases the current I EAu,i (t) and the lower current I EAd,i (t) cannot be performed simultaneously, similar to the charging and discharging power of the energy storage device, then the upward and downward current constraints are: where u EA,i (t) is a 0-1 variable, bigM is I EAmax,i -I EAmin,i .

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