CCHP system multi-time scale coordinated operation optimization method considering power demand response

Abstract

The invention belongs to the field of energy system optimization, and particularly relates to a CCHP system multi-time-scale coordinated operation optimization method considering power demand response, which comprises the following steps: step 1, determining the total load demand, renewable energy output and time-sharing energy purchase price of a system according to historical day-ahead load data; 2, constructing a power demand side response model; step 3, according to the operation characteristics of the equipment, establishing each equipment model; 4, establishing an equipment operation characteristic constraint and an energy balance constraint; step 5, constructing objective functions of a day-ahead stage, an intra-day stage and a real-time stage; and step 6, determining a real-time output plan of the equipment based on the established price type demand response model, the equipment model, constraint conditions and the established target functions of the day-ahead stage, the intra-day stage and the real-time stage, and finally obtaining smooth output of the equipment. The method provided by the invention can realize stable, economic and efficient operation of the system.

Description

Technical Field

[0001] The invention belongs to the field of energy system optimization, and particularly relates to a multi-time scale coordinated operation optimization method for a CCHP system considering power demand response. Background Art

[0002] The CCHP system integrates an energy system, an energy conversion system and an energy storage system, and can flexibly dispatch various energy devices to simultaneously meet various energy demands of users, which is of great significance in realizing the comprehensive utilization, effective complementarity, energy conservation and environmental protection of energy and promoting the utilization of renewable energy. At present, renewable energy has been widely used in the CCHP system. However, due to the random influence of the output of renewable energy on external environment and meteorological conditions fluctuations, combined with the volatility of user load, the safety and economy of the CCHP system operation are greatly affected.

[0003] Most of the previous technologies only consider the operation optimization in the long time period of the day-ahead based on historical load data, and rarely consider the influence of step-by-step optimization of equipment under multi-time scales and the user-side demand response mechanism. Therefore, it is necessary to consider the comprehensive influence of user demand response characteristics and intra-day multi-time scale step-by-step optimization on the operation characteristics of the integrated energy system, and further improve the economy, stability and efficiency of system operation. Summary of the Invention

[0004] The purpose of the invention is to provide a multi-time scale coordinated operation optimization method for a CCHP system considering power demand response. The method performs day-ahead, intra-day and real-time step-by-step operation optimization on the CCHP system, and finally determines the real-time output plan of the equipment to achieve stable, economic and efficient operation of the system.

[0005] The technical solution for realizing the purpose of the invention is as follows:

[0006] A multi-time scale coordinated operation optimization method for a CCHP system considering power demand response, the method comprising:

[0007] Step 1, determine the total load demand, renewable energy output and time-of-use energy purchase price of the system according to historical day-ahead load data;

[0008] Step 2, construct a power demand side response model;

[0009] Step 3, establish models for each device according to the device operation characteristics;

[0010] Step 4, establish device operation characteristic constraints and energy balance constraints;

[0011] Step 5, construct objective functions for the day-ahead stage, intra-day stage and real-time stage;

[0012] Step 6: Based on the established price-based demand response model, equipment models, and constraint conditions, construct the objective functions for the day-ahead stage, intra-day stage, and real-time stage, determine the real-time output plan of the equipment, and finally obtain the smooth output of the equipment.

[0013] The said Step 2 includes: establishing a price-based demand response model, considering the shiftable electric load and the uncertainty of the response of the user's shiftable electric load, using the load transfer rate to characterize the response degree of the end-user, and using the load transfer rates in the peak-valley, peak-flat, and flat-valley periods to fit the electric load after demand response.

[0014] The said Step 3 includes: establishing a micro gas turbine model, a waste heat boiler model, a photovoltaic power generation system model, a refrigeration machine model, a gas boiler model, and an energy storage device model.

[0015] The said Step 4 includes:

[0016] Step 4.1: Establish the equipment operation characteristic constraints: various equipment should meet the upper and lower limits of the output power, the ramp power constraint, and the operation state constraint during operation;

[0017] Step 4.2: Establish the energy balance constraints, including: cold power balance constraint, heat power balance constraint, and electric power balance constraint.

[0018] The objective function for the day-ahead stage in the said Step 5 is:

[0019]

[0020] In the formula: T is the day-ahead scheduling period, 24h; C po,t is the total system energy purchase cost, yuan; C om,t is the total system operation cost, yuan; C en,t is the total system environmental cost, yuan; C Da is the system daily operation cost, yuan.

[0021] The objective function for the intra-day stage in the said Step 5 is:

[0022]

[0023] In the formula: C Din is the operation cost within the control time domain, yuan; M is the length of the control time domain, taking 4h; C po,r,t is the total system energy purchase cost within the control time domain, yuan; C st,r,t is the total penalty cost for the power change of the energy storage device within the control time domain, yuan; l is the current time period, h.

[0024] The objective function for the real-time stage in the said Step 5 is:

[0025]

[0026] In the formula: where α 1 and α 2 are respectively the equipment power adjustment coefficients; the numerator term is the real-time power adjustment amount corresponding to various equipment in the real-time stage, in kW; the denominator term is the rated output power of various equipment, in kW.

[0027] The said step 6 includes:

[0028] Step 6.1: Establish a price-based demand response model, equipment model, and constraint conditions in the Matlab environment, and use the linear programming algorithm to solve the optimal solution to determine the optimal day-ahead scheduling plan and the output status of various equipment;

[0029] Step 6.2: Conduct intraday rolling optimization based on the day-ahead optimization results, and determine the intraday optimization results through continuous rolling optimization; finally, based on the intraday rolling optimization results, determine the real-time output plan of the equipment, and finally obtain the smooth output of the equipment.

[0030] The said step 6.2 is specifically as follows: The day-ahead optimization aims to minimize the sum of the energy purchase cost, equipment operation and maintenance cost, and environmental cost within all time periods of the whole day to determine the day-ahead operation plan; the intraday rolling optimization is based on the day-ahead operation plan and aims to minimize the energy purchase cost and the penalty cost of equipment output change within the control time domain for continuous intraday rolling optimization, so as to repeatedly optimize the equipment output and determine the intraday output plan; while in the real-time optimization stage, based on the intraday rolling optimization results, aiming to minimize the sum of the equipment output power change rates within all time periods, determine the real-time output plan of the equipment, and finally obtain the smooth output of the equipment.

[0031] The beneficial technical effects of the present invention are as follows:

[0032] 1. A multi-time scale coordinated operation optimization method for a CCHP system considering power demand response provided by the present invention conducts day-ahead optimization with the lowest system daily operation cost as the goal in the day-ahead stage; conducts continuous rolling optimization with the lowest energy purchase cost and equipment power change penalty cost within the control time domain as the goal in the intraday stage; and conducts real-time optimization with the minimum equipment power change rate within all time periods as the goal in the real-time stage to determine the real-time output plan of the equipment. By performing day-ahead, intraday, and real-time multi-time scale hierarchical optimization, further optimize the equipment output within the system, and finally determine the real-time smooth output of the system to achieve coordinated, economic, and efficient operation of the system.

[0033] 2. The multi-time scale coordinated operation optimization method of a CCHP system considering power demand response provided by the present invention transfers part of the load during the original peak electricity price period to the valley electricity price period after implementing demand response, while the amount of transferred electricity load during the flat electricity price period is extremely small. Finally, the total electricity load during the peak electricity price period is reduced, and the electricity load during the valley electricity price period is increased, changing the timing distribution of energy consumption.

[0034] 3. The multi-time scale coordinated operation optimization method of a CCHP system considering power demand response provided by the present invention implements multi-time scale operation optimization based on the day-ahead operation results and power load demand response, further suppressing the output fluctuations of the photovoltaic system and user load, and realizing coordinated and economic dispatching of the system. That is, demand-side response is considered during the intraday rolling and implementation adjustment stage, and the real-time operation plan is finally determined, and the daily operation cost of the multi-time scale optimal dispatching considering demand response is calculated. Compared with the day-ahead optimization strategy without considering demand response, the multi-time scale optimization strategy considering electricity demand response can significantly save operation costs and show good economic benefits.

[0035] 4. The multi-time scale coordinated operation optimization method of a CCHP system considering power demand response provided by the present invention implements multi-time scale operation optimization, and each device adjusts its output plan in a timely manner following the change of the load. According to the output results of each device, each device quickly adjusts following the day-ahead output plan, and the output fluctuation of the device is small, which helps to quickly suppress the load fluctuation and realize economic and efficient operation. The results show that the multi-time scale optimization strategy implementing demand response can not only quickly suppress the power fluctuation of the device and realize the safe and stable operation of the device, but also ensure the economy of the system and reduce the daily operation cost. Description of the Drawings

[0036] Figure 1 It is a comprehensive energy system diagram;

[0037] Figure 2 It is a flow chart of the day-ahead, intraday rolling and real-time adjustment optimization process of the CCHP system in the method of the present invention;

[0038] Figure 3 It is the electricity load diagram before and after demand response in the embodiment of the present invention;

[0039] Figure 4 It is the power diagram of the micro gas turbine in the embodiment of the present invention;

[0040] Figure 5 It is the power diagram of the grid interaction in the embodiment of the present invention;

[0041] Figure 6 It is the power diagram of the gas boiler in the embodiment of the present invention;

[0042] Figure 7This is a comparison chart of the cost savings rate after implementing demand response in the embodiments of the present invention. Detailed implementation manners

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0044] A multi-time scale coordinated operation optimization method for a CCHP system considering power demand response provided by the present invention combines the actual demands of energy demand users and the historical load data of users to conduct user load prediction analysis and determine user load demands. At the same time, it combines relevant meteorological data in the region to conduct output power prediction and calculation of the photovoltaic system; combines local electricity price information, introduces a demand response mechanism, and adjusts the energy purchase demands on the user side; establishes a system day-ahead - intra-day rolling - real-time adjustment optimization objective function and constraint characteristic conditions, and based on the objective function, guides the equipment to adjust the output power plan, and finally realizes the optimal output of the equipment, which can provide a calculation reference for the actual system operation optimization. Specifically speaking, the method of the present invention includes the following steps:

[0045] Step 1: According to the historical day-ahead load data, use the Monte Carlo sampling method to determine the total load demand and renewable energy output of the system; according to the actual power purchase price of the power grid, determine the time-of-use energy purchase price;

[0046] Step 2: Construct a power demand side response model;

[0047] The demand side response mechanism uses price information to guide users to improve their electricity consumption behaviors, conduct partial load transfer, thereby alleviating the peak load of the power grid and reducing the total operating cost. Common demand responses are divided into price-based demand response and alternative demand response. The electricity price-based demand response combines time-of-use electricity price information to guide part of the electricity demand to be transferred to different time periods, thereby alleviating the load of the power grid during the peak electricity consumption period and reducing the power grid pressure; while the alternative demand response substitutes different types of energy demands for each other to alleviate the pressure of a single energy supply and give play to the advantages of the coordinated operation of the system.

[0048] In this method, in order to fully and maximally utilize the waste heat of the micro gas turbine and reduce energy waste, it is not considered to use electricity to replace cold energy. Therefore, this method focuses on considering the price-based demand response mechanism to guide users to adjust their electricity consumption behaviors, optimize the system operation, reduce the system operation cost, and improve the system operation efficiency.

[0049] Step 2.1: Establish a price-based demand response model

[0050] Due to the significant differences in the specific components of the electricity load at different times, some of the electricity load can be transferred according to the time-of-use electricity price information. Under the guidance of the time-of-use electricity price, the electricity consumption can be adjusted appropriately to change the time distribution of electricity consumption. Part of the electricity load is transferred from the peak electricity price period to the flat electricity price and valley electricity price periods, thereby alleviating the pressure on the external power grid, achieving "peak shaving and valley filling", enhancing the consumption of renewable energy, and reducing the system operation cost. This method mainly considers the transferable electricity load and simultaneously takes into account the uncertainty of the response of the user's transferable electricity load. By using the load transfer rate to characterize the response degree of the end-user, the load transfer rates in the peak-valley, peak-flat, and flat-valley periods are used to fit the electricity load after the demand response. The model is as follows:

[0051]

[0052] In the formula: is the load range corresponding to each period after implementing the time-of-use electricity price demand response, kW; L e0,t is the load corresponding to each period before implementing the demand response, kW; L n,m and L p,m are the average electricity loads in the flat electricity price period and the peak electricity price period before implementing the demand response, kW; are the upper and lower limits of the peak-valley, flat-valley, and peak-flat electricity load transfer rates respectively.

[0053] Step 3. Establish models for each device according to the device operation characteristics

[0054] The detailed configuration of the integrated energy system is as Figure 1 shown.

[0055] Step 3.1. Establish a micro gas turbine model

[0056] The mathematical models of the power generation power and heat production power of the micro gas turbine are shown in the following formula.

[0057] P mt,t = F mt,t η mt (2)

[0058] P h,t = F mt,t η h (3)

[0059] In the formula: P mt,t is the power generation power of the micro gas turbine, kW; F mt,t is the natural gas consumption power of the micro gas turbine at time t, kW; η mt , η h are the power generation efficiency and heat production efficiency of the micro gas turbine.

[0060] Step 3.2. Establish a waste heat boiler model

[0061] The waste heat boiler can further recover the waste heat of the micro gas turbine, and its mathematical model is:

[0062] P hr,t =P hb,t η hr (4)

[0063] Where: P hr,t is the heat release power of the waste heat boiler, kW; P hb,t is the heat power entering the waste heat boiler, kW; η hr is the flue gas recovery efficiency.

[0064] Step 3.3. Establish a photovoltaic power generation system model

[0065] The power generation of the photovoltaic system is affected by the illumination radiation intensity and the temperature of the photovoltaic panel at the same time, and its mathematical model is:

[0066]

[0067] Where: P PV,t is the power generation power of the photovoltaic system, kW; P STC is the rated power of the photovoltaic system, kW; f PV is the power derating factor of the photovoltaic system, 0.9; Gt is the actual solar illumination radiation intensity, W / m 2 ; G STC is the solar radiation intensity under standard test conditions, 1000W / m 2 ; T STC is the ambient temperature under standard test conditions, 25°C; T amb,t is the actual ambient temperature, °C; k is the power-temperature coefficient of the photovoltaic system, -0.47% / °C; T NOCT is the rated operating temperature of the battery, 47°C; T cell,t is the surface temperature of the photovoltaic system.

[0068] Step 3.4. Establish a refrigerator model

[0069] The mathematical models of the electric refrigerator and the absorption refrigerator are:

[0070] C j,t =P j,t COP j (7)

[0071] Where: C j,t is the refrigeration power of the j-th type of refrigerator, kW; P j,t is the input power of the j-th type of refrigerator, kW; COP j is the refrigeration coefficient of the j-th type of refrigerator.

[0072] Step 3.5. Establish a gas boiler model

[0073] The gas boiler can quickly meet the heat load requirements of users, and its mathematical model is as follows:

[0074] H b,t = η b F b,t (8)

[0075] Where: H b,t is the output thermal power of the gas boiler, kW; F b,t is the natural gas consumption power of the gas boiler, kW; η b is the thermal efficiency of the gas boiler.

[0076] Step 3.6: Establish the energy storage device model

[0077] The energy storage state equation of the energy storage device is:

[0078]

[0079] Where: P i,c,t and P i,disc,t are the energy storage and energy release powers of the i-th type of energy storage device, kW; W i,t is the energy stored in the i-th type of energy storage device, kWh; σ i is the self-consumption rate of the i-th type of energy storage device; U i,c,t and U i,disc,t are the energy storage and energy release flag bits of the energy storage device, which are 0-1 variables; Δt is the time period length, h; η i,c is the energy storage efficiency of the i-th type of energy storage device; η i,disc is the energy supply efficiency of the i-th type of energy storage device; W i,t+1 is the energy storage device capacity at time t+1, kWh; P i,disc,max is the maximum energy supply power of the i-th type of energy storage device, kW; P i,c,max is the maximum energy storage power of the i-th type of energy storage device, kW; W i,max is the rated capacity of the i-th type of energy storage device, kWh; W i,min is the minimum storage capacity of the i-th type of energy storage device.

[0080] Step 4: Establish equipment operation characteristic constraints and energy balance constraints

[0081] Step 4.1: Establish equipment operation characteristic constraints

[0082] All types of equipment should meet the upper and lower limits of the output power, the ramp power constraints and the operation state constraints during operation. The detailed modeling process of each constraint is shown below:

[0083]

[0084] Where: P k,maxand P k,min are the upper and lower limits of the output power of the k-th type of equipment, kW; P k,up and P k,down are the upper and lower limits of the ramping power of the k-th type of equipment, kW; U k,t is the operation status flag of the k-th type of equipment, a 0-1 variable; P k,t is the power of the k-th type of equipment at time t, kW; P k,t+1 is the power of the k-th type of equipment at time t+1, kW.

[0085] Step 4.2: Establish the energy balance constraint

[0086] Cooling power balance constraint: The sum of the cooling powers of various cooling equipment at each moment meets the cooling load demand of users;

[0087] Heating power balance constraint: The sum of the heating powers of various equipment at each moment meets the heating load demand of users;

[0088] Electric power balance constraint: The sum of the power generation of various power generation equipment and the power supply of the power grid at each moment meets the electric load demand of users;

[0089] Step 5: Construct the objective functions for the day-ahead stage, intra-day stage, and real-time stage

[0090] Step 5.1: Construct the day-ahead stage objective function

[0091]

[0092] In the formula: T is the day-ahead scheduling period, 24h; C po,t is the total system energy purchase cost, yuan; C om,t is the total system operation cost, yuan; C en,t is the total system environmental cost, yuan; C Da is the daily operation cost of the system, yuan.

[0093] Step 5.2: Construct the intra-day stage objective function

[0094]

[0095] In the formula: C Din is the operation cost within the control time domain, yuan; M is the length of the control time domain, taken as 4h; C po,r,t is the total system energy purchase cost within the control time domain, yuan; C st,r,t is the total penalty cost for the power change of the energy storage equipment within the control time domain, yuan; l is the current time period, h.

[0096] Step 5.3: Construct the real-time stage objective function

[0097]

[0098] In the formula: 1 and α 2 They are the equipment power adjustment coefficients respectively; the numerator is the real-time power adjustment corresponding to each type of equipment in the real-time stage, kW; the denominator is the rated output power of each type of equipment, kW.

[0099] Step 6: Based on the established price-based demand response model, equipment model, and constraints, the objective functions of the day-ahead phase, intraday phase, and real-time phase are constructed to determine the real-time output plan of the equipment, and finally obtain the smooth output of the equipment.

[0100] Step 6.1, establish the price-based demand response model, equipment model, and constraints in the Matlab environment, use the linear programming algorithm to solve the optimal solution, and determine the best day-ahead scheduling plan and the output status of various equipment;

[0101] In the day-ahead module, long-term optimization is performed with a time step of 1 hour. An operation plan is made every 24 hours, and the optimization is performed with the goal of minimizing the operating cost throughout the day. Based on the constraints of equipment operating characteristics and energy balance constraints, a linear programming solution algorithm is used to determine the best output plan for the day.

[0102] Step 6.2: Perform intraday rolling optimization based on the day-ahead optimization result, and determine the intraday optimization result through continuous rolling optimization; finally, determine the real-time output plan of the equipment based on the intraday rolling optimization result, and finally obtain the smooth output of the equipment.

[0103] Based on the demand response, the CCHP system is optimized step by step in three stages: day-ahead, intra-day rolling and real-time adjustment, and the real-time operation output plan of the CCHP system is finally determined. The flow chart of the optimization process is shown in the figure below. Figure 2 The day-ahead optimization aims to minimize the sum of the energy purchase cost, equipment operation and maintenance cost (including battery loss cost) and environmental cost in all periods of the day, and determines the day-ahead operation plan; the intraday rolling optimization is based on the day-ahead operation plan, and carries out continuous rolling optimization within the day with the goal of minimizing the energy purchase cost and equipment output change penalty cost within the control time domain (4h), so as to repeatedly optimize the equipment output and determine the output plan within the day; and the real-time optimization stage is based on the intraday rolling optimization results, with the goal of minimizing the sum of the equipment output power change rate in all periods, to determine the real-time output plan of the equipment, and finally obtain the smooth output of the equipment.

[0104] Day-ahead optimization: In the day-ahead module, long-term optimization is performed on the day-ahead basis. The time step of the day-ahead optimization is 1 hour. An operation plan is formulated every 24 hours. The optimization is performed with the goal of minimizing the operating cost throughout the day to determine the best output plan for the day.

[0105] Intraday rolling optimization: Based on the day-ahead operation results and intraday load, continuous rolling optimization is carried out during the day to further adjust the device output, and continuously reduce the output deviation between the source and the load. The time resolution of the intraday rolling optimization is 15 minutes, and the control time domain is 4 hours, with a total of 96 time periods. Since the intraday rolling optimization focuses on the power adjustment of the device, the power adjustment amount of the device is used as a penalty term and added to the cost objective function to further suppress the power fluctuation.

[0106] Real-time adjustment: Based on the intraday rolling optimization results, in order to further reduce the device output fluctuation, real-time optimization is carried out with a time step of 5 minutes. Therefore, the real-time adjustment is carried out with the lowest power change rate of all devices in all time periods as the goal, and finally the real-time output plan of the device is determined.

[0107] The corresponding electrical load demand response results of the required demand response are as Figure 3 shown.

[0108] Figure 3 The results show that after the implementation of the demand response, part of the load during the original peak electricity price period is transferred to the valley electricity price period, while the transferred electrical load during the flat electricity price period is extremely small. Finally, the total electrical load during the peak electricity price period is reduced, and the electrical load during the valley electricity price period is increased, changing the energy consumption time sequence distribution.

[0109] The comparison charts of the output power of the micro gas turbine, the grid interaction power, and the gas boiler power before and after the introduction of the demand response mechanism are as Figures 4 - 6 shown.

[0110] Figures 4 - 6 In [Figure], Case 1, Case 2, and Case 3 are specifically explained as follows:

[0111] Case 1: Day-ahead optimization without considering demand-side response;

[0112] Case 2: Day-ahead optimization considering demand-side response;

[0113] Case 3: Based on the day-ahead operation results of Case 2 and the electrical load demand response, Case 3 implements multi-time scale operation optimization to further suppress the output of the photovoltaic system and the load fluctuation of users, and realizes the coordinated and economic dispatch of the system. That is, demand-side response is considered during the intraday rolling and implementation adjustment stages, and finally the real-time operation plan is determined, and the daily operation cost of the multi-time scale optimal dispatch considering demand response is calculated.

[0114] Figures 4 - 6 The results show that the introduction of the demand response mechanism has significantly changed the output power of the micro gas turbine, the power of the gas boiler, and the external grid interaction power compared with before the demand response.

[0115] According to the output results of each device in the figure, each device quickly adjusts according to the day-ahead output plan, and the output fluctuation of the device is small, which helps to quickly suppress the load fluctuation and achieve economic and efficient operation. The results show that the multi-time scale optimization strategy for implementing demand response can not only quickly suppress the power fluctuation of the device and achieve the safe and stable operation of the device, but also ensure the economy of the system and reduce the daily operation cost.

[0116] Based on Scenario 1, the daily operation cost saving rates of Scenario 2 and Scenario 3 implementing demand response are as Figure 7 shown. It can be seen from Figure 7 that the cost saving rates corresponding to Scenario 3 on each typical day are higher than those corresponding to Cost 2. This shows that the multi-time scale optimization strategy after implementing demand response can further reduce the operation cost of the system and improve the economy of the system. This is because the use of the multi-time scale optimization strategy can gradually reduce the deviation between the source and the load, reduce the large fluctuation of the device power, and achieve the most economical energy supply.

[0117] The present invention has been described in detail above in conjunction with the accompanying drawings and embodiments. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the gist of the present invention. The content not described in detail in the present invention can all adopt the prior art.

Claims

1. A multi-time scale coordinated operation optimization method for CCHP system considering power demand response, characterized in that: The method comprises: Step 1: Determine the total load demand, renewable energy output, and time-of-day energy purchase price of the system based on historical day-ahead load data; Step 2: Construct a power demand side response model; Step 3: Establish each equipment model according to the equipment operation characteristics; Step 4: Establish equipment operation characteristic constraints and energy balance constraints; Step 5: Construct objective functions for the day-ahead phase, intraday phase, and real-time phase; Step 6: Based on the established price-based demand response model, equipment model, and constraints, the objective functions of the day-ahead, intraday, and real-time stages are constructed to determine the real-time output plan of the equipment and ultimately obtain the smooth output of the equipment.

2. A CCHP system multi-time scale coordinated operation optimization method taking into account power demand response according to claim 1, characterized in that: The step 2 includes: establishing a price-based demand response model, taking into account the transferable electric load and the uncertainty of the user's transferable electric load response, characterizing the end-user response degree by using the load transfer rate, and fitting the electric load after demand response by using the load transfer rate in the peak-valley, peak-flat and flat-valley time periods.

3. A CCHP system multi-time scale coordinated operation optimization method taking into account power demand response according to claim 1, characterized in that: The step 3 includes: establishing a micro gas turbine model, a waste heat boiler model, a photovoltaic power generation system model, a refrigerator model, a gas boiler model, and an energy storage device model.

4. A CCHP system multi-time scale coordinated operation optimization method taking into account power demand response according to claim 1, characterized in that: The step 4 comprises: Step 4.1, establish equipment operation characteristic constraints: various types of equipment must meet the upper and lower limits of output power, the equipment's climbing power constraints and operating state constraints during operation; Step 4.2: Establish energy balance constraints, including: cooling power balance constraints, heating power balance constraints, and electric power balance constraints.

5. The multi-time scale coordinated operation optimization method of CCHP system taking into account power demand response according to claim 1 is characterized in that: The objective function of the day-ahead phase in step 5 is: Where: T is the day-ahead scheduling period, 24 hours; C po,t is the total energy purchase cost of the system, yuan; C om,t is the total operating cost of the system, yuan; C en,t is the total environmental cost of the system, yuan; C Da is the daily operating cost of the system, yuan.

6. A CCHP system multi-time scale coordinated operation optimization method taking into account power demand response according to claim 1, characterized in that: The intraday objective function in step 5 is: Where: C Din is the operating cost within the control time domain, RMB; M is the length of the control time domain, which is 4h; C po,r,t To control the total system energy cost in the time domain, C st,r,t is the total penalty cost for controlling the power change of energy storage equipment in the time domain, RMB; l is the current time period, h.

7. The multi-time scale coordinated operation optimization method of CCHP system taking into account power demand response according to claim 1 is characterized in that: The real-time objective function in step 5 is: In the formula: α1 and α2 are the equipment power adjustment coefficients respectively; the numerator is the real-time power adjustment corresponding to each type of equipment in the real-time stage, kW; the denominator is the rated output power of each type of equipment, kW.

8. The multi-time scale coordinated operation optimization method of CCHP system taking into account power demand response according to claim 1 is characterized in that: The step 6 comprises: Step 6.1, establish the price-based demand response model, equipment model, and constraints in the Matlab environment, use the linear programming algorithm to solve the optimal solution, and determine the best day-ahead scheduling plan and the output status of various equipment; Step 6.2: Perform intraday rolling optimization based on the day-ahead optimization result, and determine the intraday optimization result through continuous rolling optimization; finally, determine the real-time output plan of the equipment based on the intraday rolling optimization result, and finally obtain the smooth output of the equipment.

9. A CCHP system multi-time scale coordinated operation optimization method taking into account power demand response according to claim 8, characterized in that: The step 6.2 is specifically as follows: the day-ahead optimization aims to minimize the sum of the energy purchase cost, equipment operation and maintenance cost, and environmental cost in all time periods throughout the day, and determines the day-ahead operation plan; the intraday rolling optimization is based on the day-ahead operation plan, and performs intraday continuous rolling optimization with the goal of minimizing the energy purchase cost and equipment output change penalty cost within the control time domain, so as to repeatedly optimize the equipment output and determine the intraday output plan; The real-time optimization stage is based on the intraday rolling optimization results. The goal is to minimize the sum of the equipment output power change rates in all time periods, determine the equipment's real-time output plan, and ultimately obtain the smooth output of the equipment.

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