CN112488374B - Generalized demand response optimization proportioning research method based on time sequence production simulation - Google Patents

Generalized demand response optimization proportioning research method based on time sequence production simulation Download PDF

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CN112488374B
CN112488374B CN202011334259.1A CN202011334259A CN112488374B CN 112488374 B CN112488374 B CN 112488374B CN 202011334259 A CN202011334259 A CN 202011334259A CN 112488374 B CN112488374 B CN 112488374B
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葛毅
李琥
史静
刘国静
陈佳铭
朱永康
胡秦然
王逸飞
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Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses a generalized demand response optimization proportioning research method based on time sequence production simulation, which comprises the following steps: establishing a conventional power generation set model, an external area electricity model and an energy storage model according to power supply parameters, power supply output conditions and external area electricity conditions; according to the resource response condition of the load side of the system, analyzing the characteristics of the residential load and the industrial load when participating in demand response, and establishing a model of the demand side response in the operation of the system; establishing a power system time sequence operation simulation model by taking the optimal operation economy of the power system as a target, and determining a starting combination of the power system; and determining the proportion of the industrial load and the resident load participating in the demand response through time sequence operation simulation by taking the lowest demand response subsidy cost as a target and combining the load side resource response condition. The research method analyzes roles and values of the residential load and the industrial load in demand response from the perspective of power system production simulation.

Description

Generalized demand response optimization proportioning research method based on time sequence production simulation
Technical Field
The invention relates to the field of generalized demand response, in particular to a generalized demand response optimization proportioning research method based on time sequence production simulation.
Background
From the view point of the operation of the power system, the demand response can reduce the power demand of a user in a short time, improve the load curve of the system, equivalently increase the reserve capacity of the power system and ensure the reliable operation of the power system. Under the emergency condition of the system, various demand response items can be implemented, and the occurrence of large-scale power failure accidents can be avoided. Except for relieving the condition of insufficient capacity of the power system, the demand response resources can participate in power market transaction as the power generation resources, so that the supply and demand are kept balanced in real time, and the reliability of a power grid is improved. At present, comprehensive test points for electric power demand side response are successfully implemented in Beijing, Jiangsu, Shanghai, Guangdong, Tianjin, Henan, Zhejiang, Jiangxi and the like in China. It is expected that future demand response projects will be further developed in China, and the influence of demand response needs to be considered in power supply optimization or power system operation analysis, so that the introduction of demand response during power system production simulation has very important practical significance.
Production simulation is an important tool for researching the operation of modern power systems, can provide important indexes such as expected values of production cost, reliability, new energy consumption rate and the like of the systems in an optimal operation mode, and is widely applied to the power industry. Part of provinces in the development of demand response test point work are definitely proposed, and most of the subsidy modes of the industrial load are subsidized directly according to the electric quantity of the industrial users participating in the demand response. The subsidy finally obtained by the resident load through the peak power temporarily reduced in demand response is related to the number of responses, and insufficient number of responses leads to a reduction in subsidy or even no subsidy. The reward and punishment mechanism is an annual system, along with the market industrialization of demand response, a monthly system or an assessment system can be adopted, a certain subsidy standard is given in advance, reward and punishment are given according to the actual performance of a user, and more users are encouraged to participate in the reward and punishment mechanism while the response capacity is ensured. Generally, when the set subsidy price is higher, more residential users voluntarily participate in demand response, but the cost burden of the operation of the power system is increased; when the subsidy price is low, the enthusiasm of the resident users for participating in demand response and the controllability of user behaviors are reduced, and further the uncertainty of the power system is increased. Therefore, a new production simulation-oriented demand response model is constructed to determine subsidy prices of residential users participating in demand response and further calculate the proportion of the residential load and the industrial load in the demand response, and the problem to be solved at present is urgently solved.
Disclosure of Invention
The invention aims to provide a generalized demand response optimization proportioning research method based on time sequence production simulation, which analyzes roles and values of residential loads and industrial loads in demand response from the perspective of power system production simulation; establishing an optimized proportion model of industrial load and resident load in demand response according to a demand response subsidy policy formulated by part of provinces in China at present; the subsidy cost of demand response can be effectively reduced, and a powerful analysis tool is provided for the operation simulation problem of the power system with the demand response.
The purpose of the invention can be realized by the following technical scheme:
a generalized demand response optimization proportioning research method based on time sequence production simulation comprises the following steps:
s1: establishing a conventional power generation set model, an external power model and an energy storage model according to power supply parameters, power supply output conditions and external power conditions;
s2: according to the resource response condition of the load side of the system, analyzing the characteristics of the residential load and the industrial load when participating in demand response, and establishing a model of the demand side response in the system operation;
s3, aiming at optimizing the running economy of the power system, establishing a power system time sequence running simulation model according to the power system time sequence load curve and the output conditions of various types of units, and determining the starting combination of the power system;
s4: and determining the proportion of the industrial load and the resident load participating in the demand response through time sequence operation simulation by taking the lowest demand response subsidy cost as a target and combining the load side resource response condition.
Further, the S1 specifically includes:
s11: analyzing the output characteristics of the conventional unit; the actual output of the thermal power generating unit is close to or reaches the rated output, the gas turbine considers that the gas turbine operates at the peak load position of the system, and the output of the gas condensing type unit cannot be smaller than the minimum technical output of the unit;
s12: forecasting the output of renewable energy sources, and introducing a new energy source cutting mechanism into an operation simulation model, so that the model cuts out part of the output of the renewable energy sources under the conditions that the system cannot provide peak regulation capacity, the system spare capacity is insufficient or the new energy sources are blocked to be sent out; controlling the actual output of the new energy to operate within a range of a predicted value or below according to the measured value before the day; according to the requirements of environmental protection and energy conservation of the operation of the power grid, the new energy is required to be completely connected to the Internet under the condition that the power grid can accept;
s13: modeling the regional external electricity by adopting a three-section type output model according to the load characteristic of a receiving-end power grid; from the angle of a receiving-end power grid, on the premise that the composition of a transmitting-end wind power and thermal power installation machine is determined, the peak regulation is carried out on the receiving-end power grid by utilizing the regulation capacity of the receiving-end power grid; modeling thinking of the out-of-area incoming call participating in production simulation;
s14: the energy storage power station charging and discharging model is as follows:
Figure BDA0002796689690000031
further, said WtThe electric quantity stored in the energy storage power station at the moment t; mu.slossThe energy loss rate of the energy storage power station is self; etach、ηdisRespectively representing the charging efficiency and the discharging efficiency of the energy storage power station; pch,t、Pdis,tRespectively charging power and discharging power of the energy storage power station at the moment t; Δ t is the time interval; u shapech,t、Udis,tThe charging and discharging states of the energy storage power station at the time t are Uch,t、Udis,tIs a variable from 0 to 1, and is,
Figure BDA0002796689690000032
the maximum charging power and the maximum discharging power of the energy storage power station are respectively.
Further, the S2 specifically includes:
s21, analyzing the demand response characteristics of the resident load; dividing the load of residents into three categories of uncontrollable load, transferable load and reducible load; the transferable load and the interruptible load can be used as an active load to participate in demand response, so that the consumption demand of the distributed power supply is met; the maximum capacity which can participate in peak clipping response and is provided by the load of residents in the system at each moment is as follows:
M'max(t)＝Rtotal·r·Pav·sim(t)·lp·cmax
s22, analyzing the demand response characteristics of the load of the residents; in a system for providing frequency modulation and non-rotating standby service only considering loads, the capacity of the industrial loads participating in demand response at t moment in a scheduling period for providing maximum participatory peak clipping response is as follows:
M”max(t)＝∑Prmax(t)+∑τiPnsmax(t)
further, said PrmaxMaximum frequency modulation capacity, tau, available for industrial enterprises participating in demand response at all timesiPnsmax(t) the maximum rotational reserve conversion capacity that can be provided for the system load at each moment; rtotalThe total number of the residential users in the power system, r is the proportion of the residential users participating in demand response, and the size of the residential users is positively correlated with the subsidy price; pavAverage value of controllable load loading amount for single resident user; sim (t) is the simultaneous utilization rate of the controllable load at each moment; lp is the average load rate of the resident users using the controllable load; c. CmaxThe maximum load shedding rate of the controllable load.
Further, the S3 specifically includes:
s31, in order to improve the economic benefit of the power grid, the selected system objective function is used for carrying out time sequence production simulation for the optimal operation economy of the power system; the objective function is as follows:
Figure BDA0002796689690000041
s32, the output restraint of the thermal power generating unit mainly comprises upper and lower limit restraint of the output, climbing and landslide restraint of the unit and minimum start-stop time restraint, and comprises the following steps:
Figure BDA0002796689690000051
s33, except the constraints of other conventional units, new energy sources and energy storage in the step 1, other constraints also exist in the power system time sequence operation simulation model as follows:
and power balance constraint:
Figure BDA0002796689690000052
standby constraint:
Figure BDA0002796689690000053
further, T is the total number of time periods in the optimization cycle; j is the subscript of the generator set, and the total number of the generator sets is Nj(ii) a The generator set can be regarded as a set of a start-stop unit and a non-start-stop unit; ciGenerating cost curves corresponding to the generator sets; pj,tThe generated energy of the unit j at the time t;
Figure BDA0002796689690000054
the start-stop cost of the unit at the time j is t; voll is the load loss of the system, LS (t) is the load power of the system at each moment; pout(t) is the electric quantity of the extra-district power, and C (t) is the unit price of the extra-district power; p iscmin，PcmaxRespectively the minimum output and the maximum output of the unit which can not be started and stopped; pfmin，PfmaxRespectively the minimum output and the maximum output of the machine set which can be started and stopped; i iscThe state variable of the unit can not be started and stopped in the day,
Figure BDA0002796689690000055
the state variable of the unit can be started and stopped in the day;
Figure BDA0002796689690000056
the climbing rate and the landslide rate of the unit which can not be started or stopped and the unit which can be started or stopped are respectively set; TO and TS are respectively the minimum starting time and the minimum stopping time of the machine set which can be started and stopped;
Figure BDA0002796689690000057
respectively are the output values of the unit which can not be started and stopped and the unit which can be started and stopped at the moment t.
Furthermore, dr (t), vf (t) are the electric quantities involved in the peak clipping response and the valley filling response in the power system at each time, and the magnitude of the electric quantities is determined by a load curve and a demand response policy; load (t) is the load of the system at each moment; n is a radical ofc、NfThe total number of the units which can not be started and stopped and the total number of the units which can be started and stopped are set; pnu(t) the output of the nuclear power generating units at each moment, and because the operating cost of the nuclear power generating units is lower and the safety is limited, the nuclear power generating units do not participate in peak shaving of the system during normal operation, and each unit operates at full load;
Figure BDA0002796689690000061
respectively setting the installed capacities of a unit which can not be started and stopped and a unit which can be started and stopped; ρ is the power system backup rate.
Further, the S4 specifically includes:
s41: considering the economy as a target, establishing an optimized proportion model of the industrial load and the residential load in the demand response, and regarding the electric quantity participating in the demand response at each moment as the sum of the residential load response and the industrial load response, wherein the model optimization target is as follows:
Figure BDA0002796689690000062
s42: the model constraints are as follows:
st.VF1(t)+VF2(t)＝VF(t)
DR1(t)+DR2(t)＝DR(t)
0≤DR1(t)≤M'max(t)
0≤DR2(t)≤M”max(t)
0≤VF1(t)≤M'max(t)
0≤VF2(t)≤M”max(t)
furthermore, T is the total time interval of the system optimization cycle, T' is a subsidy cycle of the load participation of residents in demand response, subsidies are made year by year, and a monthly system or an assessment system is adopted as the market of the demand response is more mature; cost1、cost2Subsidy prices of electric quantity per kilowatt hour corresponding to the peak clipping response and the valley filling response of the industrial load are respectively; subsidy (i) subsidizing the price per period for the corresponding residents under the participation rate; DR (digital radiography)2(t)、VF2(t) the industrial loads participating in peak clipping and valley filling demand responses, respectively; DR (digital radiography)1(t)、VF1(t) loads of residents participating in peak clipping and valley filling responses, respectively.
The invention has the beneficial effects that:
1. the research method analyzes roles and values of residential loads and industrial loads in demand response from the perspective of power system production simulation;
2. the research method establishes an optimized proportion model of industrial load and resident load in demand response according to a demand response subsidy policy formulated by part of provinces in China at present;
3. the research method can effectively reduce the subsidy cost of demand response and provide a powerful analysis tool for the operation simulation problem of the power system with the demand response.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of the research method of the present invention;
FIG. 2 is a schematic diagram of a modeling concept of an out-of-range call participating in production simulation according to the present invention;
FIG. 3 is a schematic diagram of a timing load curve structure according to the present invention;
FIG. 4 is a schematic view of the annual wind predicted output structure of the present invention;
FIG. 5 is a schematic view of the annual photovoltaic predicted output structure of the present invention;
FIG. 6 is a schematic diagram of peak clipping and valley filling power of demand response at various times throughout the year in accordance with the present invention;
FIG. 7 is a diagram of the load participation peak clipping and valley filling response electric quantity of residents at various moments throughout the year;
FIG. 8 is a schematic diagram of the peak clipping and valley filling response electric quantity of the industrial load at each time of the year according to the present invention;
fig. 9 is a schematic diagram of a line of the proportion of subsidy to participation in demand response desired by the residents of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A generalized demand response optimization proportioning research method based on time sequence production simulation comprises the following steps:
s1: establishing a conventional power generation set model, an external power model and an energy storage model according to power supply parameters, power supply output conditions and external power conditions;
the method specifically comprises the following steps:
s11: analyzing the output characteristics of the conventional unit; the actual output of the thermal power generating unit approaches or reaches the rated output, the gas turbine considers that the gas turbine operates at the peak load position of the system, and the output of the condensing gas type unit cannot be smaller than the minimum technical output of the unit;
s12: forecasting the output of renewable energy sources, and introducing a new energy source cutting mechanism into an operation simulation model, so that the model cuts out part of the output of the renewable energy sources under the conditions that the system cannot provide peak regulation capacity, the system spare capacity is insufficient or the new energy sources are blocked to be sent out; controlling the actual output of the new energy to operate within a range of a predicted value or below according to the measured value before the day; according to the requirements of environmental protection and energy conservation of the operation of the power grid, the new energy is required to be completely on line under the condition that the power grid can accept.
S13: according to the load characteristics of a receiving-end power grid, modeling the external electricity by adopting a three-stage output model; from the angle of a receiving-end power grid, on the premise that the composition of a transmitting-end wind power and a thermal power installation machine is determined, the peak regulation of the receiving-end power grid is realized by utilizing the regulation capacity of the receiving-end power grid; the modeling idea of the out-of-range call participating in the production simulation is shown in FIG. 2.
S14: the energy storage power station charging and discharging model is as follows:
Figure BDA0002796689690000081
in the formula: wtThe electric quantity stored in the energy storage power station at the moment t; mu.slossThe energy loss rate of the energy storage power station is self; etach、ηdisRespectively charging efficiency and discharging efficiency of the energy storage power station; p isch,t、Pdis,tRespectively charging power and discharging power of the energy storage power station at the moment t; Δ t is the time interval; u shapech,t、Udis,tRespectively is the charging and discharging state of the energy storage power station at the time t, Uch,t、Udis,tIs a variable of 0 to 1, and is,
Figure BDA0002796689690000091
the maximum charging power and the maximum discharging power of the energy storage power station are respectively.
S2: according to the resource response condition of the load side of the system, analyzing the characteristics of the residential load and the industrial load when participating in demand response, and establishing a model of the demand side response in the system operation;
s21, analyzing the demand response characteristics of the load of the residents; dividing the load of residents into three categories of uncontrollable load, transferable load and reducible load; the power on/off of an uncontrollable load such as lighting equipment can influence the normal life of residents, and the uncontrollable load does not participate in demand response; loads such as electric water heaters, dish washing machines and other equipment can be transferred, the electricity utilization time is flexible, and the work requirement can be completed within a certain time; the interruptible load is powered off in a short time without influencing the normal life of residents, so that the transferable load and the interruptible load can be used as an active load to participate in demand response, and the consumption demand of the distributed power supply is met;
the maximum capacity which can participate in peak clipping response and is provided by the load of residents in the system at each moment is as follows:
M'max(t)＝Rtotal·r·Pav·sim(t)·lp·cmax
in the formula: rtotalThe total number of the residential users in the power system, r is the proportion of the residential users participating in demand response, and the size of the residential users is positively correlated with the subsidy price; p isavAverage value of controllable load loading amount for single resident user; sim (t) is the simultaneous utilization rate of the controllable load at each moment; lp is the average load rate of the resident users using the controllable load; c. CmaxThe maximum load shedding rate of the controllable load.
S22, analyzing the demand response characteristics of the resident load; the industrial load is different due to different factors such as production equipment, process flow, electricity price sensitivity and the like, and the potential and the economical efficiency for providing demand response are greatly different. For example, the steel industry has higher requirement on the reliability of power supply of electric equipment, most production equipment is of primary or secondary load, the load rate is higher, the coking, smelting and other links belong to the class I load and have no potential of peak load shifting and valley filling due to the production process requirements, and partial loads in other process links, such as steel rolling equipment, belong to the class II load and have interruptible load proportion; the daily load curve presents obvious peak-valley characteristics, so that the mechanical manufacturing industry has greater peak-shifting production potential and strong load transfer capacity; the equipment such as the rush load and the electric heating furnace in the arrangement industry is arranged to be consumed in the low-ebb period of the power grid, and a large part of peak load is transferred. In a system for providing frequency modulation and non-rotating standby service only considering loads, the capacity of the industrial loads participating in demand response at t moment in a scheduling period for providing maximum participatory peak clipping response is as follows:
M”max(t)＝∑Prmax(t)+∑τiPnsmax(t)
in the formula: prmaxMaximum frequency modulation capacity, tau, available for industrial enterprises participating in demand response at all timesiPnsmax(t) the maximum spinning reserve available for system load at each time.
S3, aiming at optimizing the running economy of the power system, establishing a power system time sequence running simulation model according to the power system time sequence load curve and the output condition of each type of unit, and determining the starting combination of the power system;
s31, in order to improve the economic benefit of the power grid, the selected system objective function is used for carrying out time sequence production simulation for optimizing the operation economy of the power system; the objective function is as follows:
Figure BDA0002796689690000101
in the formula: t is the total time interval in the optimization cycle; j is subscript of generator set, and the total number of the generator sets is Nj(ii) a The generator set can be regarded as a set of a start-stop unit and a non-start-stop unit; ciGenerating cost curves corresponding to the generator sets; pj,tThe generated energy of the unit j at the time t;
Figure BDA0002796689690000102
the start-stop cost of the unit at the time j is t; voll is the load shedding loss of the system, LS (t) is the load shedding power of the system at each moment. Pout(t) is the amount of electricity from the outside, and C (t) is the unit price of electricity corresponding to the electricity from the outside.
S32, the output restraint of the thermal power generating unit mainly comprises upper and lower limit restraint of the output, climbing and landslide restraint of the unit and minimum start-stop time restraint, and comprises the following steps:
Figure BDA0002796689690000111
in the formula: pcmin，PcmaxRespectively the minimum output and the maximum output of the unit which can not be started and stopped; pfmin，PfmaxRespectively the most capable of starting and stopping the machine setSmall and maximum output; i iscThe state variable of the unit can not be started and stopped in the day,
Figure BDA0002796689690000112
the state variable of the unit can be started and stopped in the day;
Figure BDA0002796689690000113
the climbing rate and the landslide rate of the unit which can not be started or stopped and the unit which can be started or stopped are respectively; TO and TS are respectively the minimum starting time and the minimum stopping time of the machine set which can be started and stopped;
Figure BDA0002796689690000114
Figure BDA0002796689690000115
respectively are the output values of the machine set which can not be started and stopped and the machine set which can be started and stopped at the moment t.
S33, except the constraints of other conventional units, new energy sources and energy storage in the step 1, other constraints also exist in the power system time sequence operation simulation model as follows:
and power balance constraint:
Figure BDA0002796689690000116
in the formula: DR (t), VF (t) are the electric quantity participating in peak clipping response and valley filling response in the power system at each moment, and the numerical value is determined by a load curve and a demand response policy; load (t) is the load of the system at each moment; n is a radical of hydrogenc、NfThe total number of the units which can not be started or stopped is the total number of the units which can be started or stopped; pnuAnd (t) the output of the nuclear power unit at each moment, and because the operating cost of the nuclear power unit is low and the safety is limited, the nuclear power unit does not participate in peak shaving of the system during normal operation, and each unit operates at full load.
Standby constraint:
Figure BDA0002796689690000121
in the formula:
Figure BDA0002796689690000122
respectively the installed capacities of the unit which can not be started and stopped and the unit which can be started and stopped; ρ is the power system utilization.
S34, adopting a time sequence operation simulation method to simulate the production of a certain power saving network; the total time length of time sequence production simulation is 8760 time periods, and the time interval of simulation is 1 h; the time-series load curve and the wind and photovoltaic predicted force are obtained as shown in fig. 3, fig. 4 and fig. 5 respectively.
S35, obtaining peak clipping and valley filling electric quantity of the demand response at each moment of the whole year by combining the forecast data of the province according to the electric power system time sequence production simulation model established in S33, as shown in FIG. 6; the peak clipping is completed for 60 hours and the maximum response capacity is 750 ten thousand kilowatts all the year through the demand response according to the statistics of the optimized full-time load curve, and the total electric quantity is 2.16 hundred million kilowatt hours; the valley filling is completed by the demand response for 15 hours, the maximum response capacity is 218 kilowatts, and the total electric quantity is 0.123 hundred million kilowatt hours.
S4: the lowest demand response subsidy cost is taken as a target, and the proportion of the industrial load and the resident load participating in demand response is determined through time sequence operation simulation in combination with the load side resource response condition;
s41: considering the economy as a target, establishing an optimized proportion model of the industrial load and the residential load in the demand response, and regarding the electric quantity participating in the demand response at each moment as the sum of the residential load response and the industrial load response, wherein the model optimization target is as follows:
Figure BDA0002796689690000123
s42: the model constraints are as follows:
st.VF1(t)+VF2(t)＝VF(t)
DR1(t)+DR2(t)＝DR(t)
0≤DR1(t)≤M'max(t)
0≤DR2(t)≤M”max(t)
0≤VF1(t)≤M'max(t)
0≤VF2(t)≤M”max(t)
in the formula: t is the total time interval of the system optimization cycle, T' is the subsidy cycle of the resident load participating in the demand response, subsidies are subsidized year by year, and a monthly system or an assessment system is adopted as the market of the demand response is more mature; cost1、cost2Respectively subsidy prices of electric quantity per kilowatt hour corresponding to the industrial load participating in peak clipping response and valley filling response; subsidy (i) subsidizing the price per period for the corresponding residents under the participation rate; DR (digital radiography)2(t)、VF2(t) the industrial loads involved in peak clipping and valley filling demand responses, respectively. DR (digital radiography)1(t)、VF1(t) loads of residents participating in peak clipping and valley filling responses, respectively.
S43: according to the province demand response policy, in the aspect of peak clipping, the demand response of 5% of the full province maximum load of a full time sequence load curve is considered; in the aspect of filling valley, filling valley within day for the day with the peak-valley difference rate of more than 35% in the load curve characteristic;
s44: the subsidy mode of the industrial load is subsidy according to the electricity consumption participating in the demand response: the electricity price standard is 10 yuan/kilowatt hour when the regulation time is less than 60 minutes, the electricity price standard is 12 yuan/kilowatt hour when the regulation time is between 60 and 120 minutes, and the electricity price standard is 15 yuan/kilowatt hour when the regulation time is more than 120 minutes; for temporary increase (fill-in-valley) of load through demand response, promotion of renewable energy power consumption, execution of renewable energy consumption subsidy: the consumption subsidy of renewable energy sources in the appointed response valley period is 5 yuan/kilowatt, and the subsidy in the ordinary period is 8 yuan/kilowatt; the subsidy mode of the load of the residents is to give subsidies to the residents who voluntarily participate in the demand response according to the month. According to the investigation report of the province about the subsidy expected by the participation of the residential users in the demand response, the data obtained in the report are subjected to quadratic fitting, and a proportional line graph of the subsidy expected by the residential users in the province and the participation demand response is obtained, as shown in fig. 7.
S45: according to the model established in the S2, acquiring the electric quantity of the resident load and the industrial load participating in the demand response at each moment of the whole year in the whole province, wherein the resident load is shown in a figure 8, and the industrial load is shown in a figure 9; the province does not need to carry out demand response within 3-5 months and 9-11 months; the other load of each month and the load of industry participate in demand response electric quantity, proportion and subsidy cost are shown in table 1;
TABLE 1 develop demand response month subsidy case
Figure BDA0002796689690000141
The demand response times in 7-8 months are more, and 11.3 percent and 24.9 percent of residential users should be subsidized in 7 months and 8 months respectively to encourage the users to participate in the demand response, wherein the subsidizing prices are 47 yuan/month and 56 yuan/month respectively for each household; in other months, due to the smaller number of responses, it is better to schedule only the industrial load to participate in the response, so that the required subsidy cost is lower.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (7)

1. A generalized demand response optimization proportioning research method based on time sequence production simulation is characterized by comprising the following steps:
s1: establishing a conventional power generation set model, an external area electricity model and an energy storage model according to power supply parameters, power supply output conditions and external area electricity conditions;
s2: according to the resource response condition of the load side of the system, analyzing the characteristics of the residential load and the industrial load when participating in demand response, and establishing a model of the demand side response in the system operation;
s3, aiming at optimizing the running economy of the power system, establishing a power system time sequence running simulation model according to the power system time sequence load curve and the output condition of each type of unit, and determining the starting combination of the power system;
s4: the lowest demand response subsidy cost is taken as a target, and the proportion of the industrial load and the resident load participating in demand response is determined through time sequence operation simulation in combination with the load side resource response condition;
the S1 specifically includes:
s11: analyzing the output characteristics of the conventional unit; the actual output of the thermal power generating unit is close to or reaches the rated output, the gas turbine considers that the gas turbine operates at the peak load position of the system, and the output of the gas condensing type unit cannot be smaller than the minimum technical output of the unit;
s12: forecasting the output of renewable energy sources, and introducing a new energy source cutting mechanism into an operation simulation model, so that the model cuts out part of the output of the renewable energy sources under the conditions that the system cannot provide peak regulation capacity, the system spare capacity is insufficient or the new energy sources are blocked to be sent out; controlling the actual output of the new energy to operate within a range of a predicted value or below according to the measured value before the day; according to the requirements of environmental protection and energy conservation of the operation of the power grid, the new energy is required to be completely connected to the Internet under the condition that the power grid can accept;
s13: modeling the regional external electricity by adopting a three-section type output model according to the load characteristic of a receiving-end power grid; from the angle of a receiving-end power grid, on the premise that the composition of a transmitting-end wind power and a thermal power installation machine is determined, the peak regulation of the receiving-end power grid is realized by utilizing the regulation capacity of the receiving-end power grid; modeling thinking of the out-of-area incoming call participating in production simulation;
s14: the charging and discharging model of the energy storage power station is as follows:
Figure FDA0003673005690000021
the S2 specifically includes:
s21: analyzing demand response characteristics of the load of the residents; dividing the load of residents into three categories of uncontrollable load, transferable load and reducible load; the transferable load and the interruptible load can be used as an active load to participate in demand response, so that the consumption demand of the distributed power supply is met; the maximum capacity which can participate in peak clipping response and is provided by the load of residents in the system at each moment is as follows:
M’max(t)＝Rtotal·r·Pav·sim(t)·lp·cmax
s22: analyzing demand response characteristics of the load of the residents; in a system for providing frequency modulation and non-rotating standby service only considering loads, the capacity of the industrial loads participating in demand response at t moment in a scheduling period for providing maximum participatory peak clipping response is as follows:
M”max(t)＝∑Prmax(t)+∑τiPnsmax(t)
the S3 specifically includes:
s31: in order to improve the economic benefit of the power grid, a selected system objective function is used for carrying out time sequence production simulation on the optimal operation economy of the power system; the objective function is as follows:
Figure FDA0003673005690000022
s32, the output restraint of the thermal power generating unit mainly comprises upper and lower limit restraint of the output, climbing and landslide restraint of the unit and minimum start-stop time restraint, and comprises the following steps:
Figure FDA0003673005690000031
s33, except the constraints of other conventional units, new energy sources and energy storage in the step 1, other constraints also exist in the power system time sequence operation simulation model as follows:
and power balance constraint:
Figure FDA0003673005690000032
standby constraint:
Figure FDA0003673005690000033
2. the generalized demand response optimal proportioning research method based on time series production simulation of claim 1, wherein W istThe electric quantity stored in the energy storage power station at the moment t; mu.slossThe energy loss rate of the energy storage power station is self; etach、ηdisRespectively representing the charging efficiency and the discharging efficiency of the energy storage power station; pch,t,Pdis,tRespectively charging power and discharging power of the energy storage power station at the moment t; Δ t is the time interval; u shapech,t、Udis,tRespectively is the charging and discharging state of the energy storage power station at the time t, Uch,t、Udis,tIs a variable of 0 to 1, and is,
Figure FDA0003673005690000041
the maximum charging power and the maximum discharging power of the energy storage power station are respectively.
3. The generalized demand response optimal matching research method based on time series production simulation as claimed in claim 1, wherein p isrmaxMaximum frequency modulation capacity, tau, available for industrial enterprises participating in demand response at each momentiPnsmax(t) the maximum rotational reserve conversion capacity that can be provided for the system load at each moment; r istotalThe total number of the residential users in the power system, r is the proportion of the residential users participating in demand response, and the size of the residential users is positively correlated with the subsidy price; pavAverage value of controllable load loading amount for single resident user; sim (t) is eachThe simultaneous utilization rate of the controllable load at any moment; 1p is the average load rate of the resident users using the controllable load; cmaxThe maximum load shedding rate of the controllable load.
4. The generalized demand response optimal matching research method based on time series production simulation of claim 1, wherein T is a total number of time periods within an optimization cycle; j is the subscript of the generator set, and the total number of the generator sets is Nj(ii) a The generator set can be regarded as a set of a start-stop unit and a non-start-stop unit; ciGenerating cost curves corresponding to the generator sets; pj，tThe generated energy of the unit j at the time t;
Figure FDA0003673005690000046
the start-stop cost of the unit at the time j is t; voll is the load loss of the system, LS (t) is the load power of the system at each moment; pout(t) is the electric quantity of the extra-district power, and C (t) is the unit price of the extra-district power; p iscmin，PcmaxRespectively the minimum output and the maximum output of the unit which can not be started and stopped; p isfmin，PfmaxRespectively the minimum output and the maximum output of the machine set which can be started and stopped; i iscThe state variable of the unit can not be started and stopped in the day,
Figure FDA0003673005690000045
the state variable of the unit can be started and stopped in the day;
Figure FDA0003673005690000042
Figure FDA0003673005690000043
the climbing rate and the landslide rate of the unit which can not be started or stopped and the unit which can be started or stopped are respectively set; TO and TS are respectively the minimum starting time and the minimum stopping time of the machine set which can be started and stopped;
Figure FDA0003673005690000044
respectively are the output values of the unit which can not be started and stopped and the unit which can be started and stopped at the moment t.
5. The generalized demand response optimization proportioning research method based on time series production simulation of claim 1, wherein dr (t), vf (t) are the electric quantities participating in peak clipping response and valley filling response in the power system at each moment, and the magnitude of the electric quantities is determined by a load curve and a demand response policy; load (t) is the load of the system at each moment; n is a radical of hydrogenc、NcThe total number of the units which can not be started and stopped and the total number of the units which can be started and stopped are set; p isnu(t) the output of the nuclear power generating units at each moment, and because the operating cost of the nuclear power generating units is lower and the safety is limited, the nuclear power generating units do not participate in peak shaving of the system during normal operation, and each unit operates at full load;
Figure FDA0003673005690000051
respectively the installed capacities of the unit which can not be started and stopped and the unit which can be started and stopped; ρ is the power system backup rate.
6. The method of claim 1, wherein the S4 specifically includes:
s41: considering the economy as a target, establishing an optimized proportion model of the industrial load and the residential load in the demand response, and regarding the electric quantity participating in the demand response at each moment as the sum of the residential load response and the industrial load response, wherein the model optimization target is as follows:
Figure FDA0003673005690000052
s42: the model constraints are as follows:
st.VF1(t)+VF2(t)＝VF(t)
DR1(t)+DR2(t)＝DR(t)
0≤DR1(t)≤M’max(t)
0≤DR2(t)≤M”max(t)
0≤VF1(t)≤M’max(t)
0≤VF2(t)≤M”max(t)。
7. the generalized demand response optimization proportioning research method based on time series production simulation of claim 6, wherein T is the total time period of a system optimization cycle, T' is a subsidy cycle of resident load participation demand response, subsidies are annual, and a monthly system or an assessment system is adopted as the demand response market becomes more mature; cost1、cost2Subsidy prices of electric quantity per kilowatt hour corresponding to the peak clipping response and the valley filling response of the industrial load are respectively; subsidy (i) subsidizing the price per period for the corresponding residents under the participation rate; DR (digital radiography)2(t)、VF2(t) the industrial loads participating in peak clipping and valley filling demand responses, respectively; DR (digital radiography)1(t)、VF1(t) loads of residents participating in peak clipping and valley filling responses, respectively.
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