CN111932014B - Wind farm-IESP cooperative operation optimization method considering risk avoidance - Google Patents
Wind farm-IESP cooperative operation optimization method considering risk avoidance Download PDFInfo
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Abstract
The invention discloses a wind farm-IESP collaborative operation optimization method considering risk avoidance, which comprises the following steps: s1, determining IESP and energy supply equipment to participate in a main body, and constructing a wind power plant-IESP cooperative operation mode frame; s2, according to a wind power plant-IESP cooperative operation mode framework, an electric quantity deviation index existing between the consumption demand of the wind power plant and IESP service electric quantity is provided by considering the influence of wind power output and electric and thermal load uncertainty; s3, taking an electric and thermal load demand response and energy supply equipment participation main body as a wind power plant-IESP cooperative operation mode frame to reduce parameter indexes of risk loss, and further providing a comprehensive energy system response model; and S4, establishing a risk avoidance model based on the comprehensive energy system response model by taking IESP operation benefits and the maximization of the risk avoidance degree as targets, and solving the risk avoidance model by adopting a particle swarm algorithm. The risk existing in the cooperative process of the wind power plant-IESP is effectively eliminated, and the operation income and the new energy consumption level of IESP are improved.
Description
Technical Field
The invention mainly relates to the technical field of new energy permeability power systems, in particular to a wind farm-IESP collaborative operation optimization method considering risk avoidance.
Background
In recent years, due to the increasing of the new energy ratio and the insufficient regulation capability of the controllable power supply, a serious wind-discarding, light-discarding and water-discarding phenomenon is generated in the operation of the power system, and the new energy loss of China is equivalent to the annual total power generation amount of the three gorges power station in 2018. In order to solve the problem of new energy consumption, the national modification Committee (2018 energy work guidance opinion) specially provides guidance and requirements for energy guarantee; however, in different areas and periods, the peak power load is high, and power consumption limiting or orderly power supply measures have to be taken frequently. In order to solve such contradiction and improve the new energy consumption level, the current power system has achieved a relatively rich result in terms of integrated energy systems (INTEGRATEDENERGYSYSTEM, IES). With the deep advancement of the reform of the electric power system and the rapid development of the electric power emerging industry, an electric power industry ecological system which takes customers as centers, takes an electric power trade market and an energy derived market as carriers, participates in a multiparty trade main body and provides various energy services is gradually formed. Thus, the electric company will gradually change to IESP (INTEGRATED ENERGY SERVICE provider, integrated energy service). And through IESP, various energy supply devices and thermoelectric loads are regulated and controlled, so that the wind power is coordinated and consumed. However, researches on a certain risk in the coordinated wind power generation and power generation process of the wind power plant-IESP caused by the uncertainty of the electric heating load are quite fresh.
Disclosure of Invention
In view of the above, the present invention aims to provide a wind farm-IESP collaborative operation optimization method for risk avoidance, which uses the electric and thermal load demand response and energy supply equipment participation subject considering the user electricity consumption psychology and the user heat satisfaction, so as to effectively eliminate the risk existing in the collaborative process of the wind farm-IESP and improve the operation income and the new energy consumption level of IESP.
The invention discloses a wind farm-IESP collaborative operation optimization method considering risk avoidance, which comprises the following steps of:
s1, determining IESP and energy supply equipment to participate in a main body, and constructing a wind power plant-IESP cooperative operation mode frame;
s2, according to a wind power plant-IESP cooperative operation mode framework, an electric quantity deviation index existing between the consumption demand of the wind power plant and IESP service electric quantity is provided by considering the influence of wind power output and electric and thermal load uncertainty;
S3, taking an electric and thermal load demand response and energy supply equipment participation main body as a wind power plant-IESP cooperative operation mode frame to reduce parameter indexes of risk loss, and further providing a comprehensive energy system response model;
and S4, establishing a risk avoidance model based on the comprehensive energy system response model by taking IESP operation benefits and the maximization of the risk avoidance degree as targets, and solving the risk avoidance model by adopting a particle swarm algorithm.
Further, the step S1 is specifically expressed as:
s11, on the basis of comprehensive energy supply of a plurality of traditional energy sources, combining wind farm resources, and simultaneously aggregating user comprehensive demand response resources to establish a IESP operation mode;
s12, determining IESP an operation mode and selecting energy supply equipment for supplying power and heat to participate in a main body;
s13, combining the IESP operation modes with a wind farm to construct a collaborative operation mode framework of the wind farm-IESP.
Further, the expression of the power deviation index in step S2 is:
|ΔPt|＝|Pt dem-ΔPt IESP| (1)
Wherein Δp t is an electric quantity deviation index, P t dem is a consumption demand of the wind farm, and obeys gaussian distribution, Δp t IESP is electric power obtained from a power grid in a period t compared with the electric power obtained before the wind farm operates cooperatively, and when Δp t IESP is more than 0, IESP is to increase the electric power to consume surplus power of the wind farm; when Δp t IESP < 0 indicates IESP to reduce the electrical power to provide an equivalent output to the wind farm; when Δp t IESP =0, IESP is not involved in wind power absorption.
Further, the consumption demand P t dem of the wind farm is a difference between the wind power output and the wind power prescheduled output, and is specifically expressed as follows: when the wind power output is lower than the wind power pre-dispatching output, IESP is required to call the common output of each participating main body to compensate the system dispatching requirement; when the wind power output is higher than the wind power pre-scheduling output, IESP is required to reduce the amount of electricity purchased from the grid, i.e. equivalent to providing an equivalent output to the wind farm.
Further, the comprehensive energy system response model in the step S3 is specifically as follows:
1) Electric load model:
Wherein Δp 1、ΔP2、ΔPm represents the relative increase in power demand in the 1 st period of the scheduling period, the relative increase in power demand in the 2 nd period of the scheduling period, and the relative increase in power demand in the m th period of the scheduling period, respectively; q 1、q2、qm represents the electricity price of the 1 st period before demand response, the electricity price of the 2 nd period before demand response, and the electricity price of the m th period before demand response, respectively; deltaq 1、Δq2、Δqm represents the relative increase in power rates at the 1 st period in the schedule period, the relative increase in power rates at the 2 nd period in the schedule period, the relative increase in power rates at the m-th period in the schedule period, Respectively represent the error amounts, and/>K represents an error correction coefficient, and E represents an average value.
2) Thermal load model:
wherein Δh 1、ΔH2、ΔHn represents the relative increment of heat H, Δq 1、Δq2、Δqm represents the relative increment of heat price, and s represents the user satisfaction function with heat, and the expression is:
Wherein f '(H', t), f (H, t) respectively represent the heat consumption amount in the period of t before and after the peak-valley time-of-use heat price is carried out.
Further, the step S2 further includes generating a deviation electric quantity, where the calculation mode of the deviation electric quantity is as follows:
Where, η t denotes a random prediction error coefficient, Representing a predicted wind power consumption demand.
Further, the risk avoidance model in step S4 is specifically a risk avoidance model that is built to maximize the benefits and the degree of risk avoidance under the constraint condition of IESP.
Further, the constraint conditions comprise one or more of deviation electric quantity constraint, electrothermal power balance constraint and equipment participation main body output constraint.
Further, the deviation electricity quantity constraint condition is:
|ΔPt|≤ΔPtmax (15)
wherein Δp tmax is the maximum value of the offset electric quantity.
Compared with the prior art, the invention takes the electric and thermal load demand response reflecting the user electricity consumption psychology and the user heat satisfaction as a means for risk avoidance in the wind power plant-IESP cooperative operation, aims at maximizing IESP benefits and risk avoidance degree, establishes a wind power plant-IESP cooperative operation mode frame considering risk avoidance, and can effectively reduce the deviation electric quantity in the wind power plant-IESP cooperative operation by dispatching controllable electric and thermal load demand response resources, thereby reducing penalty cost. In addition, the efficiency of the coordinated operation of the wind power plant-IESP can be effectively improved by considering the deviation electric quantity inhibition target while optimizing IESP benefits.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a frame diagram of a wind farm-IESP collaborative mode of operation.
FIG. 2 is a flow chart of a wind farm-IESP collaborative operation optimization method that accounts for risk avoidance in accordance with the present embodiment.
FIG. 3 is a process diagram of constructing a wind farm-IESP collaborative mode framework.
Fig. 4 shows the electrical and thermal load prediction curves used in the specific application example.
Fig. 5 shows the amount of systematic bias power in three ways in a specific application example.
Fig. 6 is a comparison of the amount of change in electrical load in three ways in the specific example.
FIG. 7 is a comparison of thermal load variation in three modes of implementation.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
In the invention, IESP in a wind farm-IESP cooperative operation mode framework is established on the basis of traditional comprehensive energy supply (multiple energy sources such as electric power, fuel gas and heat) and on the basis of combining wind farm resources, simultaneously aggregating user comprehensive demand response resources, realizing multi-energy cooperative supply and improving the efficiency of an energy system, IESP is used for aggregating heat energy and electric energy provided by a micro gas turbine cogeneration (microturbine-CHP, MT-CHP) unit, a fuel gas boiler and an electric boiler, and meanwhile, IDR loads governed by IESP comprise electric loads and heat loads, wherein the electric loads take the psychological influence of user electricity into consideration, and the heat loads take the satisfaction degree of the user in a heat mode into consideration, and particularly see FIG. 1.
Meanwhile, as shown in fig. 2 to 3, the wind farm-IESP collaborative operation optimization method taking risk avoidance into consideration in the embodiment includes the following steps:
s1, determining IESP and energy supply equipment to participate in a main body, and constructing a wind power plant-IESP cooperative operation mode frame; it should be noted that, in this embodiment, the main participation bodies of the energy supply device are an MT-CHP unit, a gas boiler, and an electric boiler;
S2, according to a wind power plant-IESP cooperative operation mode framework, an electric quantity deviation index existing between the consumption demand of the wind power plant and IESP service electric quantity is provided by considering the influence of wind power output and electric and thermal load uncertainty; it should be noted that, the consumption demand P t dem of the wind farm is defined as the difference between the wind power output and the wind power pre-dispatching output, specifically, in order to meet the wind power grid connection, the wind farm needs to provide output according to the system dispatching instruction, and the wind power predicted output and the wind power pre-dispatching output have a certain difference, so the example adopts a wind farm-IESP cooperative operation mode, and the wind farm-IESP cooperative operation process means that when the wind power output is lower than the wind power pre-dispatching output, IESP is required to call the common output of the electric boiler, the gas boiler and the MT-CHP unit to make up the system dispatching demand; when the wind power output is higher than the wind power pre-dispatching output, IESP is needed to reduce the electric quantity purchased from the power grid, namely equivalent to providing equivalent output to a wind power plant;
S3, taking an electric and thermal load demand response and energy supply equipment participation main body as a wind power plant-IESP cooperative operation mode frame to reduce parameter indexes of risk loss, and further providing a comprehensive energy system response model;
and S4, establishing a risk avoidance model based on the comprehensive energy system response model by taking IESP operation benefits and the maximization of the risk avoidance degree as targets, and solving the risk avoidance model by adopting a particle swarm algorithm.
As shown in fig. 3, in step S1 of this embodiment, on the basis of the conventional comprehensive energy supply (multiple energy sources such as electric power, gas and heat), wind farm resources are combined, and meanwhile comprehensive demand response resources (specifically expressed as user electricity consumption psychology and user heat satisfaction) of users are aggregated, so as to establish a IESP operation mode; determining IESP an operation mode and selecting each energy supply device for supplying power and heat to participate in the main body; and combining the IESPIESP operation modes with a wind farm to construct a collaborative operation mode framework of the wind farm-IESP.
In a further technical solution, in step S2, the electric quantity deviation index has the following expression:
|ΔPt|＝|Pt dem-ΔPt IESP| (1)
Wherein Δp t is an electric quantity deviation index, P t dem is a consumption demand of the wind farm, and obeys gaussian distribution, Δp t IESP is electric power obtained from a power grid in a period t compared with the electric power obtained before the wind farm operates cooperatively, and when Δp t IESP is more than 0, IESP is to increase the electric power to consume surplus power of the wind farm; when Δp t IESP < 0 indicates IESP to reduce the electrical power to provide an equivalent output to the wind farm; when Δp t IESP =0, IESP is not involved in wind power absorption.
The step S2 further comprises the generation of deviation electric quantity, and the calculation mode of the deviation electric quantity is as follows:
Where, η t denotes a random prediction error coefficient, Representing a predicted wind power consumption demand.
The invention fully exploits the adjustment potential of the power source side and the demand side, optimizes the comprehensive energy system response model by utilizing the comprehensive demand response, and the mathematical expression of the comprehensive energy system response model in the embodiment is specifically as follows:
1) Power supply side device
Electric boiler:
gas-fired boiler:
micro gas turbine cogeneration unit MT-CHP:
Wherein, The electricity and heating power of the electric boiler i in the period t are respectively; /(I)Is the electrothermal conversion efficiency of the electric boiler i,/>The gas consumption and the heating power of the gas boiler j in the period t are respectively; /(I)The gas-heat conversion efficiency of the gas boiler j; l gas is the natural gas calorific value; Δt is the unit duration. /(I)The waste heat power and the generating power of the MT-CHP unit k in the period t are respectively; /(I)The power generation efficiency and the heat dissipation loss efficiency are respectively; /(I)Heating power of the MT-CHP unit k in a period t; /(I)The heating coefficient and the flue gas recovery rate are respectively.
2) Comprehensive demand side load model
The electrical load is the subject of conventional demand response and may utilize price levers or incentive mechanisms. The invention adopts a price demand response (demandprice, DR) model based on a price demand elastic matrix as follows:
because DR is affected by the electricity psychology of users, the load response has a certain uncertainty, and the uncertainty is approximately reflected by adopting normal distribution in combination with the big number theorem:
In the method, in the process of the invention, The error amount is represented by k, the error correction coefficient is represented by E (Δp t), and the average value of the response change amount is represented by E.
Wherein Δp 1、ΔP2、ΔPm represents the relative increase in power demand in the 1 st period of the scheduling period, the relative increase in power demand in the 2 nd period of the scheduling period, and the relative increase in power demand in the m th period of the scheduling period, respectively; q 1、q2、qm represents the electricity price of the 1 st period before demand response, the electricity price of the 2 nd period before demand response, and the electricity price of the m th period before demand response, respectively; deltaq 1、Δq2、Δqm represents the relative increase in power rates at the 1 st period in the schedule period, the relative increase in power rates at the 2 nd period in the schedule period, the relative increase in power rates at the m-th period in the schedule period,Respectively represent the error amounts, and/>K represents an error correction coefficient, and E represents an average value.
According to the principle of economy and the similarity of the electric power market and the thermal market, price excitation is carried out in the thermal market to change the heat utilization mode of a user, and when facing price signals of different time periods, a reasonable thermal user can autonomously and reasonably arrange and adjust the heat utilization mode and the heat utilization time of the user to realize peak-valley time-sharing heat price.
Wherein Δh f1、ΔHg1、ΔHp1 represents the amount of heat change before and after the time-of-use heat price.
According to the principle of the electric quantity and electricity price elastic coefficient matrix, the heat energy and heat price elastic matrix is shown as follows:
self elastic coefficient:
Cross elastic coefficient:
By defining the user thermal satisfaction by the amount of change in the user's thermal behavior, the user thermal satisfaction function is as follows:
where f '(H', t), f (H, t) represent the heat consumption amount in the period of t before and after the peak-to-valley time-of-use heat price is performed.
The thermal load demand response taking into account the user's thermal satisfaction is as follows:
Where Δh 1、ΔH2、ΔHn represents the relative increase in heat H, and Δq 1、Δq2、Δqm represents the relative increase in heat price.
In this embodiment, the risk avoidance model in step S4 is specifically a risk avoidance model that is built to maximize the running yield and the risk avoidance degree under the constraint condition of IESP, where the constraint condition includes one or more constraint conditions of deviation electric quantity constraint, electrothermal power balance constraint, and equipment participation subject output constraint, where:
the constraint condition of the deviation electric quantity is as follows:
|ΔPt|≤ΔPtmax (15)
wherein Δp tmax is the maximum value of the offset electric quantity.
The equipment participation main body output constraint comprises boiler operation constraint and MT-CHP unit operation constraint
1 Electrothermal Power balance constraint
2 Boiler operation constraints
Wherein,Maximum power consumption of the electric boiler i in a period t; /(I)The maximum heating power of the gas boiler j in the period t is respectively.
3MT-CHP unit operation constraint
Wherein,Respectively refers to the minimum continuous running time and the minimum continuous stopping time of the unit; /(I) Respectively refers to continuous running time and continuous stopping time of the unit in a period t; /(I)Respectively refers to the upper limit and the lower limit of the generating power of the unit; /(I)Respectively refers to the up-down climbing speed of the unit; /(I)Refers to the maximum start-stop times of the machine set.
In the step S4, the operation income and the risk avoidance degree are maximized in advance, a risk avoidance model is established based on the constraint conditions, the operation of the wind power plant-IESP cooperative operation mode frame is controlled based on the risk avoidance model, and particularly, the risk avoidance model with the maximized operation income and risk avoidance degree under the constraint of deviation electric quantity is established by taking the electricity consumption psychology of a user and the satisfaction degree of the heat consumption mode of the user into consideration of electricity and heat load demand response and energy supply equipment participation subjects, so that the risk existing in the cooperative process of the wind power plant-IESP can be effectively eliminated, and the operation income and new energy consumption level of IESP can be improved.
When the wind power circumvention model is constructed in the embodiment,
Wherein f 1 0 is the income of IESP before the wind farm-IESP operates cooperatively; the method is a wind power consumption requirement before the wind power plant-IESP cooperatively operates.
The method specifically comprises the following steps:
1) Revenue target for IESP:
2) Deviation electricity amount suppression target:
Wherein: c e、ch1 refers to the electricity and heat selling price of IESP respectively; c gas denotes the natural gas price; c ud1、cud2、cud3 is the single start-stop cost of the electric boiler, the gas boiler and the MT-CHP unit respectively; c pun is unit penalty cost; e e、Eh1 refers to the total power and heat usage in IESP, respectively; c w、cg is the unit price of IESP for servicing the wind farm and the unit price of purchasing electricity to the grid; n EB、NGB、NMT is the number of the electric boiler, the gas boiler and the MT-CHP unit respectively; t refers to the number of time periods within the scheduling period; u i,t、vj,t、wk,t indicates the operation states of the electric boiler i, the gas boiler j and the MT-CHP unit k at the time t, 1 indicates operation, and 0 indicates stop.
In order to solve the risk avoidance model of the wind power plant-IESP with multiple targets, a linear weighting method is adopted to convert the risk avoidance model into a single-target planning problem, and a particle swarm algorithm is adopted to carry out optimal economic dispatch solving of the wind power plant-IESP.
For verifying the effectiveness of the invention, IESP which provides electric and thermal energy services for all users in a certain area of China is taken as an example, one scheduling period is set to be 24h, and each scheduling period is set to be 1h. Table 1 shows system equipment parameters. And the flue gas exhausted by the micro-combustion engine is completely supplied to the waste heat boiler. FIG. 4 is a graph of predicted electrothermal load output. Table 2 shows peak-valley time-of-use electricity and heat price parameters, the electricity price type demand response is obtained by taking-0.2 from the elastic coefficient, 0.03 from the cross elastic coefficient, and the electricity purchasing price connected with the power grid is 1.2 yuan/kilowatt-hour, and the electricity selling price is 0.7 yuan/kilowatt-hour.
Table 1 system equipment parameters
TABLE 2 peak-valley time-of-use electricity and heat valence parameters
In order to verify the effectiveness of the wind power plant-IESP cooperative operation mode, 3 different optimization operation modes are selected for comparison analysis with the aim of maximizing IESP benefits and maximizing new energy consumption. Mode 1: consider only target one IESP where revenue is maximum; mode 2: only the minimum deviation electric quantity of the target II is considered; mode 3: and simultaneously, IESP is considered to have the maximum benefit and the minimum deviation electric quantity. The optimized operation results are shown in fig. 5-7:
As can be seen from analysis of fig. 5, the deviation power is maximum when only IESP benefits are considered, and the deviation power is minimum when only deviation power suppression targets are considered; when two targets are considered, the method can ensure better benefits, and simultaneously can reduce the deviation electric quantity caused by uncertainty in the cooperative operation process of the wind power plant-IESP, so that the effectiveness of the cooperative operation of the wind power plant-IESP is improved. As can be seen from an analysis of fig. 6 to 7, for 3 different optimized operating modes, there is a greater fluctuation in the electrical load during period 8, with a smaller thermal load variation overall than the electrical load variation. It follows that the power usage of the electrical load directly affects IESP the power drawn from the grid, and that the electrical load therefore plays an important role in suppressing the amount of offset compared to the thermal load.
TABLE 3 IESP benefit in three modes
From the analysis of table 3, it can be seen that when only IESP gains are considered as targets, the deviation electricity quantity is maximum and the penalty cost is also maximum although the operation benefit is maximum; when only the minimum deviation electric quantity is considered as a target, the minimum deviation electric quantity and the minimum punishment cost are considered; when two targets are considered, under the condition that IESP benefits are kept high, the deviation electric quantity is obviously reduced, so that balance among the targets is achieved.
The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. While the invention has been described with reference to preferred embodiments, it is not intended to be limiting. Therefore, any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present invention shall fall within the scope of the technical solution of the present invention.
Claims (4)
1. The wind farm-IESP collaborative operation optimization method considering risk avoidance is characterized by comprising the following steps of:
s1, determining IESP and energy supply equipment to participate in a main body, and constructing a wind power plant-IESP cooperative operation mode frame;
s2, according to a wind power plant-IESP cooperative operation mode framework, an electric quantity deviation index existing between the consumption demand of the wind power plant and IESP service electric quantity is provided by considering the influence of wind power output and electric and thermal load uncertainty;
S3, taking an electric and thermal load demand response and energy supply equipment participation main body as a wind power plant-IESP cooperative operation mode frame to reduce parameter indexes of risk loss, and further providing a comprehensive energy system response model;
S4, establishing a risk avoidance model based on a comprehensive energy system response model by taking IESP operation benefits and maximization of risk avoidance degree as targets, and solving the risk avoidance model by adopting a particle swarm algorithm;
the step S1 is specifically expressed as follows:
s11, on the basis of comprehensive energy supply of a plurality of traditional energy sources, combining wind farm resources, and simultaneously aggregating user comprehensive demand response resources to establish a IESP operation mode;
s12, determining IESP an operation mode and selecting energy supply equipment for supplying power and heat to participate in a main body;
S13, combining the IESP operation modes with a wind farm to construct a collaborative operation mode frame of the wind farm-IESP;
the expression of the electric quantity deviation index in the step S2 is as follows:
Wherein DeltaP t is an electric quantity deviation index, Is used for absorbing the demand of the wind power plant and obeys Gaussian distribution,/>For IESP electrical power drawn from the grid during period t compared to before the wind farm co-operates, when/>The time indicates IESP that the electric power is increased to consume the redundant power of the wind power plant; when/>Time indication IESP reduces the electrical power to provide an equivalent force to the wind farm; when/>The time indicates IESP is not involved in wind power consumption;
consumption demand of the wind farm The difference between the wind power output and the wind power prescheduled output is specifically expressed as follows: when the wind power output is lower than the wind power pre-dispatching output, IESP is required to call the common output of each participating main body to compensate the system dispatching requirement; when the wind power output is higher than the wind power pre-dispatching output, IESP is needed to reduce the electric quantity purchased from the power grid, namely equivalent to providing equivalent output to a wind power plant;
the risk avoidance model in the step S4 is specifically a risk avoidance model that is built to maximize the running yield and the risk avoidance degree of IESP under constraint conditions;
the constraint conditions comprise one or more constraint conditions of deviation electric quantity constraint, electrothermal power balance constraint and equipment participation main body output constraint;
when the wind power avoidance model is constructed,
In the method, in the process of the invention,The benefit of IESP before the wind farm-IESP is operated cooperatively; /(I)The method is characterized by comprising the steps of providing wind power consumption requirements before the wind power plant-IESP cooperatively operates;
The method specifically comprises the following steps:
1) Revenue target for IESP:
2) Deviation electricity amount suppression target:
Wherein: c e、ch1 refers to the electricity and heat selling price of IESP respectively; c gas denotes the natural gas price; c ud1、cud2、cud3 is the single start-stop cost of the electric boiler, the gas boiler and the MT-CHP unit respectively; c pun is unit penalty cost; e e、Eh1 refers to the total power and heat usage in IESP, respectively; c w、cg is the unit price of IESP for servicing the wind farm and the unit price of purchasing electricity to the grid; n EB、NGB、NMT is the number of the electric boiler, the gas boiler and the MT-CHP unit respectively; t refers to the number of time periods within the scheduling period; u i,t、vj,t、wk,t respectively indicates the running states of the electric boiler i, the gas boiler j and the MT-CHP unit k at the time t, wherein 1 indicates running and 0 indicates stopping; The gas consumption of the gas boiler j in the period t; l gas is the natural gas calorific value; Δt is the unit duration,/> Generating power for MT-CHP unit k in period t; /(I)Is the power generation efficiency.
2. The wind farm-IESP collaborative operation optimization method considering risk avoidance according to claim 1, wherein the comprehensive energy system response model in step S3 is specifically as follows:
1) Electric load model:
Wherein Δp 1、ΔP2、ΔPm represents the relative increase in power demand in the 1 st period of the scheduling period, the relative increase in power demand in the 2 nd period of the scheduling period, and the relative increase in power demand in the m th period of the scheduling period, respectively; q 1、q2、qm represents the electricity price of the 1 st period before demand response, the electricity price of the 2 nd period before demand response, and the electricity price of the m th period before demand response, respectively; deltaq 1、Δq2、Δqm represents the relative increase in power rates at the 1 st period in the schedule period, the relative increase in power rates at the 2 nd period in the schedule period, the relative increase in power rates at the m-th period in the schedule period, Respectively represent the error amounts, and/>K represents an error correction coefficient, and E represents an average value;
2) Thermal load model:
wherein Δh 1、ΔH2、ΔHn represents the relative increment of heat H, Δq 1、Δq2、Δqm represents the relative increment of heat price, and s represents the user satisfaction function with heat, and the expression is:
Wherein f '(H', t), f (H, t) respectively represent the heat consumption amount in the period of t before and after the peak-valley time-of-use heat price is carried out.
3. The risk avoidance based wind farm-IESP collaborative operation optimization method according to claim 2, wherein the step S2 further includes generating a deviation electric quantity, and the calculation method of the deviation electric quantity is as follows:
Where, η t denotes a random prediction error coefficient, Representing a predicted wind power consumption demand.
4. A wind farm-IESP collaborative operation optimization method with risk avoidance according to claim 3, wherein the bias power constraints are:
|ΔPt|≤ΔPtmax (15)
wherein Δp tmax is the maximum value of the offset electric quantity.
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