CN111523729A - Virtual power plant bidding optimization control method based on IGDT and demand response - Google Patents

Virtual power plant bidding optimization control method based on IGDT and demand response Download PDF

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CN111523729A
CN111523729A CN202010327348.7A CN202010327348A CN111523729A CN 111523729 A CN111523729 A CN 111523729A CN 202010327348 A CN202010327348 A CN 202010327348A CN 111523729 A CN111523729 A CN 111523729A
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李嘉媚
艾芊
孙东磊
李雪亮
李文升
王男
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Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention discloses an optimal control method for virtual power plant bidding based on IGDT and demand response, which comprises the following steps: establishing a deterministic bidding model of the virtual power plant and solving; establishing a system model by using an information gap decision theory; establishing an uncertainty model by using an information gap decision theory; selecting a corresponding decision strategy in the information gap decision theory model according to different risk management strategies and the required performance requirements; solving an optimization result of the system model according to the selected decision strategy; and determining the optimal planned output of the virtual power plant. Demand response resources can be integrated, a virtual power plant joint market bidding model based on IGDT is constructed, and internal resources are coordinated and scheduled to participate in the power market, so that economic optimization is realized.

Description

Virtual power plant bidding optimization control method based on IGDT and demand response
Technical Field
The invention relates to the technical field of virtual power plant bidding optimization control, in particular to an optimization control method for virtual power plant bidding based on IGDT and demand response.
Background
With the large-scale development and utilization of distributed energy sources and the gradual release of the power market, the participation of distributed energy sources in the power market becomes a major focus of future power market research. However, the uncertainty of the output of the distributed energy is strong, the single capacity is small, and the unit cost of power generation is high compared with the traditional power generation resource, so that the competitiveness of the distributed energy is small when the distributed energy participates in the bidding of the power market alone. As a new form of energy aggregation for realizing reliable aggregation of distributed energy, a Virtual Power Plant (VPP) can overcome the disadvantages of small capacity, random output, and the like of distributed energy, and can improve the competitiveness of the distributed energy participating in the power market, thus being an effective form of the distributed energy participating in the power market.
There has been some research currently focused on the bidding strategy of VPPs participating in the electricity market. In the prior art, a virtual power plant is considered to participate in an energy market and an auxiliary service market at the same time, a bidding strategy of the virtual power plant is put forward, but uncertain factors of an electric power market are not considered. With the release of the electricity selling side in China, the competition of the electricity market is increasingly violent, the electricity price of the market is uncertain to a certain extent, uncertainty factors of the output and load requirements of the renewable energy unit in the VPP are integrated, and the participation of the VPP in a market bidding strategy and the internal optimization operation research are particularly urgent under multiple uncertainty factors. Most methods for processing uncertainty are robust optimization and random optimization, and in the prior art, uncertainty of electricity price and wind power output is respectively processed by adopting random planning and robust optimization. However, due to the problem that the wind-solar output probability is difficult to accurately depict in the probability-based analysis methods such as opportunity constrained planning and stochastic planning, a large number of scene samples need to be generated to improve the reliability of the model, so that the complexity of the problem is inevitably increased sharply; although the reliability of the system is improved by the robust optimization, the economy is reduced, and therefore, the robust optimization result is always conservative.
Therefore, those skilled in the art are dedicated to developing an Information Gap Decision Theory (IGDT) and demand response-based virtual power plant bidding optimization control method, integrating demand response resources, constructing an IGDT-based virtual power plant joint market bidding model, and coordinating and scheduling internal resources to participate in a power market, thereby implementing economic optimization.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is how to provide an optimal control method for virtual power plant bidding based on IGDT and demand response, integrate demand response resources, construct a virtual power plant joint market bidding model based on IGDT, coordinate and schedule internal resources to participate in the power market, thereby achieving economic optimization.
In order to achieve the above object, the present invention provides an optimal control method for virtual plant bidding based on IGDT and demand response, the method comprising the steps of:
step 1, establishing a deterministic bidding model of a virtual power plant and solving the deterministic bidding model;
step 2, establishing a system model by using an information gap decision theory;
step 3, establishing an uncertainty model by using an information gap decision theory;
step 4, selecting a corresponding decision strategy in the information gap decision theoretical model according to different risk management strategies and the required performance requirements;
step 5, solving the optimization result of the system model according to the selected decision strategy;
and 6, determining the optimal planned output of the virtual power plant.
Further, the objective of the deterministic bidding model of the virtual power plant in step 1 is to maximize the profit of the virtual power plant operator in the electric power market, and the objective function is as follows:
Figure BDA0002463690600000021
in the formula: t is a scheduling period; the first term represents the revenue of the virtual power plant in the day-ahead market and the equilibrium market, Pt DA、
Figure BDA0002463690600000022
Respectively represent the amount of investment and the price of electricity in the market at the day-ahead,
Figure BDA0002463690600000023
respectively representing positive and negative throw scalar and electricity price in the equilibrium market; the second term represents the quadratic cost of the gas turbine, expressed by piecewise linearization, NgIndicating the number of gas turbines, Pt mRepresents gas turbine output; the third term represents the demand side revenue, P, of the virtual power plantt LOAD、
Figure BDA0002463690600000024
Respectively representing load power and load electricity price, Pt curtRepresenting the power of the interruptible load project participated by the demand side, a and b are compensation coefficients of the demand response project, representing that the virtual power plant compensates the load participated by the demand response project.
Further, the constraint conditions of the deterministic power plant bidding model in step 1 include:
and power balance constraint:
Figure BDA0002463690600000025
in the formula: pt wind、Pt disAnd Pt chRespectively representing wind power output, energy storage discharge output and energy storage charge output;
gas turbine restraint:
Figure BDA0002463690600000026
Figure BDA0002463690600000027
Figure BDA0002463690600000028
Figure BDA0002463690600000029
in the formula:
Figure BDA00024636906000000210
is the maximum output of the gas turbine,
Figure BDA00024636906000000211
is the maximum output of the gas turbine section,
Figure BDA00024636906000000212
and
Figure BDA00024636906000000213
respectively the upward climbing rate and the downward climbing rate of the gas turbine;
energy storage restraint:
Figure BDA00024636906000000214
Figure BDA0002463690600000031
SOC1＝SOCT+1(9)
0≤Pt ch≤Pch,MAX(10)
0≤Pt dis≤Pdis,MAX(11)
in the formula: SOCt、SOCminAnd SOCmaxRepresenting the time period t of stored energy, the minimum and maximum charge respectively ηchAnd ηdisCharging efficiency and discharging efficiency for the energy storage device; pch,MAXAnd Pdis,MAXMaximum charging power and maximum charging power for the energy storage device;
and (3) constraint of demand response:
0≤Pt curt≤υPt LOAD(12)
in the formula: v represents the maximum proportion of the load participating in the interruptible load item;
price constraint:
Figure BDA0002463690600000032
further, the system model in the step 2 is B (P, v), and represents the profit of the joint bidding of the virtual power plant in the day-ahead market and the equilibrium market, where P ═ { P ═ Pt DA,Pt m,Pt curt,Pt dis,Pt chV and v are decision variables representing the virtual plant day-ahead operating plan
Figure BDA0002463690600000033
Respectively, as the actual and predicted values of the uncertain variable.
Further, the uncertainty model in step 3 is used to describe the actual data v and the predicted data v
Figure BDA0002463690600000034
The gap therebetween:
Figure BDA0002463690600000035
wherein the content of the first and second substances,
Figure BDA0002463690600000036
explaining uncertain factors, the day-ahead market price and wind power output are considered, α is uncertainThe fluctuation range of the amount.
Further, the decision strategy in the step 4 includes a robust model decision strategy and an opportunity model decision strategy.
Further, the robust model decision strategy is a preset robust model target function threshold lower than a reference target value, the fluctuation range of the uncertain variable is maximized under the condition that the optimization result is not lower than the robust model target function threshold, the optimization target is a deviation coefficient of the maximized uncertain variable, and the specific expression is as follows:
Figure BDA0002463690600000037
in the formula: b isRIs a robust model objective function threshold.
Further, the opportunity model decision strategy is that an opportunity model target function threshold higher than a reference target value is preset, the fluctuation range of the uncertain variable is minimized on the basis that the optimization result is not lower than the opportunity model target function threshold, the optimization target is the deviation coefficient of the uncertain variable, and the specific expression is as follows:
Figure BDA0002463690600000038
in the formula: b isOIs an opportunistic model objective function threshold.
Further, the simplified model of the robust model decision strategy is:
Figure BDA0002463690600000041
constraint conditions are as follows: formula (2) to formula (13),
Figure BDA0002463690600000042
Figure BDA0002463690600000043
B≥BR＝B0(1-σR) (35)
Figure BDA0002463690600000044
further, the simplified model of the opportunity model decision strategy is:
Figure BDA0002463690600000045
constraint conditions are as follows: formula (2) to formula (13),
Figure BDA0002463690600000046
Figure BDA0002463690600000047
B≥BO＝B0(1+σO) (41)
Figure BDA0002463690600000048
the invention has the beneficial effects that:
1. demand response resources are integrated on the basis of the prior art, and multiple uncertainties, namely wind power output and day-ahead market output power price, of the VPP in the process of participating in market bidding are fully considered.
2. The method has the advantages that a probability density function of uncertain factors is not needed, uncertainty is quantified under the condition that probability distribution and a fluctuation range are unknown, a virtual power plant joint market bidding model based on an information gap decision theory is constructed, a robust model decision strategy and an opportunity model decision strategy are provided, and the virtual power plant participation market bidding strategy is determined.
Drawings
FIG. 1 is a functional block diagram of virtual plant bidding in accordance with a preferred embodiment of the present invention;
FIG. 2 is a general flow diagram of an optimization control method in accordance with a preferred embodiment of the present invention;
figure 3 is a bid amount result under different strategies for a preferred embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings for clarity and understanding of technical contents. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
The invention researches the bidding problem of virtual power plants participating in day-ahead market (DAM) and real-time balance market (RM). Firstly, a joint bidding model of the virtual power plant in the day-ahead market and the real-time market is built with the aim of maximizing the income of the virtual power plant. And then, the uncertainty of wind power output and the day-ahead market discharge price is processed by adopting the IGDT, so that a virtual power plant joint market bidding model based on an information gap decision theory is constructed, and a virtual power plant participation market bidding strategy is determined.
1. Virtual power plant bidding process
FIG. 1 is a functional block diagram of virtual plant bidding. The energy flow is indicated by the arrows. The distributed energy source 1 in the virtual power plant considers a renewable energy source unit 11, a controllable unit 12, a demand response resource 13, and an Energy Storage System (ESS) 14. In general, virtual power plants may coordinate and schedule internal resources to participate in the electricity market. The VPP operation center 2 can sell its surplus power to the grid and purchase power from the grid to meet load demands, thereby realizing economic optimization. The virtual power plant participates in both the day-ahead market 31 and the real-time balancing market 32. Since the aggregated capacity of the virtual power plant is smaller than that of other subjects in the electric power market, the virtual power plant is treated as a price acceptor and does not have the capability of influencing the discharge price of the electric power market.
The specific bidding process can be divided into two phases.
Stage one: the virtual power plant determines a bidding strategy in the day-ahead market 31 and an operation plan of the internal distributed energy source 1 according to the electricity prices and the day-ahead forecast of the renewable energy source unit 11.
And a second stage: at real-time operation, there is a discrepancy between the actual output of the virtual plant and the bid plan. Thus, any deficit/redundant bids thereof should be cleared in the real-time equilibrium market 32. In particular, if it actually outputs more than the bid amount, the excess portion is settled at a price lower than the price of the fresh electricity from the market 31 in the day ahead. Meanwhile, if the amount of its bid is more than the actual output, the insufficient portion is settled at a price higher than the price of the clear electricity from the market 31 in the day ahead. Obviously, the virtual power plant should reduce the economic risk of the forecast deviation to the bidding strategy.
2. Deterministic virtual power plant bidding model
First, assuming uncertainty is not considered, a deterministic model is built.
(1) An objective function:
the bidding goal of the virtual power plant is to maximize the revenue of the virtual power plant operator in the electricity market:
Figure BDA0002463690600000051
in the formula: t is a scheduling period; the first term represents the revenue of the virtual power plant in the day-ahead market and the equilibrium market, Pt DA、
Figure BDA0002463690600000052
Respectively representing the amount of the bid and the price of electricity in the day-ahead market, Pt +(Pt -)、
Figure BDA0002463690600000053
Respectively representing positive (negative) bid amounts and electricity prices in the equilibrium market; the second term represents the quadratic cost of the gas turbine, expressed by piecewise linearization, NgIndicating the number of gas turbines, Pt mRepresents gas turbine output; the third term represents the demand side revenue, P, of the virtual power plantt LOAD、
Figure BDA0002463690600000054
Respectively representing load power and load electricity price, Pt curtRepresenting the power of the interruptible load project participated by the demand side, a and b are compensation coefficients of the demand response project, representing that the virtual power plant compensates the load participated by the demand response project.
(2) Constraint conditions are as follows:
the power balance needs to be ensured in the virtual power plant,
Figure BDA0002463690600000061
in the formula: pt windAnd Pt dis(Pt ch) Respectively representing wind power output and energy storage discharge (charging) output.
Gas turbine restraint:
Figure BDA0002463690600000062
Figure BDA0002463690600000063
Figure BDA0002463690600000064
Figure BDA0002463690600000065
in the formula:
Figure BDA0002463690600000066
is the maximum output of the gas turbine,
Figure BDA0002463690600000067
is the maximum output of the gas turbine section,
Figure BDA0002463690600000068
and
Figure BDA0002463690600000069
respectively the gas turbine uphill and downhill rates.
Energy storage restraint:
Figure BDA00024636906000000610
Figure BDA00024636906000000611
SOC1＝SOCT+1(9)
0≤Pt ch≤Pch,MAX(10)
0≤Pt dis≤Pdis,MAX(11)
in the formula: SOCt、SOCminAnd SOCmaxRepresenting the time period t of stored energy, the minimum and maximum charge respectively ηch(ηdis) Efficiency of charging (discharging) the energy storage device; pch,MAX(Pdis,MAX) The maximum charge (discharge) power for the energy storage device.
And (3) constraint of demand response:
0≤Pt curt≤υPt LOAD(12)
in the formula: v represents the maximum proportion of load participating in the interruptible load item.
It should be noted that there are certain relationships between prices of different electricity markets.
Figure BDA00024636906000000612
Is lower than
Figure BDA00024636906000000613
While
Figure BDA00024636906000000614
Is higher than
Figure BDA00024636906000000615
Since the virtual plant operator has the right to manage demand side resources, it should offer lower electricity prices in return for the load than the day-ahead market versus the equilibrium market. Thus, the relationship of the different types of prices can be derived as follows:
Figure BDA00024636906000000616
3. information gap decision theory
When the randomness of the uncertain variable is processed by adopting a conventional method (such as a probability method, a fuzzy programming method and the like), a large amount of historical data of the uncertain variable needs to be obtained, and error probability distribution between a predicted value and an actual value of the uncertain variable is modeled, so that the error random distribution of the uncertain variable is determined according to an error probability model.
While the information gap decision theory is a non-probabilistic method of dealing with uncertainty, a key difference with other probabilistic decision theories is that this method achieves an efficient, high priority and risk-avoidance solution by modeling the gap error between actual data and predicted data, rather than by probability. An information gap decision theory is commonly used for solving uncertain scenes which are difficult to describe accurately, and the uncertainty of data can be quantified under the condition that the probability distribution and the fluctuation range of the data are unknown by adopting an information gap decision theory model. The basic idea is as follows: the fluctuation interval of the uncertain variable is maximized as much as possible while ensuring that the set target value is within an acceptable range of the fluctuation up and down of the reference target value, so as to obtain a greater possibility of the target value satisfying the condition.
The information gap decision theory model generally comprises three parts, a system model, an uncertainty model and a performance requirement.
(1) The system model is a mathematical model, usually denoted by B (P, v), built for the particular uncertainty problem and the decision variables that need to be solved. Wherein P ═ { P ═ Pt DA,Pt m,Pt curt,Pt dis,Pt chIs a blockPolicy variables, representing the virtual plant day-ahead operating plan, v and
Figure BDA0002463690600000076
the actual value and the predicted value are represented as uncertain variables respectively, and the system model represents the income of joint bidding of the virtual power plant in the day-ahead market and the balance market.
(2) The uncertainty model is a mathematical expression of the uncertainty quantity, which is used to describe the actual data v and the predicted data
Figure BDA0002463690600000071
In which the gap between the first and second side walls, wherein,
Figure BDA0002463690600000072
the uncertainty factor is explained by considering the day-ahead market price and wind power output, and is generally expressed as follows:
Figure BDA0002463690600000073
in the formula: alpha is the fluctuation range of the uncertain quantity.
(3) According to different risk management strategies, a corresponding decision strategy can be selected in an information gap decision theory model according to performance requirements needed by actual conditions, and a robust model decision strategy and an opportunity model decision strategy are common.
The robust model decision strategy belongs to a risk avoidance type decision strategy, and the basic principle is to avoid the influence of uncertain variables on the optimization result of the whole system model as much as possible. When a risk avoidance type strategy is adopted, a decision maker needs to preset a target function threshold value lower than a reference target value, the fluctuation range of uncertain variables is maximized under the condition that the optimization result is not lower than the target function threshold value, the optimization target is the deviation coefficient of the maximized uncertain variables, and the specific expression is as follows:
Figure BDA0002463690600000074
in the formula: b isRIs a robust model objective function threshold.
Correspondingly, the opportunity model decision strategy belongs to a risk preference type decision strategy, and the basic principle is to search the maximum expected gain in uncertain risks as much as possible. When a risk preference type strategy is adopted, a decision maker can preset a target function threshold higher than a reference target value, the fluctuation range of uncertain variables is minimized on the basis that the optimization result is not lower than the target function threshold, the optimization target is the deviation coefficient of the uncertain variables, and the specific expression is as follows:
Figure BDA0002463690600000075
in the formula: b isOIs an opportunistic model objective function threshold.
4. Bidding model based on information gap decision theory
Information gapping decision theory is used to address economic risks in the electricity market, with the goal of the decision maker being to maximize the range of uncertainty factors while meeting key profits. This chapter considers two uncertain parameters, including the price of the outgoing fresh electricity and the wind power output of the day-ahead market. According to the predicted electricity price and wind power, a virtual power plant operator determines a day-ahead operation plan of a scalar input amount and distributed energy, then the difference between the predicted wind power output and the actual wind power output is eliminated in a balance market, and meanwhile, the economic risk of the virtual power plant operator is considered through an information gap decision theory.
As previously mentioned, there is a need to smooth wind power output fluctuations in the equilibrium market. Therefore, there are two cases: power shortage and residual power.
(1) Power shortage: this occurs when the predicted value of the wind power is greater than the actual value. Therefore, the virtual plant operator needs to purchase energy in the equilibrium market, whose mathematical formula is as follows:
Figure BDA0002463690600000081
Figure BDA0002463690600000082
thus, the revenue of a virtual power plant operator can be written as
Figure BDA0002463690600000083
(2) Residual electricity: this occurs when the predicted value of the wind power is less than the actual value. Therefore, the virtual plant operator needs to sell energy in the equilibrium market, with the mathematical formula:
Figure BDA0002463690600000084
Figure BDA0002463690600000085
thus, the revenue of a virtual power plant operator can be written as
Figure BDA0002463690600000086
A robust model decision strategy and an opportunity model decision strategy are provided based on an information gap decision theory.
(1) Robust model decision strategy
In a robust model decision strategy, the goal is to maximize the range of uncertain parameters while ensuring the lowest specified profit. Thus, the two-layer robust optimization model is represented as follows:
Figure BDA0002463690600000087
constraint conditions are as follows: formula (2) to formula (13)
Figure BDA0002463690600000088
Constraint conditions are as follows:
Figure BDA0002463690600000089
Figure BDA00024636906000000810
in the formula: sigmaRIs a robust profit level factor.
(2) Opportunistic model decision strategy
In an opportunity model decision strategy, the goal is to minimize the range of uncertain parameters while the operator pursues higher profits. Thus, the two-layer opportunistic optimization model is represented as follows:
Figure BDA0002463690600000091
constraint conditions are as follows: formula (2) to formula (13)
Figure BDA0002463690600000092
Constraint conditions are as follows:
Figure BDA0002463690600000093
Figure BDA0002463690600000094
in the formula: sigmaOIs an opportunity profit level factor.
Model processing:
because the traditional solver cannot solve the double-layer problem, the double-layer problem is converted into an equivalent single-layer problem.
For the robust model decision strategy, the set P is a decision variable of the upper layer optimization problem, and thus can be regarded as constant processing when solving the lower layer optimization problem. Equation (24) is therefore a linear optimization problem. Considering formula (13) and formula (17) -formula (22) simultaneously, when the profit of the virtual power plant is minimum, the virtual power plant is in a power shortage state, and the actual output of wind power is minimum. Meanwhile, when the virtual power plant purchases energy from the day-ahead market and the actual price of the day-ahead market price is higher than expected, or the virtual power plant sells energy to the day-ahead market and the actual price of the day-ahead market price is lower than expected.
Figure BDA0002463690600000095
Figure BDA0002463690600000096
Can be rewritten as
Figure BDA0002463690600000097
Thus, the robust model decision strategy can be rewritten as:
Figure BDA0002463690600000098
constraint conditions are as follows: formulae (2) to (13), formula (31) and formula (33)
B≥BR＝B0(1-σR) (35)
Figure BDA0002463690600000099
For the opportunistic model decision strategy, the same pattern (28) is a linear optimization problem. Meanwhile, by considering the expressions (13) and (17) -expression (22), the virtual power plant is in a residual power state when the profit of the virtual power plant is the maximum, and the actual output of the wind power is the maximum. Meanwhile, when the virtual power plant purchases energy from the day-ahead market and the actual price of the day-ahead market price is lower than expected, or the virtual power plant sells energy to the day-ahead market and the actual price of the day-ahead market price is higher than expected.
Figure BDA00024636906000000910
Figure BDA00024636906000000911
Can be rewritten as
Figure BDA0002463690600000101
Thus, the opportunistic model decision strategy can be rewritten as:
Figure BDA0002463690600000102
constraint conditions are as follows: formulas (2) to (13), formula (37) and formula (39)
B≥BO＝B0(1+σO) (41)
Figure BDA0002463690600000103
The general flow chart of the optimization control method of the present invention is shown in fig. 2. The method comprises the following specific steps:
(1) determining a virtual power plant bidding process: and the virtual power plant carries out electric quantity bidding in the day-ahead market according to the wind power and the day-ahead market price clearing predicted value, wherein the real-time output deviation is traded in the real-time balance market.
(2) And establishing a deterministic bidding model of the virtual power plant and solving.
(3) And processing the uncertainty of the wind power output and the electricity price by using an information gap decision theory, and selecting a robust model decision strategy or an opportunity model decision strategy.
(4) And setting a robust/opportunity profit level factor according to the selected decision strategy, and solving the robust/opportunity model.
(5) And determining the optimal planned output of the virtual power plant.
The following are example analyses and example results.
1. Analysis by calculation example:
the parameters for each component in the VPP are as follows: the model of the gas turbine was TAU5670, and the specific parameters were taken from "virtual plant optimization trading strategy based on two-stage stochastic planning" (Zhou Bo, Lulin, high Red average, Tan Xinyi, Wu hong appliance. electric power construction, 2018,39(09): 70-77.). Wind power, predicted load data and predicted DAM prices were taken from "virtual plant optimization trading strategy based on two-stage stochastic programming". The positive and negative prices in the equilibrium market are 0.9 and 1.1 of the price of DAM, respectively. The compensation factors a and b are 1 and 90, respectively. The maximum proportion upsilon of the load participation interruptible load items is set to 0.15. The capacity of the ESS is 15MW · h, the maximum charge and discharge power is 3.5 and 4.0MW, respectively, the charge and discharge efficiency is 0.8, the ESS maximum and minimum charge levels are 0.9 and 0.1, and the initial charge level of the ESS is 0.5.
2. The results of the examples are as follows:
1) a deterministic bidding strategy: in this section, the load and DAM prices can be predicted perfectly, giving results that do not take uncertainty into account. The optimal amount of bid power in the DAM is shown in fig. 3. The maximum profit for the VPP operator without uncertainty is $ 18312 (B)0)。
2) Robust bidding strategy: first, considering the uncertainty of wind power and DAM price, the robust profit level factor is set to 0.1 and the lowest specified profit is $ 16481 (B)R) The bid power results are shown in FIG. 3. compared to the deterministic bidding strategy, VPPs sell less energy on DAMs, receive lower profits, which is consistent with the performance requirements of risk-evading decisioners.
3) The opportunity bidding strategy is as follows: first, considering the uncertainty of the load and the DAM price, the opportunity profit level factor is set to 0.1 and the maximum specified profit is $ 20143 (B)O) The bid charge results are shown in FIG. 3. compared to the deterministic bidding strategy, VPPs sell more energy and receive higher profits on DAMs, which is consistent with the performance requirements of the opportunity seeking decider.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. An optimal control method for virtual plant bidding based on IGDT and demand response, the method comprising the steps of:
step 1, establishing a deterministic bidding model of a virtual power plant and solving the deterministic bidding model;
step 2, establishing a system model by using an information gap decision theory;
step 3, establishing an uncertainty model by using an information gap decision theory;
step 4, selecting a corresponding decision strategy in the information gap decision theoretical model according to different risk management strategies and the required performance requirements;
step 5, solving the optimization result of the system model according to the selected decision strategy;
and 6, determining the optimal planned output of the virtual power plant.
2. The IGDT-and-demand-response-based optimal control method for virtual plant bidding as claimed in claim 1, wherein the objective of the deterministic bidding model of the virtual plant in step 1 is to maximize the virtual plant operator's revenue in the electricity market with an objective function of:
Figure FDA0002463690590000011
in the formula: t is a scheduling period; the first term represents the revenue of the virtual power plant in the day-ahead market and the equilibrium market, Pt DA、
Figure FDA0002463690590000012
Respectively represent the dayAmount of bid and price of electricity, P, in the pre-markett +、Pt -、
Figure FDA0002463690590000013
Respectively representing positive and negative throw scalar and electricity price in the equilibrium market; the second term represents the quadratic cost of the gas turbine, expressed by piecewise linearization, NgIndicating the number of gas turbines, Pt mRepresents gas turbine output; the third term represents the demand side revenue, P, of the virtual power plantt LOAD、
Figure FDA0002463690590000014
Respectively representing load power and load electricity price, Pt curtRepresenting the power of the interruptible load project participated by the demand side, a and b are compensation coefficients of the demand response project, representing that the virtual power plant compensates the load participated by the demand response project.
3. The method for optimal control of virtual plant bidding based on IGDT and demand response of claim 2, wherein the constraints of the deterministic virtual plant bidding model in step 1 include:
and power balance constraint:
Figure FDA0002463690590000015
in the formula: pt wind、Pt disAnd Pt chRespectively representing wind power output, energy storage discharge output and energy storage charge output;
gas turbine restraint:
Figure FDA0002463690590000016
Figure FDA0002463690590000017
Figure FDA0002463690590000018
Figure FDA0002463690590000019
in the formula:
Figure FDA00024636905900000110
is the maximum output of the gas turbine,
Figure FDA00024636905900000111
is the maximum output of the gas turbine section,
Figure FDA00024636905900000112
and
Figure FDA00024636905900000113
respectively the upward climbing rate and the downward climbing rate of the gas turbine;
energy storage restraint:
Figure FDA0002463690590000021
Figure FDA0002463690590000022
SOC1＝SOCT+1(9)
0≤Pt ch≤Pch,MAX(10)
0≤Pt dis≤Pdis,MAX(11)
in the formula: SOCt、SOCminAnd SOCmaxRepresenting the time period t of stored energy, the minimum and maximum charge respectively ηchAnd ηdisCharging efficiency and discharging efficiency for the energy storage device; pch,MAXAnd Pdis,MAXIs the most important energy storage deviceA large charging power and a maximum charging power;
and (3) constraint of demand response:
0≤Pt curt≤υPt LOAD(12)
in the formula: v represents the maximum proportion of the load participating in the interruptible load item;
price constraint:
Figure FDA0002463690590000023
4. the IGDT-and-demand-response-based optimal control method for virtual plant bidding according to claim 3, wherein the system model in step 2 is B (P, v) representing the profit of the joint bidding of the virtual plant in the day-ahead market and the equilibrium market, wherein P ═ { P ═ P { (P } P)t DA,Pt m,Pt curt,Pt dis,Pt chV and v are decision variables representing the virtual plant day-ahead operating plan
Figure FDA0002463690590000027
Respectively, as the actual and predicted values of the uncertain variable.
5. The method for optimal control of virtual plant bidding based on IGDT and demand response according to claim 4, wherein the uncertainty model in step 3 is used to describe actual data v and forecast data v
Figure FDA0002463690590000028
The gap therebetween:
Figure FDA0002463690590000024
wherein the content of the first and second substances,
Figure FDA0002463690590000025
the uncertain factors are explained by considering the day-ahead market electricity price and wind power output, and α is the fluctuation range of the uncertain quantity.
6. The IGDT-and-demand-response-based optimal control method for virtual plant bidding according to claim 5, wherein the decision strategies in step 4 comprise robust model decision strategies and opportunity model decision strategies.
7. The IGDT-and-demand-response-based virtual plant bidding optimization control method according to claim 6, wherein the robust model decision strategy is to preset a robust model objective function threshold value lower than a reference target value, and maximize a fluctuation range of the uncertain variable under the condition that the optimization result is not lower than the robust model objective function threshold value, and the optimization goal is to maximize a deviation coefficient of the uncertain variable, and the specific expression is as follows:
Figure FDA0002463690590000026
in the formula: b isRIs a robust model objective function threshold.
8. The IGDT-and-demand-response-based virtual plant bidding optimization control method according to claim 6, wherein the opportunity model decision strategy is a preset opportunity model objective function threshold value higher than a reference target value, and the fluctuation range of the uncertain variable is minimized on the basis of ensuring that the optimization result is not lower than the opportunity model objective function threshold value, and the optimization target is to minimize the deviation coefficient of the uncertain variable, and the specific expression is as follows:
Figure FDA0002463690590000031
in the formula: b isOIs an opportunistic model objective function threshold.
9. The IGDT-and-demand-response-based optimal control method for virtual plant bidding as claimed in claim 7, wherein the simplified model of the robust model decision strategy is:
Figure FDA0002463690590000032
constraint conditions are as follows: formula (2) to formula (13),
Figure FDA0002463690590000033
Figure FDA0002463690590000034
B≥BR＝B0(1-σR) (35)
Figure FDA0002463690590000035
10. the IGDT-and-demand-response-based optimal control method for virtual plant bidding according to claim 8, wherein the simplified model of the opportunistic model decision strategy is:
Figure FDA0002463690590000036
constraint conditions are as follows: formula (2) to formula (13),
Figure FDA0002463690590000037
Figure FDA0002463690590000038
B≥BO＝B0(1+σO) (41)
Figure FDA0002463690590000039
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