CN114118574A - Virtual power plant operation optimization method, electronic device, storage medium, and program product - Google Patents
Virtual power plant operation optimization method, electronic device, storage medium, and program product Download PDFInfo
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Abstract
The invention provides a virtual power plant operation optimization method, electronic equipment, a storage medium and a program product, wherein the method comprises the steps of determining an electric-heat combined virtual power plant to be optimized, wherein the electric-heat combined virtual power plant comprises a thermoelectric coupling device; determining an operation optimization model corresponding to the electric-heating combined virtual power plant, wherein an objective function of the operation optimization model is constructed based on the total operation cost of the electric-heating combined virtual power plant, and the total operation cost comprises the operation cost of the thermoelectric coupling device; and optimizing the electric-heating combined virtual power plant based on the operation optimization model. According to the invention, the electric-heat combined virtual power plant comprising the thermoelectric coupling device is constructed to participate in the electric power market, and the operation optimization model corresponding to the electric-heat combined virtual power plant is constructed based on the operation cost of the thermoelectric coupling device, so that the electric-heat combined virtual power plant is optimized, the operation cost of the electric-heat combined virtual power plant is further reduced, and the benefit of the electric-heat combined virtual power plant is maximized.
Description
Technical Field
The invention relates to the technical field of power grid analysis, in particular to a virtual power plant operation optimization method, electronic equipment, a storage medium and a program product.
Background
Virtual Power Plant (VPP) refers to a carrier that combines a distributed Power generation unit, a flexible load, and a distributed energy storage facility, and integrates and controls distributed energy sources through an advanced information communication technology and a software system.
The distributed energy can participate in market trading through the virtual power plant, and the development of the distributed energy is an important choice for changing an energy utilization mode, adjusting an energy structure, improving energy utilization efficiency and improving environmental pollution.
At present, distributed energy sources in a virtual power plant are mostly fans or photovoltaic, and the requirements of new energy consumption and heat supply in cold regions are not considered. Therefore, how to optimize the operation of the virtual power plant including the thermoelectric coupling device is an important issue to be solved in the industry at present.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a virtual power plant operation optimization method, electronic equipment, a storage medium and a program product.
The invention provides a virtual power plant operation optimization method, which comprises the following steps:
determining an electric-thermal combined virtual power plant to be optimized, wherein the electric-thermal combined virtual power plant comprises a thermoelectric coupling device;
determining an operation optimization model corresponding to the electric-heating combined virtual power plant, wherein an objective function of the operation optimization model is constructed based on the total operation cost of the electric-heating combined virtual power plant, and the total operation cost comprises the operation cost of the thermoelectric coupling device;
and optimizing the electric-heating combined virtual power plant based on the operation optimization model.
According to the operation optimization method of the virtual power plant provided by the invention, the optimization of the electric-heat combined virtual power plant based on the operation optimization model comprises the following steps:
determining constraint conditions corresponding to the operation optimization model, wherein the constraint conditions comprise operation constraints corresponding to the thermocouple device;
and optimizing the electric-heat combined virtual power plant based on the operation optimization model and the constraint conditions.
According to the operation optimization method of the virtual power plant provided by the invention, the constraint condition comprises at least one of operation constraint of a cogeneration unit, operation constraint of a gas unit, operation constraint of an electric boiler, operation constraint of electric energy storage, operation constraint of a heat storage device, operation constraint of a wind turbine, operation constraint of a photovoltaic unit and electric heat balance constraint of the electric-heat combined virtual power plant.
According to the operation optimization method of the virtual power plant provided by the invention, the optimization of the electric-heat combined virtual power plant based on the operation optimization model comprises the following steps:
determining a scene set of the electric-heating combined virtual power plant, wherein the scene set is determined based on prediction errors of a wind turbine set or a photovoltaic set in the electric-heating combined virtual power plant;
and optimizing the electric-heating combined virtual power plant based on the operation optimization model and the scene set.
According to the virtual power plant operation optimization method provided by the invention, the operation optimization model is a model based on condition risk value;
the operation optimization model corresponding to the electric heating combined virtual power plant is determined, and the method comprises the following steps:
determining a risk aversion degree corresponding to the electric-heating combined virtual power plant, wherein the risk aversion degree is customized by a decision maker of the electric-heating combined virtual power plant;
determining the operational optimization model based on the degree of risk aversion.
According to the operation optimization method of the virtual power plant provided by the invention, the thermocouple device comprises at least one of a cogeneration unit, an electric boiler and a heat storage device, and the electric-heat combined virtual power plant further comprises at least one of a gas unit, an electric energy storage device, a wind turbine unit and a photovoltaic unit;
the total operation cost comprises at least one of the operation cost of a cogeneration unit, the operation cost of a gas unit, the operation cost of an electric boiler, the operation cost of electric energy storage and the operation cost of a heat storage device, and the operation cost of the gas unit comprises the fuel cost and the start-stop cost of the gas unit.
According to the virtual power plant operation optimization method provided by the invention, the objective function is constructed by the following method:
and constructing the objective function based on the total operation cost, the corresponding risk aversion degree of the electric-heat combined virtual power plant, the occurrence probability, the risk value and the relaxation variable of each scene in the electric-heat combined virtual power plant.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of any one of the virtual power plant operation optimization methods.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for virtual plant operation optimization as described in any one of the above.
The invention also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the method for optimizing the operation of a virtual power plant as described in any one of the above.
According to the virtual power plant operation optimization method, the electronic equipment, the storage medium and the program product, an electric-heat combined virtual power plant to be optimized is determined, wherein the electric-heat combined virtual power plant comprises a thermoelectric coupling device; determining an operation optimization model corresponding to the electric-heating combined virtual power plant, wherein an objective function of the operation optimization model is constructed on the basis of the total operation cost of the electric-heating combined virtual power plant, and the total operation cost comprises the operation cost of a thermoelectric coupling device; and optimizing the electric heating combined virtual power plant based on the operation optimization model. Through the mode, the electric-heat combined virtual power plant comprising the thermoelectric coupling device is constructed to participate in the electric power market, and the operation optimization model corresponding to the electric-heat combined virtual power plant is constructed based on the operation cost of the thermoelectric coupling device, so that the electric-heat combined virtual power plant is optimized, the operation cost of the electric-heat combined virtual power plant is reduced, and the benefit of the electric-heat combined virtual power plant is maximized.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a virtual power plant operation optimization method provided by the present invention;
FIG. 2 is a second schematic flow chart of the method for optimizing the operation of a virtual power plant according to the present invention;
FIG. 3 is a schematic diagram of the electrical and thermal characteristics of the extraction CHP unit according to the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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.
Fig. 1 is a schematic flow diagram of a virtual power plant operation optimization method provided by the present invention, and as shown in fig. 1, the virtual power plant operation optimization method provided by the present invention includes the following steps 110 and 130:
Wherein, the electric heat is united virtual power plant for the virtual power plant that needs to carry out operation optimization. The electric-heat combined virtual power plant is a combined electric and heat virtual power plant, namely the electric-heat combined virtual power plant comprises a thermoelectric coupling device.
Wherein, the thermocouple device includes but is not limited to: combined Heat and Power (CHP), electric boilers, heat storage devices, and the like. The cogeneration unit can be an extraction type CHP unit; the heat storage device may be a high capacity heat storage device.
In addition, the electric-heat combined virtual power plant may further include: distributed energy equipment such as a gas turbine unit, an electric energy storage device, a wind turbine unit, a photovoltaic unit and the like. The gas engine set can be a micro gas engine set or a micro gas turbine and the like.
In one embodiment, the thermoelectric coupling device comprises at least one of a cogeneration unit, an electric boiler and a heat storage device, and the electric-thermal combined virtual power plant further comprises at least one of a gas unit, an electric energy storage device, a wind turbine unit and a photovoltaic unit.
It should be noted that the thermocouple device participates in the operation of the power market through the electric and thermal combination virtual power plant.
And 120, determining an operation optimization model corresponding to the electric-heating combined virtual power plant, wherein an objective function of the operation optimization model is constructed based on the total operation cost of the electric-heating combined virtual power plant, and the total operation cost comprises the operation cost of the thermoelectric coupling device.
The operation optimization model is used for optimizing the operation of the electric heating combined virtual power plant. The operation optimization model is constructed by taking the minimum operation cost of the electric-heat combined virtual power plant as a target.
The target function of the operation optimization model is used for combining constraint conditions of the electric-heat combined virtual power plant to obtain a loss function, and then the operation optimization is carried out on the electric-heat combined virtual power plant according to the loss function so as to reduce the operation cost of the electric-heat combined virtual power plant.
Wherein the total operating cost comprises the operating cost of each member in the electric-heat combined virtual power plant.
It should be noted that, if the virtual power plant based on electric-heat combination includes a cogeneration unit, an electric boiler, a heat storage device, a gas unit, and an electric energy storage device, the total operating cost includes the operating cost of the cogeneration unit, the operating cost of the electric boiler, the operating cost of the heat storage device, the operating cost of the gas unit, and the operating cost of the electric energy storage device. The operating cost may be a fuel cost or a start-stop cost, etc.
In an embodiment, the total operation cost includes at least one of a cogeneration unit operation cost, a gas unit operation cost, an electric boiler operation cost, an electric energy storage operation cost, and a heat storage device operation cost, and the gas unit operation cost includes a fuel cost and a start-stop cost of the gas unit.
And 130, optimizing the electric-heat combined virtual power plant based on the operation optimization model.
Specifically, the operation of the electric-heat combined virtual power plant is optimized based on an objective function of an operation optimization model, so that the operation cost of the electric-heat combined virtual power plant is reduced, and the benefit of the electric-heat combined virtual power plant is maximized.
According to the operation optimization method of the virtual power plant, an electric-heat combined virtual power plant to be optimized is determined, wherein the electric-heat combined virtual power plant comprises a thermoelectric coupling device; determining an operation optimization model corresponding to the electric-heating combined virtual power plant, wherein an objective function of the operation optimization model is constructed on the basis of the total operation cost of the electric-heating combined virtual power plant, and the total operation cost comprises the operation cost of a thermoelectric coupling device; and optimizing the electric heating combined virtual power plant based on the operation optimization model. Through the mode, the electric-heat combined virtual power plant comprising the thermoelectric coupling device is constructed to participate in the electric power market, and the operation optimization model corresponding to the electric-heat combined virtual power plant is constructed based on the operation cost of the thermoelectric coupling device, so that the electric-heat combined virtual power plant is optimized, the operation cost of the electric-heat combined virtual power plant is reduced, and the benefit of the electric-heat combined virtual power plant is maximized.
Further, based on the above embodiment, another embodiment of the virtual power plant operation optimization method of the present invention is provided. Fig. 2 is a second schematic flow chart of the virtual power plant operation optimization method provided by the present invention, as shown in fig. 2, in this embodiment, the step 130 includes the following steps 131 and 132:
and 131, determining constraint conditions corresponding to the operation optimization model, wherein the constraint conditions comprise operation constraints corresponding to the thermoelectric coupling device.
And the constraint condition is a constraint condition corresponding to an objective function of the running optimization model. The constraint conditions are used for combining with a target function of the electric-heat combined virtual power plant to obtain a loss function, and then the electric-heat combined virtual power plant is optimized in operation according to the loss function so as to reduce the operation cost of the electric-heat combined virtual power plant.
The corresponding operating constraints of the thermoelectric coupling device include, but are not limited to: the operation restriction of the cogeneration unit, the operation restriction of the electric boiler and the operation restriction of the heat storage device.
In addition, the constraint conditions further comprise distributed energy equipment operation constraints such as gas turbine set operation constraint, electric energy storage operation constraint, wind turbine set operation constraint, photovoltaic set operation constraint and the like, and electric heat balance constraint of the electric-heat combined virtual power plant.
In one embodiment, the constraint condition includes at least one of a cogeneration unit operation constraint, a gas unit operation constraint, an electric boiler operation constraint, an electric energy storage operation constraint, a heat storage device operation constraint, a wind turbine unit operation constraint, a photovoltaic unit operation constraint, and an electric heat balance constraint of the electric heat combined virtual power plant.
In some embodiments, the cogeneration unit may be an extraction CHP unit, the extraction CHP unit may simultaneously supply heat and power to the outside, the heat supply is mainly performed by extracting a part of steam from the turbine, the power supply is mainly performed by driving the turbine to work through the steam, an electrical-thermal characteristic curve of the extraction CHP unit is shown in fig. 3, fig. 3 is a schematic diagram of electrical-thermal characteristics of the extraction CHP unit provided by the present invention, fig. 3 totally includes five electrical-thermal characteristic extreme points a-E, H represents thermal power, P represents electric power, and u represents a subscript of the CHP unit.
The operation point of the CHP unit should fall in the feasible region of its output, based on which the operation constraint of the cogeneration unit (CHP unit operation constraint) is as follows:
wherein u represents the subscript of the CHP unit,
and
respectively the electric power and the thermal power generated by the CHP unit u in the scene s at the time period t,
and
the extreme point of the feasible output area of the CHP unit u is shown in fig. 3 as an example,
and
the coordinates of the extreme point A, B, C, D, E in the figure.
Representing the output coefficient of the CHP unit u at the extreme point r at the moment t under the scene S, Scv,chpIs the set of the vertices of the feasible output domain of the CHP unit.
In addition, the CHP unit operation constraints also include a hill climbing constraint, which is as follows:
wherein the content of the first and second substances,
and
respectively represents the climbing rate and the descending rate of the CHP unit u, tau represents the time interval of the electric power market,
for the electrical power generated by the CHP unit u during the time period t in the scene s,
the electric power generated by the CHP unit u in the t-1 time period under the scene s.
In some embodiments, the gas train may be a micro gas train having gas train operating constraints as follows:
wherein i represents the number of the micro gas unit;
representing the active power of the micro gas turbine set i in a scene s at a time period t;
the electric power of the micro gas turbine set i in a t-1 time period under a scene s;
the starting and stopping states of the micro gas turbine set i in the t time period under the scene s are represented, and the starting and stopping states have two values of 0 and 1 when the values are
When the micro gas unit i is in the closed state in the scene s for the time period t, the micro gas unit i is in the closed state in the scene s
When the time is up, the micro gas turbine set i is in an opening state in a scene s at a time period t;
and
respectively representing the minimum value and the maximum value of the i active output of the micro gas unit; τ represents the time interval of the electricity market;
and
respectively representing the upward climbing rate and the downward climbing rate of the gas turbine unit i;
and
respectively representing the starting cost and the shutdown cost of the micro gas unit i in the scene s for the time period t,
and
respectively representing the cost of starting and shutting down the gas unit i once. In addition, the third expression and the fourth expression are climbing constraints of the micro gas unit; and the fifth expression and the sixth expression are the start-stop cost constraint of the gas turbine set.
In some embodiments, the electrical boiler operating constraints of the electrical boiler are as follows:
wherein l represents the number of the electric boiler;
represents the electric power consumed by the electric boiler l during the period t under the scene s;
generating thermal power for the electric boiler l in a scene s in a period t;
an efficiency coefficient representing the conversion of electrical power to thermal power, which is typically above 90%;
and
respectively the minimum and maximum value of the electric power consumed by the electric boiler l.
In some embodiments, the electrical energy storage operation constraint of the electrical energy storage device (electrical energy storage for short) is as follows:
wherein m represents the number of the electrical energy storage,
representing the charging power of the electrical energy storage means m for a period t under the scene s,
and
respectively representing the minimum and maximum charging power of the electrical energy storage means m,
representing the discharge power of the electrical energy storage means m for a period t under the scene s,
and
respectively representing the minimum discharge power and the maximum discharge power of the electrical energy storage device m;
and
is a state variable for controlling the charge and discharge of the electric energy storage, and the two variables are both 0-1 variable when
When it is, it means that the electric energy storage device m is in a charged state for a period t under the scene s, when
When the current state is in the discharge state, the electric energy storage device m is in the discharge state in the scene s at the time period t;
representing the total amount of stored electricity of the electrical energy storage means m for a period t under the scene s,
and
respectively representing the maximum and minimum capacity of the electrical energy storage means m,
and
respectively representing the charging and discharging efficiency of the electric energy storage, and T representing the total time interval number of the electric power market. In addition, the third formula limits that the electric energy storage device m cannot be in the charging state and the discharging state at the same time in the scene s for the period t; the fifth formula represents the energy time coupling relation of the electric energy storage device m; the seventh formula indicates that the final charge of the electrical energy storage device m is the same as the initial charge.
In some embodiments, the thermal storage device operating constraints of the thermal storage device (also referred to as thermal energy storage) are as follows:
wherein n represents a number of thermal energy storage;
representing the thermal power of the heat storage device n in a t period under the scene s;
and
respectively representing the minimum thermal power and the maximum thermal power of the heat storage device n;
representing the heat release power of the heat storage device n for a period t under the scene s;
and
respectively representing the minimum heat release power and the maximum heat release power of the heat storage device n;
and
is a state variable for controlling heat storage, heat charging and heat discharging, and the two variables are 0-1 variable when
When the heat storage device n is in a heat charging state in the scene s for a period t
When the heat storage device n is in a heat release state in the scene s for the time period t;
representing the total heat stored by the heat storage device n in the scene s for a period t;
and
respectively representing the maximum of the heat storage unit nCapacity and minimum capacity;
the heat load loss rate is expressed, the heat load loss rate is related to the temperature of the heat storage system, the heat load loss is constant under the phase change heat storage, and is in direct proportion to the heat storage amount under the explicit heat storage. In addition, the third formula limits that the heat storage device n cannot be in a heat charging state and a heat discharging state at the same time in the scene s for the period t; the fifth formula represents the heat time coupling relation of the heat storage device n under the form of considering phase change heat storage; the seventh formula indicates that the final amount of stored heat of the heat storage device n is the same as the initial amount of stored heat.
In some embodiments, for the wind turbine generator operation constraint and the photovoltaic turbine generator operation constraint, the output of the wind turbine generator and the photovoltaic turbine generator is predicted based on the prediction of the temperature, the solar radiation intensity, the wind speed and the like and historical data of the output of the wind turbine generator and the photovoltaic turbine generator, and the uncertainty of the output of the photovoltaic generator and the wind turbine generator is described by a scene set.
It can be understood that based on the wind turbine generator operation constraint and the photovoltaic generator operation constraint, uncertainty of wind power and photovoltaic output and risk control of operation cost of the electric-heat combined virtual power plant can be fully considered.
In some embodiments, the electric-to-heat balance constraints of the electric-to-heat combined virtual power plant are as follows:
wherein N isgas、Nwind、Nsolar、Nes、Nchp、NtsAnd NebRespectively representing the number of a gas turbine set, a wind turbine set, a photovoltaic set, an electric energy storage device, a CHP set, a heat storage device and an electric boiler,
is the electric load for the time period t,
reporting the electric quantity for the day before the t period,
is the thermal load for the period t. Further, the first expression represents an electrical balance constraint; the second expression represents the thermal balance constraint, i.e. the heat released by the electric boiler, the CHP train and the heat storage means is equal to the demand of the thermal load.
It can be understood that the fluctuation of the renewable energy sources is stabilized through the inertia of the thermodynamic system, so that the output of each distributed energy source is coordinated and optimized, and the income of the virtual power plant in the market in the future is improved.
And 132, optimizing the electric-heat combined virtual power plant based on the operation optimization model and the constraint conditions.
Specifically, a loss function is determined and obtained based on an objective function and a constraint condition of an operation optimization model, and then operation optimization is performed on the electric-heat combined virtual power plant based on the loss function, so that the operation cost of the electric-heat combined virtual power plant is reduced, and the income of the electric-heat combined virtual power plant is maximized. I.e. the running optimization model takes into account the constraints.
According to the operation optimization method of the virtual power plant, the electric-heat combined virtual power plant is optimized according to the constraint conditions on the basis of the operation optimization model, and the electric-heat combined virtual power plant is more accurately optimized, so that the operation cost of the electric-heat combined virtual power plant is further reduced, and the benefit of the electric-heat combined virtual power plant is further maximized. Meanwhile, the electric-heat combined virtual power plant comprising the thermoelectric coupling device is optimized by constructing a constraint condition comprising the operation constraint of the thermoelectric coupling device.
Further, based on the above embodiment, another embodiment of the virtual power plant operation optimization method of the present invention is provided. In the present embodiment, the step 130 includes the following steps 133-134:
step 133, determining a scene set of the electric-heating combined virtual power plant, where the scene set is determined based on a prediction error of a wind turbine set or a photovoltaic set in the electric-heating combined virtual power plant.
And 134, optimizing the electric heating combined virtual power plant based on the operation optimization model and the scene set.
Wherein the scene set is used to describe the randomness of the variables. The more this set of scenarios, the more accurate the description of the random variable.
In this embodiment, the scene set is determined based on the distribution of prediction errors of the wind turbine generator set or the photovoltaic generator set in the electric-heat combined virtual power plant. Namely, the scene set needs to cover the output range of wind power or photovoltaic power in the time interval as much as possible, and the setting of different scene weights also needs to meet the distribution of prediction errors.
The generated power prediction error of the wind turbine generator or the photovoltaic generator can be described by adopting normal distribution, beta distribution, Weibull distribution and the like.
In one embodiment, a normal distribution is used to fit the prediction error, as follows:
ΔP|Pf～N(μ,σ)，
wherein, PfRepresenting the predicted power, P, of a wind or photovoltaic unittRepresenting the actual output, P, of the wind or photovoltaic unitmaxThe method is characterized by representing the rated capacity (maximum power) generated by the wind turbine generator or the photovoltaic generator, wherein delta P represents the prediction error of the wind turbine generator or the photovoltaic generator, and the second expression is a normal distribution expression.
It can be understood that under the condition of low requirement on precision, the generation efficiency of the scene set can be improved by adopting normal distribution fitting to predict errors.
Further, the more scene sets, the greater the computational burden. Based on the method, a representative scene set can be selected by adopting a scene set reduction method, so that the prediction precision is improved. Specifically, scene reduction is performed on a scene set acquired in advance, and a reduced scene set is obtained.
Methods of scene set pruning include, but are not limited to: backward subtraction, fast forward selection, scene tree construction, k-means clustering, etc.
In one embodiment, a k-means clustering method is adopted to perform scene reduction on a scene set. Specifically, N scene sets are clustered into k classes, and the overall target of the clustering is as follows:
wherein, mujRepresents the jth cluster center, SjRepresents by mujA set of elements that are a class of cluster centers.
It should be noted that the overall partitioning objective is a non-convex optimization problem, and the solution thereof may be in an iterative manner.
According to the operation optimization method of the virtual power plant, the electric-heating combined virtual power plant is optimized based on the operation optimization model and the scene set, so that the description accuracy of random variables is improved, the electric-heating combined virtual power plant is more accurately optimized, the operation cost of the electric-heating combined virtual power plant is further reduced, and the benefit of the electric-heating combined virtual power plant is further maximized.
Further, based on the above embodiment, another embodiment of the virtual power plant operation optimization method of the present invention is provided. In this embodiment, the operation optimization model is a model based on conditional risk value; the step 120 comprises the following steps 121-122:
and 121, determining a risk aversion degree corresponding to the electric-heating combined virtual power plant, wherein the risk aversion degree is customized by a decision maker of the electric-heating combined virtual power plant.
Step 122, determining the operation optimization model based on the risk aversion degree.
Specifically, an objective function for running the optimization model is determined based on the degree of risk aversion.
Wherein the risk aversion degree is used for representing the degree of risk aversion of a decision maker of the electric-heat combined virtual power plant.
In addition, the operation optimization model can be obtained based on the total operation cost of the electric-heat combined virtual power plant, the occurrence probability, the risk value and the relaxation variable of each scene in the electric-heat combined virtual power plant.
Specifically, the step 122 includes:
and determining the operation optimization model based on the total operation cost, the risk aversion degree, the occurrence probability of each scene in the electric-heat combined virtual power plant, the risk value and the relaxation variable.
It should be noted that Conditional Value-at-Risk (CVaR) is used to measure the Risk, and specifically, it refers to the expected loss Value when the loss of the system exceeds the Value of the Risk (VaR). The VaR value refers to the maximum loss of the system at a certain confidence level, and compared to the VaR value, CVaR focuses on the tail of the risk distribution, considering the case where the probability of occurrence is low but the risk value is high.
In one embodiment, a run optimization model based on conditional risk values is constructed as follows.
First, the expression for VaR and CVaR at a confidence level for a certain random variable is determined.
The expression of VaR at the confidence level is shown below:
where z is a random variable and α is the confidence level.
The expression of CVaR at confidence level α is as follows:
where z is a random variable and α is the confidence level.
Then, based on the expression of VaR and CVaR at the confidence level, the following expression is determined:
where z is a random variable, α is a confidence level, and f (z) represents a probability density function for the random variable z. This formula can be modified as follows:
wherein CVaR is the minimum value under the condition that VaR is used as an independent variable.
Finally, an objective function for running the optimization model is determined, which is as follows:
wherein γ ∈ [0,1 ]]γ represents the magnitude of the risk preference, and smaller γ represents a higher degree of risk aversion of the decision maker, and when γ is 0, it represents the risk aversion of the decision maker, and γ is 1, which represents the risk neutrality of the decision maker.
Represents the expected operating cost of the electric-heat combined virtual power plant, which is specifically as follows:
where Ω represents the set of scene sets, ρsRepresents the probability of each scene occurring, and
OCsrepresenting the expected operating cost under each scenario. This formula can be discretized into:
further, a relaxation variable may be introduced to deform the objective function. Specifically, based on the following expression:
ηs≥OCs-VaR，
ηs≥0，
a deformed objective function expression can be obtained:
s.t. ηs≥OCs-VaR,ηs≥0，
wherein eta issIs the relaxation variable.
According to the virtual power plant operation optimization method provided by the embodiment of the invention, the operation optimization model based on the condition risk value is constructed based on the condition risk value modeling method, so that different day-ahead reporting strategies can be obtained according to the risk preference degree of a decision maker, and the balance between the expected operation cost of the minimized virtual power plant and the operation cost risk of the minimized virtual power plant is realized. Meanwhile, an operation optimization model of the electric heating combined virtual power plant participating in the electric power market is established based on the condition risk value, so that the risk control of the electric heating combined virtual power plant participating in the electric power market can be realized.
Further, based on the above embodiment, another embodiment of the virtual power plant operation optimization method of the present invention is provided. In this embodiment, the objective function is constructed by the following method:
and 140, constructing the objective function based on the total operation cost, the corresponding risk aversion degree of the electric-heat combined virtual power plant, the occurrence probability, the risk value and the relaxation variable of each scene in the electric-heat combined virtual power plant.
In the present embodiment, the total operating cost includes the operating cost in each scenario.
Wherein the objective function is as follows:
s.t. ηs≥OCs-VaR,ηs≥0,0≤γ≤1，
wherein γ ∈ [0,1 ]]γ is a risk aversion degree, which represents the magnitude of the risk preference, and the smaller γ represents the higher degree of the risk aversion of the decision maker, and when γ is 0, it represents the risk aversion of the decision maker, and γ is 1, which represents the neutral risk of the decision maker; rhosRepresents the occurrence probability of each scene, and
OCsrepresenting the operating cost under each scenario; VaR is the risk value; α is the confidence level; etasIs the relaxation variable.
In one embodiment, the total operation cost includes a cogeneration unit operation cost, a gas unit operation cost, an electric boiler operation cost, an electric energy storage operation cost, and a heat storage device operation cost, and the gas unit operation cost includes a fuel cost and a start-stop cost of the gas unit.
In particular, the total operating cost OC under the scene ssAs follows:
wherein the content of the first and second substances,
for the fuel cost of the gas turbine set i at time t under the scene s,
and
respectively representing the starting cost and the shutdown cost of the gas turbine set i in a time period t under a scene s;
representing the operation cost of the electric energy storage device m in the scene s at the time period t;
representing the running cost of the CHP unit u in the t period under the scene s;
representing the operation cost of the electric boiler l in the scene s for the time period t;
representing the operating cost of the heat storage device n in the scene s for the period t;
reporting the electric quantity for the day ahead.
It should be noted that, because the capacity of the gas turbine unit is small and convenient to adjust, the CHP unit always keeps the running state, and therefore, the decision variable at this stage is the reported electric quantity in the future
In the real-time operation stage, the value of the decision variable is determined after the value of the random variable is known, and the decision variable mainly comprises the following variables:
wherein the content of the first and second substances,
and the output of the photovoltaic or wind power reduced in the t period is shown. Based on this, the optimization model of the real-time run phase is as follows:
wherein the content of the first and second substances,
represents the real-time electricity rate for the period t,
actual output, psi, of a combined electric and thermal virtual plant representing a time period ttRepresenting a deviation penalty factor, Δ PtThe absolute value t of the difference between the actual output of the electric heating combined virtual power plant and the output reported day before is represented0Representing a period of real-time operation.
It is to be appreciated that the above expression may be viewed as an electric power balance constraint that takes into account the new energy contribution cutback. Meanwhile, after the cost functions of the gas unit and the CHP unit are linearized, the decision variables of the model only comprise continuous variables and 0-1 variables, and the model is a linear mixed integer programming model and can be solved by adopting CPLEX in a Matlab environment.
In one embodiment, the cogeneration unit operating costs are as follows:
wherein the content of the first and second substances,
representing the cost of the CHP unit u at the time t under the scene s, au、bu、cu、du、euAnd fuCoefficients representing an operating cost function of the CHP plant.
In one embodiment, the cogeneration unit operating costs are as follows:
wherein the content of the first and second substances,
representing the fuel cost of the micro gas turbine unit i in the time period t under the scene s, ai、biAnd ciIs the fuel cost coefficient of the gas turbine set i.
In one embodiment, the electric boiler operates at the following cost:
wherein the content of the first and second substances,
representing the operating cost of the electric boiler/for a period t under the scene s,
average operating costs for the electric boiler l to generate 1MW of heat.
In one embodiment, the electrical energy storage operating cost is as follows:
wherein the content of the first and second substances,
representing the operating cost of the electrical energy storage device m for a period t under the scene s,
represents the operating cost per unit of charge or discharge of the electrical energy storage means m.
In one embodiment, the heat storage device operates at the cost shown below:
wherein the content of the first and second substances,
representing the operating cost of the heat storage apparatus n for a period t under the scene s,
the operating cost of the heat storage device n for charging and discharging the unit heat is shown.
According to the operation optimization method of the virtual power plant, provided by the embodiment of the invention, the objective function is constructed based on the total operation cost, the risk aversion degree corresponding to the electric-heat combined virtual power plant, the occurrence probability, the risk value and the relaxation variable of each scene in the electric-heat combined virtual power plant, so that the electric-heat combined virtual power plant is optimized, the operation cost of the electric-heat combined virtual power plant is further reduced, and the income of the electric-heat combined virtual power plant is maximized.
The virtual power plant operation optimization device provided by the invention is described below, and the virtual power plant operation optimization device described below and the virtual power plant operation optimization method described above can be referred to correspondingly.
In this embodiment, the virtual power plant operation optimization device includes:
the system comprises a first determination module, a second determination module and a control module, wherein the first determination module is used for determining an electric-heat combined virtual power plant to be optimized, and the electric-heat combined virtual power plant comprises a thermoelectric coupling device;
a second determining module, configured to determine an operation optimization model corresponding to the virtual power plant for electric heating combination, where an objective function of the operation optimization model is constructed based on a total operation cost of the virtual power plant for electric heating combination, and the total operation cost includes an operation cost of the thermoelectric coupling device;
and the optimization module is used for optimizing the electric-heat combined virtual power plant based on the operation optimization model.
Fig. 4 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 4, the electronic device may include: a processor (processor)410, a communication Interface 420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke logic instructions in the memory 430 to perform a virtual plant operation optimization method, the method comprising: determining an electric-thermal combined virtual power plant to be optimized, wherein the electric-thermal combined virtual power plant comprises a thermoelectric coupling device; determining an operation optimization model corresponding to the electric-heating combined virtual power plant, wherein an objective function of the operation optimization model is constructed based on the total operation cost of the electric-heating combined virtual power plant, and the total operation cost comprises the operation cost of the thermoelectric coupling device; and optimizing the electric-heating combined virtual power plant based on the operation optimization model.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being stored on a non-transitory computer readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing the virtual power plant operation optimization method provided by the above methods, the method comprising: determining an electric-thermal combined virtual power plant to be optimized, wherein the electric-thermal combined virtual power plant comprises a thermoelectric coupling device; determining an operation optimization model corresponding to the electric-heating combined virtual power plant, wherein an objective function of the operation optimization model is constructed based on the total operation cost of the electric-heating combined virtual power plant, and the total operation cost comprises the operation cost of the thermoelectric coupling device; and optimizing the electric-heating combined virtual power plant based on the operation optimization model.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a virtual plant operation optimization method provided by the above methods, the method comprising: determining an electric-thermal combined virtual power plant to be optimized, wherein the electric-thermal combined virtual power plant comprises a thermoelectric coupling device; determining an operation optimization model corresponding to the electric-heating combined virtual power plant, wherein an objective function of the operation optimization model is constructed based on the total operation cost of the electric-heating combined virtual power plant, and the total operation cost comprises the operation cost of the thermoelectric coupling device; and optimizing the electric-heating combined virtual power plant based on the operation optimization model.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A virtual power plant operation optimization method is characterized by comprising the following steps:
determining an electric-thermal combined virtual power plant to be optimized, wherein the electric-thermal combined virtual power plant comprises a thermoelectric coupling device;
determining an operation optimization model corresponding to the electric-heating combined virtual power plant, wherein an objective function of the operation optimization model is constructed based on the total operation cost of the electric-heating combined virtual power plant, and the total operation cost comprises the operation cost of the thermoelectric coupling device;
and optimizing the electric-heating combined virtual power plant based on the operation optimization model.
2. The virtual power plant operation optimization method of claim 1, wherein optimizing the electric-thermal combination virtual power plant based on the operation optimization model comprises:
determining constraint conditions corresponding to the operation optimization model, wherein the constraint conditions comprise operation constraints corresponding to the thermocouple device;
and optimizing the electric-heat combined virtual power plant based on the operation optimization model and the constraint conditions.
3. The virtual power plant operation optimization method of claim 2, wherein the constraint condition comprises at least one of a cogeneration unit operation constraint, a gas unit operation constraint, an electric boiler operation constraint, an electric energy storage operation constraint, a heat storage device operation constraint, a wind turbine unit operation constraint, a photovoltaic unit operation constraint, and an electric heat balance constraint of the electric-thermal combined virtual power plant.
4. The virtual power plant operation optimization method of claim 1, wherein optimizing the electric-thermal combination virtual power plant based on the operation optimization model comprises:
determining a scene set of the electric-heating combined virtual power plant, wherein the scene set is determined based on prediction errors of a wind turbine set or a photovoltaic set in the electric-heating combined virtual power plant;
and optimizing the electric-heating combined virtual power plant based on the operation optimization model and the scene set.
5. The virtual power plant operation optimization method of claim 1, wherein the operation optimization model is a model based on conditional risk value;
the operation optimization model corresponding to the electric heating combined virtual power plant is determined, and the method comprises the following steps:
determining a risk aversion degree corresponding to the electric-heating combined virtual power plant, wherein the risk aversion degree is customized by a decision maker of the electric-heating combined virtual power plant;
determining the operational optimization model based on the degree of risk aversion.
6. The virtual power plant operation optimization method according to any one of claims 1 to 5, wherein the thermoelectric coupling device comprises at least one of a cogeneration unit, an electric boiler and a heat storage device, and the electric-thermal virtual power plant further comprises at least one of a gas unit, an electric energy storage device, a wind turbine unit and a photovoltaic unit;
the total operation cost comprises at least one of the operation cost of a cogeneration unit, the operation cost of a gas unit, the operation cost of an electric boiler, the operation cost of electric energy storage and the operation cost of a heat storage device, and the operation cost of the gas unit comprises the fuel cost and the start-stop cost of the gas unit.
7. The virtual power plant operation optimization method according to any one of claims 1 to 5, wherein the objective function is constructed by:
and constructing the objective function based on the total operation cost, the corresponding risk aversion degree of the electric-heat combined virtual power plant, the occurrence probability, the risk value and the relaxation variable of each scene in the electric-heat combined virtual power plant.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the virtual plant operation optimization method according to any one of claims 1 to 7.
9. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the virtual plant operation optimization method of any of claims 1 to 7.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, realizes the steps of the virtual plant operation optimization method according to any of the claims 1 to 7.
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