CN114498625A - Income prediction method and system of wind-solar-storage integrated power supply - Google Patents

Income prediction method and system of wind-solar-storage integrated power supply Download PDF

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CN114498625A
CN114498625A CN202210009404.1A CN202210009404A CN114498625A CN 114498625 A CN114498625 A CN 114498625A CN 202210009404 A CN202210009404 A CN 202210009404A CN 114498625 A CN114498625 A CN 114498625A
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刘傲
李江南
程韧俐
史军
祝宇翔
张炀
车诒颖
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Shenzhen Power Supply Co ltd
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Abstract

The invention provides a method and a system for predicting profits of a wind-solar-energy storage integrated power supply, wherein the method comprises the steps of acquiring operation plan data of wind power, photovoltaic and energy storage devices in the wind-solar-energy storage integrated system; calculating system expected revenue and system expected cost according to the operation plan data, and outputting the difference between the system expected revenue and the system expected cost as system expected income; and judging whether the system expected income meets a preset constraint standard or not through a preset constraint model, and if so, outputting the system expected income as an income prediction result of the wind-solar-energy-storage integrated power supply. According to the method, the influence of the new energy prediction deviation on the expected transaction income and the expected transaction risk is fully considered, and the transaction risk constraint under the new energy prediction deviation based on the condition value risk is constructed, so that the declaration strategy is adaptive to the new energy prediction deviation requirement.

Description

Income prediction method and system of wind-solar-storage integrated power supply
Technical Field
The invention relates to the technical field of power dispatching operation, in particular to a profit prediction method and a profit prediction system for a wind-solar-storage integrated power supply.
Background
In order to promote new energy consumption and continuously accelerate the construction of two integrated systems of wind, light, water, fire and storage and source network charge and storage, the complementary regulation potential of multiple types of power supplies is exploited. The wind, light and energy storage integrated system is a typical form of the two integrated systems, and is characterized in that an integrated system is formed by integrating three types of power supplies, such as wind power, photovoltaic and energy storage, and an integrated system operator participates in response adjustment of a large power grid by digging the operation adjustment potential of the three types of power supplies, so that the operation income of the integrated system is improved, and the consumption of new energy resources of the whole grid is promoted.
Compared with conventional power supplies such as water, electricity and thermal power, the wind, light and storage integrated system is influenced by the prediction deviation of new energy due to the fact that the new energy accounts for a high ratio, and the competitive price of the spot market trading faces greater challenges. Especially in the current spot market transaction, the prediction deviation of new energy resources such as wind power, photovoltaic and the like is larger than that of the current spot market in the day, and if the declaration strategy is unreasonable to make, when the large prediction deviation occurs, the new energy resources of the integrated system are not easily consumed, and the integrated system operator is likely to suffer large economic loss.
The current day-ahead market declaring strategy of the wind-solar-energy storage integrated system is made by mainly considering expected transaction income maximization as a declaring decision target, and on the basis, various power supply operation characteristic constraints and the like of the integrated system are comprehensively considered to construct a declaring decision model as a decision basis of an integrated system operator. In the current research, the uncertainty influence of the spot market trading price is mainly considered in the expected trading yield measurement, the maximization of the trading yield expectation under the multi-scene day-ahead market trading price is taken as a target, and the influence of the market trading price fluctuation on the trading result is adapted.
The existing research only considers the influence of the price fluctuation of the spot market faced by the wind-solar-energy storage integrated system, only considers the influence of the price fluctuation on the expected income, and does not comprehensively consider possible transaction risks. In addition, compared with conventional power supplies such as thermal power, hydroelectric power and the like, the wind-solar-energy storage integrated system has the advantages that the installed scale of new energy such as wind power, photovoltaic power and the like is large, and the influence of the prediction deviation of the new energy on the transaction result is obvious. The existing prediction method does not fully consider the problems, and may cause a large risk caused by the prediction deviation of new energy.
Disclosure of Invention
The invention aims to provide a method and a system for predicting profits of a wind-light-storage integrated power supply, and solves the technical problem that impression factors are not fully considered in the conventional method, so that the prediction deviation of new energy is large.
On one hand, the method for predicting the income of the wind-solar-storage integrated power supply comprises the following steps:
acquiring operation plan data of wind power, photovoltaic and energy storage devices in the wind-solar-energy storage integrated system;
calculating system expected revenue and system expected cost according to the operation plan data, and outputting the difference between the system expected revenue and the system expected cost as system expected income;
and judging whether the system expected income meets a preset constraint standard or not through a preset constraint model, and if so, outputting the system expected income as an income prediction result of the wind-solar-energy-storage integrated power supply.
Preferably, the operation plan data of the wind power, photovoltaic and energy storage device at least comprises: market price under a current spot goods forecasting scene before the day, the number of current spot goods market forecasting scenes, integrated system time interval exchange power, wind power forecasting cost, photovoltaic forecasting cost, energy storage forecasting cost, the number of predicted scenes of wind power multiple scenes, scene occurrence probability, wind power generation power under the scenes, quadratic term coefficients of a wind power forecasting cost function, primary term coefficients of a wind power forecasting cost function, constant term coefficients of a wind power forecasting cost function, the number of predicted scenes of the photovoltaic multiple scenes, the power generation power of scene photovoltaics, secondary term coefficients of a photovoltaic forecasting cost function, primary term coefficients of a photovoltaic forecasting cost function, and constant term coefficients of a photovoltaic forecasting cost function; the energy storage device exchanges power net in a period of time, the quadratic term coefficient of the energy storage expected cost function, the primary term coefficient of the energy storage expected cost function and the constant term coefficient of the energy storage expected cost function.
Preferably, the system expected revenue is calculated according to the following formula:
Figure RE-GDA0003577923100000031
wherein, IISRepresents the expected revenue of the integrated system, deltat represents the interval of time periods, NT represents the number of time periods,
Figure RE-GDA0003577923100000032
showing the market price of the time period t under the spot goods forecast scene ps before the day, and NPS showing the number of spot goods market price forecast scenes before the day, Pt ISIndicating an integrated system time period exchange power.
Preferably, the system expected cost is calculated according to the following formula:
CIS＝Cw+Cp+Cs
Figure RE-GDA0003577923100000033
Figure RE-GDA0003577923100000034
Figure RE-GDA0003577923100000035
wherein, CISRepresents the expected cost of the integrated system, CwRepresenting wind powerExpected cost, CpRepresents the photovoltaic expected cost, CsRepresenting the expected cost of energy storage, NWS representing the number of wind power multi-scenario predicted scenarios, ρwsWhich represents the probability of occurrence of the scene ws,
Figure RE-GDA0003577923100000041
representing the generated power of the wind-power interval t under the scene ws, awCoefficient of quadratic term representing wind power expected cost function, bwCoefficient of first order term representing wind power expected cost function, cwConstant term coefficients representing a wind power expected cost function; NPS represents the number of scenes, rho, of photovoltaic multi-scene predictionpsWhich represents the probability of occurrence of the scene ps,
Figure RE-GDA0003577923100000042
representing the generated power of the photovoltaic period t under the scene ps, apCoefficient of quadratic term representing the photovoltaic expected cost function, bpCoefficient of first order term representing photovoltaic desired cost function, cpConstant term coefficients representing a photovoltaic desired cost function; pt SRepresenting net exchange power of the energy storage device period, asCoefficient of quadratic term of the expected cost function of stored energy, bsCoefficient of first order of expected cost function of stored energy, csConstant term coefficients representing an expected cost function of stored energy.
Preferably, the preset constraint model specifically includes:
Max INIS
Figure RE-GDA0003577923100000051
where max represents the maximization planning problem, s.t. represents the constraint, Pt W,SRepresents the planned generated output, P, of the wind power time interval tt W,ARepresenting planned wind curtailment output, P, of a wind power period tt P,SRepresents the planned generated output, P, of the photovoltaic period tt P,ARepresenting the planned rejected light output, P, of the photovoltaic time period tt S,DRepresenting the discharge power of the energy storage device over a period t, Pt S,CRepresenting the charging power of the energy storage device over a period t, PSDmaxRepresenting the maximum discharge power, P, of the energy storage deviceSCmaxRepresents the maximum charging power of the energy storage device,
Figure RE-GDA0003577923100000052
represents the energy storage device time period t discharge state variable,
Figure RE-GDA0003577923100000053
represents the energy storage device time period t state-of-charge variable,
Figure RE-GDA0003577923100000061
indicating the initial charge of the energy storage device, ESmaxRepresents the maximum energy storage capacity of the energy storage device, ESminIndicates the minimum electric storage quantity, eta of the energy storage deviceSRepresenting the loss factor, E, converted to the charging sideAsetThe limit value of wind and light abandoning electric quantity is shown,
Figure RE-GDA0003577923100000062
representing the generated power rho under the whole scene wps of the wind power and photovoltaic new energywpsRepresenting the occurrence probability of a new energy random variable scene wps, wherein the NWPS represents the number of new energy random variable scenes]+Indicating taking a positive function.
On the other hand, a revenue prediction system of the wind-solar-storage integrated power supply is also provided, which is used for realizing the revenue prediction method of the wind-solar-storage integrated power supply, and comprises the following steps:
the data acquisition module is used for acquiring operation plan data of wind power, photovoltaic and energy storage devices in the wind-solar-energy storage integrated system;
the expected income module is used for calculating the expected revenue and the expected cost of the system according to the operation plan data and outputting the difference between the expected revenue and the expected cost of the system as the expected income of the system;
and the constraint module is used for judging whether the system expected income meets a preset constraint standard or not through a preset constraint model, and if so, outputting the system expected income as an income prediction result of the wind-light-storage integrated power supply.
Preferably, the acquiring of the operation plan data of the wind power, the photovoltaic and the energy storage device by the data acquiring module at least includes: market price under a current spot goods forecasting scene before the day, the number of current spot goods market forecasting scenes, integrated system time interval exchange power, wind power forecasting cost, photovoltaic forecasting cost, energy storage forecasting cost, the number of predicted scenes of wind power multiple scenes, scene occurrence probability, wind power generation power under the scenes, quadratic term coefficients of a wind power forecasting cost function, primary term coefficients of a wind power forecasting cost function, constant term coefficients of a wind power forecasting cost function, the number of predicted scenes of the photovoltaic multiple scenes, the power generation power of scene photovoltaics, secondary term coefficients of a photovoltaic forecasting cost function, primary term coefficients of a photovoltaic forecasting cost function, and constant term coefficients of a photovoltaic forecasting cost function; the energy storage device exchanges power net in a period of time, the quadratic term coefficient of the energy storage expected cost function, the primary term coefficient of the energy storage expected cost function and the constant term coefficient of the energy storage expected cost function.
Preferably, the expected revenue module is further configured to calculate the system expected revenue according to the following formula:
Figure RE-GDA0003577923100000071
wherein, IISRepresents the expected revenue of the integrated system, deltat represents the interval of time periods, NT represents the number of time periods,
Figure RE-GDA0003577923100000072
showing the market price of the time period t under the spot goods forecast scene ps before the day, and NPS showing the number of spot goods market price forecast scenes before the day, Pt ISIndicating an integrated system time period exchange power.
Preferably, the expected revenue module is further configured to calculate a system expected cost according to the following formula:
CIS＝Cw+Cp+Cs
Figure RE-GDA0003577923100000073
Figure RE-GDA0003577923100000074
Figure RE-GDA0003577923100000075
wherein, CISRepresents the expected cost of the integrated system, CwRepresenting the expected cost of wind power, CpRepresents the photovoltaic expected cost, CsRepresenting the expected cost of energy storage, NWS representing the number of wind power multi-scenario predicted scenarios, ρwsWhich represents the probability of occurrence of the scene ws,
Figure RE-GDA0003577923100000076
representing the generated power of the wind-power interval t under the scene ws, awCoefficient of quadratic term representing wind power expected cost function, bwCoefficient of first order term representing wind power expected cost function, cwConstant term coefficients representing a wind power expected cost function; NPS represents the number of scenes, rho, of photovoltaic multi-scene predictionpsWhich represents the probability of occurrence of the scene ps,
Figure RE-GDA0003577923100000081
representing the generated power of the photovoltaic period t under the scene ps, apCoefficient of quadratic term representing the photovoltaic expected cost function, bpCoefficient of first order term representing photovoltaic desired cost function, cpConstant term coefficients representing a photovoltaic desired cost function; pt SRepresenting net exchange power of the energy storage device period, asCoefficient of quadratic term of the energy storage expected cost function, bsCoefficient of first order of expected cost function of stored energy, csConstant term coefficients representing an expected cost function of stored energy.
Preferably, the preset constraint model specifically includes:
Max INIS
Figure RE-GDA0003577923100000091
wherein, Pt W,SRepresents the planned generated output, P, of the wind power time interval tt W,ARepresenting planned wind curtailment output, P, of a wind power period tt P,SRepresents the planned generated output, P, of the photovoltaic period tt P,ARepresenting the planned rejected light output, P, of the photovoltaic time period tt S,DRepresenting the discharge power of the energy storage device over a period t, Pt S,CRepresenting the charging power of the energy storage device over a period t, PSDmaxRepresenting the maximum discharge power, P, of the energy storage deviceSCmaxRepresents the maximum charging power of the energy storage device,
Figure RE-GDA0003577923100000092
represents the energy storage device time period t discharge state variable,
Figure RE-GDA0003577923100000093
represents the energy storage device time period t state-of-charge variable,
Figure RE-GDA0003577923100000094
indicates the initial charge capacity of the energy storage device, ESmaxRepresents the maximum energy storage capacity of the energy storage device, ESminIndicates the minimum electric storage quantity, eta of the energy storage deviceSRepresenting the loss factor, E, converted to the charging sideAsetThe limit value of wind and light abandoning electric quantity is shown,
Figure RE-GDA0003577923100000101
representing the generated power rho under the whole scene wps of the wind power and photovoltaic new energywpsRepresenting the occurrence probability of a new energy random variable scene wps, and NWPS representing the number of new energy random variable scenes, and]+indicating taking a positive function.
In summary, the embodiment of the invention has the following beneficial effects:
according to the income prediction method and the income prediction system of the wind-solar-storage integrated power supply, on the basis of the design of the declaration strategy of the current integrated system, the influence of the new energy source prediction deviation on the expected transaction income and the expected transaction risk is fully considered, and the transaction risk constraint under the new energy source prediction deviation based on the conditional value risk is constructed, so that the declaration strategy is adaptive to the requirement of the new energy source prediction deviation. The method fully considers the high installation scale of new energy such as wind power, photovoltaic and the like in the wind-solar-storage integrated system, and the influence of the new energy prediction deviation on a transaction result can reduce the risk of the new energy prediction deviation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive labor.
Fig. 1 is a main flow diagram of a revenue prediction method of a wind, photovoltaic and energy storage integrated power supply in an embodiment of the invention.
Fig. 2 is a schematic diagram of a revenue prediction system of a wind, photovoltaic and energy storage integrated power supply in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram illustrating a method for predicting the benefit of a wind, photovoltaic and energy storage integrated power supply according to an embodiment of the present invention. In this embodiment, the method comprises the steps of:
acquiring operation plan data of wind power, photovoltaic and energy storage devices in the wind-solar-energy storage integrated system; in other words, factors such as new energy prediction deviation, current price fluctuation before the day and the like are considered, and the operation plan of the wind power, photovoltaic and energy storage device in the integrated system is considered, so that a data basis is provided for the subsequent calculation and prediction process. Specifically, the operation plan data of the wind power, photovoltaic and energy storage device at least comprises: market price under a current spot goods forecasting scene before the day, the number of current spot goods market forecasting scenes, integrated system time interval exchange power, wind power forecasting cost, photovoltaic forecasting cost, energy storage forecasting cost, the number of predicted scenes of wind power multiple scenes, scene occurrence probability, wind power generation power under the scenes, quadratic term coefficients of a wind power forecasting cost function, primary term coefficients of a wind power forecasting cost function, constant term coefficients of a wind power forecasting cost function, the number of predicted scenes of the photovoltaic multiple scenes, the power generation power of scene photovoltaics, secondary term coefficients of a photovoltaic forecasting cost function, primary term coefficients of a photovoltaic forecasting cost function, and constant term coefficients of a photovoltaic forecasting cost function; the energy storage device exchanges power net in a period of time, the quadratic term coefficient of the energy storage expected cost function, the primary term coefficient of the energy storage expected cost function and the constant term coefficient of the energy storage expected cost function.
Further, calculating system expected revenue and system expected cost according to the operation plan data, and outputting the difference between the system expected revenue and the system expected cost as system expected income; namely, factors such as new energy prediction deviation, spot price fluctuation before the day and the like are considered, and expected revenue under the integrated system declaration strategy is calculated; and calculating the expected cost according to the operation plan of the wind power, photovoltaic and energy storage devices in the integrated system.
In the specific embodiment, a multi-scenario prediction model is adopted to evaluate the new energy prediction deviation and the day-ahead spot market price fluctuation. The multi-scenario prediction model belongs to a mature technical method at present, does not influence the main innovation of the invention, and is not repeated for the specific implementation process. The integrated system expected revenue is expected value based on multi-scene prediction model, and the system expected revenue is calculated according to the following formula:
Figure RE-GDA0003577923100000121
wherein, IISRepresents the expected revenue of the integrated system, deltat represents the interval of time periods, NT represents the number of time periods,
Figure RE-GDA0003577923100000122
showing the market price of the time period t under the spot goods forecast scene ps before the day, and NPS showing the number of spot goods market price forecast scenes before the day, Pt ISIndicating an integrated system time period exchange power.
Specifically, the costs of the wind power, the photovoltaic and the energy storage device can be divided into fixed costs with a low relation with the operation plan and variable costs with a high relation with the operation plan. The expected cost of the integrated system is the sum of the expected costs of wind power, photovoltaic and energy storage devices, and the expected cost of the system is calculated according to the following formula:
CIS＝Cw+Cp+Cs
Figure RE-GDA0003577923100000123
Figure RE-GDA0003577923100000124
Figure RE-GDA0003577923100000125
wherein, CISRepresents the expected cost of the integrated system, CwRepresenting the expected cost of wind power, CpRepresents the photovoltaic expected cost, CsRepresenting the expected cost of energy storage, NWS representing the number of wind power multi-scenario predicted scenarios, ρwsWhich represents the probability of occurrence of the scene ws,
Figure RE-GDA0003577923100000131
representing the generated power of the wind-power interval t under the scene ws, awCoefficient of quadratic term representing wind power expected cost function, bwCoefficient of first order term representing wind power expected cost function, cwConstant term coefficients representing a wind power expected cost function; NPS represents the number of scenes, rho, of photovoltaic multi-scene predictionpsWhich represents the probability of occurrence of the scene ps,
Figure RE-GDA0003577923100000132
representing the generated power of the photovoltaic period t under the scene ps, apCoefficient of quadratic term representing the photovoltaic expected cost function, bpCoefficient of first order term representing photovoltaic desired cost function, cpConstant term coefficients representing a photovoltaic desired cost function; pt SRepresenting net exchange power over time of the energy storage device, asCoefficient of quadratic term of the expected cost function of stored energy, bsCoefficient of first order of expected cost function of stored energy, csConstant term coefficients representing an expected cost function of stored energy.
And specifically, comprehensively considering the expected revenue and the expected cost of the integrated system, and constructing a decision target based on expected profit maximization.
The expected revenue may be expressed as the difference between the expected revenue and the expected cost, and may be expressed as:
INIS＝IIS-CIS
in the formula INISRevenue is expected for the integrated system.
Further, whether the system expected income meets a preset constraint standard or not is judged through a preset constraint model, and if the system expected income meets the preset constraint standard, the system expected income is output to be an income prediction result of the wind-light-storage integrated power supply. Namely, a constraint model taking expected income maximization as a decision target and taking adjustable power supply operation constraint and risk control constraint as constraint conditions is constructed, and a prediction result, namely a reporting strategy, is obtained by solving.
In a specific embodiment, the preset constraint model specifically includes:
Max INIS
Figure RE-GDA0003577923100000141
where max represents the maximization planning problem, s.t. represents the constraint, Pt W,SRepresents the planned generated output, P, of the wind power time interval tt W,ARepresenting planned wind curtailment output, P, of a wind power period tt P,SWhen representing photovoltaicsSection t planning the power generationt P,ARepresents the planned abandoned light output, P, of the photovoltaic time period tt S,DRepresenting the discharge power of the energy storage device over a period t, Pt S,CRepresenting the charging power of the energy storage device over a period t, PSDmaxRepresenting the maximum discharge power, P, of the energy storage deviceSCmaxRepresents the maximum charging power of the energy storage device,
Figure RE-GDA0003577923100000142
represents the energy storage device time period t discharge state variable,
Figure RE-GDA0003577923100000143
represents the energy storage device time period t state-of-charge variable,
Figure RE-GDA0003577923100000151
indicating the initial charge of the energy storage device, ESmaxRepresents the maximum energy storage capacity of the energy storage device, ESminIndicates the minimum electric storage quantity, eta of the energy storage deviceSRepresenting the loss factor, E, converted to the charging sideAsetThe limit value of wind and light abandoning electric quantity is shown,
Figure RE-GDA0003577923100000152
representing the generated power rho under the whole scene wps of the wind power and photovoltaic new energywpsRepresenting the occurrence probability of a new energy random variable scene wps, and NWPS representing the number of new energy random variable scenes, and]+indicating taking a positive function.
Wherein, according to wind-powered electricity generation, photovoltaic, energy memory operating characteristic, establish its operation restraint, specifically, the electricity operation restraint is the power generation power balance restraint, can express as:
Figure RE-GDA0003577923100000153
in the formula, Pt W,S、Pt W,AAnd respectively planning the power generation output and the abandoned wind output for the wind power time interval t.
The photovoltaic operating constraint is a generated power balance constraint, which can be expressed as:
Figure RE-GDA0003577923100000154
in the formula, Pt P,S、Pt P,AAnd respectively planning power generation output and light abandoning output for the photovoltaic time period t.
Specifically, the energy storage device operating characteristic constraints include an exchange power constraint, a discharge capability constraint, a charge-discharge state constraint, an electric storage quantity constraint, and an electric storage quantity invariant constraint, which can be expressed as:
Pt S＝Pt S,D-Pt S,C
Figure RE-GDA0003577923100000155
Figure RE-GDA0003577923100000156
Figure RE-GDA0003577923100000157
Figure RE-GDA0003577923100000161
Figure RE-GDA0003577923100000162
in the formula, Pt S,D、Pt S,CRespectively the discharge power and the charge power of the energy storage device in a period t, PSDmax、 PSCmaxRespectively the maximum discharge power and the maximum charge power of the energy storage device,
Figure RE-GDA0003577923100000163
respectively a discharge state variable and a charge state variable of the energy storage device in a time period t,
Figure RE-GDA0003577923100000164
for the initial storage of the energy storage device, ESmax、ESminMaximum and minimum electric storage capacity, eta of the energy storage deviceSTo convert to a loss factor to the charging side. The integrated system also needs to satisfy an overall outgoing balance constraint, which can be expressed as:
Pt W,S+Pt P,S+Pt S＝Pt IS
specifically, the influence of the new energy source prediction deviation on the operation is fully considered, and risk control constraints are constructed. The impact of the new energy source prediction bias includes two aspects. On one hand, the actual new energy power generation output is higher than the planned arrangement, which may cause the loss of the abandoned wind and abandoned light, on the other hand, the actual new energy power generation output is lower than the planned arrangement, and the integral system sends the penalty lower than the planned requirement. The wind curtailment and light curtailment loss risk constraint requires that the expected wind curtailment and light curtailment quantity does not exceed a limit value, which can be expressed as:
Figure RE-GDA0003577923100000165
in the formula, EAsetIn order to abandon wind and light electric quantity limit value,
Figure RE-GDA0003577923100000166
generating power rho under wind power and photovoltaic new energy overall scene wpswpsAnd the probability of occurrence of a new energy random variable scene wps is shown, and the NWPS is the number of the new energy random variable scenes. []+To take a positive function, when the result of the function is positive, its own value is output, otherwise 0 is output, which can be expressed as:
Figure RE-GDA0003577923100000171
the power generation power of the whole multi-scene prediction of new energy is the random variable superposition of wind power and photovoltaic multi-scene prediction, and can be expressed as follows:
Figure RE-GDA0003577923100000172
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0003577923100000173
representing the addition of random variables.
The integrated system penalty risk constraint requires that the penalty incurred by the integrated system plan deviation does not exceed a limit, which can be expressed as:
Figure RE-GDA0003577923100000174
in the formula, CAsetIn order to plan for the deviation penalty limit,
Figure RE-GDA0003577923100000176
plan for the time period t the penalty electricity price]-To take a negative function, when the result of the function is negative, the inverse of its own value is output, otherwise 0 is output, which can be expressed as:
Figure RE-GDA0003577923100000175
fig. 2 is a schematic diagram of a revenue prediction system of a wind, photovoltaic and energy storage integrated power supply according to an embodiment of the present invention. In this embodiment, the method includes:
the data acquisition module is used for acquiring operation plan data of wind power, photovoltaic and energy storage devices in the wind-solar-energy storage integrated system; specifically, the acquiring of the operation plan data of the wind power, the photovoltaic and the energy storage device by the data acquiring module at least comprises the following steps: market price under a current spot goods forecasting scene before the day, the number of current spot goods market forecasting scenes, integrated system time interval exchange power, wind power forecasting cost, photovoltaic forecasting cost, energy storage forecasting cost, the number of predicted scenes of wind power multiple scenes, scene occurrence probability, wind power generation power under the scenes, quadratic term coefficients of a wind power forecasting cost function, primary term coefficients of a wind power forecasting cost function, constant term coefficients of a wind power forecasting cost function, the number of predicted scenes of the photovoltaic multiple scenes, the power generation power of scene photovoltaics, secondary term coefficients of a photovoltaic forecasting cost function, primary term coefficients of a photovoltaic forecasting cost function, and constant term coefficients of a photovoltaic forecasting cost function; the energy storage device exchanges power net in a period of time, the quadratic term coefficient of the energy storage expected cost function, the primary term coefficient of the energy storage expected cost function and the constant term coefficient of the energy storage expected cost function.
The expected income module is used for calculating the expected revenue and the expected cost of the system according to the operation plan data and outputting the difference between the expected revenue and the expected cost of the system as the expected income of the system; specifically, the expected revenue module is further configured to calculate a system expected revenue according to the following formula:
Figure RE-GDA0003577923100000181
wherein, IISRepresents the expected revenue of the integrated system, deltat represents the interval of time periods, NT represents the number of time periods,
Figure RE-GDA0003577923100000182
showing the market price of the time period t under the spot goods forecast scene ps before the day, and NPS showing the number of spot goods market price forecast scenes before the day, Pt ISIndicating an integrated system time period exchange power.
The expected revenue module is further configured to calculate a system expected cost according to the following equation:
CIS＝Cw+Cp+Cs
Figure RE-GDA0003577923100000183
Figure RE-GDA0003577923100000184
Figure RE-GDA0003577923100000191
wherein, CISRepresents the expected cost of the integrated system, CwRepresenting the expected cost of wind power, CpRepresents the photovoltaic expected cost, CsRepresenting the expected cost of energy storage, NWS representing the number of wind power multi-scenario predicted scenarios, ρwsWhich represents the probability of occurrence of the scene ws,
Figure RE-GDA0003577923100000192
representing the generated power of the wind-power interval t under the scene ws, awCoefficient of quadratic term representing wind power expected cost function, bwCoefficient of first order term representing wind power expected cost function, cwConstant term coefficients representing a wind power expected cost function; NPS represents the number of scenes, rho, of photovoltaic multi-scene predictionpsWhich represents the probability of occurrence of the scene ps,
Figure RE-GDA0003577923100000193
representing the generated power of the photovoltaic period t under the scene ps, apCoefficient of quadratic term representing the photovoltaic expected cost function, bpCoefficient of first order term representing photovoltaic desired cost function, cpConstant term coefficients representing a photovoltaic desired cost function; pt SRepresenting net exchange power of the energy storage device period, asCoefficient of quadratic term of the expected cost function of stored energy, bsCoefficient of first order of expected cost function of stored energy, csConstant term coefficients representing an expected cost function of stored energy.
And the constraint module is used for judging whether the system expected income meets a preset constraint standard or not through a preset constraint model, and if so, outputting the system expected income as an income prediction result of the wind-light-storage integrated power supply.
Specifically, the preset constraint model specifically includes:
Max INIS
Figure RE-GDA0003577923100000201
wherein, Pt W,SRepresents the planned generated output, P, of the wind power time interval tt W,ARepresenting planned wind curtailment output, P, of a wind power period tt P,SRepresents the planned generated output, P, of the photovoltaic time period tt P,ARepresenting the planned rejected light output, P, of the photovoltaic time period tt S,DRepresenting the discharge power of the energy storage device over a period t, Pt S,CRepresenting the charging power of the energy storage device over a period t, PSDmaxRepresenting the maximum discharge power, P, of the energy storage deviceSCmaxRepresents the maximum charging power of the energy storage device,
Figure RE-GDA0003577923100000202
represents the energy storage device time period t discharge state variable,
Figure RE-GDA0003577923100000203
represents the energy storage device time period t state-of-charge variable,
Figure RE-GDA0003577923100000204
indicating the initial charge of the energy storage device, ESmaxRepresents the maximum energy storage capacity of the energy storage device, ESminIndicates the minimum electric storage quantity, eta of the energy storage deviceSRepresents a loss factor converted to the charging side, EAsetThe limit value of wind and light abandoning electric quantity is shown,
Figure RE-GDA0003577923100000211
representing the generated power rho under the whole scene wps of the wind power and photovoltaic new energywpsRepresenting the occurrence probability of a new energy random variable scene wps, and NWPS representing the number of new energy random variable scenes, and]+indicating taking a positive function.
The above-described system embodiments 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 multiple 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.
It should be noted that the system described in the foregoing embodiment corresponds to the method described in the foregoing embodiment, and therefore, portions of the system described in the foregoing embodiment that are not described in detail can be obtained by referring to the content of the method described in the foregoing embodiment, and details are not described here.
In summary, the embodiment of the invention has the following beneficial effects:
according to the income prediction method and the income prediction system of the wind-solar-storage integrated power supply, on the basis of the design of the declaration strategy of the current integrated system, the influence of the new energy source prediction deviation on the expected transaction income and the expected transaction risk is fully considered, and the transaction risk constraint under the new energy source prediction deviation based on the conditional value risk is constructed, so that the declaration strategy is adaptive to the requirement of the new energy source prediction deviation. The method fully considers the high installation scale of new energy such as wind power, photovoltaic and the like in the wind-solar-storage integrated system, and the influence of the new energy prediction deviation on a transaction result can reduce the risk of the new energy prediction deviation. The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.

Claims (10)

1. A profit prediction method for a wind-solar-storage integrated power supply is characterized by comprising the following steps:
acquiring operation plan data of wind power, photovoltaic and energy storage devices in the wind-solar-energy storage integrated system;
calculating system expected revenue and system expected cost according to the operation plan data, and outputting the difference between the system expected revenue and the system expected cost as system expected income;
and judging whether the system expected income meets a preset constraint standard or not through a preset constraint model, and if so, outputting the system expected income as an income prediction result of the wind-solar-energy-storage integrated power supply.
2. The method of claim 1, wherein the operational schedule data for the wind, photovoltaic, and energy storage devices comprises at least: market price under a current spot goods forecasting scene before the day, the number of current spot goods market forecasting scenes, integrated system time interval exchange power, wind power forecasting cost, photovoltaic forecasting cost, energy storage forecasting cost, the number of predicted scenes of wind power multiple scenes, scene occurrence probability, wind power generation power under the scenes, quadratic term coefficients of a wind power forecasting cost function, primary term coefficients of a wind power forecasting cost function, constant term coefficients of a wind power forecasting cost function, the number of predicted scenes of the photovoltaic multiple scenes, the power generation power of scene photovoltaics, secondary term coefficients of a photovoltaic forecasting cost function, primary term coefficients of a photovoltaic forecasting cost function, and constant term coefficients of a photovoltaic forecasting cost function; the energy storage device exchanges power net in a period of time, the quadratic term coefficient of the energy storage expected cost function, the primary term coefficient of the energy storage expected cost function and the constant term coefficient of the energy storage expected cost function.
3. The method of claim 2, wherein the system expected revenue is calculated according to the formula:
Figure FDA0003456771050000011
wherein, IISRepresents the expected revenue of the integrated system, deltat represents the interval of time periods, NT represents the number of time periods,
Figure FDA0003456771050000012
showing the market price of the time period t under the spot goods forecast scene ps before the day, and NPS showing the number of spot goods market price forecast scenes before the day, Pt ISIndicating an integrated system time period exchange power.
4. The method of claim 3, wherein the system expected cost is calculated according to the formula:
CIS＝Cw+Cp+Cs
Figure FDA0003456771050000021
Figure FDA0003456771050000022
Figure FDA0003456771050000023
wherein, CISRepresents the expected cost of the integrated system, CwRepresenting the expected cost of wind power, CpRepresents the photovoltaic expected cost, CsRepresenting the expected cost of energy storage, NWS representing the number of wind power multi-scenario predicted scenarios, ρwsWhich represents the probability of occurrence of the scene ws,
Figure FDA0003456771050000024
representing the generated power of the wind-power interval t under the scene ws, awCoefficient of quadratic term representing wind power expected cost function, bwCoefficient of first order term representing wind power expected cost function, cwConstant term coefficients representing a wind power expected cost function; NPS represents the number of scenes, rho, of photovoltaic multi-scene predictionpsWhich represents the probability of occurrence of the scene ps,
Figure FDA0003456771050000025
representing the generated power of the photovoltaic period t under the scene ps, apCoefficient of quadratic term representing the photovoltaic expected cost function, bpCoefficient of first order term representing photovoltaic desired cost function, cpConstant term coefficients representing a photovoltaic desired cost function; pt SRepresenting net exchange power over time of the energy storage device，asCoefficient of quadratic term of the expected cost function of stored energy, bsCoefficient of first order of expected cost function of stored energy, csConstant term coefficients representing an expected cost function of stored energy.
5. The method according to claim 4, wherein the preset constraint model specifically comprises:
Max INIS
Figure FDA0003456771050000031
where max represents the maximization planning problem, s.t. represents the constraint, Pt W,SRepresents the planned generated output, P, of the wind power time interval tt W,ARepresenting planned wind curtailment output, P, of a wind power period tt P,SRepresents the planned generated output, P, of the photovoltaic period tt P,ARepresenting the planned rejected light output, P, of the photovoltaic time period tt S,DRepresenting the discharge power of the energy storage device over a period t, Pt S,CRepresenting the charging power of the energy storage device over a period t, PSDmaxRepresenting the maximum discharge power, P, of the energy storage deviceSCmaxRepresents the maximum charging power of the energy storage device,
Figure FDA0003456771050000032
represents the energy storage device time period t discharge state variable,
Figure FDA0003456771050000033
represents the energy storage device time period t state-of-charge variable,
Figure FDA0003456771050000034
indicating the initial charge of the energy storage device, ESmaxRepresents the maximum energy storage capacity of the energy storage device, ESminIndicates the minimum electric storage quantity, eta of the energy storage deviceSRepresenting the loss factor, E, converted to the charging sideAsetThe limit value of wind and light abandoning electric quantity is shown,
Figure FDA0003456771050000041
representing the generated power rho under the whole scene wps of the wind power and photovoltaic new energywpsRepresenting the occurrence probability of a new energy random variable scene wps, and NWPS representing the number of new energy random variable scenes, and]+indicating taking a positive function.
6. A profit prediction system of a wind-solar-storage integrated power supply for realizing the profit prediction method of the wind-solar-storage integrated power supply according to any one of claims 1 to 5, comprising:
the data acquisition module is used for acquiring operation plan data of wind power, photovoltaic and energy storage devices in the wind-solar-energy storage integrated system;
the expected income module is used for calculating the expected revenue and the expected cost of the system according to the operation plan data and outputting the difference between the expected revenue and the expected cost of the system as the expected income of the system;
and the constraint module is used for judging whether the system expected income meets a preset constraint standard or not through a preset constraint model, and if so, outputting the system expected income as an income prediction result of the wind-light-storage integrated power supply.
7. The system of claim 6, wherein the data acquisition module acquiring the operation plan data of the wind power, photovoltaic and energy storage device at least comprises: market price under a current spot goods forecasting scene before the day, the number of current spot goods market forecasting scenes, integrated system time interval exchange power, wind power forecasting cost, photovoltaic forecasting cost, energy storage forecasting cost, the number of predicted scenes of wind power multiple scenes, scene occurrence probability, wind power generation power under the scenes, quadratic term coefficients of a wind power forecasting cost function, primary term coefficients of a wind power forecasting cost function, constant term coefficients of a wind power forecasting cost function, the number of predicted scenes of the photovoltaic multiple scenes, the power generation power of scene photovoltaics, secondary term coefficients of a photovoltaic forecasting cost function, primary term coefficients of a photovoltaic forecasting cost function, and constant term coefficients of a photovoltaic forecasting cost function; the energy storage device exchanges power net in a period of time, the quadratic term coefficient of the energy storage expected cost function, the primary term coefficient of the energy storage expected cost function and the constant term coefficient of the energy storage expected cost function.
8. The system of claim 7, wherein the expected revenue module is further configured to calculate a system expected revenue according to the following formula:
Figure FDA0003456771050000051
wherein, IISRepresents the expected revenue of the integrated system, deltat represents the interval of time periods, NT represents the number of time periods,
Figure FDA0003456771050000052
showing the market price of the time period t under the spot goods forecast scene ps before the day, and NPS showing the number of spot goods market price forecast scenes before the day, Pt ISIndicating an integrated system time period exchange power.
9. The system of claim 8, wherein the expected revenue module is further configured to calculate a system expected cost according to the following formula:
CIS＝Cw+Cp+Cs
Figure FDA0003456771050000053
Figure FDA0003456771050000054
Figure FDA0003456771050000055
wherein, CISRepresents the expected cost of the integrated system, CwRepresenting the expected cost of wind power, CpRepresents the photovoltaic expected cost, CsRepresenting the expected cost of energy storage, NWS representing the number of wind power multi-scenario predicted scenarios, ρwsWhich represents the probability of occurrence of the scene ws,
Figure FDA0003456771050000056
representing the generated power of the wind-power interval t under the scene ws, awCoefficient of quadratic term representing wind power expected cost function, bwCoefficient of first order term representing wind power expected cost function, cwConstant term coefficients representing a wind power expected cost function; NPS represents the number of scenes, rho, of photovoltaic multi-scene predictionpsWhich represents the probability of occurrence of the scene ps,
Figure FDA0003456771050000061
representing the generated power of the photovoltaic period t under the scene ps, apCoefficient of quadratic term representing the photovoltaic expected cost function, bpCoefficient of first order term representing photovoltaic desired cost function, cpConstant term coefficients representing a photovoltaic desired cost function; p is a radical oft SRepresenting net exchange power of the energy storage device period, asCoefficient of quadratic term of the expected cost function of stored energy, bsCoefficient of first order of expected cost function of stored energy, csConstant term coefficients representing an expected cost function of stored energy.
10. The system according to claim 9, wherein the preset constraint model specifically comprises:
Max INIS
Figure FDA0003456771050000063
wherein, Pt W,SRepresents the planned generated output, P, of the wind power time interval tt W,ARepresenting planned wind curtailment output, P, of a wind power period tt P,SRepresents the planned generated output of the photovoltaic time period t,Pt P,Arepresenting the planned rejected light output, P, of the photovoltaic time period tt S,DRepresenting the discharge power of the energy storage device over a period t, Pt S,CRepresenting the charging power of the energy storage device over a period t, PSDmaxRepresenting the maximum discharge power, P, of the energy storage deviceSCmaxRepresents the maximum charging power of the energy storage device,
Figure FDA0003456771050000071
representing the energy storage device time period tdischarge state variable,
Figure FDA0003456771050000072
represents the energy storage device time period t state-of-charge variable,
Figure FDA0003456771050000073
indicating the initial charge of the energy storage device, ESmaxRepresents the maximum energy storage capacity of the energy storage device, ESminIndicates the minimum electric storage quantity, eta of the energy storage deviceSRepresenting the loss factor, E, converted to the charging sideAsetThe limit value of wind and light abandoning electric quantity is shown,
Figure FDA0003456771050000074
representing the generated power rho under the whole scene wps of the wind power and photovoltaic new energywpsRepresenting the occurrence probability of a new energy random variable scene wps, and NWPS representing the number of new energy random variable scenes, and]+indicating taking a positive function.
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* Cited by examiner, † Cited by third party
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CN115983518B (en) * 2022-12-22 2024-06-11 浙江电力交易中心有限公司 Reporting method of wind-solar-energy-storage integrated system and related components

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