CN117010625A - Virtual power plant optimal scheduling method and system for demand response and prediction error - Google Patents
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Abstract
The invention discloses a virtual power plant optimal scheduling method and a system for demand response and prediction error, which relate to the technical field of power demand response analysis and comprise the steps of constructing an adjustable load model based on user response demands and optimizing the adjustable load model according to scheduling cost; setting an initial optimization target and initial constraint conditions, and constructing a day-ahead scheduling optimization model; setting a final optimization target and a final constraint condition, and constructing a real-time optimal scheduling model; obtaining an optimal scheduling plan, judging whether the optimal scheduling plan is feasible or not by a virtual power plant control center, and implementing if the optimal scheduling plan is feasible; if the plan is not feasible, continuing to set an optimization target to establish an optimal scheduling plan. The invention can effectively smooth the power fluctuation of the virtual power plant caused by the future prediction error of the new energy, optimize the load curve of the user, reduce the running cost of the virtual power plant and realize the economic, safe and stable running of the virtual power plant.
Description
Technical Field
The invention relates to the technical field of power demand response analysis, in particular to a virtual power plant optimal scheduling method and system for demand response and prediction errors.
Background
The proportion of the distributed energy is continuously increased, and the virtual power plant realizes collaborative management and control by aggregating various distributed new energy technologies such as controllable load, energy storage and the like, so that the influence of new energy such as wind power, photovoltaic and the like on a power system is reduced.
At present, due to the limitation of a prediction method, the output power prediction precision of new energy sources such as wind power, photovoltaic and the like is not high, and the prediction error can reach 20% -30%; in addition, the influence of randomness and intermittence of new energy output on the operation of the virtual power plant is considered in the current scheduling scheme, but the multi-time scale optimization of the virtual power plant is not performed, the operation cost of the virtual power plant is reduced, and the stable operation is realized.
Therefore, a virtual power plant optimal scheduling method with demand response and prediction errors is needed, so that virtual power plant power fluctuation caused by the future prediction errors of new energy sources is effectively smoothed, a user load curve is optimized, the running cost of the virtual power plant is reduced, and economic, safe and stable running of the virtual power plant is realized.
Disclosure of Invention
The invention is provided in view of the problems of high optimal scheduling cost and low efficiency of the existing virtual power plant.
Therefore, the problem to be solved by the invention is that the influence caused by prediction errors such as wind power generation, photovoltaic power generation, load change and the like is not considered when the influence of wind power and photovoltaic on the virtual power plant is considered in the traditional method, and the two-stage multi-time scale optimization of the virtual power plant based on price and excitation-based demand response is not considered simultaneously when the virtual power plant is optimally scheduled through demand response.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for optimizing and scheduling a virtual power plant for demand response and prediction error, where the method is characterized in that: constructing an adjustable load model based on user response demands, and optimizing the adjustable load model according to the dispatching cost; setting an initial optimization target and initial constraint conditions, and constructing a day-ahead scheduling optimization model; setting a final optimization target and a final constraint condition, and constructing a real-time optimal scheduling model; obtaining an optimal scheduling plan, judging whether the optimal scheduling plan is feasible or not by a virtual power plant control center, and implementing if the optimal scheduling plan is feasible; if the plan is not feasible, continuing to set an optimization target to establish an optimal scheduling plan.
As a preferable scheme of the virtual power plant optimal scheduling method for demand response and prediction error, the invention comprises the following steps: the construction of the adjustable load model specifically comprises the steps of introducing a peak-to-valley electricity price strategy to transfer and adjust the power load, and calculating the load demand based on a user response to the electricity price and a load demand elasticity theory, wherein a load demand calculation formula is as follows:
wherein L is t ,ρ t To load demand and electricity prices before demand response,for load demand and electricity price after demand response, deltaL t For load demand change, s, t are determined moments e tt Is electrically elastic; when s=t, e st Is the self-elasticity coefficient; when s+.t, e st Is the coefficient of mutual elasticity.
As a preferable scheme of the virtual power plant optimal scheduling method for demand response and prediction error, the invention comprises the following steps: the calculation formula of the scheduling cost is as follows:
wherein C is ibdr For incentive-based scheduling costs ρ e In order to compensate for the price,is the amount of load interruption.
As a preferable scheme of the virtual power plant optimal scheduling method for demand response and prediction error, the invention comprises the following steps: the initial optimization target is that the day-ahead scheduling cost is minimum; the initial constraint condition is a day-ahead scheduling power balance constraint, a gas unit output constraint, an energy storage device constraint, a power purchase constraint, a price demand response based constraint and an excitation demand response based constraint; the daily schedule power balance constraint is as follows:
the gas unit output constraint is as follows:
wherein eta mt For generating efficiency of gas unit, L NG Is the low-grade heat value of the natural gas,the minimum output and the maximum output of the gas unit are;
the energy storage device is constrained as follows:
wherein E is t For the storage capacity of the energy storage battery at time t, tau 1 ,η ch ,η dis E is the self-loss rate, the charging efficiency and the discharging efficiency of the battery respectively min ,E max Is the lower limit and the upper limit of the energy storage device,for maximum charge-discharge power of the energy storage device, +.>Is the state of charge and discharge of the battery at time t as a 0-1 variable;
the electricity purchasing constraint is as follows:
wherein,is the maximum value of the power purchased from the external grid at time t;
the price demand response constraint based formula is as follows:
wherein DeltaL t Positive values indicate load transfer, negative values indicate load transfer, Δl min ,ΔL max Minimum and maximum values are shifted for load shifting; the incentive-based demand response constraints are as follows:
wherein,for a maximum value of load interruption in a period of time t, lambda is the ratio coefficient of interruption load to total load.
As a preferable scheme of the virtual power plant optimal scheduling method for demand response and prediction error, the invention comprises the following steps: the day-ahead scheduling optimization model specifically comprises,
wherein,respectively the electricity purchase cost, the scheduling cost, the maintenance service cost and the fuel cost, P t pv ,P t wt Predicting the force at time t for wind and photovoltaic, P t mt For the output of the gas unit at time t, P t ch ,P t dis Charging and discharging power for the energy storage device at time t, < >>For the electricity purchase cost at time t, P t buy To purchase electric power, c pv ,c wt ,c mt ,c ess Respectively lightCost factor of photovoltaic, wind power, gas turbine and energy storage device, < >>And is the natural gas purchase cost at time t.
As a preferable scheme of the virtual power plant optimal scheduling method for demand response and prediction error, the invention comprises the following steps: the final optimization target is that the real-time scheduling cost is minimum; the final constraint conditions comprise real-time power balance constraint, gas unit output constraint, energy storage device constraint, electricity purchasing constraint and response constraint based on excitation demand, and the rest constraint conditions are the same as the initial constraint conditions;
the real-time power balance constraint is as follows:
wherein,the method is used for real-time gas unit output, photovoltaic output and load demand.
As a preferable scheme of the virtual power plant optimal scheduling method for demand response and prediction error, the invention comprises the following steps: the real-time optimal scheduling model is as follows:
where k is the sampling time point, N is the scroll time length,delta P is the electric power purchase cost, the scheduling cost, the fuel cost and the energy storage adjustment cost respectively t buy Mu, for the electricity purchasing adjustment in time t buy Adjusting the cost for the purchased power unit, +.>Mu, as an adjustment of IBDR response capability in time t e The cost per unit volume is adjusted for the IBDR,for the output adjustment quantity, mu, of the gas unit in time t gs The unit capacity of the gas unit is adjusted to be the cost delta P t ch ,ΔP t dis For adjusting the charge and discharge of the energy storage device, mu ess The cost is adjusted for the unit capacity of the energy storage device.
In a second aspect, an embodiment of the present invention provides a virtual power plant optimization scheduling system for demand response and prediction error, which includes an optimization module for constructing and optimizing an adjustable load model based on user response and scheduling cost; the model construction module is used for constructing a virtual power plant scheduling optimization model and constructing a real-time optimal scheduling model based on an optimization target and constraint conditions; the analysis module is used for judging whether the optimal scheduling plan is feasible or not by the virtual power plant control center, and if so, implementing the optimal scheduling plan; if the plan is not feasible, continuing to set an optimization target to establish an optimal scheduling plan.
In a third aspect, embodiments of the present invention provide a computer apparatus comprising a memory and a processor, the memory storing a computer program, wherein: and the processor executes the computer program to realize any step of the virtual power plant optimized scheduling method for the demand response and the prediction error.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: the computer program, when executed by the processor, implements any of the steps of the virtual power plant optimized scheduling method for demand response and prediction error described above.
The method has the advantages that virtual power plant power fluctuation caused by new energy daily prediction errors is effectively smoothed through demand response and prediction error management, stability and reliability of a power system are improved, energy distribution and scheduling of the virtual power plant are matched with user load demands through optimal scheduling, optimization of a user load curve is achieved, user satisfaction is improved, operation cost of the virtual power plant is reduced through optimal scheduling and prediction error management, an optimal scheduling strategy can reasonably utilize energy resources, energy waste is reduced, operation cost of the virtual power plant is reduced, and factors such as demand response, prediction error management and optimal scheduling are comprehensively considered, so that economic, safe and stable operation of the virtual power plant is achieved. The method can improve the sustainable development and the operation efficiency of the power system.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of a method for optimizing scheduling of a virtual power plant for demand response and prediction error.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a method for optimizing and scheduling a virtual power plant for demand response and prediction error, including:
s1: and constructing an adjustable load model based on the user response requirement, and optimizing the adjustable load model according to the scheduling cost.
Specifically, a peak-to-valley electricity price strategy is introduced to transfer and adjust the power load, and the load demand is calculated based on the user response to the electricity price and the load demand elasticity theory, wherein the load demand calculation formula is as follows:
wherein L is t ,ρ t To load demand and electricity prices before demand response,for load demand and electricity price after demand response, deltaL t For load demand change, s, t are determined moments e tt Is electrically elastic; when s=t, e st Is the self-elasticity coefficient; when s+.t, e st Is the coefficient of mutual elasticity.
The scheduling cost calculation formula is as follows:
wherein C is ibdr For incentive-based scheduling costs ρ e In order to compensate for the price,is the amount of load interruption.
S2: setting an initial optimization target and initial constraint conditions, and constructing a day-ahead scheduling optimization model; and setting a final optimization target and a final constraint condition, and constructing a real-time optimal scheduling model.
The initial optimization target is that the day-ahead scheduling cost is minimum; the initial constraint conditions are a day-ahead scheduling power balance constraint, a gas unit output constraint, an energy storage device constraint, a power purchase constraint, a price demand response based constraint and an incentive demand response based constraint.
The day-ahead schedule power balance constraint is as follows:
the gas unit output constraint is as follows:
wherein eta mt For generating efficiency of gas unit, L NG Is the low-grade heat value of the natural gas,the gas turbine set has minimum output and maximum output.
The energy storage device is constrained as follows:
wherein E is t For the storage capacity of the energy storage battery at time t, tau 1 ,η ch ,η dis E is the self-loss rate, the charging efficiency and the discharging efficiency of the battery respectively min ,E max Is the lower limit and the upper limit of the energy storage device,for maximum charge-discharge power of the energy storage device, +.>Is the state of charge and discharge of the battery at time t as a 0-1 variable.
The power purchase constraints are as follows:
wherein,is the maximum value of the power purchased from the external grid at time t.
The constraint formula based on price demand response is as follows:
wherein DeltaL t Positive values indicate load transfer, negative values indicate load transfer, Δl min ,ΔL max The minimum and maximum values are shifted for load shifting.
The excitation-based demand response constraints are as follows:
wherein,for a maximum value of load interruption in a period of time t, lambda is the ratio coefficient of interruption load to total load.
The day-ahead scheduling optimization model is as follows:
wherein,respectively the electricity purchase cost, the scheduling cost, the maintenance service cost and the fuel cost, P t pv ,P t wt Predicting the force at time t for wind and photovoltaic, P t mt For the output of the gas unit at time t, P t ch ,P t dis Charging and discharging power for the energy storage device at time t, < >>For the electricity purchase cost at time t, P t buy To purchase electric power, c pv ,c wt ,c mt ,c ess Cost coefficients of photovoltaic, wind power, gas turbine set and energy storage device respectively, < >>And is the natural gas purchase cost at time t.
The final optimization target is that the real-time scheduling cost is minimum; the final constraint conditions comprise real-time power balance constraint, gas unit output constraint, energy storage device constraint, electricity purchasing constraint and response constraint based on excitation demand, and the rest constraint conditions are the same as the initial constraint conditions.
The real-time power balance constraints are as follows:
wherein,the method is used for real-time gas unit output, photovoltaic output and load demand.
The real-time optimal scheduling model is as follows:
where k is the sampling time point, N is the scroll time length,delta P is the electric power purchase cost, the scheduling cost, the fuel cost and the energy storage adjustment cost respectively t buy Mu, for the electricity purchasing adjustment in time t buy Adjustment for purchased power unitsCost (S)/(S)>Mu, as an adjustment of IBDR response capability in time t e The cost per unit volume is adjusted for the IBDR,for the output adjustment quantity, mu, of the gas unit in time t gs The unit capacity of the gas unit is adjusted to be the cost delta P t ch ,ΔP t dis For adjusting the charge and discharge of the energy storage device, mu ess The cost is adjusted for the unit capacity of the energy storage device.
S3: obtaining an optimal scheduling plan, judging whether the optimal scheduling plan is feasible or not by a virtual power plant control center, and implementing if the optimal scheduling plan is feasible; if the plan is not feasible, continuing to set an optimization target to establish an optimal scheduling plan.
By considering the changes of photovoltaic units, wind power units and load prediction on the day-ahead and real-time scheduling time scales, a virtual power plant two-stage optimization scheduling model based on price and excitation-based demand response and prediction errors is established.
The embodiment also provides a virtual power plant optimization scheduling system for demand response and prediction errors, which comprises a prediction module, wherein the prediction module is used for calculating the predicted wind power, photovoltaic solar power and solar load values of a virtual power plant control center.
And the model construction module is used for constructing a daily scheduling optimization model of the virtual power plant.
The analysis module is used for judging whether the plan is feasible or not in the future by the virtual power plant control center, and if the plan is completed, the flow is ended; if the plan is not feasible, continuing to establish a day-ahead plan with the minimum day-ahead scheduling cost as an optimization objective.
The embodiment also provides computer equipment which is suitable for the situation of the multisource power grid information fusion method based on the Internet of things and comprises a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the multi-source power grid information fusion method based on the Internet of things, which is provided by the embodiment.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium, on which a computer program is stored, which when executed by a processor, implements a method for implementing multi-source power grid information fusion based on the internet of things as set forth in the foregoing embodiment.
The storage medium according to the present embodiment belongs to the same inventive concept as the data storage method according to the above embodiment, and technical details not described in detail in the present embodiment can be seen in the above embodiment, and the present embodiment has the same advantageous effects as the above embodiment.
According to the invention, through demand response and prediction error management, virtual power plant power fluctuation caused by new energy daily prediction error is effectively smoothed, the stability and reliability of a power system are improved, the energy distribution and the scheduling of the virtual power plant are matched with the user load demand through optimal scheduling, the optimization of a user load curve is realized, the user satisfaction is improved, the running cost of the virtual power plant is reduced through optimal scheduling and prediction error management, an optimal scheduling strategy can reasonably utilize energy resources, energy waste is reduced, the running cost of the virtual power plant is reduced, and factors such as demand response, prediction error management, optimal scheduling and the like are comprehensively considered, so that the economic, safe and stable running of the virtual power plant is realized. The method can improve the sustainable development and the operation efficiency of the power system.
Example 2
Referring to tables 1 and 2, for another embodiment of the present invention, further explanation is provided for verifying the advantageous effects thereof based on the above-described method.
If the prediction error before the day is 20%, 20% and 3%, the real-time prediction error is 5%, 5% and 1%, the electricity price self-elasticity coefficient is-0.2, the mutual elasticity coefficient is 0.033, and the natural gas price is 3.24 yuan/m 3 The unit heating value is 9.78 kWh/m 3, the interruptible load compensation price is 0.5 yuan/kWh, and the response capacity is 3% of the total load. The results are shown in the following table by comparing the effectiveness of the comparison model of the incentive-based demand response and the price-based demand response with the case one without considering the demand response, the case two with considering the incentive-based demand response and the case three.
Table 1 cost comparison graph for different cases
| Case (B) | Cost of electricity purchase/yuan | Scheduling costs/primitives | Operation and maintenance cost/element | Total cost/meta |
| Case one | 4198 | 0 | 4909 | 9107 |
| Case two | 3774 | 0 | 5020 | 8794 |
| Case three | 3189 | 364 | 5018 | 8571 |
As can be seen from the table, the consideration of both incentive-based demand response and price-based demand response results in a 24% reduction in electricity purchase cost and a 5.9% reduction in total cost compared to case one, indicating that the model can improve the economics of virtual power plants by reducing loads and stimulating customers to self-adjust energy use strategies. The virtual power plant optimizing and scheduling method based on the demand response and the prediction error optimizes the user load curve, reduces the running cost of the virtual power plant and improves the safety and stability of the virtual power plant.
Table 2 comparison table of technical characteristics of the present method and conventional method
According to the invention, through demand response and prediction error management, virtual power plant power fluctuation caused by new energy daily prediction error is effectively smoothed, the stability and reliability of a power system are improved, the energy distribution and the scheduling of the virtual power plant are matched with the user load demand through optimal scheduling, the optimization of a user load curve is realized, the user satisfaction is improved, the running cost of the virtual power plant is reduced through optimal scheduling and prediction error management, an optimal scheduling strategy can reasonably utilize energy resources, energy waste is reduced, the running cost of the virtual power plant is reduced, and factors such as demand response, prediction error management, optimal scheduling and the like are comprehensively considered, so that the economic, safe and stable running of the virtual power plant is realized. The method can improve the sustainable development and the operation efficiency of the power system.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered by the scope of the claims of the present invention.
Claims (10)
1. A virtual power plant optimal scheduling method for demand response and prediction error is characterized by comprising the following steps of: comprising the steps of (a) a step of,
constructing an adjustable load model based on user response demands, and optimizing the adjustable load model according to the dispatching cost;
setting an initial optimization target and initial constraint conditions, and constructing a day-ahead scheduling optimization model; setting a final optimization target and a final constraint condition, and constructing a real-time optimal scheduling model;
obtaining an optimal scheduling plan, judging whether the optimal scheduling plan is feasible or not by a virtual power plant control center, and implementing if the optimal scheduling plan is feasible; if the plan is not feasible, continuing to set an optimization target to establish an optimal scheduling plan.
2. The virtual power plant optimal scheduling method of demand response and prediction error according to claim 1, wherein: the construction of the adjustable load model specifically comprises the following steps of,
introducing a peak-valley electricity price strategy to transfer and adjust the power load;
based on the user response to electricity price and the load demand elasticity theory, calculating the load demand, wherein the load demand calculation formula is as follows:
wherein L is t ,ρ t To load demand and electricity prices before demand response,for load demand and electricity price after demand response, deltaL t For load demand change, s, t are determined moments e tt Is electrically elastic; when s=t, e st Is the self-elasticity coefficient; when s+.t, e st Is the coefficient of mutual elasticity.
3. The virtual power plant optimal scheduling method of demand response and prediction error according to claim 1, wherein: the calculation formula of the scheduling cost is as follows:
wherein C is ibdr For incentive-based scheduling costs ρ e In order to compensate for the price,is the amount of load interruption.
4. A virtual power plant optimized scheduling method for demand response and prediction error as claimed in claim 1 or 3, wherein: the initial optimization target is that the day-ahead scheduling cost is minimum; the initial constraint condition is a day-ahead scheduling power balance constraint, a gas unit output constraint, an energy storage device constraint, a power purchase constraint, a price demand response based constraint and an excitation demand response based constraint;
the daily schedule power balance constraint is as follows:
the gas unit output constraint is as follows:
wherein eta mt For generating efficiency of gas unit, L NG Is the low-grade heat value of the natural gas,the minimum output and the maximum output of the gas unit are;
the energy storage device is constrained as follows:
wherein E is t For the storage capacity of the energy storage battery at time t, tau 1 ,η ch ,η dis E is the self-loss rate, the charging efficiency and the discharging efficiency of the battery respectively min ,E max Is the lower limit and the upper limit of the energy storage device,for maximum charge-discharge power of the energy storage device, +.>Is the state of charge and discharge of the battery at time t as a 0-1 variable;
the electricity purchasing constraint is as follows:
wherein,is the maximum value of the power purchased from the external grid at time t;
the price demand response constraint based formula is as follows:
wherein DeltaL t Positive values indicate load transfer, negative values indicate load transfer, Δl min ,ΔL max Minimum and maximum values are shifted for load shifting;
the incentive-based demand response constraints are as follows:
wherein,for a maximum value of load interruption in a period of time t, lambda is the ratio coefficient of interruption load to total load.
5. The virtual power plant optimal scheduling method of demand response and prediction error according to claim 1, wherein: the day-ahead scheduling optimization model specifically comprises,
wherein,respectively the electric power purchase cost, the dispatching cost and the maintenance serviceCost and fuel cost, P t pv ,P t wt Predicting the force at time t for wind and photovoltaic, P t mt For the output of the gas unit at time t, P t ch ,P t dis Charging and discharging power for the energy storage device at time t, < >>For the electricity purchase cost at time t, P t buy To purchase electric power, c pv ,c wt ,c mt ,c ess Cost coefficients of photovoltaic, wind power, gas turbine set and energy storage device respectively, < >>And is the natural gas purchase cost at time t.
6. The virtual power plant optimal scheduling method of claim 1 or 5, wherein: the final optimization target is that the real-time scheduling cost is minimum; the final constraint conditions comprise real-time power balance constraint, gas unit output constraint, energy storage device constraint, electricity purchasing constraint and response constraint based on excitation demand, and the rest constraint conditions are the same as the initial constraint conditions;
the real-time power balance constraint is as follows:
wherein,the method is used for real-time gas unit output, photovoltaic output and load demand.
7. The virtual power plant optimal scheduling method of demand response and prediction error according to claim 1, wherein: the real-time optimal scheduling model is as follows:
where k is the sampling time point, N is the scroll time length,C ess delta P is the electric power purchase cost, the scheduling cost, the fuel cost and the energy storage adjustment cost respectively t buy Mu, for the electricity purchasing adjustment in time t buy Adjusting the cost for the purchased power unit, +.>Mu, as an adjustment of IBDR response capability in time t e Cost per unit volume adjustment for IBDR, < >>For the output adjustment quantity, mu, of the gas unit in time t gs The unit capacity of the gas unit is adjusted to be the cost delta P t ch ,ΔP t dis For adjusting the charge and discharge of the energy storage device, mu ess The cost is adjusted for the unit capacity of the energy storage device.
8. A virtual power plant optimal scheduling system for demand response and prediction error, based on the method for virtual power plant optimal scheduling for demand response and prediction error according to any one of claims 1 to 7, characterized in that:
the optimizing module is used for constructing and optimizing the adjustable load model based on the user response and the scheduling cost;
the model construction module is used for constructing a virtual power plant scheduling optimization model and constructing a real-time optimal scheduling model based on an optimization target and constraint conditions;
the analysis module is used for judging whether the optimal scheduling plan is feasible or not by the virtual power plant control center, and if so, implementing the optimal scheduling plan; if the plan is not feasible, continuing to set an optimization target to establish an optimal scheduling plan.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the virtual power plant optimized scheduling method of demand response and prediction error of any one of claims 1-7.
10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor performs the steps of the virtual power plant optimized scheduling method of any one of claims 1 to 7.
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