CN117439186A - Virtual power plant economic dispatching method and system considering source-load double-side uncertainty - Google Patents
Virtual power plant economic dispatching method and system considering source-load double-side uncertainty Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- H—ELECTRICITY
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The utility model provides a virtual power plant economic dispatching method and system considering source load double-side uncertainty, an objective function with maximized income and minimum abandoned wind and light rate is established, constraint conditions of the objective function are determined, stable operation of the system is guaranteed, similarity of wind and light output scenes is considered, a scene screening model is established to delete similar scenes so as to overcome the uncertainty of wind and light output, an optimized operation model is established based on an IGDT theory, influence caused by load side uncertainty is further overcome, the obtained optimized operation model can further enrich income ways of providing peak regulation service for the virtual power plant, and the method can be widely applied to the field of virtual power plant participation peak regulation auxiliary service.
Description
Technical Field
The invention belongs to the field of economic dispatch of power systems, and particularly relates to a virtual power plant economic dispatch method and system considering source-load double-side uncertainty.
Background
With the continuous deep development of the electric power market reform, the first batch of spot market reform test points are formally operated, and the second batch of spot market test points are gradually put into the test operation stage. However, for other areas, peak shaving auxiliary service is still an effective means for guaranteeing stable operation of the power grid and promoting clean energy consumption. The virtual power plant realizes flexible output adjustment and quick response by aggregating distributed clean energy and flexible resources, and has the capability of participating in peak shaving auxiliary service markets.
The virtual power plant participates in peak shaving to obtain peak shaving benefits, so that the overall flexibility of the virtual power plant can be improved by introducing flexible resources, controllable loads and the like, wherein new energy is introduced as a main trend. The new energy source depends on more weather and natural conditions in the output process, has the defects of intermittence, uncertainty and the like, and the uncertainty of the output of the new energy source on the load side can cause instability of the power plant. How to realize the maximization of transaction benefit through reasonable economic dispatch, and then improve distributed energy utilization, reduce the influence of traditional power generation mode to the environment, is the current problem that needs to be solved urgently.
Disclosure of Invention
Based on the method and the system, the economic dispatching method and the system of the virtual power plant, which take the uncertainty of the two sides of the source load into account, further weaken the influence of the uncertainty of the load side on the basis of taking the uncertainty of the power output of the power source into consideration, and enrich the income way of the virtual power plant for providing peak shaving service.
The invention relates to a virtual power plant economic dispatching method considering source-load double-side uncertainty, wherein a unit of a virtual power plant at least comprises a gas unit, a wind-light unit and an electric conversion unit, and the method comprises the following steps:
establishing an objective function with the maximum benefit and the minimum wind and light rejection rate according to a unit of a virtual power plant, and determining constraint conditions of the objective function;
establishing a scene screening model to screen the output scene of the wind-light unit to obtain a scene screening result, calculating the net gain of the wind-light unit according to the scene screening result, and updating an objective function according to the net gain of the wind-light unit;
and establishing an optimized operation model based on the IGDT theory, solving the optimized operation model based on the updated objective function and constraint conditions, wherein the optimized operation model is used for guiding economic dispatch of the virtual power plant, and uncertain parameters in the optimized operation model are set according to a load side.
Further, the objective function is specifically expressed as:
f 1 ＝max(r vpp +r g )
wherein f 1 Representing the maximum objective function of benefit, f 2 Represents the minimum objective function of the wind and light rejection rate, r vpp Representing the electricity selling income of a virtual power plant, r g Representing peak shaving income of virtual power plant, U c Indicating the actual amount of abandoned wind and abandoned light, U t And the total power generation amount of the wind-solar unit is represented.
Further, establishing a scene screening model to overcome uncertainty of wind and light output, and optimizing an objective function according to a scene screening result comprises:
extracting an output scene of the wind-solar unit by using a Latin hypercube sampling method;
the extracted output scenes are reduced, and M output scenes are obtained;
solving the average value of wind-light power generation output of M output scenes to obtain a wind-light output curve;
the net gain of the wind turbine unit is calculated according to the wind-light output curve as follows:
wherein,and->Respectively representing output power, p of wind turbine generator and photovoltaic turbine generator after uncertainty is overcome vpp Indicating electricity price, c w Representing the electricity-measuring cost of the wind turbine generator system, c pv And the electricity cost of the photovoltaic unit is represented, and t represents time.
Further, the step of reducing the extracted wind-light output scenes to obtain M wind-light output scenes comprises:
s1, reducing similar scenes by using scene distance measurement and calculation, and calculating the average distance of any two scenes as follows:
wherein,and->Representing the average distance, X, of the sum of scenes i and j, respectively iw And X jw Sample values under scenes i and j, respectively;
s2, removing samples closest to the scene data set, and calculating distance-probability values S of scenes i and j ij ＝p j s ij Wherein p is j Representing the probability of occurrence of scene j，s ij Representing the distance between scenes i and j;
s3, calculating distance-probability values of the scene i and all scenes, and deleting a scene j corresponding to the minimum distance-probability value d ；
S4, updating the occurrence probability of the sample i as followsp i Representing the probability of occurrence of a scene before update,representing deleted scene j d Probability of occurrence;
s5, repeating the steps S1 to S4 until the number of the output scenes is reduced to M.
Further, establishing an optimized operation model based on the IGDT theory, and solving the optimized operation model according to the updated objective function comprises:
the optimized operation model based on IGDT theory is established as follows
Wherein Y represents an uncertain parameter, d represents a decision variable, F (Y, d) represents an objective function, H (Y, d) and G (Y, d) represent an equality constraint and an inequality constraint, respectively, alpha represents the fluctuation range of the uncertain parameter, alpha is equal to or larger than 0,indicating that the uncertainty parameter Y deviates from the predicted value by no more than +.>F 0 Representing the optimal target value, beta, of the deterministic model a And beta s Representing deviation from predicted valueTo a poor extent, the robust decision value ensures that the expected value does not exceed (1+β) for any disturbance within the decision maker's acceptable range a )F 0 At least one Y exists for the opportunity decision value within an acceptable range such that the expected value does not exceed (1-beta) s )F 0 ；
Substituting the predicted values of the following uncertain parameters into the optimized operation model to solve,
wherein y is b1 And y b2 Representing an optimized value obtained by the deterministic model;
setting the variation range of the uncertain parameters asThe subscript l denotes the load;
the optimal operation model taking the above uncertain parameters into account is simplified into the following form:
constructing a pessimistic decision model based on risk resistance according to a robust decision strategy, and setting the actual load demand as the minimum value of predicted force deviationSetting deviation degree beta of actual output disturbance influencing objective function value i I=1, 2, the maximum pessimistic value of each target is (1- β 1 )y b1 ，(1+β 2 )y b2 The robust decision strategy in the optimized operation model is expressed as:
constructing an optimistic decision model based on opportunity pursuit according to an opportunity decision strategy, and setting the actual load demand as the maximum value of the predicted force deviationThe opportunistic decision strategy in the optimization run model described above is expressed as:
further, the constraint conditions comprise system power balance constraint, gas unit constraint, wind power unit output constraint, photovoltaic unit output constraint and system reserve capacity constraint.
Further, the system power balance constraint is:
wherein g i,g Active output g of gas unit i j,w Active output g of wind turbine generator j m,pv The active output of the photovoltaic unit m is represented, and D represents the load demand value of the power system.
Further, the gas unit constraint comprises a gas unit output constraint, a gas unit climbing constraint and a gas unit start-stop constraint;
the output constraint of the gas unit is as follows:
wherein,representing the maximum schedulable output of the gas unit i,/->Representing the minimum schedulable output of the gas unit i;
the climbing constraint of the gas unit is as follows:
wherein,and->Representing the power lifting constraint of the gas unit i;
the start-stop constraint of the gas unit is as follows:
(T i on (t-1)-MT i on )(u i (t-1)-u i (t))≥0
(T i off (t-1)-MT i off )(u i (t)-u i (t-1))≥0
wherein T is i on (t-1) represents the operation time of the gas turbine unit i at the time t-1, MT i on Representing the shortest operating time, T, of the gas unit i i off (t-1) shows the shutdown time of the gas turbine unit i at the time t-1, MT i off For the shortest downtime of the gas turbine unit i, u represents the start-stop parameter.
Further, the output constraint of the wind turbine generator is as follows:
wherein,representing the maximum schedulable output of wind turbine j, < >>And representing the minimum schedulable output of the wind turbine j.
Further, the photovoltaic unit output constraint is:
wherein,representing the maximum schedulable output of the photovoltaic unit m,/->Representing the minimum dispatchable output of the photovoltaic unit m.
Further, the system spare capacity constraint is:
wherein D (t) is the load demand of the system at the moment of t, and R (t)For the standby requirement of the system at the time t, l represents the line loss rate of the system, θ represents the unit self-power consumption rate, g max And (t) represents the maximum unit output of the unit at the time t.
The invention also provides a virtual power plant economic dispatch system considering the source-load double-side uncertainty, wherein the virtual power plant at least comprises a gas unit, a wind-light unit and an electric conversion gas unit, and the system comprises:
the objective function construction module is used for establishing an objective function with the maximum benefit and the minimum wind and light rejection rate and determining constraint conditions of the objective function;
the scene screening module is used for establishing a scene screening model to screen the output scene of the wind-light unit to obtain a scene screening result, calculating the net benefit of the wind-light unit according to the scene screening result, and updating the objective function according to the net benefit of the wind-light unit;
the operation model construction module is used for establishing an optimized operation model based on the IGDT theory, solving the optimized operation model based on the updated objective function and the constraint condition, wherein the optimized operation model is used for guiding the economic dispatch of the virtual power plant, and uncertain parameters in the optimized operation model are set according to the load side.
In addition, the invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the virtual power plant economic dispatch method accounting for the source load double-side uncertainty when executing the computer program.
The present invention provides a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the virtual power plant economic dispatch method described above that accounts for source-load double-sided uncertainty.
The invention has the following beneficial effects:
the invention provides a virtual power plant economic dispatching method and a system considering source-load bilateral uncertainty, which are used for considering function transfer of peak shaving auxiliary services of all output units in a virtual power plant from virtual power plant marketing transaction, and considering uncertainty of a source side and a load side, establishing an operation model of virtual power plant economic dispatching, further enriching paths of virtual power plants participating in market transaction, and can be widely applied to the market transaction field of virtual power plants participating in peak shaving auxiliary services.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a virtual power plant according to an embodiment of the present invention;
FIG. 2 is an alternative execution flow of a virtual power plant economic dispatch method accounting for source-load double-side uncertainty provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a virtual power plant economic dispatch system accounting for source-load double-side uncertainty according to an embodiment of the present invention;
fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, which shows the structure of a virtual power plant participating in peak shaving assistance services, power plant components include a gas turbine (illustrated as a gas turbine), a wind turbine, a photovoltaic turbine, and an electric conversion device, and fig. 1 also shows the power flow direction and the natural gas flow direction of each power plant component.
Referring to fig. 2, an optional execution flow of a virtual power plant economic dispatch method accounting for source-load double-side uncertainty according to an embodiment of the present invention is shown, including:
and S11, establishing an objective function with the maximum income and the minimum wind and light rejection rate, and determining constraint conditions of the objective function.
The virtual power plant component mainly comprises a gas unit, a wind turbine unit and a photovoltaic unit, and the maximum revenue objective function of the system can be further determined according to the physical model and the revenue model of each output unit.
The electric conversion equipment can convert electric energy into natural gas or hydrogen, the obtained gas is stored in a natural gas pipe network or natural gas storage equipment, the gas is converted and stored in the renewable energy output peak period, and energy is supplied in the electric power shortage period, so that the capacity of absorbing renewable energy sources of the system is improved. And the electric gas conversion equipment is introduced into the virtual power plant, so that the natural gas can be effectively circulated. The electric conversion technology can convert the undigested electric energy into methane for storage, and the methane is converted into electric energy again through the gas unit when necessary, so that the clean energy power generation and the absorption are positively achieved, and the system energy is converted from unidirectional coupling to bidirectional coupling, and in a further embodiment, the maximum income objective function can also consider the income brought by the electric conversion equipment.
S12, establishing a scene screening model to screen the output scene of the wind-light unit to obtain a scene screening result, calculating the net gain of the wind-light unit according to the scene screening result, and updating the objective function according to the net gain of the wind-light unit.
Specifically, scene screening is mainly used for overcoming the uncertainty of wind and light unit output, namely, the uncertainty of a source side is listed into a factor of an optimized operation model, and the Latin hypercube sampling method can be used for sampling and obtaining sample data of an accumulated probability curve in a layered manner, so that similar scenes are deleted, and the uncertainty of wind and light output is overcome.
S13, establishing an optimized operation model based on the IGDT theory, solving the optimized operation model based on the updated objective function and constraint conditions, wherein the optimized operation model is used for guiding economic dispatch of the virtual power plant, and uncertain parameters in the optimized operation model are set according to a load side.
The IGDT theory, namely the information gap decision theory, is a non-probability and non-fuzzy uncertainty risk management method, and the method does not need to know probability distribution and fluctuation intervals of uncertain parameters, and comprises two strategies, namely a risk avoidance strategy and a risk preference strategy, wherein the former represents that a decision maker resists risks and fear of bearing losses, and is a risk resistant person; the latter represents the decision maker regarding risk as an opportunity to strive for more benefits, being a risk finder. And (3) establishing an optimized operation model based on the IGDT theory based on the updated objective function of the previous step, and enabling all components of the virtual power plant to stably operate around the objective function and the constraint condition according to the optimized operation model.
In a specific embodiment, the objective function in step S10 is specifically expressed as:
f 1 ＝max(r vpp +r g )
wherein f 1 Representing the maximum objective function of benefit, f 2 Represents the minimum objective function of the wind and light rejection rate, r vpp Representing the electricity selling income of a virtual power plant, r g Representing peak shaving income of virtual power plant, U c Indicating the actual amount of abandoned wind and abandoned light, U t And the total power generation amount of the wind-solar unit is represented.
When the embodiment of the invention considers the maximization of the benefits of the virtual power plant, the benefits brought by the gas unit, the wind-light unit and the electric converting gas unit are mainly considered, and the benefits of the three units are calculated by the following processes:
(1) Gas engine set
For a gas turbine, its output model can be expressed as:
g i,g (t)＝F GT (t)·η G (t)·HHV
wherein F is GT (t) represents the natural gas consumption of the gas unit i in the period t, g i,g (t) represents the electric energy supplied by the gas turbine unit in the period t, eta G (t) represents the power generation efficiency of the gas turbine unit in the t period, and HHV is natural gasHigh heating value, preferably 36MJ/m in a further embodiment 3 。
The carbon dioxide emission formula of the gas unit is as follows:
e i,g (t)＝F GT (t)·e G
wherein e i,g (t) is the carbon dioxide emission of the gas unit, e G Is the unit carbon dioxide emission of the gas unit.
The gas turbine unit revenue includes peak shaving auxiliary service compensation and power generation revenue, so its net revenue model can be expressed as:
wherein r is G Indicating the net income of the gas unit g i,g (t) represents the generated energy of the fan squeeze occupying the combustion engine in the period of t, P a (t) represents the auxiliary service price for the period t, c g Representing the total cost of the gas,representing the operation and maintenance cost delta g For the price of fuel gas->Indicating that the gas unit is using natural gas from the gas storage tank.
(2) Wind-solar unit
The wind-solar unit generally comprises a wind turbine unit and a photovoltaic unit, wherein the wind turbine unit depends on natural wind output, natural wind has strong randomness, the output power of the wind turbine unit fluctuates along with wind speed, the natural wind speed is simulated by combining Weibull distribution, and a probability density function is as follows:
wherein v represents the natural wind speed, phi andrepresenting the shape and scale parameters of the distribution function, respectively.
When the wind speed is within the bearable range of the wind turbine, the power of the wind turbine is increased along with the increase of the wind speed, but if the wind speed exceeds the bearable range, namely the wind speed is too low or the wind speed is too high, the wind turbine is not started to avoid the damage of a machine body, and the functional relation between the output of the wind turbine and the wind speed is expressed as follows:
wherein g j,w (t) is the available output of the wind turbine j at the moment t, g r V is the rated output power of the wind turbine generator i,w And v o,w Respectively represent the wind speed of cut-in and cut-out, v r,w And v (t) is the actual wind speed at the moment t for the rated wind speed.
The output curve of a photovoltaic unit generally satisfies the following Beta distribution:
wherein, alpha and Beta represent the shape parameters of Beta distribution, theta is the radiance correlation coefficient, and the parameters for calculating Beta by introducing the average value and standard deviation value of irradiance are as follows:
wherein μ and δ represent the mean and normal distribution values, respectively, of solar radiation.
Calculation of the Beta distribution integral:
wherein θ c And theta d The upper and lower limits of the solar radiation degree θ are respectively indicated.
The output model of the photovoltaic unit can be expressed as:
g m,pv (t)＝η PV ×S PV ×θ t
wherein eta PV Work efficiency for photovoltaic unit S PV Is the total area of the photovoltaic unit, theta t The solar energy generating device is used for generating sunlight for the photovoltaic unit.
The net gain model for the wind turbine is then expressed as:
wherein r is wpv Indicating net income of wind-solar unit, p vpp Indicating electricity price, c w Representing the electricity-measuring cost of the wind turbine generator system, c pv And the electricity cost of the photovoltaic unit is represented.
The method comprises the following steps of considering the uncertainty of the wind-light unit output, establishing a scene screening model to overcome the uncertainty of wind-light output, wherein the specific process comprises the following steps:
s121, reducing similar scenes by using scene distance measurement and calculation, and calculating the average distance of any two scenes as follows:
wherein,and->Respectively representAverage distance, X, of scenes i and j iw And X jw Representing sample values under scenarios i and j, respectively.
S122, removing samples closest to the scene data set, and calculating distance-probability values S of scenes i and j ij ＝p j s ij Wherein p is j Representing the probability of occurrence of scene j, s ij Representing the distance between scenes i and j.
S123, calculating distance-probability values of the scene i and all scenes, and deleting a scene j corresponding to the minimum distance-probability value d 。
S124, updating the occurrence probability of the sample i asp i Representing the probability of occurrence of a scene before update, +.>Representing deleted scene j d Probability of occurrence.
S125, repeating the steps S121-S124 until the number of the wind-light output scenes is reduced to M.
After overcoming the uncertainty of the output, the net gain model of the wind-solar unit can be expressed as:
wherein,and->Respectively representing output power, p of wind turbine generator and photovoltaic turbine generator after uncertainty is overcome vpp Indicating electricity price, c w Representing the electricity-measuring cost of the wind turbine generator system, c pv And the electricity cost of the photovoltaic unit is represented, and t represents time.
(3) Electric gas converting unit
The electric gas conversion technology is combined with surplus electric power in the valley period to electrolyze water, and methane is generated through a methanation process. The operation principle is as follows:
Q pg,t ＝P pg,t η pg
wherein Q is pg,t P being the amount of gas produced by electroconversion gas conversion pg,t For converting electricity into electricity consumption, eta pg The electrical conversion efficiency is represented by the number of the electrodes,generating capacity representing natural gas output of gas turbine unit by using gas storage tank>Represents the natural gas quantity, eta from a gas storage tank when the gas unit is used MT Representing the gas-electricity generating efficiency of the gas turbine unit.
The gas storage tank is used for storing the converted natural gas and reasonably distributing the converted natural gas in combination with the price, and can utilize the gas storage to generate electricity in the peak load time or select to sell the converted natural gas to a natural gas network, and then the natural gas flow in the gas storage tank can be expressed as:
wherein Q is GST,t Is the gas storage quantity of the gas storage tank at the time t,for the initial state of gas storage quantity, +.>Represents the natural gas amount stored after electric conversion of qi, < >>And the natural gas quantity input to the gas unit by the gas storage tank at the time t is shown.
Meanwhile, the operation that the gas storage tank cannot store and release gas at the same time is set as follows:
based on the above construction of the physical model and the benefit model of each output unit, the portion of the objective function regarding the benefit maximization can be expressed as:
f 1 ＝max(r vpp +r g )＝max(r' wpv +r G -P pg,t ·p vpp )
aiming at the established objective function, constraint conditions for ensuring the stable operation of the system comprise system power balance constraint, gas unit constraint, wind turbine unit output constraint, photovoltaic unit output constraint and system reserve capacity constraint.
The system power balance constraint is:
wherein g i,g Active output g of gas unit i j,w Active output g of wind turbine generator j m,pv Active output of the photovoltaic unit m is represented, D represents a load demand value of the power system, NG represents the number of gas units in the virtual power plant, NW represents the number of wind power units in the virtual power plant, and NPV represents the number of photovoltaic units in the virtual power plant.
The gas unit constraint comprises a gas unit output constraint, a gas unit climbing constraint and a gas unit start-stop constraint;
the output constraint of the gas unit is as follows:
wherein,representing the maximum schedulable output of the gas unit i,/->Representing the minimum schedulable output of the gas unit i;
the climbing constraint of the gas unit is as follows:
wherein,and->Representing the power lifting constraint of the gas unit i;
the start-stop constraint of the gas unit is as follows:
(T i on (t-1)-MT i on )(u i (t-1)-u i (t))≥0
(T i off (t-1)-MT i off )(u i (t)-u i (t-1))≥0
wherein T is i on (t-1) represents the operation time of the gas turbine unit i at the time t-1, MT i on Representing the shortest operating time, T, of the gas unit i i off (t-1) shows the shutdown time of the gas turbine unit i at the time t-1, MT i off For the shortest downtime of the gas turbine unit i, u represents the start-stop parameter.
The output constraint of the wind turbine generator is as follows:
wherein,representing the maximum schedulable output of wind turbine j, < >>And representing the minimum schedulable output of the wind turbine j.
The output constraint of the photovoltaic unit is as follows:
wherein,representing the maximum schedulable output of the photovoltaic unit m,/->Representing the minimum dispatchable output of the photovoltaic unit m.
The system spare capacity constraint is:
wherein D (t) is the load demand of the system at the moment t, R (t) is the standby demand of the system at the moment t, l represents the line loss rate of the system, θ represents the unit self-power consumption rate, g max And (t) represents the maximum unit output of the unit at the time t.
Specifically, step S13 builds an optimized operation model based on the IGDT theory, and solves the optimized operation model based on the updated objective function and constraint conditions, where the process includes:
the IGDT theoretical model taking uncertainty into account mainly contains 3 elements: deterministic models (max/min models), uncertainty models, and performance requirements.
Setting the optimization objective F as a minimization function, the general expression of the uncertainty model is as follows:
where Y represents an uncertainty parameter, d represents a decision variable, F (Y, d) represents an objective function, and H (Y, d) and G (Y, d) represent an equality constraint and an inequality constraint, respectively.
Uncertainty parameter Y surrounds the predicted valueThe fluctuations may be expressed as:
wherein alpha represents the fluctuation range of an uncertain parameter, alpha is more than or equal to 0,indicating that the uncertainty parameter Y deviates from the predicted value by no more than +.>
Therefore, for the unit operation of the virtual power plant, based on two decision directions in the IGDT theory, the uncertainty of the load side is considered, and an optimal operation model considering risk avoidance and risk preference can be established as follows:
F 0 representing the optimal target value, beta, of the deterministic model a And beta s Representing the deviation degree from the predicted value, the robust decision value can ensure that the predicted value does not exceed (1+beta) for any disturbance within the acceptable range of the decision maker a )F 0 At least one Y is present for the opportunity decision valueWithin an acceptable range, the expected value does not exceed (1-beta) s )F 0 。
Substituting the predicted values of the following uncertain parameters into the optimized operation model to solve,
/>
wherein y is b1 And y b2 Representing the optimized value derived by the deterministic model.
The range of variation of the uncertain parameters is set as follows:
wherein the subscript l denotes the load.
The optimal operation model taking the above uncertain parameters into account is simplified into the following form:
constructing a pessimistic decision model based on risk resistance according to a robust decision strategy, and setting the actual load demand as the minimum value of predicted force deviationSetting deviation degree beta of actual output disturbance influencing objective function value i I=1, 2, the maximum pessimistic value of each target is (1- β 1 )y b1 ，(1+β 2 )y b2 The risk avoidance maneuver in the optimized operation model is expressed as:
constructing an optimistic decision model based on opportunity pursuit according to an opportunity decision strategy, and setting the actual load demand as the maximum value of the predicted force deviationThe risk preference policy in the optimized operation model described above is expressed as:
as shown in FIG. 3, embodiments of the present invention also provide a virtual power plant economic dispatch system 300 that accounts for source-load double-sided uncertainty, comprising:
the objective function construction module 310 is configured to establish an objective function with the largest benefit and the smallest wind and light rejection rate, and determine constraint conditions of the objective function.
The scene screening module 320 is configured to establish a scene screening model to screen the output scene of the wind turbine unit to obtain a scene screening result, calculate the net gain of the wind turbine unit according to the scene screening result, and update the objective function according to the net gain of the wind turbine unit.
The operation model construction module 330 is configured to establish an optimized operation model based on the IGDT theory, solve the optimized operation model based on the updated objective function and the constraint condition, where the optimized operation model is used to guide economic dispatch of the virtual power plant, and uncertain parameters in the optimized operation model are set according to the load side.
The specific implementation logic of the above modules can refer to the related description of the virtual power plant economic dispatching method part considering the source load double-side uncertainty, and will not be repeated here.
The virtual power plant economic dispatch system 300 provided by the embodiment of the application, which takes account of the source-load double-side uncertainty, can be applied to electronic equipment. Fig. 4 shows a block diagram of a hardware structure of the electronic device, where the hardware structure may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;
in the embodiment of the application, the number of the processor 1, the communication interface 2, the memory 3 and the communication bus 4 is at least one, and the processor 1, the communication interface 2 and the memory 3 complete communication with each other through the communication bus 4;
processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention, etc.;
the memory 3 may comprise a high-speed RAM memory, and may further comprise a non-volatile memory (non-volatile memory) or the like, such as at least one magnetic disk memory;
wherein the memory stores a program, the processor is operable to invoke the program stored in the memory, the program operable to: and realizing each processing flow in the virtual power plant economic dispatch scheme considering the source load double-side uncertainty.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. The foregoing embodiments are only for illustrating the present invention, wherein the structures, connection modes, manufacturing processes, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solutions of the present invention should not be excluded from the protection scope of the present invention.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will appreciate that: the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (14)
1. The utility model provides a virtual power plant economic dispatch method that takes account of source lotus two-sided uncertainty, characterized in that the unit cell of virtual power plant includes gas unit, scene unit and electricity gas unit at least, the method includes:
establishing an objective function with the maximum benefit and the minimum wind and light rejection rate according to a unit of a virtual power plant, and determining constraint conditions of the objective function;
establishing a scene screening model to screen the output scene of the wind-light unit to obtain a scene screening result, calculating the net gain of the wind-light unit according to the scene screening result, and updating the objective function according to the net gain of the wind-light unit;
an optimized operation model based on an IGDT theory is established, the optimized operation model is solved based on the updated objective function and the constraint conditions, the optimized operation model is used for guiding economic dispatch of the virtual power plant, and uncertain parameters in the optimized operation model are set according to a load side.
2. The method according to claim 1, characterized in that the objective function is expressed in particular as:
f 1 ＝max(r vpp +r g )
wherein f 1 The maximum objective function of the benefit is represented,f 2 represents the minimum objective function of the wind and light rejection rate, r vpp Representing the electricity selling income of a virtual power plant, r g Representing peak shaving income of virtual power plant, U c Indicating the actual amount of abandoned wind and abandoned light, U t And the total power generation amount of the wind-solar unit is represented.
3. The method of claim 1, wherein the establishing a scene screening model to screen the output scene of the wind turbine to obtain a scene screening result, and calculating the net gain of the wind turbine based on the scene screening result comprises:
extracting an output scene of the wind-solar unit by using a Latin hypercube sampling method;
the extracted output scenes are reduced, and M output scenes are obtained;
solving an average value of wind-light power generation output of the M output scenes to obtain a wind-light output curve;
the net gain of the wind turbine unit is calculated according to the wind-light output curve as follows:
wherein,and->Respectively representing output power, p of wind turbine generator and photovoltaic turbine generator after uncertainty is overcome vpp Indicating electricity price, c w Representing the electricity-measuring cost of the wind turbine generator system, c pv And the electricity cost of the photovoltaic unit is represented, and t represents time.
4. The economic dispatch method of claim 3, wherein the pruning the extracted output scenarios to obtain M output scenarios comprises:
s1, reducing similar scenes by using scene distance measurement and calculation, and calculating the average distance of any two scenes as follows:
wherein,and->Representing the average distance, X, of scenes i and j, respectively iw And X jw Sample values under scenes i and j, respectively;
s2, removing samples closest to the scene data set, and calculating distance-probability values S of scenes i and j ij ＝p j s ij Wherein p is j Representing the probability of occurrence of scene j, s ij Representing the distance between scenes i and j;
s3, calculating distance-probability values of the scene i and all scenes, and deleting a scene j corresponding to the minimum distance-probability value d ；
S4, updating the occurrence probability of the sample i to be p i '＝p i +p jd ，p i Representing the probability of occurrence of a scene before update, p jd Representing deleted scene j d Probability of occurrence;
s5, repeating the steps S1 to S4 until the number of the output scenes is reduced to M.
5. The economic dispatch method of claim 1, wherein the establishing an optimized operation model based on IGDT theory, and the solving the optimized operation model based on the updated objective function and the constraint condition comprises:
the optimized operation model based on IGDT theory is established as follows
Wherein Y represents an uncertain parameter, d represents a decision variable, F (Y, d) represents an objective function, H (Y, d) and G (Y, d) represent an equality constraint and an inequality constraint, respectively, alpha represents the fluctuation range of the uncertain parameter, alpha is equal to or larger than 0,indicating that the uncertainty parameter Y deviates from the predicted value by no more than +.>F 0 Representing the optimal target value, beta, of the deterministic model a And beta s Representing the deviation degree from the predicted value, the robust decision value can ensure that the predicted value does not exceed (1+beta) for any disturbance within the acceptable range of the decision maker a )F 0 At least one Y exists for the opportunity decision value within an acceptable range such that the expected value does not exceed (1-beta) s )F 0 ；
Substituting the predicted values of the following uncertain parameters into the optimized operation model based on the IGDT theory for solving,
wherein y is b1 And y b2 Representing an optimized value obtained by the deterministic model;
setting the variation range of the uncertain parameters asThe subscript l denotes the load;
the optimal operation model taking into account the uncertainty parameters is reduced to the following form:
constructing a pessimistic decision model based on risk resistance according to a robust decision strategy, and setting the actual load demand as the minimum value of predicted force deviationSetting deviation degree beta of actual output disturbance influencing objective function value i I=1, 2, the maximum pessimistic value of each target is (1- β 1 )y b1 ，(1+β 2 )y b2 The robust decision strategy of the optimized operation model is expressed as:
constructing an optimistic decision model based on opportunity pursuit according to an opportunity decision strategy, and setting the actual load demand as the maximum value of the predicted force deviationThe opportunistic decision strategy of the optimized operational model is expressed as:
6. the economic dispatch method of claim 1, wherein the constraint conditions include a system power balance constraint, a gas turbine constraint, a wind turbine output constraint, a photovoltaic turbine output constraint, and a system backup capacity constraint.
7. The economic dispatch method of claim 6, wherein the system power balancing constraints are:
wherein g i,g Active output g of gas unit i j,w Active output g of wind turbine generator j m,pv The active output of the photovoltaic unit m is represented, and D represents the load demand value of the power system.
8. The economic dispatch method of claim 6, wherein the gas unit constraints include gas unit output constraints, gas unit ramp-up constraints, and gas unit start-stop constraints;
the gas unit output constraint is as follows:
wherein,representing the maximum schedulable output of the gas unit i,/->Representing the minimum schedulable output of the gas unit i;
the climbing constraint of the gas unit is as follows:
wherein,and->Representing the power lifting constraint of the gas unit i;
the start-stop constraint of the gas unit is as follows:
(T i on (t-1)-MT i on )(u i (t-1)-u i (t))≥0
(T i off (t-1)-MT i off )(u i (t)-u i (t-1))≥0
wherein T is i on (t-1) represents the operation time of the gas turbine unit i at the time t-1, MT i on Representing the shortest operating time, T, of the gas unit i i off (t-1) shows the shutdown time of the gas turbine unit i at the time t-1, MT i off For the shortest downtime of the gas turbine unit i, u represents the start-stop parameter.
9. The economic dispatch method of claim 6, wherein the wind turbine generator output constraints are:
wherein,representing the maximum schedulable output of wind turbine j, < >>And representing the minimum schedulable output of the wind turbine j.
10. The economic dispatch method of claim 6, wherein the photovoltaic unit output constraints are:
wherein,representing the maximum schedulable output of the photovoltaic unit m,/->Representing the minimum dispatchable output of the photovoltaic unit m.
11. The economic dispatch method of claim 6, wherein the system reserve capacity constraint is:
wherein D (t) is the load demand of the system at the moment t, and R (t) is the standby demand of the system at the moment tCalculating, i represents the line loss rate of the system, θ represents the unit self-power consumption rate, g max And (t) represents the maximum unit output of the unit at the time t.
12. A virtual power plant economic dispatch system accounting for source-load double-sided uncertainty, wherein the unit cells of the virtual power plant include at least a gas unit, a wind-solar unit, and an electric gas-to-air unit, the system comprising:
the objective function construction module is used for establishing an objective function with the maximum benefit and the minimum wind and light rejection rate according to unit units of the virtual power plant and determining constraint conditions of the objective function;
the scene screening module is used for establishing a scene screening model to screen the output scene of the wind-light unit to obtain a scene screening result, calculating the net gain of the wind-light unit according to the scene screening result, and updating the objective function according to the net gain of the wind-light unit;
the operation model construction module is used for establishing an optimized operation model based on the IGDT theory, solving the optimized operation model based on the updated objective function and the constraint conditions, wherein the optimized operation model is used for guiding economic dispatch of the virtual power plant, and uncertain parameters in the optimized operation model are set according to a load side.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the virtual power plant economic dispatch method of any one of claims 1-11 that accounts for source-load double-sided uncertainty when executing the computer program.
14. A readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the virtual power plant economic dispatch method of any one of claims 1 to 11 taking account of source load double-sided uncertainty.
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