CN116505512A - Micro-grid optimization operation method considering load demand response - Google Patents
Micro-grid optimization operation method considering load demand response Download PDFInfo
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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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load 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
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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
- H02J3/381—Dispersed generators
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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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- 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
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- 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
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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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
- 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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- 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
- 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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Abstract
The invention relates to a micro-grid optimization operation method considering load demand response, which comprises the following steps: obtaining a plurality of typical scenes of wind-light output and occurrence probability thereof based on a preset method; the method comprises the steps of constructing a micro-grid optimization operation model considering wind-light uncertainty and load demand response based on a plurality of typical scenes of wind-light output and occurrence probability of the typical scenes by taking the minimum micro-grid operation cost as a target; and solving the microgrid optimizing operation model through a preset optimizing algorithm to obtain an optimizing result. According to the micro-grid optimizing operation method considering the load demand response, the transferable load is introduced into the micro-grid, and the load can be reduced to participate in the demand response, so that the operation benefit of the micro-grid is improved, and the total operation cost of the micro-grid is reduced.
Description
Technical Field
The invention relates to the technical field of power distribution network operation optimization, in particular to a micro-grid optimization operation method considering load demand response.
Background
In recent years, renewable energy power generation technology is considered as an important measure for reducing carbon emissions, and wind power generation and solar power generation are rapidly developed under the support and the excitation of policies of various countries. However, the renewable energy sources have randomness and fluctuation, and the wind power generation and the photovoltaic power generation are connected in a grid mode to influence the operation stability of the power system. The microgrid can effectively solve the problem of grid-connected digestion of renewable energy sources to a certain extent. The micro-grid is a small electric power balance system comprising an uncontrollable power source such as wind power, a controllable unit such as a gas turbine, a load, a monitoring and protecting device and the like, and can flexibly adjust the working state of the controllable unit and coordinate and distribute the output of the controllable unit, thereby improving the running reliability of the micro-grid system.
In actual micro-grid dispatching, loads are various, operation characteristics are different, and a simple load demand response model is difficult to comprehensively measure the influence caused by participation of actual electric loads in energy management. In addition, compared with a common photovoltaic power station, the photovoltaic power station is additionally provided with the heat storage system module and the steam turbine generator module, so that the photovoltaic power station becomes schedulable equipment, and the reliability of power supply of the system can be improved when the photovoltaic power station is bundled and connected with wind power.
The micro-grid implements the required energy management of the load on the load side, so that the optimal operation of the micro-grid can be realized, and the scheduling flexibility is improved. However, existing microgrid optimization operation models rarely consider both the load demand response and the related constraints of the photo-thermal power plant.
Disclosure of Invention
The invention aims to provide a micro-grid optimizing operation method considering load demand response, so as to improve the operation benefit of the micro-grid and reduce the total operation cost of the micro-grid.
Based on the above object, the present invention provides a method for optimizing operation of a micro-grid in consideration of load demand response, comprising the steps of:
s100: obtaining a plurality of typical scenes of wind-light output and occurrence probability thereof based on a preset method;
s200: the method comprises the steps of constructing a micro-grid optimization operation model considering wind-light uncertainty and load demand response based on a plurality of typical scenes of wind-light output and occurrence probability of the typical scenes by taking the minimum micro-grid operation cost as a target;
s300: and solving the microgrid optimizing operation model through a preset optimizing algorithm to obtain an optimizing result.
Further, the step S100 specifically includes:
s110: randomly generating a plurality of initial scenes of wind power output according to a preset probability distribution function, and adopting a preset scene reduction planning model to perform scene reduction on the initial scenes of the wind power output to obtain typical scenes of the wind power output and occurrence probability of the typical scenes;
s120: randomly generating a plurality of initial scenes of the photoelectric output according to a preset probability distribution function, and adopting a preset scene reduction planning model to carry out scene reduction on the initial scenes of the photoelectric output to obtain a typical scene of the photoelectric output and occurrence probability of the typical scene;
s130: and obtaining a plurality of typical scenes of wind and light output and the occurrence probability thereof according to the typical scenes of the wind and light output and the occurrence probability thereof and the typical scenes of the photoelectric output.
Further, the preset scene reduction planning model is as follows:
wherein omega is an initial scene set, i and j are initial scene indexes, g respectively i For the probability of occurrence of the initial scene i, y i,j Assigning a variable to a scene, a value of 1 representing scene u i Assigned to scene v j Otherwise, 0; u (u) i And v j Respectively the ith and jth initial scenes; d (u) i ,v j ) For scene u i And scene v j Euclidean distance between u i,t And v j,t The specific values of the ith and jth initial scenes at the time t are respectively z i Selecting a variable (0 or 1) for a typical scene, z i Let 1 illustrate that the i-th initial scene is determined as a typical scene, and 0 illustrate that the i-th initial scene is not determined as a typical scene; z is the number of clusters of a typical scene, and T is the scheduling period.
Further, step S130 specifically includes:
combining the plurality of wind power output typical scenes and the plurality of photoelectric output typical scenes to obtain a plurality of wind power output typical scenes, wherein the occurrence probability of the wind power output typical scenes is equal to the product of the wind power output typical scenes and the photoelectric output typical scenes which participate in the combination.
Further, the objective function of the microgrid optimization operation model satisfies the following relation:
wherein obj is an objective function, S is the number of typical scenes of wind-light output, g s The occurrence probability corresponding to a typical scene s of wind and light output force is C 0 C is the starting and stopping cost of the gas turbine 1 For the operation and maintenance cost of the fuel of the gas turbine, C 2 For the operation and maintenance cost of the photo-thermal power station, C 3 For wind-powered electricity generation machinesCost of group operation and maintenance, C 4 Compensating costs for load demand response, C 5 Punishment cost for wind and light abandoning, C 6 The method is the micro-grid electricity purchasing cost.
Further, the load demand response compensation cost satisfies the following relation:
wherein ρ is s,load To offset the cost of power per transferable load,is the variable quantity of transferable load at t under the typical scene s of wind-light output, +.>Unit power compensation cost for load reduction for h-level load reduction, +.>The load is reduced for the h-level load which can reduce the load at t under the typical scene s of wind and light output.
Further, constraint conditions of the microgrid optimization operation model comprise a microgrid power balance constraint, a microgrid electricity purchase constraint, a gas turbine operation constraint, a photo-thermal power station operation constraint, a wind farm operation constraint and a load demand response constraint.
Further, the load demand response constraints include a transferable load variation constraint and a load curve peak-to-valley constraint.
Further, the transferable load variation constraint satisfies the following relationship:
wherein T is a scheduling period,is the variable quantity of the transferable load at t;
the load curve peak-valley difference constraint satisfies the following relation:
wherein,,for the total load of the microgrid at t before demand response, +.>Is the variable quantity of transferable load at t under the typical scene s of wind-light output, +.>The actual reduction amount of load can be reduced after load demand response is implemented in a typical scene s of wind-light output.
Further, the optimization result comprises a load reduction amount capable of being reduced for typical scenes of different wind and light output under different demand response intensities, a load transfer amount capable of being transferred for typical scenes of different wind and light output, micro-grid operation cost and the like.
According to the micro-grid optimizing operation method considering the load demand response, a wind turbine, a gas turbine, a photo-thermal power station and a power grid are combined, and the four are combined to supply electric loads; based on a scene reduction planning model based on Wasserstein probability distance, a typical scene of wind-light output is obtained to consider the uncertainty of wind-light resources; the load can be transferred and reduced in the micro-grid, so as to participate in the demand response of the micro-grid load, bring economic benefits to users, change the power utilization mode of the users, smooth the load curve of the micro-grid, reduce the peak-valley difference of the load curve, finally improve the operation benefit of the micro-grid and reduce the total operation cost of the micro-grid. The method can provide reference for the micro-grid optimization scheduling technology under the rapid development background of renewable energy sources, and has obvious practical significance and theoretical significance.
Drawings
FIG. 1 is a flow chart of a method of optimizing operation of a microgrid that takes into account load demand response in accordance with an embodiment of the present invention;
FIG. 2 is a graph of wind power output predictions, light field thermal power predictions and load predictions over time for a photo-thermal power plant according to an embodiment of the invention;
FIG. 3 is a graph of the time-dependent response of total load, no-demand response of total load, load shedding amount reducible and load transferability amount transferable according to an embodiment of the present invention;
fig. 4 is a schematic diagram of the running cost of the micro-grid under different load demand response strengths according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides a method for optimizing operation of a micro-grid in consideration of load demand response, including the steps of:
s100: and obtaining a plurality of typical scenes of wind and light output and occurrence probability thereof based on a preset method.
The step S100 specifically includes:
s110: and randomly generating a plurality of initial scenes of the wind power output according to a preset probability distribution function, and adopting a preset scene reduction planning model to perform scene reduction on the initial scenes of the wind power output to obtain typical scenes of the wind power output and occurrence probability of the typical scenes.
In some embodiments, the probability distribution function may be selected as desired, e.g., the error value of the actual wind power output from the predicted force satisfies a normal distribution. After the selection, a plurality of (e.g. 200) wind-light output initial scenes are randomly generated by utilizing Latin hypercube sampling according to a probability distribution function, and the occurrence probability of each initial scene and the values of different moments of each initial scene are obtained.
The scene reduction planning model is as follows;
the objective function of the model is:
the constraint conditions of the model are as follows:
y i,j ≤z j (6)
wherein omega is an initial scene set, i and j are initial scene indexes, g respectively i For the probability of occurrence of the initial scene i, y i,j Assigning a variable to a scene, a value of 1 representing scene u i Assigned to scene v j Otherwise, 0; u (u) i And v j Initial scenes i and j, respectively; d (u) i ,v j ) For scene u i And scene v j Euclidean distance between u i,t And v j,t Specific values of initial scenes i and j at time t (namely wind power output) and z i Selecting a variable (with a value of 0 or 1) for the typical scene, wherein 1 indicates that the initial scene i is determined to be the typical scene, and 0 indicates that the initial scene i is not determined to be the typical scene; z is the number of clusters of a typical scene, and T is the scheduling period.
Equation (2) represents all of the initial valuesThe sum of the discrete occurrence probabilities of the initial scene is equal to 1, and equation (3) represents the initial scene u i And v j Euclidean distance between them, equation (4) represents the initial scene u i Uniqueness of allocation, i.e. only one typical scene can be allocated, formula (5) constrains the number of clusters of typical scenes, formula (6) allocates a variable y to a scene i,j And a typical scene selection variable z i Is a logical constraint of (1) representing the initial scene u i Can only be assigned to typical scenes.
The scene reduction planning model can be solved by a CPLEX solver, and a scene distribution variable y can be obtained i,j Then, a typical scene of wind power output and occurrence probability thereof can be obtained according to the following formula (7).
Wherein p is j Is the probability of occurrence of the typical scene j.
The construction and solving principle of the scene reduction planning model can be seen in the literature:
dong Xiao, sun Yingyun, pu Tianjiao, chen Naishi, sun Ke. An optimal scene reduction method based on Wasserstein distance and effectiveness index [ J ]. Chinese Motor engineering Programming, 2019,39 (16): 4650-4658+4968.DOI:10.13334/j.0258-8013.Pcsee.181494.
S120: and randomly generating a plurality of initial scenes of the photoelectric output according to a preset probability distribution function, and adopting a preset scene reduction planning model to carry out scene reduction on the initial scenes of the photoelectric output to obtain a typical scene of the photoelectric output and occurrence probability of the typical scene.
The method for reducing the scenario of the photoelectric output is the same as the method for reducing the scenario of the wind power output, and is not repeated here.
S130: and obtaining a plurality of typical scenes of wind and light output and the occurrence probability thereof according to the typical scenes of the wind and light output and the occurrence probability thereof and the typical scenes of the photoelectric output.
Any one of the typical scenes of wind power output and any one of the typical scenes of photoelectric output are mutually combined to obtain a typical scene of wind power output (also called as a typical scene of wind power output), and the occurrence probability of the combined typical scene of wind power output and the occurrence probability of the typical scene of photoelectric output are multiplied to obtain the occurrence probability of the typical scene of wind power output. And combining the typical scenes of the wind power output and the typical scenes of the photoelectric output in pairs to obtain the typical scenes of the wind power output and the occurrence probability of the scenes. For example, if the number of typical scenes of wind power output is m and the number of typical scenes of photovoltaic power output is n, the number of typical scenes of wind power output is s=m×n. In some embodiments, m and n may both be 2.
S200: and constructing a micro-grid optimization operation model considering wind-light uncertainty and load demand response based on a plurality of typical scenes of wind-light output and occurrence probability thereof by taking the minimum micro-grid operation cost as a target.
Considering the load demand response means that transferable loads and reducible loads are introduced in the operation of the micro-grid, so that the transferable loads and the reducible loads can participate in the optimized dispatching of the micro-grid, and the demand energy management of the loads is implemented, namely, the load demand is not required to be met by 100 percent. The partial load quantity of the transferable load can be transferred from the current period to another period, for example, the washing machine can work at eight night or at one early morning; the load reduction means that the whole load is not required in a certain period of time, for example, one lamp may be turned on or two lamps may be turned on. The function of demand response is to improve the flexibility of power grid dispatching, ensure power time balance, maintain the safe and stable operation of the power grid, and simultaneously reduce the operation cost of the power grid.
The demand response strength of the magnitude of the load participation demand response in the reactive microgrid refers to the ratio of transferable load or reducible load in the total load, and satisfies the following relation:
in the method, in the process of the invention,and->The demand response intensity at t is respectively the transferable load and the load-shedding load, +.>And->Maximum load amounts which can be transferred and reduced, respectively, and which can be involved in demand response at t, < +.>Is the total load of the microgrid at t before demand response.
The core idea of the demand response of the transferable load is to motivate a user to change the power utilization mode and transfer the power in the load peak period to the load valley period, and the method is as follows:
wherein, T is a scheduling period,for transferring the change of load at tQuantity of transformation, tex>For the transferable load transfer load at k to t,/load amount>Load transfer amount for transferable load transferred to k at t,/>A T-order matrix with diagonal elements of 0, indicating that the transferable load transfer load from T to T is 0, P t s,load The actual load amount of the load can be transferred after the demand response. The expression (10) indicates that the amount of change in the transferable load is affected by both the amount of the load transferred in and the amount of the load transferred out. Equation (11) indicates that the transferable load transfer load amount at t cannot exceed the maximum load amount involved in the transferable load demand response. Equation (12) represents that the actual transferable load is the sum of the initial load amount of the transferable load and the load variation amount.
The demand response of the load reduction can be determined in advance through the contract between the power grid dispatching center and the user, and the load reduction amount and the compensation cost can be reduced, and the method is concretely as follows:
wherein P is t c,load For the actual reduction amount by which the load can be reduced after the demand response is implemented, H and H are the index of the number of stages and the total number of stages by which the load demand response can be reduced,as binary variables, a reduction state of h-level when the load demand response t can be reduced is represented, a value of 1 represents that the load corresponding to h-level is reduced, otherwise, the value is 0, < >>The actual reduction amount of the load can be reduced for the h-stage at t. The actual reduction amount capable of reducing the load is represented by formula (13) and the sum of reduction amounts capable of reducing the load demand in response to each stage is represented by formula (14)>The value range constraint of (2) and (15) indicates that there is one or only one of the t-time excited states that can reduce the load demand response.
The micro-grid operation cost comprises gas turbine start-stop cost, gas turbine fuel operation and maintenance cost, photo-thermal power station operation and maintenance cost, wind turbine generator operation and maintenance cost, load demand response compensation cost, waste wind and waste light punishment cost and micro-grid electricity purchasing cost.
Wherein, the gas turbine start-stop cost satisfies the following relation:
wherein C is 0 The starting and stopping cost of the gas turbine is that N is the number of the gas turbines,And->The starting cost and the stopping cost of the nth gas turbine at T are respectively, and T is a scheduling period.
The fuel operation and maintenance cost of the gas turbine meets the following relation:
wherein C is 1 For gas turbine fuel operating maintenance costs, Δt is the time interval, which may be 1 hour for example,the output of the nth gas turbine at t under a typical scenario S (s=1, 2..s) for wind and light output; />Fuel operating costs per unit of electrical energy for the nth gas turbine.
The operation and maintenance cost of the photo-thermal power station meets the following relation:
wherein C is 2 For the operation and maintenance cost of the photo-thermal power station, ρ SP Unit thermal energy cost coefficient ρ for providing thermal energy power generation for light field in photo-thermal power station TP Providing a unit thermal energy cost coefficient of thermal energy power generation for the heat storage system module,thermal power provided to the turbo generator module by the light field at time t under a typical scene s of wind-light output,/->And the thermal power of the heat storage system module flowing into the turbine generator module at t time under a typical scene s of wind-light output.
The operation and maintenance cost of the wind turbine generator meets the following relation:
wherein C is 3 For the operation and maintenance cost of the wind turbine generator system, ρ W The operation and maintenance cost of the unit electric energy of the wind turbine generator system,the actual output of the wind turbine generator set at t in a typical scene s of wind and light output.
The load demand response compensation cost satisfies the following relation:
wherein C is 4 Compensating cost for load demand response ρ s,load To offset the cost of power per transferable load,is the variable quantity of transferable load at t under the typical scene s of wind-light output, +.>Unit power compensation cost for load reduction for h-level load reduction, +.>The load is reduced for the h-level load which can reduce the load at t under the typical scene s of wind and light output.
The wind and light discarding punishment cost meets the following relation:
wherein C is 5 Penalty cost for wind and light rejection ρ WC And ρ SC The punishment cost of wind energy per unit of wind generation set and punishment cost of heat energy per unit of heat rejection of the light field are respectively,and->Wind curtailment power of wind turbine generator set under typical scene s of wind-light output respectivelyAnd the reject power of the light field.
The micro-grid electricity purchasing cost meets the following relation:
wherein C is 6 For the cost of micro-grid electricity purchase,for the grid electricity price at t +.>The method is the electricity purchasing quantity of the micro-grid at t under a typical scene s of wind-light output.
The objective function of the microgrid optimization operation model satisfies the following relation:
wherein obj is an objective function, S is the number of typical scenes of wind-light output, g s The occurrence probability corresponding to a typical scene s of wind and light power.
The constraint conditions of the microgrid optimization operation model comprise a microgrid power balance constraint, a microgrid electricity purchase constraint, a gas turbine operation constraint, a photo-thermal power station operation constraint, a wind power plant operation constraint and a load demand response constraint.
Wherein, the microgrid power balance constraint is:
wherein N is the number of gas turbines,is the output of the nth gas turbine at t under the typical scene s of wind-light output, +.>Is the actual output of the wind turbine generator set at t under the typical scene s of wind and light output, +.>Is the output of the photo-thermal power station at t time under the typical scene s of wind-light output, +.>Is the electricity purchasing quantity of the micro-grid at t under a typical scene s of wind-light output, < + >>For the total load of the micro-grid at t before the load demand response,/>Is the variable quantity of transferable load at t under the typical scene s of wind-light output, +.>The actual reduction amount of load can be reduced after load demand response is implemented in a typical scene s of wind-light output. The equation represents that the real-time balance of power production and consumption needs to be met for operation of the microgrid taking into account the load demand response.
The micro-grid electricity purchasing constraint is as follows:
in the method, in the process of the invention,the maximum transmission power of the interconnection line of the micro-grid and the power grid. Equation (25) indicates that when the output of the power supply in the micro-grid is difficult to meet the requirement of self-load or the output economy is poor, the micro-grid can purchase electricity from the power grid, and the purchase electricity quantity needs to meet the maximum transmission power constraint of the interconnection line of the micro-grid and the power grid.
Gas turbine operating constraints include:
a) Start-stop constraint
The gas turbine is required to meet the relevant technical requirements from start to stop or from stop to start, and the following steps are shown:
in the method, in the process of the invention,and->The single start cost and stop cost of the nth gas turbine are respectively +.>Andstarting and stopping costs of the nth gas turbine at t, respectively, +.>And->The operation states of the nth gas turbine at t-1 time and t time are respectively 1, and 0 represents the unit stop; />For the minimum number of operating hours after start-up of the gas turbine,/-for the gas turbine>Is the minimum number of shutdown hours after a gas turbine shutdown. Equations (26) and (27) describe the start-stop cost constraints of the unit, and equations (28) and (29) represent the minimum start-up time and minimum shut-down time constraints of the unit.
b) Force constraint
In the method, in the process of the invention,and->The minimum output and the maximum output of the nth gas turbine are respectively.
c) Climbing rate constraint
In the method, in the process of the invention,and->The maximum downward and upward ramp rates of the nth gas turbine are respectively.
d) Standby constraint
Because of the uncertainty in renewable energy output, the microgrid needs to reserve a certain reserve capacity to cope with the power surplus and deficiency, in some embodiments, the positive and negative rotational reserve capacity of the microgrid may be taken to be 5% of the total load before demand response, including the reserve of the fuel turbine and the reserve of the microgrid and grid tie-line. The backup constraints are as follows:
the photo-thermal power plant operational constraints include:
a) Heat storage system related constraints
The mathematical model of the heat storage system module is similar to an energy storage battery, including heat storage capacity, heat storage and release, and initial heat storage constraints, as follows:
in the method, in the process of the invention,and->The heat storage capacity and lambda of the heat storage system module at t time under a typical scene s of wind-light output respectively Tes For the dissipation factor of the heat storage system module, +.>For the heat storage efficiency of the heat storage system module, +.>Heat storage power flowing into the heat storage system module from the light field at t time under a typical scene s of wind-light output is +.>For the heat release efficiency of the heat storage system module, +.>T is the minimum heat storage capacity of the heat storage system module full For the number of load hours of the heat storage system module, i.e. the number of hours in which the heat storage system module can support the maximum output of the turbo generator module without illumination, η Stg Is the photo-thermal conversion coefficient of the turbo generator module,for maximum output of the turbo generator module, < >>For the exothermic state of the heat storage system module, +.>And->And the initial period heat storage capacity and the end period heat storage capacity of the heat storage system module are respectively under a typical scene s of wind-light output.
Equation (34) indicates that the current heat storage amount of the heat storage system module is related to the heat storage amount at the previous time, and the heat storage power of the heat storage system module; equation (35) gives the heat storage capacity range of the heat storage system module; formulas (36) and (37) prevent the heat storage power of the heat storage system module from exceeding a rated value; formula (38) indicates that the heat storage system module cannot store heat and release heat at the same time; to ensure that the new periodic heat storage system module is adjustable, equation (39) illustrates that the initial and final heat storage amounts of the heat storage system module are equal.
b) Light field correlation constraints
The heat collection device of the light field converts solar energy into heat energy, and the heat energy is divided into three parts, wherein the three parts comprise heat power provided by the light field to the steam turbine generator module, heat storage power of the light field flowing into the heat storage system module and heat rejection power of the light field, and the heat storage power is shown as follows;
in the method, in the process of the invention,the thermal-arrest power is predicted for the light field at t under the typical scene s of wind-light output (namely, the photoelectric output of the typical scene of photoelectric output contained in the typical scene s of wind-light output at t).
c) Turbo generator module related constraints
In order to ensure safe operation of the turbo generator module, it is necessary to constrain the input thermal power, the output electric power and the climbing rate:
in the method, in the process of the invention,and->Is the maximum downward and upward ramp rate of the turbo generator module. The formula (41) gives the photo-thermal conversion relation of the turbo generator module; equations (42) and (43) describe the ramp rate and the output range, respectively, of the turbo generator module.
The wind farm operation constraints are:
the predicted output of the wind power field at t under the typical scene s of the wind power output (namely, the wind power output at t of the typical scene s of the wind power output contained in the typical scene s of the wind power output).
The load demand response constraints include:
a) Transferable load variation constraint
The transferable load participates in demand response, and the electricity demand of the user is adjusted in different time intervals according to the change of electricity price/signals, but the total load quantity of the transferable load in one period is kept unchanged, namely the sum of the change quantity of the transferable load in one period is 0, and the sum meets the following relation:
b) Load curve peak-valley difference constraint
The load demand response is advantageous for smoothing the load curve of the microgrid, reducing its peak-to-valley difference, so that the load after the demand response is within the minimum and maximum values of the initial load before the demand response.
S300: and solving the microgrid optimizing operation model through a preset optimizing algorithm to obtain an optimizing result.
The preset optimization algorithm can be any existing optimization algorithm. In some embodiments, a CPLEX solver may be utilized to solve the microgrid optimization run model. The CPLEX solver is internally provided with a plurality of existing optimization algorithms, and when solving, the CPLEX defaults to call the solving method which is considered to be the most suitable for solving, but the CPLEX solver can also select the suitable algorithm according to the characteristics of the model so as to accelerate the solving speed. In some embodiments, the optimization algorithm may be a branch-and-bound method. After the constructed micro-grid optimizing operation model is input into a CPLEX solver, the CPLEX solver outputs an optimizing result, namely values of all unknown variables (decision variables), and then the micro-grid is operated according to the values of the decision variables by setting corresponding parameters.
The optimization result comprises load reduction amount capable of being reduced to typical scenes of different wind and light output under different demand response intensities, load transfer amount capable of being transferred to typical scenes of different wind and light output, micro-grid operation cost and the like.
According to the micro-grid optimizing operation method considering the load demand response, which is disclosed by the embodiment of the invention, the wind turbine, the gas turbine and the photo-thermal power station are combined with a power grid, so that the four are combined to supply the electric load; based on a scene reduction planning model based on Wasserstein probability distance, a typical scene of wind-light output is obtained to consider the uncertainty of wind-light resources; the load can be transferred and reduced in the micro-grid, so as to participate in the demand response of the micro-grid load, bring economic benefits to users, change the power utilization mode of the users, smooth the load curve of the micro-grid, reduce the peak-valley difference of the load curve, finally improve the operation benefit of the micro-grid and reduce the total operation cost of the micro-grid. The method can provide reference for the micro-grid optimization scheduling technology under the rapid development background of renewable energy sources, and has obvious practical significance and theoretical significance.
The flow and effects of the method of the present invention will be described below using a micro-grid system comprising a wind turbine, a photo-thermal power station and three gas turbines as an example.
The operation parameters of the gas turbine are shown in table 1, the operation parameters of the photo-thermal power station are shown in table 2, the peak-valley time electricity price of the power grid is shown in table 3, and the load demand response related parameters are shown in table 4. The method is characterized in that a day is taken as a scheduling period and is divided into 24 time periods, and the running states of the devices in each time period are the same. The predicted wind power output value, the predicted heat collection power value of the light field of the photo-thermal power station and the predicted total load quantity value before load demand response are shown in figure 2. Assuming that probability distribution of actual wind power output and actual light field heat collection power meets normal distribution with a corresponding predicted value as a mean value and a standard deviation of 0.1 times of the predicted value. 200 initial scenes are generated by using a Meng Daka method respectively, and three typical scenes are obtained by using a scene reduction method based on Wasserstein probability distance. Given: operation and maintenance cost rho of unit electric energy of wind turbine generator system W Penalty cost ρ of wind energy of unit abandoned wind of wind turbine generator is 0.01 per kW.h WC Penalty cost ρ of heat energy per heat rejection of light field SC The maximum transmission power of the connection lines of the micro-grid and the power grid is 20kW, and the maximum transmission power is 0.23 kW.h. The optimization results obtained by the method of the present invention are shown in fig. 3, fig. 4 and table 5. As shown in fig. 4, the total load profile for the load demand response is lower than the initial total load profile during the 8:00-23:00 (peak load) period, indicating that the load demand for the segment of the microgrid is reduced. The total load profile for the load demand response is higher than the initial total load profile during the 24:00-next day 7:00 (load underestimation), indicating an increase in load demand for the microgrid during this period, since the microgrid is favoring when the load demand response is implementedThe beneficial chase down will change the power usage pattern of the user. Specifically, the load that can be cut down at the time of the partial load peak will be cut down, and the partial load that can be transferred at the time of the load peak will be transferred to the load off-peak period. The peak-valley difference of the micro-grid total load curve with the demand response is 54.56kW, the peak-valley difference of the micro-grid total load curve without the demand response is 38.56kW, and 29.33% is reduced, so that the demand response of the load is beneficial to smoothing the load curve of the micro-grid, and the peak-valley difference of the load curve is reduced. As can be seen from Table 5, implementing the load demand response increases the cost of the composite demand response compensation for the micro-grid, but the load curve is smoother, which is beneficial to reducing the fuel operation cost of the gas turbine unit and the electricity purchasing cost to the power grid, reducing the total operation cost of the micro-grid after implementing the load demand response by 6.09%, and improving the operation benefit of the micro-grid.
TABLE 1 operating parameters of gas turbines
Table 2 operating parameters of the photo-thermal power plant
Table 3 peak-valley time-of-use electricity prices of the grid
TABLE 4 load demand response related parameters
TABLE 5 running costs of the micro-net with or without the demand response
In this example, set up A total of 36 scenarios, corresponding to the microgrid operating costs are shown in fig. 4. />Andthe larger the controllable load in the microgrid, the greater the intensity of the response that can participate in the load demand. As can be seen from fig. 4, as the demand response intensity of the transferable load increases, the operation cost of the micro-grid becomes smaller, and the reduction of the operation cost of the micro-grid also decreases. The same rules apply to load shedding, but the reduction in microgrid operating costs is less. In particular, when the demand response intensity of the transferable load and the reducible load is increased at the same time, the operation cost of the micro-net is most reduced. For example, when the micro-grid responds to no load demand, the running cost of the micro-grid is 731.67, and when +.>0.1 @, @>At 0.2, the running cost of the micro-grid is 652.39, and 79.28 is reduced. The load demand response is beneficial to smoothing the load curve of the micro-grid, the output of the gas turbine unit with high fuel operation and maintenance cost and the purchase power to the power grid are reduced, and compared with the load reduction, the load transfer smoothing effect is more remarkable. Of course, as the load demand response intensity increases, the effect of smoothing the microgrid load curve also decreases. />
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and various modifications can be made to the above-described embodiment of the present invention. All simple, equivalent changes and modifications made in accordance with the claims and the specification of this application fall within the scope of the patent claims. The present invention is not described in detail in the conventional art.
Claims (10)
1. The micro-grid optimizing operation method considering the load demand response is characterized by comprising the following steps:
s100: obtaining a plurality of typical scenes of wind-light output and occurrence probability thereof based on a preset method;
s200: the method comprises the steps of constructing a micro-grid optimization operation model considering wind-light uncertainty and load demand response based on a plurality of typical scenes of wind-light output and occurrence probability of the typical scenes by taking the minimum micro-grid operation cost as a target;
s300: and solving the microgrid optimizing operation model through a preset optimizing algorithm to obtain an optimizing result.
2. The method for optimizing operation of a microgrid according to claim 1, wherein step S100 specifically comprises:
s110: randomly generating a plurality of initial scenes of wind power output according to a preset probability distribution function, and adopting a preset scene reduction planning model to perform scene reduction on the initial scenes of the wind power output to obtain typical scenes of the wind power output and occurrence probability of the typical scenes;
s120: randomly generating a plurality of initial scenes of the photoelectric output according to a preset probability distribution function, and adopting a preset scene reduction planning model to carry out scene reduction on the initial scenes of the photoelectric output to obtain a typical scene of the photoelectric output and occurrence probability of the typical scene;
s130: and obtaining a plurality of typical scenes of wind and light output and the occurrence probability thereof according to the typical scenes of the wind and light output and the occurrence probability thereof and the typical scenes of the photoelectric output.
3. The method for optimizing operation of a micro-grid taking into account load demand response of claim 2, wherein the preset scene reduction planning model is:
wherein omega is an initial scene set, i and j are initial scene indexes, g respectively i For the probability of occurrence of the initial scene i, y i,j Assigning a variable to a scene, a value of 1 representing scene u i Assigned to scene v j Otherwise, 0; u (u) i And v j Respectively the ith and jth initial scenes; d (u) i ,v j ) For scene u i And scene v j Euclidean distance between u i,t And v j,t The specific values of the ith and jth initial scenes at the time t are respectively z i Selecting variables for a typical scene, z i Let 1 illustrate that the i-th initial scene is determined as a typical scene, and 0 illustrate that the i-th initial scene is not determined as a typical scene; z is the number of clusters of a typical scene, and T is the scheduling period.
4. The method for optimizing operation of a microgrid according to claim 2, wherein step S130 specifically comprises:
combining the plurality of wind power output typical scenes and the plurality of photoelectric output typical scenes to obtain a plurality of wind power output typical scenes, wherein the occurrence probability of the wind power output typical scenes is equal to the product of the wind power output typical scenes and the photoelectric output typical scenes which participate in the combination.
5. The method for optimizing operation of a microgrid in consideration of load demand response according to claim 1, wherein an objective function of the microgrid optimizing operation model satisfies the following relation:
wherein obj is the objectStandard function, S is the number of typical scenes of wind-light output, g s The occurrence probability corresponding to a typical scene s of wind and light output force is C 0 C is the starting and stopping cost of the gas turbine 1 For the operation and maintenance cost of the fuel of the gas turbine, C 2 For the operation and maintenance cost of the photo-thermal power station, C 3 C is the operation and maintenance cost of the wind turbine generator system 4 Compensating costs for load demand response, C 5 Punishment cost for wind and light abandoning, C 6 The method is the micro-grid electricity purchasing cost.
6. The method for optimized operation of a microgrid taking into account load demand response according to claim 5, wherein said load demand response compensation cost satisfies the following relation:
wherein ρ is s,load To offset the cost of power per transferable load,is the variable quantity of transferable load at t under the typical scene s of wind-light output, +.>Unit power compensation cost for load reduction for h-level load reduction, +.>The load is reduced for the h-level load which can reduce the load at t under the typical scene s of wind and light output.
7. The method for optimizing operation of a microgrid in consideration of load demand response according to claim 1, wherein the constraint conditions of the microgrid optimizing operation model comprise a microgrid power balance constraint, a microgrid power purchase constraint, a gas turbine operation constraint, a photo-thermal power plant operation constraint, a wind farm operation constraint and a load demand response constraint.
8. The method of claim 7, wherein the load demand response constraints include transferable load variation constraints and load curve peak-to-valley constraints.
9. The method of optimizing operation of a microgrid taking into account load demand response of claim 8, wherein the transferable load delta constraint satisfies the following relationship:
wherein T is a scheduling period,the variable quantity of the transferable load at t under a typical scene s of wind-light output is as follows;
the load curve peak-valley difference constraint satisfies the following relation:
wherein,,for the total load of the microgrid at t before demand response, +.>Is the variable quantity of transferable load at t under the typical scene s of wind-light output, +.>The actual reduction amount of load can be reduced after load demand response is implemented in a typical scene s of wind-light output.
10. The method for optimizing operation of a micro-grid in consideration of load demand response according to claim 1, wherein the optimization result comprises a load reduction amount capable of being reduced for typical scenes of different wind-light outputs under different demand response intensities, a load transfer amount capable of being transferred for typical scenes of different wind-light outputs and micro-grid operation cost.
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