CN111832217A - Virtual power plant optimized operation method considering wind power consumption - Google Patents
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Abstract
The invention provides a virtual power plant optimized operation method considering wind power consumption, which comprises the following steps: determining a preliminary configuration scheme of the virtual power plant by combining an actual research object; determining a target system of the optimized operation of the virtual power plant, constructing the target system by taking the wind power consumption and the system operation cost as indexes, and constructing a corresponding target function; determining equality constraint conditions and inequality constraint conditions capable of reflecting safe and stable operation of the system, and constructing expressions of various constraint conditions; constructing a multi-objective optimization model according to the determined objective system and various constraint conditions; and solving the multi-objective optimization model based on the artificial honeycomb algorithm, and checking the feasibility of the solved result. The method provided by the invention is based on the idea that the maximum wind power consumption and the maximum overall economic benefit of the system are fully considered on the premise of ensuring the safe and stable operation of the power grid, so that the control strategy of the virtual power plant in the operation management process is determined.
Description
Technical Field
The invention belongs to the field of operation analysis and control of a power system, and particularly relates to a virtual power plant optimized operation method considering wind power consumption.
Background
The method has the advantages of reducing the power production and supply cost, realizing the optimal configuration of resources and introducing a competitive mechanism in the power industry, so that the distributed power generation technology becomes a new research hotspot in a power system. The distributed power supply has the advantages that the distributed power supply has a plurality of problems, such as high cost of single machine access of the distributed power supply, difficult control and the like. In addition, distributed power is an uncontrollable source relative to large grids. Therefore, large systems often adopt a limited and isolated mode to treat distributed power supplies in order to reduce the impact of the distributed power supplies on large power grids; meanwhile, the network access standard of the distributed energy is regulated, and when the power system breaks down, the distributed power supply must be immediately quitted from operation. This greatly limits the full exploitation of the efficiency of distributed energy resources. In order to coordinate contradictions between a large power grid and a distributed power supply and fully excavate the value and benefit of the distributed energy for the power grid and users, researchers put forward the concept of a virtual power plant.
A virtual power plant is an integrated power plant consisting of an energy management system and small and ultra-small decentralized generators for control. Owners and operators of virtual power plants can gain technical, economic and ecological benefits through computerized operational planning. After distributed power sources such as wind power generation gradually enter a family, the virtual power plant technology can realize the possibility that the family or individual load can feed surplus electric quantity back to a power grid, and work time of periodic distributed power sources and distributable distributed power sources is reasonably distributed, so that regional electric energy demand and electric power demand of an electric power wholesale market are effectively coordinated.
Therefore, by comprehensively considering various factors such as the output limit of the distributed power supply, the constraint condition of the power grid operation and the like, various distributed power supplies are virtualized into a power plant unit, so that the power plant unit participates in the actual operation process of the power grid, and a specific implementation method is provided for the optimized operation of the virtual power plant by constructing a target system reflecting the energy efficiency utilization rate of the distributed power supplies and the operation cost of the power grid.
Disclosure of Invention
The invention aims to provide a virtual power plant optimized operation method considering wind power consumption, which takes wind power consumption as an important index for formulating virtual power plant operation and provides ideas and methods for researching new energy sources such as wind power and the like and avoiding resource waste.
In order to achieve the purpose, the invention provides a virtual power plant optimized operation method considering wind power consumption, which comprises the following steps:
step 1: determining a preliminary configuration scheme of the virtual power plant by combining an actual research object;
step 2: determining a target system and a target function of the optimized operation of the virtual power plant by taking the wind power consumption and the system operation cost as indexes;
and step 3: determining equality constraint conditions and inequality constraint conditions reflecting safe and stable operation of the system, and constructing expressions of various constraint conditions;
and 4, step 4: constructing a multi-objective optimization model with the maximum wind power consumption and the minimum system operation cost according to the determined target system and various constraint conditions;
and 5: and solving the multi-target optimization model based on an artificial honeycomb algorithm, and checking the feasibility of the solved result.
In step 2, a function expression embodying the wind power consumption electric quantity is as follows:
wherein T is the number of time periods in the scheduling period; n is a radical ofWThe number of wind power plants;
the active scheduling output of the wind power plant i in the time period t;
the function expression for embodying the system operation cost is as follows:
CWT(t),CPV(t),CFC(t),CMT(t) and Cj(t) the tender prices of a wind generating set, a photovoltaic generating set, a fuel cell set, a micro generating set and energy storage equipment in t hours are respectively; sGi(t) and SSj(t) cost price of the distributed power supply and the energy storage unit when starting or stopping respectively; pGrid(t) is the real power bought or sold from the grid during time period t, CGrid(t) grid electricity prices for time period t; n is a radical ofgAnd NsRepresenting the number of power sources and energy storage power sources, respectively; u shapeWT(t),UPV(t),UFC(t),UMT(t) and Uj(t) respectively representing the on/off state of each unit in a t time period of a certain day; Δ P (t) represents the difference between the original and the new loss of the feeder, CΔP(t) represents the cost price corresponding to Δ p (t); riAnd IiRespectively representing the resistance and the actual current of the ith branch.
In step 3, the equality constraint condition of the system operation is the system power balance in any time period, and the expression is as follows:
the inequality constraint conditions of system operation are wind power generation limitation, photovoltaic power generation limitation, fuel cell limitation, micro steam turbine limitation, power grid limitation and charging and discharging limitation of storage equipment, and the expressions of various inequality constraint conditions are as follows:
wind power generation limitation during t period
PWTmin(t)≤PWT(t)≤PWTmax(t)；t＝1,...,T (6)
Photovoltaic power generation limitation at time t
PPVmin(t)≤PPV(t)≤PPVmax(t)；t＝1,...,T (7)
time t fuel cell limitation
PFCmin(t)≤PFC(t)≤PFCmax(t)；t＝1,...,T (8)
Limitation of micro steam turbine in t period
PMTmin(t)≤PMT(t)≤PMTmax(t)；t＝1,...,T (9)
time t grid limitation
PGridmin(t)≤PGrid(t)≤PGridmax(t)；t＝1,...,T (10)
Limitation of accumulator
PSjmin(t)≤PSj(t)≤PSjmax(t)；t＝1,...,T (11)
Since the charge-discharge rate of the storage device is limited at each time period, the following equations and constraints may be considered:
the storage battery can not be charged and discharged simultaneously
X(t)+Y(t)≤1；t＝1,...,24；XandY∈{0,1} (13)
Wess(t) and Wess(t-1) the energy storage capacity inside the battery at time t and time t-1, PchargeAnd PdischargeIs the allowable charge-discharge rate, eta, over a certain period of timechargeAnd ηdischargeIs the charge-discharge efficiency.
The multi-objective optimization model constructed in the step 4 is as follows:
wherein f ═ E (-E)W，CGH) Is an objective function; x is a decision vector consisting of optimization variables; h isj(x) Is an equality constraint function; gk(x) Is an inequality constraint function.
In step 5, the number of the initialized population and the maximum storage number of the external archives are NP, the number of the leading bees and the number of the following bees are NP/2, the maximum evaluation time FEAS, the maximum elimination time of the scout bees is Limit, and the maximum elimination time of the elite individuals of the external archives is elite.
Step 501: initializing and calculating individual fitness values through chaotic mirror images, selecting optimal NP individuals according to a domination relation, and selecting non-domination individuals to enter an external archive; the chaotic mirror image initialization step is to generate a population A according to the chaotic initialization of the formula (15), wherein the number of the population is NP; generating a population B from the population A through mirror image operation; selecting NP better individuals as an initial population according to the fitness values of A and B;
viw＝xiw+riw(xiw-xkw) (15)
during initialization, assume the position of honey source i as xi＝(xi1,xi2,...,xid-1,xid) After the mirroring operation, the new mirror solution position miriCan be defined as miri＝(xid,xid-1,...,xi2,xi1) When a certain one-dimensional value xijWhen the upper and lower bounds are exceeded, new x is generated using regularization and de-regularization operationsij；
Step 502: updating the positions of the leading bees according to the formula (15), reserving the positions if the positions dominate the original honey sources after updating, and comparing the positions with individuals in an external archive to determine whether to reserve the positions if the positions do not dominate each other;
step 503: calculating the selection probability of the external elite individual according to the formula (16), selecting whether to carry out deep development on the elite individual through probability, wherein the evolution formula is the formula (17), the number of leading times of the elite individual after evolution is reduced by 1, and the retention strategy which is the same as that of the leading bee is executed on the descendant,
wherein elitei·Crown is the crowding distance, elite, of the ith individual in the external archivei·Choice is the number of times that the ith elite individual in the external file leads to evolve;
viw＝eliteiw+riw(eliteiw-xiw) (17)
wherein r isiw∈[-(φm+1),+(φm+1)]；
Step 504: when the offspring individuals generated by the leading bees and the following bees cannot be reserved, the elimination times are increased by 1; when the individual threshold Limit is reached, a new honey source is regenerated by equation (18) to replace the old honey source location,
ch1＝rand(0,1) (18)
chk+1＝sin(πchk)
wherein ch1Is a random number ranging between 0,1, k is an iteration counter, k 1, 2.., D-1;
step 505: and after each iteration is finished, maintaining the external file, judging whether the maximum evaluation time FEAS is reached, if so, ending the circulation and outputting the individuals in the external file as a final result, otherwise, turning to the step 502.
In step 502, the search radius is varied with xiThe increase and decrease of Limit, the radius formula is shown as (19),
ri＝φm+cos(πxi·Limit/2Limit) (19)
wherein: phi is amFor the base value of the search radius, xiLimit is the current honey source threshold, and Limit is the honey source development threshold.
In step 505, when updating the external file, performing mutation operation on the extreme solution to ensure that the extreme solution has been completely developed; when the number of the storage solutions exceeds the scale of the external archive, calculating a congestion distance, deleting the solution with the minimum congestion distance until the scale requirement is met, and taking the final external archive solution set as final output; the variation factor for each extreme solution is determined as formula (20),
wherein cur is the current evaluation time, FEAS is the maximum evaluation time of the algorithm, and round is the rounding function.
The method provided by the invention comprises two aspects of construction and operation management of the virtual power plant, wherein the operation management aspect takes the maximum wind power consumption electric quantity and the minimum system operation cost as indexes, so that the economic benefit of the system is ensured, the problem of wind abandonment can be effectively avoided, and ideas and methods are provided for planning construction and operation management work of the virtual power plant.
Drawings
FIG. 1 is a flow chart of a virtual power plant optimization operation method considering wind power consumption proposed by the present invention;
FIG. 2 is a schematic diagram of the composition of a virtual power plant and its trading of electrical energy with the electricity market in the method of the present invention;
FIG. 3 is a graph of the relationship between the three bees involved in the artificial bee hive algorithm in the method of the present invention;
FIG. 4 is a flow chart of a conventional manual cell algorithm involved in the method of the present invention;
FIG. 5 is a flow chart of the method for solving the multi-objective optimization model based on the improved artificial honeycomb algorithm.
Detailed Description
The preferred embodiments will be described in detail below with reference to the accompanying drawings. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.
FIG. 1 is a flow chart of a virtual power plant optimization operation management scheme considering wind power consumption, and FIG. 5 is a flow chart of a solution of a multi-objective optimization model based on an improved artificial honeycomb algorithm. Referring to fig. 1 and 5, the method for optimizing the operation of the virtual power plant considering wind power consumption provided by the invention comprises the following steps:
step 1: and determining a preliminary configuration scheme of the virtual power plant by combining with an actual research object.
As a new concept, the virtual power plant has few specific implementation cases in China, and the research time of Europe and the United states aiming at the virtual power plant is relatively early, so before the specific work is carried out, the architecture of the virtual power plant which is put into operation at home and abroad can be investigated by looking up documents, carrying out on-site investigation, consulting related experts and the like. According to the research result, relevant information such as power supply composition, load controllability and energy storage unit capacity of an actual research object, requirements of regional power companies, user power consumption requirements, power grid automation degree, power market rules and the like, and according to external characteristics of schedulable resources, electrical geographic positions, feasibility of system implementation and the like contained in the research object, the most reasonable virtual power plant architecture for the research region is provided.
Step 2: and determining a target system for the optimized operation of the virtual power plant, constructing the target system by taking the wind power consumption and the system operation cost as indexes, and constructing a corresponding target function.
The virtual power plant is a combination of a decentralized generator set, a controllable load and an energy storage device, and is used as an independent power plant to participate in an electric power market, so that the purposes of trading electric energy and reducing cost are achieved. When the electricity market price is low, the virtual power plant may purchase electricity from the electricity market and charge the energy storage system. However, when the electricity market is high in electricity prices, the virtual power plant may reduce the power of the controlled load and the controlled discharge of the energy storage system. The primary energy source utilized by the power source included in the virtual power plant may be fossil fuel or may be a renewable energy source. In the illustration of the present invention, the virtual power plant is configured as wind power, photovoltaic, fuel cell, micro gas turbine, and energy storage device. The energy management system is the core and the mind of the virtual plant operation control, and coordinates the output energy of all the generators, the capacity of the energy storage system and the load demand. FIG. 2 shows the composition of a virtual power plant and its trading of electrical energy with the electricity market.
Step 201: and taking the wind power consumption electric quantity as one of indexes of the virtual power plant for optimizing operation, and giving a corresponding target function expression.
The wind power consumption electric quantity refers to active power transmitted to a power grid after wind power grid-connected operation, and in order to fully utilize wind energy which is clean energy, the maximization of wind power consumption is ensured by regulating and controlling the output of other conventional power supplies in the power grid. According to the above definition and analysis, the functional expression embodying the wind power consumption electric quantity can be described as:
wherein T is the number of time periods in the scheduling period; n is a radical ofWThe number of wind power plants;
and the power of the wind power plant i is actively scheduled in the time period t.
Step 202: and taking the system operation cost as one of indexes of the virtual power plant optimization operation, and giving a corresponding objective function expression.
The system operation cost refers to the operation cost of the whole power grid, and comprises the operation cost of internal constituent units of the virtual power plant due to power generation, the purchase or sale cost of power interaction between the virtual power plant and the power grid, the loss expenditure cost of the interaction power flowing through a feeder line and the like. The functional expression for the operating cost of the system to which the present invention relates can therefore be expressed as:
CWT(t),CPV(t),CFC(t),CMT(t) and CjAnd (t) the tender prices of the wind generating set, the photovoltaic generating set, the fuel cell set, the micro generating set and the energy storage equipment in t hours are respectively. SGi(t) and SSj(t) cost price of the distributed power supply and the energy storage unit at the time of starting or stopping respectively. PGridWhen (t) is tActive power bought or sold by the time slot from the grid, CGridAnd (t) is the grid electricity price in the time period t. N is a radical ofgAnd NsRepresenting the number of power sources and energy storage power sources, respectively. U shapeWT(t),UPV(t),UFC(t),UMT(t) and Uj(t) represents the on/off state of each unit in a t period of a certain day, respectively. Δ P (t) represents the difference between the original and the new loss of the feeder, CΔP(t) represents the cost price corresponding to Δ p (t). RiAnd IiRespectively representing the resistance and the actual current of the ith branch.
And step 3: and determining equality constraint conditions and inequality constraint conditions which can reflect safe and stable operation of the system, and constructing expressions of various constraint conditions.
The basic condition that the system can safely and stably run is supply and demand balance, and because electric energy cannot be stored in a large scale, the power consumption and the power generation amount at any moment are equal to ensure that the frequency of a power grid is close to a rated value, and unnecessary life loss of frequency sensitive equipment is avoided. Therefore, the equality constraint condition is determined as the system power balance in any time period, and because the virtual power plant related in the example project of the invention is composed of wind power, photovoltaic, fuel cell and energy storage device, the power generation part should include the power output in the power grid, the distributed power output (the discharging process of the wind power, photovoltaic, fuel cell and energy storage device is also reduced to the power generation part) covered in the virtual power plant, and the power consumption part is the loss generated by the load, power consumption and power transmission links in the power grid, and the charging process of the energy storage device.
Step 301: determining the equality constraint condition of the system operation as the system power balance in any time period, wherein the expression is as follows:
step 302: and determining inequality constraint conditions of system operation as wind power generation limitation, photovoltaic power generation limitation, fuel cell limitation, micro gas engine limitation, power grid limitation and charging and discharging limitation of electric energy storage equipment.
Step 303: the expressions of the various inequality constraints are:
because the output of the power generation units such as wind power generation and photovoltaic power generation which use renewable energy existing in nature as primary energy is closely related to the natural environment and is limited by the installed capacity of the power generation units, the output of wind power and photovoltaic power in the t time period is limited by upper and lower limits, and the upper and lower limits have difference in different time periods, and the wind speed prediction value and the illumination intensity prediction value are preset respectively.
Wind power generation limitation during t period
PWTmin(t)≤PWT(t)≤PWTmax(t)；t＝1,...,T (6)
Photovoltaic power generation limitation at time t
PPVmin(t)≤PPV(t)≤PPVmax(t)；t＝1,...,T (7)
The fuel cell converts the chemical energy of fuel into electric energy, the output working condition is determined by the output and the group number of the cell stack, and the freedom degree of the unit capacity is large. At present, the types and the types of fuel cells are more, and when inequality constraint conditions are made, the upper limit and the lower limit of output are set according to information such as the types and the configuration of the fuel cells by combining the previous investigation result.
time t fuel cell limitation
PFCmin(t)≤PFC(t)≤PFCmax(t)；t＝1,...,T (8)
The micro gas turbine has different working performances under rated working conditions and variable working conditions, runs stably at high temperature and has higher efficiency, and the micro gas turbine runs along an isothermal line as far as possible or runs in a high-temperature area as far as possible from a surge line during working. However, the micro gas turbine inevitably enters a low-temperature operation region in the actual working process, so that strict distinction is required when the upper limit and the lower limit of the micro gas turbine are set.
Limitation of micro gas turbine in t period
PMTmin(t)≤PMT(t)≤PMTmax(t)；t＝1,...,T (9)
In addition, a certain constraint also exists on the power grid layer, when the power output vertical rotation reserve can fully cover the power shortage or surplus of the virtual power plant caused by the constraint of natural environment, attention should be paid to the thermal stability limit of the feeder of the virtual power plant grid-connected point, the phenomenon that the line is overheated due to overlarge transmission power is avoided, the service life of a power transmission line is shortened or serious accidents such as fire disasters occur is avoided, and if the power output fluctuation inside the virtual power plant is large and the power grid vertical rotation reserve capacity is insufficient, the upper limit value and the lower limit value of each distributed power supply in the virtual power plant are corrected.
Limitation of the grid during time t
PGridmin(t)≤PGrid(t)≤PGridmax(t)；t＝1,...,T (10)
Because the investment cost and the operation maintenance cost of the energy storage equipment are relatively high, the energy storage equipment arranged in the power grid generally has small capacity at present, the energy storage equipment in the market at present has different types, and the charging and discharging working performance also has differences, so that the condition of the electric energy storage equipment is limited.
Limitation of the accumulator during the period t
PSjmin(t)≤PSj(t)≤PSjmax(t)；t＝1,...,T (11)
Since the charge-discharge rate of the storage device is limited at each time period, the following equations and constraints may be considered:
the storage battery can not be charged and discharged simultaneously
X(t)+Y(t)≤1；t＝1,...,24；XandY∈{0,1} (13)
Wess(t) and Wess(t-1) the energy storage capacity inside the battery at time t and time t-1, PchargeAnd PdischargeIs the charge and discharge rate allowed over a certain time. EtachargeAnd ηdischargeIs the charge-discharge efficiency.
And 4, step 4: and constructing a multi-objective optimization model according to the determined objective system and various constraint conditions.
Determining a multi-objective optimization model with the maximum wind power consumption and the minimum system cost;
the objective functions contained in the multi-objective optimization model should have consistency, or the maximum value or the minimum value should be taken at the same time, from the view point of the two determined quantitative indexes, the wind power consumption should take the maximum value, and the system cost should take the minimum value, so in order to bring the two indexes into the same objective function, simple initialization processing needs to be carried out, namely one of the indexes is inverted, and the inverted index has no physical significance, but in the mathematical sense, the consistency requirement of the multi-objective model is met. Therefore, a multi-objective optimization model is constructed as
In the formula: f (-E)W，CGH) Is an objective function; x is a decision vector consisting of optimization variables; h isj(x) Is an equality constraint function; gk(x) Is an inequality constraint function.
And 5: and solving the multi-objective optimization model based on the artificial honeycomb algorithm, and checking the feasibility of the solved result.
By understanding the basic principle of the artificial honeycomb algorithm and combining three basic components contained in the algorithm: the honey source, the employed bees and the non-employed bees and the functional roles of all the elements in the algorithm execution process are initialized and set for the multi-objective optimization model provided by the invention.
And (4) honey source: corresponding to a feasible solution to the optimization problem.
Employed bees: leading bees, the number of leading bees in the model generally corresponds to the honey source. The leading bees have a memory function, store the relevant information (distance and direction from the bee nest pair, abundance of nectar and the like) of the honey source searched by the leading bees, and share the information with other bees with a certain probability.
Non-hired bees: two kinds of scout bees are adopted, wherein one kind of scout bees searches nearby bee sources around the bee nest, and the number of scout bees in the bee colony accounts for about 5% -20% of the number of the whole bee colony; one is following bees, bees which are close to the honeycomb and wait for leading bees to share honey source information, and the bees observe the dance of the leading bees and select bees which are considered satisfactory by the bees to follow. The number of following bees and leading bees in the bee colony is equal.
From the above analysis, the conversion relationship between the types of bees is shown in fig. 3. A flow chart of a conventional manual honeycomb algorithm is shown in fig. 4.
When the artificial bee colony algorithm is used for solving the multi-objective optimization problem, due to the fact that a plurality of solutions which are not mutually dominant exist at the same time, an individual searches a new honey source through information exchange, the whole process is high in randomness, the artificial bee colony algorithm has strong global searching capability, but the local searching capability is poor, and the development efficiency is obviously reduced when the artificial bee colony algorithm is close to the optimal solution. Therefore, aiming at the short board existing in the standard artificial bee colony algorithm, optimization adjustment is respectively carried out in the initialization stage, the leading bee stage and the following bee stage. Fig. 5 shows a flow chart of an optimized and improved artificial bee colony algorithm, the number of the initialized population and the maximum storage number of the external archives are NP, the number of the leading bees and the following bees are NP/2, the maximum evaluation number FEAS, the maximum elimination number of the scout bees is Limit, and the maximum elimination number of the elite individuals of the external archives is elite.
Step 501: initializing and calculating individual fitness values through chaotic mirror images, selecting optimal NP individuals according to a domination relation, and selecting non-domination individuals to enter an external archive; the specific steps of the chaotic mirror initialization include: generating a population A by chaos initialization according to a formula (15), wherein the number of the population is NP; generating a population B from the population A through mirror image operation; and selecting NP better individuals as an initial population according to the fitness values of A and B.
viw＝xiw+riw(xiw-xkw) (15)
During initialization, assume the position of honey source i as xi＝(xi1,xi2,...,xid-1,xid) After the mirroring operation, the new mirror solution position miriCan be defined as miri＝(xid,xid-1,...,xi2,xi1). When a certain one-dimensional value xijWhen the upper and lower bounds are exceeded, new x is generated using regularization and de-regularization operationsij。
Step 502: updating the positions of the leading bees according to the following formula (15), and after updating, if the original honey sources are dominated, keeping the positions, and if the positions are not dominated, comparing the domination relations with individuals in an external archive to determine whether the positions are kept;
step 503: calculating the selection probability of the external elite individual according to the following formula (16), selecting whether to carry out deep development on the elite individual through probability, wherein the specific evolution formula is formula (17), the number of times of leading the elite individual after evolution is finished is reduced by 1, and a retention strategy which is the same as that of a leading bee is executed on the descendant of the elite individual;
wherein elitei.Crown is the crowding distance, elite, of the ith individual in the external archivei.Choice is the number of times the ith elite individual in the external archive has conducted the remainder of the evolution.
viw＝eliteiw+riw(eliteiw-xiw) (17)
Wherein r isiw∈[-(φm+1),+(φm+1)]
Step 504: when the offspring individuals generated by the leading bees and the following bees cannot be reserved, the elimination times are increased by 1; when the individual threshold Limit is reached, regenerating a new honey source to replace the old honey source location by equation (18);
ch1＝rand(0,1) (18)
chk+1＝sin(πchk)
wherein ch1Is a random number ranging between 0 and 1, k is an iteration counter, k 1, 2.
Step 505: and after each iteration is finished, maintaining the external file, and judging whether the maximum evaluation time FEAS is reached. If so, the loop is ended and the individuals in the external file are output as the final result, otherwise, the process goes to step 502.
At the early stage of evolution, due to honey source xiThe distance to the real Pareto front edge is far, so the convergence speed can be accelerated by adopting a larger search radius; when the honey source is evolved to the end, the distance between the honey source and the real Pareto front is close, and only a small search radius is needed to improve the search precision. Therefore, the reasonable selection of the search radius can significantly accelerate the convergence rate. The invention adopts the strategy that the search radius is changed along with the individual threshold value, namely the search radius is changed along with xiAnd the increase of Limit is decreased, the radius formula is shown as (19) and the mode that an elite individual guides population evolution is adopted in the following bee stage, and the bee position updating equation is shown as (17). Through the search strategy, the honey source can be quickly converged to a real front edge in the whole evolution process, and the local optimum can be skipped. In the last stage of evolution, the search precision can be effectively improved, and the solving quality is improved.
ri＝φm+cos(πxi·Limit/2Limit) (19)
Wherein: phi is amFor the base value of the search radius, xiLimit is the current honey source threshold, and Limit is the honey source development threshold.
When updating the external file, mutation operation is performed on the extreme solution to ensure that the extreme solution has been completely developed. And when the number of the stored solutions exceeds the scale of the external archive, calculating the congestion distance, and deleting the solution with the minimum congestion distance until the scale requirement is met. And taking the final external archive solution set as the final output. The variation factor determining the extreme solution of each generation is shown in equation (20).
Wherein cur is the current evaluation time, FEAS is the maximum evaluation time of the algorithm, and round is the rounding function.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. A virtual power plant optimized operation method considering wind power consumption comprises the following steps:
step 1: determining a preliminary configuration scheme of the virtual power plant by combining an actual research object;
step 2: determining a target system and a target function of the optimized operation of the virtual power plant by taking the wind power consumption and the system operation cost as indexes;
and step 3: determining equality constraint conditions and inequality constraint conditions reflecting safe and stable operation of the system, and constructing expressions of various constraint conditions;
and 4, step 4: constructing a multi-objective optimization model with the maximum wind power consumption and the minimum system operation cost according to the determined target system and various constraint conditions;
and 5: and solving the multi-target optimization model based on an artificial honeycomb algorithm, and checking the feasibility of the solved result.
2. The method of claim 1, wherein in step 2, a function expression representing wind power consumption electric quantity is as follows:
wherein T is the number of time periods in the scheduling period; n is a radical ofWThe number of wind power plants;
the active scheduling output of the wind power plant i in the time period t;
the function expression for embodying the system operation cost is as follows:
CWT(t),CPV(t),CFC(t),CMT(t) and Cj(t) the tender prices of a wind generating set, a photovoltaic generating set, a fuel cell set, a micro generating set and energy storage equipment in t hours are respectively; sGi(t) and SSj(t) cost price of the distributed power supply and the energy storage unit when starting or stopping respectively; pGrid(t) is the real power bought or sold from the grid during time period t, CGrid(t) grid electricity prices for time period t; n is a radical ofgAnd NsRepresenting the number of power sources and energy storage power sources, respectively; u shapeWT(t),UPV(t),UFC(t),UMT(t) and Uj(t) respectively representing the on/off state of each unit in a t time period of a certain day; Δ P (t) represents the difference between the original and the new loss of the feeder, CΔP(t) represents the cost price corresponding to Δ p (t); riAnd IiRespectively representing the resistance and the actual current of the ith branch.
3. The method for optimizing the operation of a virtual power plant in consideration of wind power consumption according to claim 2, wherein in the step 3,
the equality constraint condition of system operation is system power balance in any time period, and the expression is as follows:
the inequality constraint conditions of system operation are wind power generation limitation, photovoltaic power generation limitation, fuel cell limitation, micro steam turbine limitation, power grid limitation and charging and discharging limitation of storage equipment, and the expressions of various inequality constraint conditions are as follows:
wind power generation limitation during t period
PWTmin(t)≤PWT(t)≤PWTmax(t)；t＝1,...,T (6)
Photovoltaic power generation limitation at time t
PPVmin(t)≤PPV(t)≤PPVmax(t)；t＝1,...,T (7)
time t fuel cell limitation
PFCmin(t)≤PFC(t)≤PFCmax(t)；t＝1,...,T (8)
Limitation of micro steam turbine in t period
PMTmin(t)≤PMT(t)≤PMTmax(t)；t＝1,...,T (9)
time t grid limitation
PGridmin(t)≤PGrid(t)≤PGridmax(t)；t＝1,...,T (10)
Limitation of accumulator
PSjmin(t)≤PSj(t)≤PSjmax(t)；t＝1,...,T (11)
Since the charge-discharge rate of the storage device is limited at each time period, the following equations and constraints may be considered:
the storage battery can not be charged and discharged simultaneously
X(t)+Y(t)≤1；t＝1,...,24；XandY∈{0,1} (13)
Wess(t) and Wess(t-1) the energy storage capacity inside the battery at time t and time t-1, PchargeAnd PdischargeIs the allowable charge-discharge rate, eta, over a certain period of timechargeAnd ηdischargeIs the charge-discharge efficiency.
4. The method for optimizing operation of a virtual power plant in consideration of wind power consumption according to claim 3, wherein the multi-objective optimization model constructed in the step 4 is:
wherein f ═ E (-E)W，CGH) Is an objective function; x is a decision vector consisting of optimization variables; h isj(x) Is an equality constraint function; gk(x) Is an inequality constraint function.
5. The method for optimizing the operation of the virtual power plant considering the wind power consumption according to claim 4, wherein in the step 5, the number of the initialized population and the maximum number of the external archive storages are NP, the number of the leading bees and the following bees are NP/2, the maximum evaluation time FEAS, the maximum elimination time of the scout bees is Limit, the maximum elimination time of the external archive elite individuals is elite.
Step 501: initializing and calculating individual fitness values through chaotic mirror images, selecting optimal NP individuals according to a domination relation, and selecting non-domination individuals to enter an external archive; the chaotic mirror image initialization step is to generate a population A according to the chaotic initialization of the formula (15), wherein the number of the population is NP; generating a population B from the population A through mirror image operation; selecting NP better individuals as an initial population according to the fitness values of A and B;
viw＝xiw+riw(xiw-xkw) (15)
during initialization, assume the position of honey source i as xi＝(xi1,xi2,...,xid-1,xid) After the mirroring operation, the new mirror solution position miriCan be defined as miri＝(xid,xid-1,...,xi2,xi1) When a certain one-dimensional value xijWhen the upper and lower bounds are exceeded, new x is generated using regularization and de-regularization operationsij；
Step 502: updating the positions of the leading bees according to the formula (15), reserving the positions if the positions dominate the original honey sources after updating, and comparing the positions with individuals in an external archive to determine whether to reserve the positions if the positions do not dominate each other;
step 503: calculating the selection probability of the external elite individual according to the formula (16), selecting whether to carry out deep development on the elite individual through probability, wherein the evolution formula is the formula (17), the number of leading times of the elite individual after evolution is reduced by 1, and the retention strategy which is the same as that of the leading bee is executed on the descendant,
wherein eliteiCrown is the crowding distance of the ith individual in the external archive, eliteiChoice is the number of times the ith elite individual in the external archive leads to the remaining evolution;
viw＝eliteiw+riw(eliteiw-xiw) (17)
wherein r isiw∈[-(φm+1),+(φm+1)]；
Step 504: when the offspring individuals generated by the leading bees and the following bees cannot be reserved, the elimination times are increased by 1; when the individual threshold Limit is reached, a new honey source is regenerated by equation (18) to replace the old honey source location,
ch1＝rand(0,1) (18)
chk+1＝sin(πchk)
wherein ch1Is a random number ranging between 0,1, k is an iteration counter, k 1, 2.., D-1;
step 505: and after each iteration is finished, maintaining the external file, judging whether the maximum evaluation time FEAS is reached, if so, ending the circulation and outputting the individuals in the external file as a final result, otherwise, turning to the step 502.
6. The method of claim 5, wherein in step 502, the search radius is determined as a function of xiThe increase and decrease of Limit, the radius formula is shown as (19),
ri＝φm+cos(πxi·Limit/2Limit) (19)
wherein: phi is amFor the base value of the search radius, xiLimit is the current honey source threshold, and Limit is the honey source development threshold.
7. The method for optimizing operation of a virtual power plant in consideration of wind power consumption according to claim 5, wherein in the step 505, when the external archive is updated, mutation operation is performed on the extreme solution to ensure that the extreme solution has been fully developed; when the number of the storage solutions exceeds the scale of the external archive, calculating a congestion distance, deleting the solution with the minimum congestion distance until the scale requirement is met, and taking the final external archive solution set as final output; the variation factor for each extreme solution is determined as formula (20),
wherein cur is the current evaluation time, FEAS is the maximum evaluation time of the algorithm, and round is the rounding function.
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