CN111724259A - Energy and rotation standby market clearing method considering multiple uncertainties - Google Patents

Energy and rotation standby market clearing method considering multiple uncertainties Download PDF

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CN111724259A
CN111724259A CN202010555550.5A CN202010555550A CN111724259A CN 111724259 A CN111724259 A CN 111724259A CN 202010555550 A CN202010555550 A CN 202010555550A CN 111724259 A CN111724259 A CN 111724259A
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楼楠
何越
李慧勇
辛阔
王科
陈静鹏
张勇
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China Southern Power Grid Co Ltd
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Abstract

The invention discloses an energy and rotating standby market clearing method considering multiple uncertainties, which takes an energy and rotating standby market clearing model taking a demand response plan containing a conventional unit, a wind turbine unit and a load side as clearing resources as a research object, and establishes an energy and rotating standby market clearing unit combination model considering multiple uncertainties and taking the minimum system operation cost and the minimum system risk level as targets; and determining the optimal energy and rotating standby market clearing scheme by adopting a multi-target pareto intensity evolution algorithm and a fuzzy algorithm. The market clearing scheme determined by the method effectively reduces the risk brought by wind power integration in the system and reduces the operation risk level of the system.

Description

Energy and rotation standby market clearing method considering multiple uncertainties
Technical Field
The invention relates to an energy and rotation standby market clearing method considering multiple uncertainties, and belongs to the technical field of power dispatching.
Background
Wind energy is a clean renewable energy source that is not reduced by its own conversion and utilization, nor does it pose a serious pollution problem like fossil fuels. The wind energy is reasonably used, the use of fossil fuel is reduced, and the scheduling cost can be reduced. Wind energy has many advantages, but it is also uncertain and variable. This presents challenges to the operation and safety of the power system. The power system safety analysis must take into account these aspects of wind energy and the load prediction will always have some error due to uncertainty in customer demand. This undoubtedly increases the risk level of the system.
In a competitive power market, power generators provide power and spin reserves by offering, and consumers can consume power and provide reserves. The unit scheduling plan and the standby arrangement are reasonably selected in the scheduling process, so that the system cost can be reduced, and the risk level of the system can be reduced. And a corresponding demand response system is formulated to provide rotary standby for day-ahead market compensation of wind power and load randomness, so that the dispatching risk level can be further reduced.
In the past, the influence of the output of uncertain wind power on the total system cost and the compensation effect of a load side implementation demand response plan on wind power integration are considered, but the demand side standby quotation and the uncertainty cost of demand side users are not considered, and besides, an uncertainty model of the load is not clearly defined.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an energy and rotating standby market clearing method for considering multiple uncertainties, which takes an energy and rotating standby market clearing model comprising conventional units, wind turbines and demand response plans on load sides as clearing resources as a research object, and establishes a combined model for considering multiple uncertain energy and rotating standby market clearing units, wherein the combined model aims at the minimum system operation cost and the minimum system risk level.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an energy and rotating standby market clearing method that accounts for multiple uncertainties, comprising:
establishing a clearing mechanism target function which takes the demand response plans of the conventional generator set, the wind turbine generator set and the load side as the energy of clearing resources and the rotating standby market;
the objective function includes: a total operating cost minimization objective function and a minimization system risk objective function;
the total operating cost minimization objective function is:
Minmize
Figure BDA0002544176710000021
Figure BDA0002544176710000022
CSRi(PSRi)＝xi+yiPSRi；
Cwj(Pwj)＝djPwj；
Figure BDA0002544176710000023
Figure BDA0002544176710000024
Figure BDA0002544176710000025
Figure BDA0002544176710000026
Ck(Pshd,k)＝a'k+b'k(Pshd,k)+c'k(Pshd,k)2；
wherein ,CiIs the fuel cost function of the ith conventional unit, PGiIs the real-time output value of the ith conventional unit, NGNumber of conventional units, CSRiIs a rotating reserve cost function, P, provided by the ith conventional unitSRiIs a rotary standby provided by the ith conventional unit, NWIs the number of wind turbine generator sets, CwjIs the function of the cost for directly purchasing wind power by the jth wind turbine generator set, PwjIs the wind power planned output of the jth wind turbine generator, Cr,wjWind power overestimation cost of jth wind turbine generator, Cp,wjIs the wind power underestimation cost, P, of the jth wind turbinewj,avIs the wind power available to the jth wind turbine, NLIs a load, Cr,DkIs the cost, P, due to the overestimated load of the kth loadDkIs the planned load of the kth load, PDk,avIs the predicted load of the kth load, Cp,DkIs the underestimated penalty cost, C, for the kth loadkIs the demand response cost, P, of the kth loadshd,kIs the demand response output value of the kth load, ai，bi，ciIs three cost coefficients, x, in the conventional unit fuel costi，yiIs the cost factor of the rotational reserve cost of the conventional unit, djIs the direct cost coefficient, k, of the jth wind turbiner,jIs the spare cost coefficient, k, of the jth wind turbinep,jIs the penalty cost coefficient, P, of the jth wind turbinerjIs the rated value of the jth wind turbinewj,avIs the obtainable force value, k, of the jth wind turbiner,kIs the spare cost factor for the kth load demand,
Figure BDA0002544176710000031
is predicted to be loadedLimit, lower limit, a'k，b'k，c'kCost coefficient of demand response, kp,kIs the penalty cost coefficient for the kth load demand, fp(p) is the probability density function of the wind power output power, p is the wind turbine output power, fl(l) Is the probability density distribution function of the uncertain load, l is the uncertain load;
the minimized system risk objective function is:
Figure BDA0002544176710000032
Figure BDA0002544176710000033
wherein u is the system security level, W (P)D)minIs the lower limit of wind penetration, W (P)D)maxIs the upper limit of wind penetration;
solving the two objective functions by adopting a multi-objective pareto intensity evolution algorithm to obtain an optimal solution set;
and selecting the best compromise solution from the optimal solution set by adopting a fuzzy algorithm to serve as an energy and rotation standby market clearing scheme.
Further, the objective function should satisfy the following constraint conditions:
1A, node power balance constraint:
Figure BDA0002544176710000034
Figure BDA0002544176710000035
wherein ,Gij and BijForming a nodal admittance matrix, Yij＝Gij+jBijIs the (i, j) term, P, of the node admittance matrixDiIs the load connected to the main line i, n is the number of main lines in the system, ViIs the modulus of the voltage vector on the main line i,iis a main linei the phase angle of the voltage across;
1B, constraint of total rotation standby requirement:
Figure BDA0002544176710000041
wherein ,PG,largestThe power shortage caused by the accidental shutdown of the maximum-capacity generator is indicated;
1C, power generation constraint:
Figure BDA0002544176710000042
Figure BDA0002544176710000043
wherein ,Pwj,fIs the predicted wind output for the jth wind turbine,
Figure BDA0002544176710000044
is the lower output limit of the ith conventional unit,
Figure BDA0002544176710000045
is the output of the ith conventional unit at the last moment,
Figure BDA0002544176710000046
is the downward climbing value of the ith conventional unit,
Figure BDA0002544176710000047
is the wind power lower limit of the jth wind turbine,
Figure BDA0002544176710000048
the value of upward climbing of the ith conventional unit is shown;
1D, requirement constraint:
DkP≤PDk；
wherein, DkPis the lower limit of demand;
1E, unit rotation standby constraint:
Figure BDA0002544176710000049
wherein,
Figure BDA00025441767100000410
is the maximum spare capacity;
1F, standby constraint on the demand side:
0≤Pshd,k≤PDk- DkP；
1G, safety restraint:
0≤u≤1；
1H, and further needs to satisfy:
Vi min≤Vi≤Vi max；
Figure BDA0002544176710000051
Ti min≤Ti≤Ti max；
wherein S isijIs a flow of power or a flow of power,
Figure BDA0002544176710000052
is the transmission limit, T, of the line between the i and j busesiIs the position of the tap of the transformer, Ti min，Ti maxIs the minimum and maximum values of the transformer tap settings, Vi min，Vi maxAre the minimum and maximum values of the voltage die on the main line i.
Further, the probability density function of the wind power output power is as follows:
Figure BDA0002544176710000053
Figure BDA0002544176710000054
wherein v isiIs the cut-in wind speed, vrIs rated wind speed, voIs the cut-out wind speed, prIs rated wind turbine generator output power, h ═ vr/vi) -1 is an intermediate parameter, k is a shape parameter, c is a scale parameter.
Further, the probability density distribution function of the uncertainty load is:
Figure BDA0002544176710000055
wherein u isLIs the mean value of the uncertain load, σLIs the standard deviation of the uncertain load, l is the uncertain load.
Further, the selecting a best compromise solution from the optimal solution set by using a fuzzy algorithm includes:
calculating a membership function value of each objective function in the optimal solution set;
calculating a membership function value of a non-dominated solution based on the membership function value of the objective function; defining the solutions in the optimal solution set as non-dominant solutions, wherein each non-dominant solution comprises: the method comprises the following steps of outputting power in real time by a conventional unit, rotating standby provided by the conventional unit, planned output of wind power, planned load, demand response output, total operation cost of a system and safety level of the system;
and selecting the non-dominated solution with the maximum membership function value as the optimal compromise solution.
Further, the calculating a membership function value of each objective function in the optimal solution set includes:
Figure BDA0002544176710000061
wherein u (F)i) A membership function value representing an ith objective function, i 1,2obj，Fi maxAnd Fi minIs the maximum and minimum of the ith objective function in all non-dominated solutions, FiIs the value of the ith objective function, NobjIs the number of objective functions.
Further, the calculating a membership function value of a non-dominated solution based on the membership function value of the objective function includes:
Figure BDA0002544176710000062
wherein,
Figure BDA0002544176710000063
membership function value, u (F), for the kth non-dominated solutioni k) And K is the membership function value of the ith objective function of the kth non-dominated solution, and the number of the non-dominated solutions is K.
The invention has the beneficial effects that:
according to the method, an energy and rotary standby market clearing model which takes a demand response plan containing a conventional unit, a wind turbine and a load side as clearing resources is taken as a research object, a multiple uncertain energy and rotary standby market clearing unit combination model which takes the minimum system operation cost and the minimum system risk level as targets is established, and the standby and demand response standby of the conventional unit are determined based on the multiple uncertain energy and rotary standby market clearing model, so that the risk brought by wind power grid connection in the system is effectively reduced, and the operation risk level of the system is reduced.
Drawings
FIG. 1 is a schematic diagram of a market clearing mechanism for energy and spinning reserve provided by the present invention that accounts for multiple uncertainties;
FIG. 2 is a block diagram of the basic structure of the energy and spin reserve market clearing mechanism system provided by the present invention that accounts for multiple uncertainties.
FIG. 3 is a fuzzy linear representation of wind penetration safety level in the present invention.
FIG. 4 is a flowchart of the SPEA2+ algorithm.
FIG. 5 is a comparison graph of total cost and risk level considering wind randomness and load randomness in the embodiment of the invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic drawings that illustrate, by way of illustration only, the basic structure of the invention and, therefore, show only the components that are relevant to the invention.
The invention provides an energy and rotating standby market clearing method taking multiple uncertainties into account, referring to fig. 1, comprising:
firstly, preparing data: firstly, assuming that the wind speed obeys a Weibull probability density function, and then solving a corresponding probability density function of the wind power for a market clearing model; and modeling the demand distribution by using a standard probability density distribution function. The method comprises the following specific steps:
wind power randomness analysis:
and predicting the output power of the wind turbine generator according to the weather prediction. Wind energy and therefore the output of the wind turbine are uncertain. Generally, probability distribution curves are used to model the uncertainty of wind power. Assuming that the wind speed obeys a Weibull probability density function, for a wind turbine generator, given the input of the wind speed, the obtained output power is as follows:
Figure BDA0002544176710000071
wherein p is the wind turbine output power, viIs the cut-in wind speed, vrIs rated wind speed, voIs the cut-out wind speed, prIs the rated wind turbine generator output power; in the continuous range (v)i≤v≤vr) The probability density function of the wind power output power of (1) is:
Figure BDA0002544176710000072
wherein h is (v)r/vi) -1 is an intermediate parameter. In the weibull distribution function, k is the shape parameter and c is the scale parameter.
Load randomness analysis and calculation: the load demand of the system is uncertain at any time, the invention uses a standard probability density distribution function to model the demand distribution, and the probability density distribution function of the normal distribution of the uncertain load l is as follows:
Figure BDA0002544176710000073
wherein u isLIs the mean value of the uncertain load, σLIs the standard deviation of the uncertain load.
Secondly, establishing an objective function of unit combination operation cost and risk safety of an energy and rotary standby market clearing mechanism considering wind power and load uncertainty;
the first objective function is:
the total operation cost objective function and the constraint condition of the system are as follows:
Figure BDA0002544176710000081
wherein, CiIs a function of the fuel cost of the conventional unit, PGiIs a real-time output value of a conventional unit, NGNumber of conventional units, CSRiIs a rotating reserve cost function, P, provided by a conventional unitSRiIs a rotary reserve provided by a conventional unit, NWIs the number of wind turbine generator sets, CwjIs to directly purchase the wind power cost function, PwjPlanned wind power output, Cr,wjIs the overestimated cost of wind power, Cp,wjIs the wind power underestimated cost (penalty cost), Pwj,avIs available wind power, NLIs a load, Cr,DkIs the cost due to overestimation of the load, PDkIs the planned load, PDk,avIs the predicted load (given by the above load randomness analysis), Cp,DkIs the underestimated penalty cost of the load, CkIs the cost of demand response, Pshd,kIs the demand response output value.
The terms in formula (4) are explained as follows.
The first is the fuel cost of a conventional unit:
Figure BDA0002544176710000082
wherein, ai，bi，ciIs alwaysThree cost factors in the fuel cost of the set of specifications. Wherein, ai，bi，ciIs of a given value, PGiThe value is unknown.
The second term is the rotational standby cost of the conventional unit:
CSRi(PSRi)＝xi+yiPSRi(6)
wherein x isi，yiIs the cost coefficient of the rotating reserve cost of the conventional unit, the two values are given, PSRiThe value is unknown.
The third term is the direct cost paid for the wind power to the wind farm owner for the dispatch, expressed as a linear cost function:
Cwj(Pwj)＝djPwj(7)
wherein d isjIs the direct cost coefficient of wind power, PwjIs unknown.
The fourth term is the backup demand cost, which represents the cost due to the available wind power being lower than the scheduled wind power. The cost is related to the actual power generation of the wind turbine and the deficit value of the scheduled power generation, the power deficit value is determined by a distribution function, and the standby demand cost is given by:
Figure BDA0002544176710000091
wherein k isr,jIs the spare cost coefficient of the jth wind turbine.
The fifth item is penalty cost, which is related to the surplus value of the actual power generation and the scheduled power generation of the wind turbine, and the power surplus value is determined by a distribution function:
Figure BDA0002544176710000092
wherein k isp,jIs the penalty cost coefficient, P, of the jth wind turbinerjIs the rated value of the wind power, Pwj,avIs the value of the wind power which can be obtained and is the amount of the random change between 0 and rated wind power and can pass through the Weibull secretF of degree functionpAnd (p) indirectly calculating in a 0-rated wind power region by using integral theory.
The sixth term is the load reserve cost, which is derived from the concept of overestimation of the load:
Figure BDA0002544176710000093
wherein k isr,kIs the spare cost factor for the kth load demand,
Figure BDA0002544176710000094
is the lower limit of the predicted load, PDkIs an unknown value, PDk,avIs indirectly determined by the above load uncertainty analysis, where l is only a variable in the integral theory in the formula, representing the load
Figure BDA0002544176710000095
To PDkAnd (4) integrating.
The seventh term is the penalty cost for the load, which follows from the concept of underestimating the load:
Figure BDA0002544176710000096
wherein k isp,kIs the penalty cost coefficient for the kth load demand.
The eighth item is the cost of the load participating in the demand side reserve quote, expressed as:
Ck(Pshd,k)＝a'k+b'k(Pshd,k)+c'k(Pshd,k)2(12)
wherein, a'k，b'k，c'kCost coefficient of demand response, Pshd,kIs an unknown value.
The constraint that the first objective function needs to satisfy is as follows:
1A, node power balance constraint: the power balance constraints include active power balance and reactive power balance, i.e.:
Figure BDA0002544176710000101
Figure BDA0002544176710000102
wherein, Yij＝Gij+jBijIs the (i, j) term, P, of the node admittance matrixDiIs the load connected to the main line i, n is the number of main lines in the system, ViIs the modulus of the voltage vector on the main line i,iis the phase angle of the voltage on the main line i.
1B, constraint of total rotation standby requirement: the rotational reserve requirement is considered based on the protection system from the maximum generator set unexpected shutdown and uncertainty of wind power and load, the total rotational reserve required is TSRreq：
Figure BDA0002544176710000103
Wherein, PG,largestRefers to the power deficit caused by an unexpected shutdown of the maximum capacity generator.
TSR in contrast to deterministic backup requirementsreqSeems to be overestimated, but it is critical to system safety if the cost due to demand side reserve quotes, TSR, is not consideredreqWill be provided by a conventional unit as follows:
Figure BDA0002544176710000104
TSR if the cost of demand side reserve quote generation is consideredreqWill be provided by the conventional crew and demand side responses as follows:
Figure BDA0002544176710000105
1C, power generation constraint: the output power of each unit is limited by its respective minimum and maximum output power limits and by the climbing capacity, i.e.,
Figure BDA0002544176710000106
Figure BDA0002544176710000107
wherein, Pwj,fIs the predicted wind output of the jth wind turbine, which is derived from the predicted wind speed,
Figure BDA0002544176710000111
is the lower limit of the output force of the conventional unit,
Figure BDA0002544176710000112
is the output force of the conventional machine set at the last moment,
Figure BDA0002544176710000113
is the value of the downward climbing of the conventional unit,
Figure BDA0002544176710000114
is the lower limit of the wind power,
Figure BDA0002544176710000115
is the upward climbing value of the conventional unit.
1D, requirement constraint:
DkP≤PDk(20)
wherein, DkPis the lower limit of demand.
1E, unit rotation standby constraint: the possible spinning reserve capacity depends on the operating conditions of the unit,
Figure BDA0002544176710000116
wherein,
Figure BDA0002544176710000117
is the maximum spare capacity, defined as:
Figure BDA0002544176710000118
1F, standby constraint on the demand side: the spinning reserve provided by the kth load demand is:
0≤Pshd,k≤PDk- DkP(23)
the second objective function is: minimizing a system risk level;
a sudden drop in wind speed may cause a frequency oscillation which may cause the under-frequency relay to trip and eventually cause a power outage, the only solution being to use a conventional unit to limit the wind-electricity permeability. The present invention treats wind power as schedulable, and the linear reliability fuzzy membership function for wind penetration "u" is used to represent the system safety level, which can be mathematically expressed as:
Figure BDA0002544176710000119
in the formula, PwjIs the planned output of wind power, W (P)D)minIs the lower limit of wind penetration below which the system is deemed safe, W (P)D)maxIs the upper limit of wind penetration above which the system is considered unsafe, W (P)D)minAnd W (P)D)maxAll depending on the total demand in the power schedule.
It is clear from equation (28) that as the membership function value increases, the system becomes safer. On the other hand, as wind penetration in power scheduling continues to increase, the system becomes less and less secure. Therefore, the objective function is defined as risk minimization as follows:
Figure BDA0002544176710000121
the second objective function needs to satisfy the safety constraint:
according to the definition of the reliability fuzzy membership function, the value of u should be in the region of [0,1], i.e.:
0≤u≤1 (24)
furthermore, it is also necessary to satisfy:
Vi min≤Vi≤Vi max(25)
Figure BDA0002544176710000122
Ti min≤Ti≤Ti max(27)
wherein S isijIs a flow of power or a flow of power,
Figure BDA0002544176710000123
is the transmission limit, T, of the line between the i and j busesiIs the position of the tap of the transformer, Ti min，Ti maxIs the minimum, maximum, V, of the transformer tap settingsi min，Vi maxIs the minimum, maximum value of the voltage die on main line i.
The principle of the ideal multi-objective optimization process is to find multiple compromise optimal solutions and then select one of the solutions using a higher level approach. The high-strength pareto evolutionary algorithm is a multi-objective genetic algorithm that maintains an outer population at each generation to store all the acquired non-dominant solutions. At each generation, an external population is mixed with the current population, and all non-dominant solutions in the mixed population are adapted based on the number of solutions that dominate them, the applicability of the dominant solution being worse than the applicability of any non-dominant solution.
Brief description of SPEA2 +:
SPEA2+ is a new multi-target genetic algorithm. SPEA2+ is based on SPEA2 with the following three mechanisms:
1) matching selection, reflecting all good solutions stored in the archive;
2) neighborhood crossing, allowing crossing between individuals that are close to each other in the target space;
3) different solutions are stored in the target space and the design variable space, respectively.
The matching selection is to select a next generation search population from the archive population. Neighborhood intersections refer to intersections between individuals that are close to each other in the target space. See fig. 4 for SEPA2+ algorithm process as follows:
step 1: generating an initial population P0Oak target archive population OA0And designing a variable archive population VA0The child count k is set to 0.
Step 2: assessment of all individuals P Using the fitness assignment method of SPEA2k、OAkAnd VAkThe adaptive value of (a).
And step 3: pk、OAkAnd VAkAll non-dominant individuals in (a) are replicated to OAk+1And VAk+1. If OA is presentk+1And VAk+1Exceeds the archive size, archive truncation in the target space will apply to the OAk+1And archive truncation in variable space will apply to VAk+1The number of individuals is reduced. If OA is presentk+1Or VAk+1Is less than the archive size, then use the data from Pk、OAkAnd VAkHas good adaptability to fill OAk+1And VAk+1。
And 4, step 4: if the maximum algebra is exceeded or other termination conditions are met, the search process is stopped.
And 5: pk+1By copying them to the OAk+1Performing neighborhood crossing and mutation operations. The count k is incremented by 1 and the process returns to step 2.
After determining the pareto optimal solution set from SPEA2+, the decision maker selects the best compromise solution by a fuzzy method. Considering the inaccuracy of decision-makers' judgment, it is natural to think that a decision-maker may have fuzzy or inaccurate targets for each objective function. The fuzzy sets are defined by equations called membership functions, which represent the degree of membership of the fuzzy set using values between 0 and 1, a value of 0 indicating no membership in the set and a value of 1 indicating complete membership in the set. By considering each targetMinimum and maximum values of the function and the rate of increase of membership, the decision maker has to detect the membership function u (F) in a subjective manneri). The present invention assumes u (F)i) Is a strictly monotonically decreasing and continuous function defined as:
Figure BDA0002544176710000131
wherein, i is 1,2obj，Fi maxAnd Fi minIs the maximum and minimum of the ith objective function in all K non-dominated solutions, FiIs the value of i objective functions, NobjIs the number of objective functions. The non-dominant solution is the result of iteration of the SPEA2+ algorithm, and each non-dominant solution includes: the system comprises a conventional unit, a wind power planned output, a planned load, a demand response output, a total system operation cost and a system safety level. Fi maxAnd Fi minThe value of (a) is set subjectively by humans.
The value of the membership function indicates how well the non-dominant solution has met the goal (in the range of 0 to 1). Membership function values u (F) of all targetsi) The sum can be calculated to measure the condition of each solution in satisfying the objective function as follows:
Figure BDA0002544176710000141
function in equation (31)
Figure BDA0002544176710000142
Can be regarded as a membership function of the kth non-dominated solution, in the fuzzy set and expressed as fuzzy base priority of the non-dominated solution, the maximum degree of membership is obtained in the obtained fuzzy set
Figure BDA0002544176710000143
May be selected as the best solution or the solution with the highest cardinal prioritization, i.e.:
Figure BDA0002544176710000144
the basic structure of the market clearing mechanism system which is constructed by the invention and takes the multiple energy and the rotary standby into account is shown in figure 2, and comprises four parts: wind power plants, thermal power plants, customer and demand side responses. Among them, wind power plants and thermal power plants supply energy.
Examples
The relevant parameters of each wind power plant are shown in table 1, and the cost coefficients of the wind power plant and the load are assumed as follows: d11＝2.75＄/MW，d13＝3＄/MW，kr,j＝1＄/MW，kp,j＝5＄/MW，kr,k＝1＄/MW，kp,k＝5＄/MW。WG11，WG13Two wind farms of the embodiment, d11，d13The direct purchasing coefficient of the wind power corresponding to the two wind power plants is obtained.
TABLE 1 wind farm related parameters
Figure BDA0002544176710000145
The system risk level cannot be optimized independently due to the increase in total cost. Therefore, the present invention considers multiobjective optimization, where SPEA2+ is used to form the pareto optimal front, and then a blur-based approach is used to extract the optimal compromise solution from the compromise front.
The embodiment of the invention is analyzed by the following two models: in model one, the total cost minimization objective does not take into account the cost due to the demand side reserve price (i.e., the last term in equation (4) will not exist). In model two, the total cost minimization objective takes into account the cost of the demand side backup quote generation (all terms of equation (4) will appear).
Considering the uncertainty of wind power and the uncertainty of ± 5% of the load demand forecast, the optimal total cost of the model-optimal compromise obtained in table 2 is 1289.18$/h and the system risk level is 1.795. Here, the required spare amount is 52.2 mw, which is the sum of the spare emission of the thermal power generating unit (37.03 mw), the spare capacity required by the wind farm (12.78 mw), and the spare capacity required by the load (2.39 mw).
TABLE 2 model one taking into account uncertainty of wind power and uncertainty of + -5% of load demand forecast
Figure BDA0002544176710000151
Given the uncertainty of wind power and the uncertainty of ± 10% of the load demand forecast, table 3 also shows the model-the planned generation, the reserve and the objective function values that optimize both the total cost and the system risk level. The overall cost of the best compromise solution obtained using SPEA2+ is 1299.5$/h, and the system risk level is 2.494.
TABLE 3 model one taking into account uncertainty of wind power and uncertainty of + -10% of load demand forecast
Figure BDA0002544176710000152
For model two, taking into account the uncertainty of wind power and the uncertainty of ± 5% of the load demand forecast, the total cost of the resulting optimal compromise solution is 3160.054$/h and the system risk level is 2.377 for the planned power generation, backup and objective function values shown in table 4. In model two, the required spare capacity is 86.08MW, provided by a conventional unit (32.88 megawatts) and a demand response plan (53.20 megawatts). Table 4 also considers the optimal solution for wind power uncertainty and load demand forecast uncertainty of + -10%.
TABLE 4 model II taking into account uncertainty of wind power and uncertainty of + -5% and + -10% of load demand forecast
Figure BDA0002544176710000153
Figure BDA0002544176710000161
It is clear from case analysis of the two models that as the uncertainty of wind power and load predictions increases, the total cost and system risk levels increase, and the increase in uncertainty levels also results in an increase in the total spare and the cost of the spare. This is because a compromise solution is required, necessarily considering both the cost minimization and risk minimization objectives. The solution thus found is more cost intensive than the cost minimization goal alone. A comparison of the total cost and risk level considering wind randomness and load randomness is shown in fig. 5. Therefore, the invention reduces the risk level of the system as much as possible while keeping the cost low. Compared with the single scheduling only considering low cost, the invention ensures the safety of the system and achieves the compromise of cost minimization and risk minimization.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (7)

1. An energy and spin reserve market clearing method that accounts for multiple uncertainties, comprising:
establishing a clearing mechanism target function which takes the demand response plans of the conventional generator set, the wind turbine generator set and the load side as the energy of clearing resources and the rotating standby market;
the objective function includes: a total operating cost minimization objective function and a minimization system risk objective function;
the total operating cost minimization objective function is:
Minmize
Figure FDA0002544176700000011
Figure FDA0002544176700000012
CSRi(PSRi)＝xi+yiPSRi；
Cwj(Pwj)＝djPwj；
Figure FDA0002544176700000013
Figure FDA0002544176700000014
Figure FDA0002544176700000015
Figure FDA0002544176700000016
Ck(Pshd,k)＝a'k+b'k(Pshd,k)+c'k(Pshd,k)2；
wherein, CiIs the fuel cost function of the ith conventional unit, PGiIs the real-time output value of the ith conventional unit, NGNumber of conventional units, CSRiIs a rotating reserve cost function, P, provided by the ith conventional unitSRiIs a rotary standby provided by the ith conventional unit, NWIs the number of wind turbine generator sets, CwjIs the function of the cost for directly purchasing wind power by the jth wind turbine generator set, PwjIs the wind power planned output of the jth wind turbine generator, Cr,wjWind power overestimation cost of jth wind turbine generator, Cp,wjIs the wind power underestimation cost, P, of the jth wind turbinewj,avIs the wind power available to the jth wind turbine, NLIs a load, Cr,DkIs the cost, P, due to the overestimated load of the kth loadDkIs the planned load of the kth load, PDk,avIs the predicted load of the kth load, Cp,DkIs underestimation of the k-th loadPenalty cost, CkIs the demand response cost, P, of the kth loadshd,kIs the demand response output value of the kth load, ai，bi，ciIs three cost coefficients, x, in the conventional unit fuel costi，yiIs the cost factor of the rotational reserve cost of the conventional unit, djIs the direct cost coefficient, k, of the jth wind turbiner,jIs the spare cost coefficient, k, of the jth wind turbinep,jIs the penalty cost coefficient, P, of the jth wind turbinerjIs the rated value of the jth wind turbinewj,avIs the obtainable force value, k, of the jth wind turbiner,kIs the spare cost factor for the kth load demand,
Figure FDA0002544176700000021
is the upper limit or lower limit of the predicted load, a'k，b'k，c'kCost coefficient of demand response, kp,kIs the penalty cost coefficient for the kth load demand, fp(p) is the probability density function of the wind power output power, p is the wind turbine output power, fl(l) Is the probability density distribution function of the uncertain load, l is the uncertain load;
the minimized system risk objective function is:
Figure FDA0002544176700000022
Figure FDA0002544176700000023
wherein u is the system security level, W (P)D)minIs the lower limit of wind penetration, W (P)D)maxIs the upper limit of wind penetration;
solving the two objective functions by adopting a multi-objective pareto intensity evolution algorithm to obtain an optimal solution set;
and selecting the best compromise solution from the optimal solution set by adopting a fuzzy algorithm to serve as an energy and rotation standby market clearing scheme.
2. The energy and rotating standby market clearing method taking into account multiple uncertainties according to claim 1, wherein said objective function satisfies the following constraints:
1A, node power balance constraint:
Figure FDA0002544176700000031
Figure FDA0002544176700000032
wherein G isijAnd BijForming a nodal admittance matrix, Yij＝Gij+jBijIs the (i, j) term, P, of the node admittance matrixDiIs the load connected to the main line i, n is the number of main lines in the system, ViIs the modulus of the voltage vector on the main line i,iis the voltage phase angle on main line i;
1B, constraint of total rotation standby requirement:
Figure FDA0002544176700000033
wherein, PG,largestThe power shortage caused by the accidental shutdown of the maximum-capacity generator is indicated;
1C, power generation constraint:
Figure FDA0002544176700000034
Figure FDA00025441767000000312
wherein, Pwj,fIs the predicted wind output for the jth wind turbine,
Figure FDA0002544176700000035
is the lower output limit of the ith conventional unit,
Figure FDA0002544176700000036
is the output of the ith conventional unit at the last moment,
Figure FDA0002544176700000037
is the downward climbing value of the ith conventional unit,
Figure FDA0002544176700000038
is the wind power lower limit of the jth wind turbine,
Figure FDA0002544176700000039
the value of upward climbing of the ith conventional unit is shown;
1D, requirement constraint:
DkP≤PDk；
wherein, DkPis the lower limit of demand;
1E, unit rotation standby constraint:
Figure FDA00025441767000000310
wherein,
Figure FDA00025441767000000311
is the maximum spare capacity;
1F, standby constraint on the demand side:
0≤Pshd,k≤PDk- DkP；
1G, safety restraint:
0≤u≤1；
1H, and further needs to satisfy:
Vi min≤Vi≤Vi max；
|Sij|≤Sij max；
Ti min≤Ti≤Ti max；
wherein S isijIs a flow of power or a flow of power,
Figure FDA0002544176700000041
is the transmission limit, T, of the line between the i and j busesiIs the position of the tap of the transformer, Ti min，Ti maxIs the minimum and maximum values of the transformer tap settings, Vi min，Vi maxAre the minimum and maximum values of the voltage die on the main line i.
3. The energy and rotating standby market clearing method taking into account multiple uncertainties according to claim 2, wherein the probability density function of the wind power output is:
Figure FDA0002544176700000042
Figure FDA0002544176700000043
wherein v isiIs the cut-in wind speed, vrIs rated wind speed, voIs the cut-out wind speed, prIs rated wind turbine generator output power, h ═ vr/vi) -1 is an intermediate parameter, k is a shape parameter, c is a scale parameter.
4. The energy and rotating standby market clearing method taking into account multiple uncertainties of claim 2, wherein the probability density distribution function of said uncertainty load is:
Figure FDA0002544176700000044
wherein u isLIs the mean value of the uncertain load, σLIs the standard deviation of the uncertain load,/Is the uncertainty load.
5. The energy and rotating standby market clearing method taking into account multiple uncertainties according to claim 1, wherein said selecting the best compromise solution from said optimal solution set using a fuzzy algorithm comprises:
calculating a membership function value of each objective function in the optimal solution set;
calculating a membership function value of a non-dominated solution based on the membership function value of the objective function; defining the solutions in the optimal solution set as non-dominant solutions, wherein each non-dominant solution comprises: the method comprises the following steps of outputting power in real time by a conventional unit, rotating standby provided by the conventional unit, planned output of wind power, planned load, demand response output, total operation cost of a system and safety level of the system;
and selecting the non-dominated solution with the maximum membership function value as the optimal compromise solution.
6. The method of claim 5, wherein said computing membership function values for each objective function in the optimal set of solutions comprises:
Figure FDA0002544176700000051
wherein u (F)i) A membership function value representing an ith objective function, i 1,2obj，Fi maxAnd Fi minIs the maximum and minimum of the ith objective function in all non-dominated solutions, FiIs the value of the ith objective function, NobjIs the number of objective functions.
7. The energy and rotating standby market clearing method of claim 6, wherein said calculating membership function values for non-dominated solutions based on membership function values for said objective function comprises:
Figure FDA0002544176700000052
wherein,
Figure FDA0002544176700000053
membership function value, u (F), for the kth non-dominated solutioni k) And K is the membership function value of the ith objective function of the kth non-dominated solution, and the number of the non-dominated solutions is K.
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