CN111724259B - Energy and rotary reserve market clearing method considering multiple uncertainties - Google Patents

Energy and rotary reserve market clearing method considering multiple uncertainties Download PDF

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CN111724259B
CN111724259B CN202010555550.5A CN202010555550A CN111724259B CN 111724259 B CN111724259 B CN 111724259B CN 202010555550 A CN202010555550 A CN 202010555550A CN 111724259 B CN111724259 B CN 111724259B
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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 a multi-uncertainty-based energy and rotary reserve market clearing method, which takes an energy and rotary reserve market clearing model which contains a conventional unit, a wind turbine and a load side and is designed as clearing resources as a research object, and establishes a multi-uncertainty-based energy and rotary reserve market clearing unit combination model which aims at minimum system running cost and minimum system risk level; and determining the optimal energy and rotary spare market clearing scheme by adopting a multi-target pareto strength evolution algorithm and a fuzzy algorithm. The market clearing scheme determined by the method effectively reduces the risk brought by wind power grid connection in the system and reduces the running risk level of the system.

Description

Energy and rotary reserve market clearing method considering multiple uncertainties
Technical Field
The invention relates to an energy and rotary reserve market clearing method considering multiple uncertainties, and belongs to the technical field of power dispatching.
Background
Wind energy is a clean renewable energy source, which does not decrease with its own conversion and utilization, nor does it cause serious pollution problems like fossil fuels. The wind energy is reasonably used, the use of fossil fuel is reduced, and the dispatching cost can be reduced. Wind energy has many advantages, but it is also uncertain and variable. This presents challenges for the operation and safety of the power system. The power system safety analysis must take into account these aspects of wind energy and load prediction will always have some errors due to uncertainty in the user's demand. This, in turn, clearly increases the risk level of the system.
In a competitive power market, a generator provides electricity and rotational reserve by quotation, and a user can consume the electricity and provide reserve. And the unit scheduling plan and the standby scheduling are reasonably selected in the scheduling, so that the system cost can be reduced, and the dangerous level of the system can be reduced. The rotary reserve is provided by formulating a corresponding demand response system and used for compensating the randomness of wind power and load in the market in the future, so that the scheduling dangerous level can be further reduced.
In the prior art, the influence of the output of uncertain wind power on the total cost of the system and the compensation effect of a load side implementation demand response plan on wind power grid connection are considered, but the standby quotation of the demand side and the uncertainty cost of a user on the demand side 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 a multi-uncertainty-based energy and rotary spare market clearing method, which takes an energy and rotary spare market clearing model which contains conventional units, wind turbines and load side demand response schemes as clearing resources as a research object, and establishes a multi-uncertainty-based energy and rotary spare market clearing unit combination model which aims at minimum system running cost and minimum system risk level.
The technical scheme adopted for solving the technical problems is as follows:
a method of energy and rotational reserve market clearing accounting for multiple uncertainties, comprising:
establishing a target function of a clearing mechanism of energy and rotary spare markets, which is designed by a conventional unit, a wind turbine and a demand response of a load side;
the objective function includes: the overall running cost minimizes the objective function and minimizes the system risk objective function;
the total running cost minimization objective function is:
Minmize
C SRi (P SRi )＝x i +y i P SRi ；
C wj (P wj )＝d j P wj ；
C k (P shd,k )＝a' k +b' k (P shd,k )+c' k (P shd,k ) 2 ；
wherein ,Ci Is the fuel cost function of the ith conventional unit, P Gi Is the ith conventional unitReal-time output value, N G Is the number of the conventional units, C SRi Is a rotary standby cost function provided by the ith conventional unit, P SRi Is the rotation reserve provided by the ith conventional unit, N W Is the number of wind turbine generators, C wj Is the cost function of directly purchasing wind power by the jth wind turbine, P wj The planned wind power output of the jth wind turbine generator system C r,wj Is the wind power overestimation cost of the j-th wind turbine generator, C p,wj Is the wind power underestimation cost of the j-th wind turbine generator set, P wj,av Wind power available for the jth wind turbine, N L Is the load, C r,Dk Is the cost caused by overestimated load of the kth load, P Dk Is the planned load of the kth load, P Dk,av Is the load predicted by the kth load, C p,Dk Is the underestimated penalty cost for the kth load, C k Is the demand response cost of the kth load, P shd,k Is the demand response output value of the kth load, a i ，b i ，c i Is three cost coefficients in the conventional unit fuel cost, x i ，y i Is the cost coefficient of the rotary standby cost of the conventional unit, d j Is the direct cost coefficient, k of the jth wind turbine generator system r,j Is the standby cost coefficient, k of the jth wind turbine generator system p,j Is punishment cost coefficient of the jth wind turbine, P rj Is the rated value of the j-th wind turbine generator, P wj,av Is the available output value k of the jth wind turbine r,k Is the backup cost factor for the kth load demand,is the upper and lower limits of the predicted load, a' k ，b' k ，c' k Demand response cost coefficient, k p,k Is the penalty cost coefficient for the kth load demand, f p (p) is a probability density function of wind power output power, p is wind turbine generator output power, f l (l) A probability density distribution function of an uncertainty load, l is the uncertainty load;
the minimum system risk objective function is:
where u is the security level of the system, W (P D ) min Is the lower limit of wind power penetration, W (P D ) max Is the upper limit of wind power penetration;
solving the two objective functions by adopting a multi-objective pareto strength evolution algorithm to obtain an optimal solution set;
and selecting the best compromise solution from the optimal solution set by adopting a fuzzy algorithm as an energy and rotation standby market clearing scheme.
Further, the objective function needs to satisfy the following constraint conditions:
1A, node power balancing constraint:
wherein ,Gij and Bij Forming a node admittance matrix, Y ij ＝G ij +jB ij Is the (i, j) term of the node admittance matrix, P Di Is the load connected to the main line i, n is the main line number in the system, V i Is the modulus, delta, of the voltage vector on main line i i Is the voltage phase angle on main line i;
1B, total rotational standby demand constraint:
wherein ,PG,largest Hair with maximum capacityPower shortage caused by unexpected stop of the motor;
1C, power generation constraint:
wherein ,Pwj,f Is the predicted wind power output of the j-th wind turbine,is the lower output limit of the ith conventional unit,is the force of the ith conventional unit at the moment, < >>Is the i-th conventional unit climbing down value,/->Is the wind power lower limit of the jth wind power generation set, </i >>Is the upward climbing value of the ith conventional unit;
1D, demand constraint:
Dk P≤P Dk ；
wherein, Dk Pis the lower limit of demand;
1E, unit rotation standby constraint:
wherein,is the maximum spare capacity;
1F, demand side standby constraint:
0≤P shd,k ≤P Dk - Dk P；
1G, safety constraint:
0≤u≤1；
1H, the following requirements are satisfied:
V i min ≤V i ≤V i max ；
T i min ≤T i ≤T i max ；
wherein S is ij Is the power flow of the power flow,is the transmission limit of the line between the i and j buses, T i Is the transformer tap position, T i min ，T i max Is the minimum and maximum value of the transformer tap setting, V i min ，V i max Is the minimum and maximum of the voltage die on the main line i.
Further, the probability density function of the wind power output power is as follows:
wherein v is i Is cut-in wind speed v r Is rated wind speed v o Is the cut-out wind speed, p r Is rated output power of the wind turbine generator, and h= (v) r /v i ) -1 is an intermediate parameter, k is a shape parameter, and c is a scale parameter.
Further, the probability density distribution function of the uncertainty load is:
wherein u is L Is the average value of uncertain load, sigma L Is the standard deviation of the uncertainty load, and l is the uncertainty load.
Further, the selecting the best compromise solution from the best solution set by using a fuzzy algorithm includes:
calculating a membership function value of each objective function in the optimal solution set;
calculating membership function values of non-dominant solutions based on the membership function values of the objective function; defining the solutions in the optimal solution set as non-dominant solutions, wherein each non-dominant solution comprises: the conventional unit outputs force in real time, the rotation reserve provided by the conventional unit, the wind power planned output force, the planned load, the demand response output force, the total running cost of the system and the safety level of the system;
and selecting the non-dominant solution with the largest membership function value as the optimal compromise solution.
Further, the calculating the membership function value of each objective function in the optimal solution set includes:
wherein u (F) i ) Membership function value representing the i-th objective function, i=1, 2,.. obj ，F i max And F i min Is the maximum and minimum of the ith objective function in all non-dominant solutions, F i Is the value of the ith objective function, N obj Is the number of objective functions.
Further, the calculating the membership function value of the non-dominant solution based on the membership function value of the objective function includes:
wherein,membership function value for the kth non-dominant solution, u (F i k ) Membership function value of the ith objective function which is the kth non-dominant solution, K is the number of non-dominant solutions.
The beneficial effects of the invention are as follows:
the invention takes the energy and rotary reserve market clearing model which contains conventional units, wind turbines and load side demand response plans as clearing resources as a research object, establishes a combined model which takes the minimum system running cost and the minimum system risk level as targets and takes multiple uncertain energy and rotary reserve market clearing units, determines the conventional units reserve and demand response reserve based on the combined model, effectively reduces the risk brought by wind power grid connection in the system and reduces the system running risk level.
Drawings
FIG. 1 is a schematic diagram of a market clearing mechanism for accounting for multiple uncertainty energy and rotational spares provided by the present invention;
FIG. 2 is a block diagram of the basic architecture of the system of the market clearing mechanism accounting for multiple uncertainty energy and rotational spares provided by the present invention.
Figure 3 is a fuzzy linear representation of the stroke electroosmotic safety rating of the present invention.
FIG. 4 is a SPEA2+ algorithm flow chart.
FIG. 5 is a graph comparing total cost and risk levels considering wind power randomness and load randomness in an embodiment of the present invention.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic illustrations, illustrating the basic structure of the invention only by way of illustration, and thus showing only the structures that are relevant to the invention.
The present invention provides a method of energy and rotational reserve market clearing accounting for multiple uncertainties, see fig. 1, comprising:
1. data preparation: firstly, assuming that the wind speed obeys a Weibull probability density function, and then obtaining a probability density function of corresponding wind power to be used for a market clearing model; and modeling the demand distribution by using a standard probability density distribution function. The method comprises the following steps:
wind power randomness analysis:
and predicting the output power of the wind turbine according to weather prediction. Wind energy is uncertain, and therefore the output of a wind turbine is also uncertain. Typically, probability distribution curves are used to model wind power uncertainty. Assuming that the wind speed obeys the Weibull probability density function, for a wind turbine generator, given wind speed input, the obtained output power is:
wherein p is the output power of the wind turbine generator, v i Is cut-in wind speed v r Is rated wind speed v o Is the cut-out wind speed, p r Is rated output power of the wind turbine generator; within a continuous range (v) i ≤v≤v r ) The probability density function of the wind power output power is:
wherein h= (v) r /v i ) -1 is an intermediate parameter. In the weibull distribution function, k is a shape parameter and c is a scale parameter.
Load randomness analysis calculation: the load demand of the system is uncertain at any time, the invention models the demand distribution using a standard probability density distribution function, the probability density distribution function of the normal distribution of the uncertainty load l is:
wherein u is L Is the average value of uncertain load, sigma L Is the standard deviation of the uncertainty load.
2. Establishing a set combined operation cost and risk safety objective function of an energy and rotary reserve market clearing mechanism considering wind power and load uncertainty;
the first objective function is:
the total running cost objective function of the system and constraint conditions:
wherein C is i Is the fuel cost function of the conventional unit, P Gi Is the real-time output value of the conventional unit, N G Is the number of the conventional units, C SRi Is a rotary standby cost function provided by a conventional unit, P SRi Is provided by a conventional unit for rotation, N W Is the number of wind turbine generators, C wj Is directly purchasing wind power cost function, P wj Wind power plan output, C r,wj Is the overestimated cost of wind power, C p,wj Is wind power underestimation cost (punishment cost), P wj,av Is available wind power, N L Is the load, C r,Dk Is the cost caused by overestimated load, P Dk Is the planned load, P Dk,av Is the predicted load (given by the above load randomness analysis), C p,Dk Is the load underestimated penalty cost, C k Is the cost of demand response, P shd,k Is the demand response force value.
Each term in the formula (4) is explained as follows.
The first term is the fuel cost of the conventional unit:
wherein a is i ，b i ，c i Is a conventional unitThree cost factors in fuel cost. Wherein a is i ，b i ，c i Is given by a given value, P Gi The value is unknown.
The second term is the rotational back-up cost of the conventional unit:
C SRi (P SRi )＝x i +y i P SRi (6)
wherein x is i ，y i Is a cost coefficient of the rotation standby cost of the conventional unit, and the two values are given by P SRi The value is unknown.
The third term is the direct cost paid to the wind farm owner for the scheduled wind power, expressed in terms of a linear cost function:
C wj (P wj )＝d j P wj (7)
wherein d j Is the direct cost coefficient of wind power, P wj Is unknown.
The fourth term is the standby demand cost, which represents the cost due to the available wind power being lower than the scheduled wind power. The cost and the actual power generation of the fan are related to the deficiency value of the scheduled power generation, the power deficiency value is determined by a distribution function, and the standby demand cost is given by the following formula:
wherein k is r,j Is the standby cost coefficient of the jth wind turbine generator.
The fifth term is penalty costs, which are related to the actual power generation of the fan in relation to the surplus of the scheduled power generation, the power surplus being determined by the distribution function:
wherein k is p,j Is punishment cost coefficient of the jth wind turbine, P rj Is the rated value of wind power, P wj,av Is the available output value of wind power, is the wind power in the interval 0-rated wind powerThe amount of machine variation, which can be determined by f of the Weibull density function p (p) in the region of 0 to rated wind power, indirectly using an integrator.
The sixth term is the load backup cost, which is derived from the overestimated concept of load:
wherein k is r,k Is the backup cost factor for the kth load demand,is the lower limit of the predicted load, P Dk Is an unknown value, P Dk,av Is indirectly determined by the above-mentioned load uncertainty analysis, i is only one variable in the integral of the formula, representing the load from +.>To P Dk And (5) integrating.
The seventh term is the penalty cost for the load, derived from the concept of underestimating the load:
wherein k is p,k Is the penalty cost factor for the kth load demand.
The eighth item is the cost incurred by the load participating in the demand-side reserve price quote, expressed as:
C k (P shd,k )＝a' k +b' k (P shd,k )+c' k (P shd,k ) 2 (12)
wherein a' k ，b' k ，c' k Demand response cost coefficient, P shd,k Is an unknown value.
The constraints to be met by the first objective function are as follows:
1A, node power balancing constraint: the power balance constraints include active power balance and reactive power balance, i.e.:
wherein Y is ij ＝G ij +jB ij Is the (i, j) term of the node admittance matrix, P Di Is the load connected to the main line i, n is the main line number in the system, V i Is the modulus, delta, of the voltage vector on main line i i Is the phase angle of the voltage on the main line i.
1B, total rotational standby demand constraint: the rotational reserve requirement is based on protecting the system from unexpected outages of the maximum generator set and wind power and load uncertainties, the total rotational reserve required being TSR req ：
Wherein P is G,largest Refers to the power shortage caused by the accidental shutdown of the maximum capacity generator.
Compared to deterministic standby requirements, TSR req Seems to be overestimated, but it is critical to system safety if the costs due to demand side reserve offers are not taken into account, TSR req Will be provided by a conventional unit as follows:
if the cost of the demand side reserve quote is considered, TSR req Will be provided by the conventional crew and demand side response as follows:
1C, power generation constraint: the output power of each unit is limited by its respective minimum and maximum output power limits, i.e.,
wherein P is wj,f Is the predicted wind power output of the j-th wind turbine, which is derived from the predicted wind speed,is the lower limit of the output force of the conventional unit, +.>Is the output of the conventional machine set at the moment +.>Is the downward climbing value of the conventional unit, +.>Is wind power lower limit, & lt & gt>Is the upward climbing value of the conventional unit.
1D, demand constraint:
Dk P≤P Dk (20)
wherein, Dk Pis the lower limit of demand.
1E, unit rotation standby constraint: the possible rotational reserve capacity depends on the operating state of the unit,
wherein,is the maximum spare capacity, defined as:
1F, demand side standby constraint: the rotational reserve provided by the kth load demand is:
0≤P shd,k ≤P Dk - Dk P (23)
the second objective function is: minimizing the risk level of the system;
sudden drops in wind speed may cause frequency oscillations, which may cause the under-frequency relay to trip and eventually power failure, the only solution being to limit wind power penetration using conventional units. 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 security level, as shown in FIG. 3, which can be expressed mathematically as:
wherein P is wj Is the planned output of wind power, W (P) D ) min Is the lower limit of wind penetration below which the system is considered safe, W (P D ) max Is the upper limit of wind penetration beyond which the system is considered unsafe, W (P D ) min And W (P) D ) max Depending on the overall requirements in the power schedule.
It is clear from equation (28) that as membership function values increase, the system becomes safer. On the other hand, as wind penetration in power dispatching continues to increase, the system becomes less and less secure. Thus, the objective function is defined as risk minimization as follows:
the second objective function is to satisfy the security constraint:
the value of u should be in the region of [0,1], i.e.:
0≤u≤1 (24)
in addition, the following needs to be satisfied:
V i min ≤V i ≤V i max (25)
T i min ≤T i ≤T i max (27)
wherein S is ij Is the power flow of the power flow,is the transmission limit of the line between the i and j buses, T i Is the transformer tap position, T i min ，T i max Is the minimum, maximum, V of the transformer tap arrangement i min ，V i max Is the minimum value and the maximum value of the voltage die on the 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 external population at each generation for storing all the non-explicit solutions obtained. At each generation, the outer population is mixed with the current population, and all non-dominant solutions in the mixed population are given an adaptability based on the number of solutions that are dominant in them, with the applicability of the dominant solution being worse than that of any non-dominant solution.
Brief description of SPEA2+:
SPEA2+ is a new multi-target genetic algorithm. SPEA2+ is an addition of three mechanisms to SPEA 2:
1) Matching selection, reflecting all good solutions saved in the archive;
2) Neighborhood crossing, allowing crossing between individuals that are close to each other within 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 among archive populations. Neighborhood intersections refer to intersections between individuals that are close to each other in the target space. The sepa2+ algorithm process is described in fig. 4 as follows:
step 1: generating an initial population P 0 Empty target archive population OA 0 And design variable archive population VA 0 The child count k=0 is set.
Step 2: all individuals P were evaluated using the fitness assignment method of SPEA2 k 、OA k And VA (VA) k Is used for the adaptation value of (a).
Step 3: p (P) k 、OA k And VA (VA) k All non-dominant individuals in (a) are replicated to OA k+1 And VA (VA) k+1 . If OA k+1 And VA (VA) k+1 If the number of individuals exceeds the archive size, then the archive truncation in the target space will apply to OA k+1 Is in the variable space, and archive truncation in the variable space will be applied to VA k+1 The number of individuals is reduced. If OA k+1 Or VA k+1 The number of individuals from P is less than the archive size k 、OA k And VA (VA) k Is well adapted to fill OA in individuals with good adaptability k+1 And VA (VA) k+1 。
Step 4: if the maximum algebra is exceeded or other termination conditions are met, the search process is stopped.
Step 5: p (P) k+1 By copying them to OA k+1 And (3) performing neighborhood crossing and mutation operations. The count k is incremented by 1 and the process returns to step 2.
At slave SPAfter determining the pareto optimal solution set in the EA < 2+ >, the decision maker selects the optimal compromise solution through a fuzzy method. In view of the inaccuracy of decision maker judgment, it is naturally believed that decision maker may have a fuzzy or inaccurate goal for each objective function. Fuzzy sets are defined by equations called membership functions that use values between 0 and 1 to represent membership of fuzzy sets, with a value of 0 representing non-belonging to a set and a value of 1 representing complete belonging to a set. By considering the minimum and maximum values of each objective function and the rate of increase of member satisfaction, the decision maker must subjectively detect the membership function u (F i ). The present invention assumes u (F i ) Is a strictly monotonically decreasing and continuous function defined as:
wherein i=1, 2,.. obj ，F i max And F i min Is the maximum and minimum of the ith objective function in all K non-dominant solutions, F i Is the value of i objective functions, N obj Is 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 conventional unit outputs force in real time, the rotary reserve provided by the conventional unit, the wind power planned output force, the planned load, the demand response output force, the total running cost of the system and the safety level of the system. F (F) i max And F i min The values of (2) are set manually and subjectively.
The value of the membership function indicates the degree to which the non-dominant solution has met the target (in the range of 0 to 1). Membership function values u (F) of all targets i ) The sum may be calculated to measure the condition of each solution in terms of satisfying the objective function as follows:
function in equation (31)Membership functions that can be regarded as the kth non-dominant solution, fuzzy radix priorities in fuzzy sets and expressed as non-dominant solutions, maximum membership +.>The solution of (2) may be selected as the best solution or as the solution with the highest cardinality prioritization, namely:
the basic structure of the market clearing mechanism system which is constructed by the invention and takes into account multiple energy and rotation reserve is shown in figure 2, and the market clearing mechanism system comprises four parts: wind power plants, thermal power plants, consumer and demand side responses. Wherein, the wind power plant and the thermal power plant 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: d, d 11 ＝2.75＄/MW，d 13 ＝3＄/MW，k r,j ＝1＄/MW，k p,j ＝5＄/MW，k r,k ＝1＄/MW，k p,k ＝5＄/MW。WG 11 ，WG 13 Refers to two wind farms of the embodiment, d 11 ，d 13 Is the direct purchasing coefficient of wind power corresponding to each wind power plant.
TABLE 1 wind farm related parameters
The system risk level cannot be optimized independently due to the increase in total cost. Thus, the present invention contemplates a multi-objective optimization, SPEA2+ is used to form a pareto optimal front, and then a fuzzy-based approach is used to extract the optimal trade-off solution from the trade-off front.
The embodiment of the invention is analyzed by the following two models: in model one, the total cost minimization goal does not take into account the cost due to the demand side reserve price quote (i.e., the last term in equation (4) will not exist). In model two, the total cost minimization goal considers the cost of the demand side reserve price quote (all terms of equation (4) will appear).
Considering the uncertainty of wind power and the uncertainty of + -5% of the load demand forecast, the model obtained in Table 2 has an optimal overall cost of 1289.18$/h, with a system risk level of 1.795. Here, the required standby amount is 52.2 megawatts, that is, the sum of the standby emission amount of the thermal power plant (37.03 megawatts), the standby capacity required by the wind farm (12.78 megawatts), and the standby capacity required by the load (2.39 megawatts).
Table 2 model one taking into account uncertainty of wind power and uncertainty of + -5% of load demand predictions
Table 3 also shows the planned power generation, reserve and objective function values of the model-optimizing both the total cost and the system risk level, taking into account the uncertainty of wind power and the uncertainty of + -10% of the load demand forecast. The best compromise solution obtained using SPEA2+ has a total cost of 1299.5$/h and a system risk level of 2.494.
Table 3 model one taking into account uncertainty of wind power and uncertainty of + -10% of load demand predictions
For model two, the planned power generation, standby and objective function values shown in Table 4 are given by the uncertainty of wind power and the uncertainty of + -5% of the load demand forecast, the total cost of the resulting optimal trade-off is 3160.054$/h, and the system risk level is 2.377. Under model two, the required inventory was 86.08MW, provided by a conventional unit (32.88 megawatts) and a demand response program (53.20 megawatts). Table 4 also considers the optimal solution resulting from uncertainty in wind power and uncertainty of + -10% of the load demand forecast.
Table 4 model two taking into account uncertainty of wind power and uncertainty of + -5% and + -10% of load demand predictions
It is clear from the case analysis of the two models that as the uncertainty of wind power and load predictions increases, the overall cost and system risk level increases, and the increase in uncertainty level also results in an increase in the cost of the total and reserve. This is because a compromise is required that must take into account both the cost minimization and risk minimization objectives. The solution thus found is more cost efficient than a single cost minimization target. A comparison of the total cost and risk levels considering wind power randomness and load randomness is shown in fig. 5. Therefore, the invention reduces the risk level of the system as much as possible while reducing the cost. Compared with single scheduling which only considers low cost, the invention ensures the safety of the system and achieves the compromise of cost minimization and risk minimization.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.

Claims (7)

1. A method of energy and rotational reserve market clearing that accounts for multiple uncertainties, comprising:
establishing a target function of a clearing mechanism of energy and rotary spare markets, which is designed by a conventional unit, a wind turbine and a demand response of a load side;
the objective function includes: the overall running cost minimizes the objective function and minimizes the system risk objective function;
the total running cost minimization objective function is:
Minmize
C SRi (P SRi )＝x i +y i P SRi ；
C wj (P wj )＝d j P wj ；
C k (P shd,k )＝a' k +b' k (P shd,k )+c' k (P shd,k ) 2 ；
wherein C is i Is the fuel cost function of the ith conventional unit, P Gi Is the real-time output value of the ith conventional unit, N G Is the number of the conventional units, C SRi Is a rotary standby cost function provided by the ith conventional unit, P SRi Is the rotation reserve provided by the ith conventional unit, N W Is the number of wind turbine generators, C wj Is the cost function of directly purchasing wind power by the jth wind turbine, P wj The planned wind power output of the jth wind turbine generator system C r,wj Is the wind power overestimation cost of the j-th wind turbine generator, C p,wj Is the wind power underestimation cost of the j-th wind turbine generator set, P wj,av Wind power available for the jth wind turbine, N L Is the load, C r,Dk Is the cost caused by overestimated load of the kth load, P Dk Is the planned load of the kth load, P Dk,av Is the load predicted by the kth load, C p,Dk Is the underestimated penalty cost for the kth load, C k Is the demand response cost of the kth load, P shd,k Is the demand response output value of the kth load, a i ，b i ，c i Is three cost coefficients in the conventional unit fuel cost, x i ，y i Is the cost coefficient of the rotary standby cost of the conventional unit, d j Is the direct cost coefficient, k of the jth wind turbine generator system r,j Is the standby cost coefficient, k of the jth wind turbine generator system p,j Is punishment cost coefficient of the jth wind turbine, P rj Is the rated value of the j-th wind turbine generator, P wj,av Is the available output value k of the jth wind turbine r,k Is the backup cost factor for the kth load demand,is the upper and lower limits of the predicted load, a' k ，b' k ，c' k Demand response cost coefficient, k p,k Is the penalty cost coefficient for the kth load demand, f p (p) is a probability density function of wind power output power, p is wind turbine generator output power, f l (l) A probability density distribution function of an uncertainty load, l is the uncertainty load;
the minimum system risk objective function is:
where u is the security level of the system, W (P D ) min Is the lower limit of wind power penetration, W (P D ) max Is the upper limit of wind power penetration;
solving the two objective functions by adopting a multi-objective pareto strength evolution algorithm to obtain an optimal solution set;
and selecting the best compromise solution from the optimal solution set by adopting a fuzzy algorithm as an energy and rotation standby market clearing scheme.
2. A method of energy and rotational reserve market clearing taking into account multiple uncertainties according to claim 1, wherein the objective function is required to satisfy the following constraints:
1A, node power balancing constraint:
wherein G is ij And B ij Forming a node admittance matrix, Y ij ＝G ij +jB ij Is the (i, j) term of the node admittance matrix, P Di Is the load connected to the main line i, n is the main line number in the system, V i Is the modulus, delta, of the voltage vector on main line i i Is the voltage phase angle on main line i;
1B, total rotational standby demand constraint:
wherein P is G,largest Refers to the power shortage caused by the accidental shutdown of the maximum capacity generator;
1C, power generation constraint:
wherein P is wj,f Is the predicted wind power output of the j-th wind turbine,is the lower limit of the output force of the ith conventional unit,/->Is the force of the ith conventional unit at the moment, < >>Is the i-th conventional unit climbing down value,/->Is the wind power lower limit of the jth wind power generation set, </i >>Is the upward climbing value of the ith conventional unit;
1D, demand constraint:
Dk P≤P Dk ；
wherein, Dk Pis the lower limit of demand；
1E, unit rotation standby constraint:
wherein,is the maximum spare capacity;
1F, demand side standby constraint:
0≤P shd,k ≤P Dk - Dk P；
1G, safety constraint:
0≤u≤1；
1H, the following requirements are satisfied:
V i min ≤V i ≤V i max ；
|S ij |≤S ij max ；
T i min ≤T i ≤T i max ；
wherein S is ij Is the power flow of the power flow,is the transmission limit of the line between the i and j buses, T i Is the transformer tap position, T i min ，T i max Is the minimum and maximum value of the transformer tap setting, V i min ，V i max Is the minimum and maximum of the voltage die on the main line i.
3. A method of energy and rotational reserve market clearing taking into account multiple uncertainties according to claim 2, wherein the probability density function of wind power output power is:
wherein v is i Is cut-in wind speed v r Is rated wind speed v o Is the cut-out wind speed, p r Is rated output power of the wind turbine generator, and h= (v) r /v i ) -1 is an intermediate parameter, k is a shape parameter, and c is a scale parameter.
4. A method of energy and rotational reserve market clearing taking into account multiple uncertainties according to claim 2, wherein the probability density distribution function of the uncertainty load is:
wherein u is L Is the average value of uncertain load, sigma L Is the standard deviation of the uncertainty load, and l is the uncertainty load.
5. The energy and rotational reserve market clearing method of claim 1, wherein said employing a fuzzy algorithm to select a best compromise solution from said set of optimal solutions comprises:
calculating a membership function value of each objective function in the optimal solution set;
calculating membership function values of non-dominant solutions based on the membership function values of the objective function; defining the solutions in the optimal solution set as non-dominant solutions, wherein each non-dominant solution comprises: the conventional unit outputs force in real time, the rotation reserve provided by the conventional unit, the wind power planned output force, the planned load, the demand response output force, the total running cost of the system and the safety level of the system;
and selecting the non-dominant solution with the largest membership function value as the optimal compromise solution.
6. The energy and rotational reserve market clearing method according to claim 5, wherein said calculating membership function values for each objective function in said optimal solution set comprises:
wherein u (F) i ) Membership function value representing the i-th objective function, i=1, 2,.. obj ，F i max And F i min Is the maximum and minimum of the ith objective function in all non-dominant solutions, F i Is the value of the ith objective function, N obj Is the number of objective functions.
7. The energy and rotational reserve market clearing method according to claim 6, wherein said calculating membership function values of non-dominant solutions based on membership function values of said objective function comprises:
wherein,membership function value for the kth non-dominant solution, u (F i k ) Membership function value of the ith objective function which is the kth non-dominant solution, K is the number of non-dominant solutions.
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