Optimal configuration method of distributed generation considering active and reactive power uncertainty coupling

Abstract

A method for optimizing the configuration of distributed power sources considering the coupling of active and reactive power uncertainty belongs to the field of energy storage technology for distribution networks. Calculate the feasible domain of active and reactive output of wind power and photovoltaic power under confidence; establish the joint probability density function of active power of wind power and photovoltaic power; establish the joint probability density function of reactive power; construct a multivariate normal joint probability distribution function; calculate the loss load and power reduction model; determine the backup energy of the output uncertainty of distributed power sources; establish an optimization model for the joint configuration of active and reactive power of wind power and photovoltaic power considering the annual cost; obtain the configuration scheme of the capacity of distributed power sources and the backup energy scheme. The present invention takes into account the uncertainty of renewable energy output, constructs an optimization model for the joint configuration of active and reactive power of distributed power sources, can determine the optimal configuration and backup energy of distributed power sources, and is suitable for solving the economic operation problem of distribution networks containing a high proportion of renewable energy while ensuring the penetration level of distributed power sources.

Description

Technical Field

[0001] The invention discloses a distributed power supply optimization configuration method considering active and reactive power uncertainty coupling, belonging to the field of distribution network energy storage technology. Background Art

[0002] As global energy conservation and emission reduction efforts continue to deepen, the penetration of renewable energy sources such as wind power and photovoltaics into distribution networks continues to increase. However, renewable energy is volatile and random. The integration of large amounts of renewable energy into distribution networks can introduce significant volatility and uncertainty. This is especially true when wind power and photovoltaics are simultaneously integrated into distribution networks, resulting in the coupling of active and reactive power with uncertainty, posing a serious challenge to the safe and stable operation of power systems. Coordinated configuration of active and reactive power from distributed generation is an effective approach to addressing this issue. Therefore, research on the coordinated configuration of active and reactive power from distributed generation in this uncertain coupling environment is crucial for ensuring the safe and economical operation of distribution networks.

[0003] At present, the main methods to solve the uncertainty problems brought about by the high penetration rate of wind power and photovoltaic power are: grid reinforcement, including changing the power source combination, adding system lines, and increasing power source procurement and construction. However, the investment cost increases significantly, which is not conducive to the economic efficiency of the distribution network system; adjusting the transformer taps. The high-frequency tap change seriously affects the life of the transformer and has great limitations; the reduction of renewable energy has caused the waste of renewable energy, affected the development of the renewable energy industry, and had a great impact on the economic efficiency of the distribution network; the reactive power compensation of the renewable energy inverter is limited by the power factor, and the reactive power and voltage adjustment capabilities are not strong.

[0004] From the above analysis, it can be seen that the current method cannot effectively guarantee the economy of the system when dealing with the uncertain coupling of renewable energy sources and when a high proportion of renewable energy is connected to the grid. It not only wastes natural resources but also increases the operating cost of the distribution network system. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a distributed power supply optimization configuration method that can determine the minimized annual cost and obtain the optimal configuration of distributed power supplies and a backup energy scheduling plan taking into account the coupling of active and reactive power uncertainty.

[0006] The technical solution adopted by the present invention to solve the technical problem is: the distributed power supply optimization configuration method considering the active and reactive power uncertainty coupling is characterized by comprising the following steps:

[0007] Calculate the feasible region of active and reactive power output of wind power and photovoltaic power under confidence level;

[0008] Establish the joint probability density function of wind power and photovoltaic active power;

[0009] The joint probability density function of reactive power is established based on the joint probability density function of active power;

[0010] Construct a multivariate normal joint probability distribution function based on the uncertainty prediction errors of wind power and photovoltaic power;

[0011] Calculate loss load and curtailment models;

[0012] Determine the reserve energy for the uncertainty of distributed generation output;

[0013] Establish an optimization model for the joint configuration of active and reactive power of wind power and photovoltaic power taking into account annual costs;

[0014] The active and reactive power joint configuration optimization model is solved to obtain the configuration scheme of distributed power generation capacity and the backup energy scheme.

[0015] Preferably, the feasible domain of active and reactive output of wind power and photovoltaic power under confidence is:

[0016]

[0017] Among them, P IRE ~N(μIRE,BIRE),P=(P IRE，1 , P IRE，2 ) represent the active power output of wind power and photovoltaic power respectively; μIRE=(μ IRE，1 , μ IRE，2 ) represent the predicted output values ​​of wind power and photovoltaic power respectively; B IRE represents the covariance matrix of wind power and photovoltaic power output; Q IRE =(Q IRE，1 , Q IRE，2 ) represent the reactive power output of wind power and photovoltaic power, SIRE=(S IRE，1 , S IRE，2 ) represent the rated capacity of wind power and photovoltaic power respectively; PF IRE =(P FIRE，1 , P FIRE，2 ) represent the power factors of wind power and photovoltaic power respectively; and Respectively represent the minimum and maximum values ​​of active output.

[0018] Preferably, the joint probability density function model of wind power and photovoltaic active power is established as follows:

[0019]

[0020]

[0021] Among them, φ(P IRE，i) is the probability density function of the active power output of wind power and photovoltaic power; i = 1, 2; P IRE，1 、P IRE，2 represent the active power output of wind power and photovoltaic power respectively; ρ is the product-moment correlation coefficient; q is the intermediate variable; μ IRE =(μ IRE，1 , μ IRE，2 ) represent the predicted output values ​​of wind power and photovoltaic power respectively; σ IRE，1 , σ IRE，2 are the prediction errors of wind power and photovoltaic power, respectively.

[0022] Preferably, the product-moment correlation coefficient ρ is:

[0023]

[0024] Preferably, the joint probability density function model of reactive power is:

[0025] l(Q IRE，1 , Q IRE，2 )=φ(Q IRE，1 , Q IRE，2) |J(Q IRE，1 , Q IRE，2 )|;

[0026] Among them, J(Q IRE，1 , Q IRE，2 ) is the joint Jacobian matrix of wind power and photovoltaic reactive power, φ(Q IRE，1 , Q IRE，2 ) is the probability density function of the reactive power output of wind power and photovoltaic power.

[0027] Preferably, the multivariate normal joint probability distribution function process established based on the uncertainty prediction error of wind power and photovoltaic power generation is:

[0028] Establish the joint cumulative probability function of active power based on the uncertainty prediction error of wind power and photovoltaic power:

[0029]

[0030] Establish a joint cumulative probability function of reactive power based on the uncertainty prediction errors of wind power and photovoltaic power:

[0031]

[0032] Preferably, the loss load model is:

[0033]

[0034]

[0035] in, are active and reactive load loss powers, P IRE，1 、P IRE，2 Represent the active power output of wind power and photovoltaic power respectively, Respectively represent the minimum and maximum values ​​of active output, Q IRE =(Q IRE，1 , Q IRE，2 ) represent the reactive power output of wind power and photovoltaic power respectively;

[0036] The power reduction model is:

[0037]

[0038]

[0039] in, Respectively, they are active and reactive power reduction.

[0040] Preferably, the energy reserve model of wind power and photovoltaic coupling considering prediction uncertainty is:

[0041]

[0042] Among them, E MT The apparent power of the micro-turbine that provides backup energy, are the active and reactive load loss powers respectively.

[0043] Preferably, the distributed generation active and reactive power joint configuration optimization model based on the annual cost F is:

[0044]

[0045]

[0046] Among them, ρ AC (t),ρ RE (t) respectively represent the prices of active power and reactive power purchased by the distribution network from the large power grid at time t; P B (i, t), Q B (i, t) represent the active and reactive power purchased by the distribution network of node i from the large power grid at time t; INV Gen , OC Gen , MC Gen are the annualized investment cost, annualized operating cost, and annualized management cost of wind power and photovoltaic power respectively; CRF Gen =r(r+1) LT / [(r+1) LT -1], CRF Gen is the annualized cost conversion factor, r is the discount rate, and LT is the service life of wind power and photovoltaic equipment; is the active power reduction; a-, b, c are the cost coefficients of the micro-turbine that provides backup energy; v0 is the penalty electricity price for power reduction; P loss (i, t), Q loss (i, t) are respectively the active power loss and reactive power loss of node i in the distribution network at time t; P D (i, t), Q D (i, t) are the active and reactive loads at node i at time t, respectively; are the active and reactive loads at node i at time t; U(i, t) is the voltage at node i at time t, U min 、U max They represent the minimum and maximum voltages of the distribution network respectively; R(i, t) and X(i, t) are the resistance and reactance of node i at time t, respectively; Y(i, t) and θ(i, t) are the amplitude and phase angle of the admittance matrix between nodes i, respectively; δ(i, t) is the voltage phase angle at node i at time t, and δ(j, t) is the voltage phase angle at node j at time t; ρ AC，min (t),ρ AC，max (t) represents the maximum and minimum price of active power purchased by the distribution network from the large power grid, ρ AC (t) represents the price of active power purchased by the distribution network from the large power grid, ρ AC，min (t)=0.1ρAC(t),ρ AC，max (t)=2ρAC(t);ρ RE，min (t),ρ RE，max (t) represents the maximum and minimum price of reactive power purchased by the distribution network from the large power grid, ρ RE (t) represents the reactive power price purchased by the distribution network from the large power grid, ρ RE，min (t)=0.1ρRE(t),ρ RE，max (t)=2ρRE(t);E MT The apparent power of the micro turbine that provides backup energy.

[0047] Preferably, the active and reactive power joint configuration optimization model is solved based on the evolutionary predatory intelligent optimization algorithm to minimize the annual cost and obtain the optimal configuration of the distributed power source and the backup energy scheduling plan.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] This distributed power generation optimization configuration method considering the coupling of active and reactive power uncertainty takes into account the uncertainty of renewable energy output, constructs a distributed power generation active and reactive joint configuration optimization model, can determine the optimal configuration and backup energy of distributed power sources, while ensuring the penetration level of distributed power sources, it is suitable for solving the economic operation problems of distribution networks with a high proportion of renewable energy; through the joint configuration strategy of active and reactive power, it can effectively deal with the problems caused by the uncertainty mismeasurement errors of wind power and photovoltaic power, determine the optimal configuration and backup energy of distributed power sources, and improve the economy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Flowchart of the distributed generation optimization configuration method considering the coupling of active and reactive power uncertainties.

[0051] Figure 2 A schematic diagram of the ratio of wind energy and load predicted for a typical day.

[0052] Figure 3 A schematic diagram of the ratio of photovoltaic power to load for a typical day forecast.

[0053] Figure 4 This is a schematic diagram of power factor based on the joint configuration of active and reactive power.

[0054] Figure 5 The figure shows the comparison characteristic curve between a joint configuration active and reactive power control strategy based on uncertain coupling and a single configuration active power control strategy.

[0055] Figure 6 Box plot of a joint configuration active and reactive power control strategy based on uncertain coupling and a single configuration active power control strategy. DETAILED DESCRIPTION

[0056] The present invention will be further described below in conjunction with specific embodiments. However, people familiar with the art should understand that the detailed description given here in conjunction with the drawings is for better explanation, and the structure of the present invention necessarily exceeds these limited embodiments. For some equivalent replacement solutions or common means, they will not be described in detail herein, but they still fall within the scope of protection of this application.

[0057] Figures 1 to 6 The best embodiment of the present invention is shown below in conjunction with the attached Figures 1 to 6 The present invention is further described.

[0058] like Figure 1 As shown in FIG: A distributed power optimization configuration method considering active and reactive power uncertainty coupling includes the following steps:

[0059] Calculate the feasible region of active and reactive power output of wind power and photovoltaic power under confidence level.

[0060] Assume that the forecast error of renewable energy output follows a multivariate normal distribution, that is:

[0061] P IRE ~N(μ IRE , B IRE );

[0062] Where P = (P IRE，1 , P IRE，2 ) represent the active power output of wind power and photovoltaic power respectively; μ IRE =(μ IRE，1 , μ IRE，2 ) represent the predicted output values ​​of wind power and photovoltaic power respectively; B IRE Represents the covariance matrix of wind power and photovoltaic power output.

[0063] The feasible domain of active power output of renewable energy, that is, the feasible domain of active power output of wind power and photovoltaic power under confidence level is:

[0064]

[0065] Among them, α is the significance level, Z 1-a / 2 is the standard quantile.

[0066] The feasible domain of renewable energy, that is, the feasible domain of active and reactive output of wind power and photovoltaic power under confidence level, is expressed as:

[0067]

[0068] Among them, Q IRE =(Q IRE，1 , Q IRE，2 ) represent the reactive power output of wind power and photovoltaic power, S IRE =(S IRE，1 , S IRE，2 ) represent the rated capacity of wind power and photovoltaic power respectively; PF IRE =(P FIRE，1 , P FIRE，2 ) represent the power factors of wind power and photovoltaic power respectively; and Respectively represent the minimum and maximum values ​​of active output.

[0069] Establish the joint probability density function of wind power and photovoltaic active power.

[0070] The process of establishing the joint probability density function model of wind power and photovoltaic active power is as follows:

[0071] The probability density function φ of the active power of wind power and photovoltaic power can be expressed as:

[0072]

[0073] The cumulative probability function Φ of the active power of wind power and photovoltaic power is:

[0074]

[0075] Calculate the joint probability density function of wind power and photovoltaic active power:

[0076]

[0077]

[0078] Among them, φ(P IRE，i ) is the probability density function of the active power output of wind power and photovoltaic power; i = 1, 2; P IRE，1 、P IRE，2 represent the active power output of wind power and photovoltaic power respectively; ρ is the product-moment correlation coefficient; q is the intermediate variable; μ IRE =(μ IRE，1 , μ IRE，2 ) represent the predicted output values ​​of wind power and photovoltaic power respectively; σ IRE，1 , σ IRE，2 are the prediction errors of wind power and photovoltaic power respectively. The product-moment correlation coefficient ρ is:

[0079]

[0080] The joint probability density function of reactive power is established based on the joint probability density function of active power.

[0081] The process of establishing a reactive power joint probability density function model based on the active power joint probability density function is as follows: Calculate the joint probability density function of wind power and photovoltaic reactive output:

[0082] l(Q IRE，1 , Q IRE，2 )=φ(Q IRE，1 , Q IRE，2 )|J(Q IRE，1 , Q IRE，2 )|;

[0083] Among them, J(Q IRE，1 , Q IRE，2 ) is the joint Jacobian matrix of the reactive power of wind power and photovoltaic power, and its expression is:

[0084]

[0085] Among them, C IRE =(C IRE，1 , C IRE，2 ) T =min{S IRE， P IRE / PFIRE}.

[0086] A multivariate normal joint probability distribution function is constructed based on the uncertainty prediction errors of wind power and photovoltaic power.

[0087] The process of establishing a multivariate normal joint probability distribution function based on the uncertainty prediction errors of wind power and photovoltaic power is as follows: Establishing a joint cumulative probability function of active power based on the uncertainty prediction errors of wind power and photovoltaic power:

[0088]

[0089] Establish a joint cumulative probability function of reactive power based on the uncertainty prediction errors of wind power and photovoltaic power:

[0090]

[0091] Calculate lost load and curtailment models.

[0092] The expressions for active and reactive load loss of wind power and photovoltaic power are as follows:

[0093]

[0094] in, are the active and reactive load loss powers respectively.

[0095] The expressions for active and reactive load reduction of wind power and photovoltaic power are as follows:

[0096]

[0097]

[0098] in, Respectively, they are active and reactive power reduction.

[0099] Determine the reserve energy for distributed generation output uncertainty.

[0100] The process of determining the reserve energy of distributed generation output uncertainty is as follows:

[0101] The energy reserve model of wind power and photovoltaic coupling considering prediction uncertainty is:

[0102]

[0103] Among them, E MT The apparent power of the micro turbine that provides backup energy.

[0104] Establish an optimization model for the active and reactive joint configuration of wind power and photovoltaic power considering annual costs. The process of establishing an optimization model for the active and reactive joint configuration of distributed power sources based on annual costs is as follows: The optimization model for the active and reactive joint configuration of distributed power sources based on annual costs F is:

[0105]

[0106]

[0107] Among them, ρ AC (t),ρ RE (t) respectively represent the prices of active power and reactive power purchased by the distribution network from the large power grid at time t; P B (i, t), Q B (i, t) represent the active and reactive power purchased by the distribution network of node i from the large power grid at time t; INV Gen 、PC Gen , MC Gen are the annualized investment cost, annualized operating cost, and annualized management cost of wind power and photovoltaic power respectively; CRF Gen =r(r+1) LT / [(r+1) LT -1], CRF Gen is the annualized cost conversion factor, r is the discount rate, and LT is the service life of wind power and photovoltaic equipment; is the active power reduction; a, b, c are the cost coefficients of the micro-turbine that provides backup energy; v0 is the penalty electricity price for power reduction; P loss (i, t), Q loss (i, t) are respectively the active power loss and reactive power loss of node i in the distribution network at time t; P D (i, t), Q D (i, t) are the active and reactive loads at node i at time t, respectively; are the active and reactive loads at node i at time t; U(i, t) is the voltage at node i at time t, U min 、U max They represent the minimum and maximum voltages of the distribution network respectively; R(i, t) and X(i, t) are the resistance and reactance of node i at time t, respectively; Y(i, t) and θ(i, t) are the amplitude and phase angle of the admittance matrix between nodes i, respectively; δ(i, t) is the voltage phase angle at node i at time t, and δ(j, t) is the voltage phase angle at node j at time t; ρ AC，min (t),ρ AC，max (t) represents the maximum and minimum price of active power purchased by the distribution network from the large power grid, ρ AC (t) represents the price of active power purchased by the distribution network from the large power grid, ρAC，min (t)=0.1ρAC ( t), ρ AC，max (t)=2ρAC(t);ρ RE，min (t),ρ RE，max (t) represents the maximum and minimum price of reactive power purchased by the distribution network from the large power grid, ρ RE (t) represents the reactive power price purchased by the distribution network from the large power grid, ρ RE，min (t)=0.1ρRE(t),ρ RE，max (t)=2ρRE(t);E MT The apparent power of the micro turbine that provides backup energy.

[0108] The active and reactive power joint configuration optimization model is solved to obtain the configuration scheme of distributed power generation capacity and the backup energy scheme.

[0109] The process of solving the distribution network optimization model and obtaining the optimal configuration of distributed power sources and the backup energy scheduling plan while minimizing the annual cost is as follows: solving the collaborative optimization model based on the evolutionary predator intelligent optimization algorithm, minimizing the annual cost, and obtaining the optimal configuration of distributed power sources and the backup energy scheduling plan.

[0110] An evolutionary predatory intelligent optimization algorithm was used to optimize the annual cost-based model for the combined active and reactive power configuration of wind and photovoltaic power. The algorithm is described in detail in the journal Applied Energy, titled "Risk-aware short-term hydro-wind-thermal scheduling using a probability interval optimization model," and will not be further elaborated here.

[0111] The combined active and reactive power configuration strategy of the present invention can effectively address the distribution network planning problems caused by uncertain mismeasurement errors of wind power and photovoltaic power, determine the optimal configuration and backup energy of distributed power sources, and improve the economy of the system.

[0112] The following example illustrates the present invention's method for optimizing the combined active and reactive power configuration of distributed generation systems under an uncertain coupling environment. This example uses an IEEE 33-bus system as the simulation target, with wind power and photovoltaic power connected to nodes 18 and 23, respectively. The peak active load is 3.715 MW, and the peak reactive load is 2.3 MVar. For ease of presentation, the peak load is expressed as 1. The prediction error for wind power and photovoltaic power is 20%. Table 1 lists the annual cost coefficients for wind power, photovoltaic power, and micro-turbines.

[0113] Table 1 Annual cost coefficients of wind power, photovoltaic power and micro-turbines

[0114]

[0115] Figure 2 The predicted ratios of wind power, photovoltaic power, and load for a typical day are given. Table 2 shows the optimal configuration strategy for wind power and photovoltaic power for a typical day. As can be seen from Table 2, wind power and photovoltaic power output during peak hours are significantly higher than during off-peak and normal hours. Furthermore, the higher wind power and photovoltaic power output matches the high load ratio during peak hours. Furthermore, the proposed method for combined active and reactive power configuration effectively addresses the uncertainties of wind power and photovoltaic power, constrains their optimal output, and ensures the safe operation of the system.

[0116] Table 2 Optimal configuration of wind power and photovoltaic power on a typical day

[0117]

[0118] Figure 3 A comparison of power factors based on the combined configuration of active and reactive power is given, that is, the ability to utilize reactive power. Reactive power is of great significance to voltage support, so studying reactive power is very necessary for the safe operation of distribution networks. Figure 3 The power factor (PF) constraint for wind and photovoltaic power generation shows two scenarios: PF = 1 and 0.9 ≤ PF ≤ 1. As the PF increases, the active power generated by wind and photovoltaic power generation also increases. However, the annual cost for PF = 1 is $3,094,181.919, while the annual cost for 0.9 ≤ PF ≤ 1 is $2,891,561.657. The sum of the load loss cost and power curtailment penalty cost is $24,293.939 and $18,679.821, respectively. While satisfying the reactive power and voltage constraints, the system's economic viability is guaranteed.

[0119] Figure 4 The figure compares the active and reactive power control strategy of a combined configuration based on uncertain coupling with that of a single configuration on a typical day. The total active power and reactive power of wind power and photovoltaic power for a typical day in the combined configuration are 45.224 MW and 6.584 MVar, respectively. The total active power and reactive power of wind power and photovoltaic power for a typical day in the single configuration are higher, at 43.096 MW and 5.544 MWar, respectively. Therefore, the figure shows that the combined configuration achieves a higher penetration level of wind power and photovoltaic power than the single configuration.

[0120] Figure 5A boxplot comparison of the active and reactive power control strategy based on a combined configuration of uncertain coupling and a single configuration of active and reactive power control is presented for a typical day. This figure shows that the combined configuration of active and reactive power control demonstrates a wider range of wind and photovoltaic power outputs, greater flexibility, and a higher median. Therefore, the combined configuration of active and reactive power control based on uncertain coupling is more beneficial for distribution network planning and output adjustment.

[0121] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

Claims

1. A distributed power supply optimization configuration method considering active and reactive power uncertainty coupling, characterized by: The steps include: Calculate the feasible region of active and reactive power output of wind power and photovoltaic power under confidence level; Establish the joint probability density function of wind power and photovoltaic active power; The joint probability density function of reactive power is established based on the joint probability density function of active power; Construct a multivariate normal joint probability distribution function based on the uncertainty prediction errors of wind power and photovoltaic power; Calculate loss load models and power curtailment models; Determine the reserve energy for the uncertainty of distributed generation output; Establish an optimization model for the joint configuration of active and reactive power of wind power and photovoltaic power taking into account annual costs; The active and reactive power joint configuration optimization model is solved to obtain the configuration scheme of distributed power generation capacity and the backup energy scheme.

2. The distributed power supply optimization configuration method considering active and reactive power uncertainty coupling according to claim 1 is characterized in that: The feasible domain of active and reactive output of wind power and photovoltaic power under confidence level is: ； in, , Represent the active power output of wind power and photovoltaic power respectively; Represent the predicted output values ​​of wind power and photovoltaic power respectively; represents the covariance matrix of wind power and photovoltaic power output; Represent the reactive power output of wind power and photovoltaic power respectively, Represent the rated capacity of wind power and photovoltaic power respectively; Represent the power factors of wind power and photovoltaic power respectively; and Respectively represent the minimum and maximum values ​​of active output, is the significance level, is the standard quantile.

3. The distributed power supply optimization configuration method considering active and reactive power uncertainty coupling according to claim 1 is characterized in that: The joint probability density function model for wind power and photovoltaic active power is established as follows: ； ； in, is the probability density function of active power output of wind power and photovoltaic power; ; 、 Represent the active power output of wind power and photovoltaic power respectively; is the product-moment correlation coefficient; is an intermediate variable; Represent the predicted output values ​​of wind power and photovoltaic power respectively; 、 are the prediction errors of wind power and photovoltaic power, respectively.

4. The distributed power supply optimization configuration method considering active and reactive power uncertainty coupling according to claim 3 is characterized by: The product-moment correlation coefficient for: 。 5. The distributed power supply optimization configuration method considering active and reactive power uncertainty coupling according to claim 3 is characterized in that: The joint probability density function model of reactive power is: ； in, is the joint Jacobian matrix of the reactive power of wind power and photovoltaic power, is the probability density function of the reactive power output of wind power and photovoltaic power.

6. The distributed power supply optimization configuration method considering active and reactive power uncertainty coupling according to claim 5 is characterized in that: The multivariate normal joint probability distribution function process established based on the uncertainty prediction error of wind power and photovoltaic power generation is: Establish the joint cumulative probability function of active power based on the uncertainty prediction error of wind power and photovoltaic power: ； Establish a joint cumulative probability function of reactive power based on the uncertainty prediction errors of wind power and photovoltaic power: 。 7. The distributed power supply optimization configuration method considering active and reactive power uncertainty coupling according to claim 1 is characterized by: The loss load model is: ； ； in, 、 are the active and reactive load loss powers, 、 Represent the active power output of wind power and photovoltaic power respectively, 、 Respectively represent the minimum and maximum values ​​of active output, Represent the reactive output of wind power and photovoltaic power respectively; The power reduction model is: ； ； in, 、 are the active and reactive power reductions, is the significance level, is the standard quantile, l (x, y) is the joint probability density function of the reactive output of variables x and y.

8. The distributed power supply optimization configuration method considering active and reactive power uncertainty coupling according to claim 1 is characterized by: The energy reserve model of wind power and photovoltaic coupling considering prediction uncertainty is: ； in, The apparent power of the micro-turbine that provides backup energy, 、 are the active and reactive load loss powers respectively.

9. The distributed power supply optimization configuration method considering active and reactive power uncertainty coupling according to claim 1 is characterized by: The active and reactive power joint configuration optimization model is solved based on the evolutionary predatory intelligent optimization algorithm to minimize the annual cost and obtain the optimal configuration of distributed power sources and the backup energy scheduling plan.

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