Voltage and line loss optimization method under seasonal load access high photovoltaic permeability power distribution network

Abstract

The invention discloses a voltage and line loss optimization method under the condition that a seasonal load is accessed to a high photovoltaic permeability power distribution network. The method comprises the following steps: 1, establishing a high photovoltaic permeability power distribution network mathematical model comprising photovoltaic power generation output and a seasonal load time sequence; 2, constructing an optimization model which comprehensively considers the voltage deviation, the active power loss of the power distribution network and the photovoltaic reduction quantity; 3, proposing an objective function of the voltage and line loss optimization model of the seasonal load access high photovoltaic permeability power distribution network; and 4, solving the objective function by adopting a particle swarm multi-objective optimization algorithm to obtain the minimum values of the total voltage deviation, the total active power loss and the total photovoltaic reduction quantity of the power distribution network. According to the method, photovoltaic power generation volatility and seasonal load change in the power distribution network are fully considered, active and reactive coordination control is combined, voltage and line loss are optimized, photovoltaic consumption is increased, and safety and operation efficiency of the power distribution network under the high photovoltaic penetration condition are remarkably improved.

Description

Technical Field

[0001] The invention relates to a method for optimizing voltage and line loss when seasonal loads are connected to a distribution network with high photovoltaic penetration rate, and belongs to the technical field of power system operation control. Background Art

[0002] With the rapid development of renewable energy, especially the widespread application of photovoltaic power generation, the penetration rate of photovoltaic power generation in distribution networks has increased year by year. Photovoltaic power generation has obvious volatility and intermittent characteristics. Especially in the context of seasonal load changes, the matching between photovoltaic power generation output and load demand is poor, which brings new challenges to the stable operation of distribution networks. How to optimize the voltage and line loss of distribution networks under the condition of high photovoltaic penetration rate has become an important topic in the current power system research.

[0003] A high photovoltaic penetration distribution network refers to a distribution network with a high proportion of photovoltaic power generation and large fluctuations in photovoltaic output in different time periods. In this case, photovoltaic power generation has a significant impact on the voltage and load scheduling of the distribution network. The volatility of photovoltaic power generation makes the temporal and spatial matching of load and power generation incomplete, resulting in voltage deviation and increased power loss in the power grid, and may even cause instability in the power system. Therefore, how to effectively regulate voltage and line loss to ensure the safe and reliable operation of the distribution network is an urgent problem to be solved.

[0004] Seasonal load refers to the periodic fluctuation of load demand in the distribution network with the change of seasons. For example, in summer and winter, the load demand is higher, while in spring and autumn, the load demand is lower. In addition, the change of seasonal load is closely related to factors such as climate change and electricity consumption habits. The change of seasonal load increases the complexity of distribution network dispatching. In the case of high photovoltaic penetration, this load fluctuation is combined with the fluctuation of photovoltaic power generation, which puts higher requirements on the regulation and optimization of the power grid.

[0005] In distribution networks with high PV penetration, the high output of PV power generation during the day may cause voltage rises at some nodes, while at night or in rainy weather, the PV output is insufficient and the voltage may drop. At the same time, the active power loss and reactive power loss of the distribution network will also be affected during PV curtailment and voltage regulation. How to reduce line losses in the distribution network while meeting voltage constraints is a key issue in the optimal scheduling of the distribution network.

[0006] At present, the optimization of distribution network voltage and line loss mainly focuses on voltage regulation and power loss control. Traditional optimization methods are usually based on static load models and photovoltaic power generation models, ignoring the seasonal fluctuations of loads and the spatiotemporal uncertainty of photovoltaic power generation. In addition, existing optimization methods mostly adopt single-objective optimization strategies, usually focusing on the optimization of one aspect, such as voltage deviation or line loss, and rarely considering the comprehensive optimization of voltage and line loss. Summary of the invention

[0007] The present invention aims to solve the deficiencies of the above-mentioned prior art and proposes a method for optimizing voltage and line loss of a distribution network with high photovoltaic penetration rate under seasonal load access, so as to fully consider the volatility of photovoltaic power generation and seasonal load changes in the distribution network, and combine active and reactive coordinated control to optimize voltage and line loss, and increase photovoltaic absorption, thereby significantly improving the safety and operation efficiency of the distribution network under high photovoltaic penetration conditions.

[0008] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0009] The voltage and line loss optimization method of the present invention when seasonal load is connected to a distribution network with high photovoltaic penetration rate is characterized in that it comprises the following steps:

[0010] Step 1: Use equations (1) to (3) to establish a high photovoltaic penetration distribution network model including photovoltaic power generation output and seasonal load time series:

[0011] (1)

[0012] (2)

[0013] (3)

[0014] In formula (1) to formula (3), represents the active power generated by the photovoltaic power plant at node i at time t, represents the function of photovoltaic power generation, I is the solar irradiance, T is the ambient temperature, is the photovoltaic conversion efficiency; represents the actual power of the seasonal load on node i at time t, represents the base load of the seasonal load on node i at time t, represents the seasonal load fluctuation part on node i at time t, represents the fluctuation amplitude of seasonal load on node i at time t; represents the fluctuation frequency of seasonal load on node i at time t, represents the initial phase of seasonal load fluctuation at node i;

[0015] Step 2: Use equations (4) to (6) to establish the total voltage deviation of the distribution network at time t: , the total active power loss of the distribution network at time t and total reduction in PV :

[0016] (4)

[0017] (5)

[0018] (6)

[0019] In formula (4) to formula (6), represents the node set of the distribution network, represents the voltage on node i at time t; and Respectively represent the active power and reactive power on the branch connected to node i and node j, represents the resistance value of the branch connected to node i and node j, Represents a set of branches of a distribution network; represents the total reduction of photovoltaic power generation at time t, Represents the set of distribution network nodes connected to photovoltaics, represents the maximum active power of photovoltaic power generation at node i;

[0020] Step 3: According to formula (7), the objective function J of the voltage and line loss optimization model of the distribution network with seasonal load access and high photovoltaic penetration is established:

[0021] (7)

[0022] In formula (7), represents the weight coefficient of node voltage deviation, represents the weight coefficient of the distribution network line loss, The weight coefficient representing the reduction in photovoltaic power generation;

[0023] Step 4: Establish the constraints of the voltage and line loss optimization model for seasonal load access to the distribution network with high photovoltaic penetration:

[0024] Step 5: Use the particle swarm multi-objective optimization algorithm to solve the voltage and line loss optimization model of the distribution network with seasonal load access and high photovoltaic penetration rate, and obtain the total voltage deviation of the distribution network at time t. The minimum value of the total active power loss The minimum value and total photovoltaic reduction The minimum value of .

[0025] The voltage and line loss optimization method for seasonal load access to a distribution network with high photovoltaic penetration rate described in the present invention is also characterized in that step 4 includes the following steps:

[0026] Step 4.1: Use equations (8) to (12) to establish the power balance constraint of the distribution network:

[0027] (8)

[0028] (9)

[0029] (10)

[0030] (11)

[0031] (12)

[0032] In formula (8) to formula (12), and They represent the active power and reactive power of node j at time t respectively; , They represent the active power and reactive power of the branches connected to node i and node j at time t, respectively. , Respectively represent the active power and reactive power of the branches connected to node j and node k at time t; Represents the reactance value of the branch connected to node i and node j; Represents the current value of the branch connected to node i and node j at time t; and They represent the active power and reactive power generated by the generator at node i at time t, represents the reactive power generated by the photovoltaic power plant at node i at time t; represents the reactive load on node i at time t; represents the power factor of the load on node i;

[0033] Step 4.2: Establish node voltage equation constraints according to equations (13) and (14):

[0034] (13)

[0035] (14)

[0036] In formula (13)-formula (14), and Respectively represent the voltage values ​​at node i and node j at time t;

[0037] Step 4.3: Establish line power flow constraints according to formula (15):

[0038] (15)

[0039] In formula (15), It represents the maximum capacity allowed by the branch connecting node i and node j;

[0040] Step 4.4: Establish distributed photovoltaic power generation constraints according to equations (16) to (20):

[0041] (16)

[0042] (17)

[0043] (18)

[0044] (19)

[0045] (20)

[0046] In formula (16) to formula (20), represents the reduction value of active power generated by the photovoltaic power generation at node i at time t, represents the reactive power generated by the photovoltaic power generation at node i at time t, represents the power factor angle of photovoltaic power generation at node i; represents the photovoltaic power generation capacity at node i at time t, and They represent the minimum and maximum reactive power that the photovoltaic power source at node i can generate respectively;

[0047] Step 4.5: Establish the control constraints for seasonal load fluctuations according to formula (21):

[0048] (twenty one)

[0049] In formula (21), It represents the maximum amplitude of seasonal load fluctuation on node i.

[0050] Further, step 5 includes the following steps:

[0051] Step 5.1, define the number of iterations as k, and initialize k=0, define the maximum number of iterations as K max ;

[0052] Randomly generate ζ particles in the kth iteration, where the position of the ath particle in the kth iteration is , the velocity of the ath particle at the kth iteration is ,in, represents the active power generated by the photovoltaic power on node i at time t in the a-th particle in the k-th iteration;

[0053] Step 5.2: Use formula (22) to calculate the fitness value of the ath particle in the kth iteration: :

[0054] (twenty two)

[0055] In formula (22), represents the total voltage deviation of the ath particle at time t in the kth iteration, represents the total active power loss of the distribution network of the ath particle at time t in the kth iteration, represents the total PV reduction of the ath particle at time t in the kth iteration;

[0056] Step 5.3: Update the velocity of the ath particle according to equations (23) and (24): and location , thus obtaining the velocity of the ath particle in the k+1th iteration and location :

[0057] (twenty three)

[0058] (twenty four)

[0059] In formula (23)-formula (24), and They represent the individual learning factor at the kth iteration and the group learning factor at the kth iteration respectively, and are obtained by formula (25); represents the inertia weight at the kth iteration and is obtained by formula (26); and Respectively represent two random numbers in the range [0,1], represents the optimal position of the ath particle in the kth iteration, represents the global optimal position of all particles at the kth iteration;

[0060] (25)

[0061] In formula (25), c 1min and c 1max are the minimum and maximum values ​​of the individual learning factor, c 2min and c 2max are the minimum and maximum values ​​of the group learning factor, respectively;

[0062] (26)

[0063] In formula (26), and are the minimum and maximum values ​​of the inertia weight, respectively;

[0064] Step 5.4: Update the optimal position of a single particle and the global optimal position at the kth iteration :

[0065] Step 5.5: If k=K max or , then the iteration is stopped and the optimal position of the ath particle in the kth iteration is obtained. , and output the total voltage deviation of the distribution network at time t corresponding to the global optimal fitness under the kth iteration The minimum value of the total active power loss The minimum value and total photovoltaic reduction On the contrary, after assigning k+1 to k, return to step 5.3 and execute sequentially; To set the threshold.

[0066] Further, step 5.4 includes the following steps:

[0067] Step 5.4.1: Use formula (22) to get the fitness value of the ath particle in the kth iteration: , if any < , then let the optimal position of the ath particle in the kth iteration be = , otherwise, let = ;

[0068] Step 5.4.2: Select the particle position with the best fitness from all particles in the kth iteration as the global optimal position in the kth iteration. , and then use formula (22) to calculate The global optimal fitness corresponding to the kth iteration.

[0069] An electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the voltage and line loss optimization model and objective function when the seasonal load is connected to a distribution network with a high photovoltaic penetration rate, and the processor is configured to execute the program stored in the memory.

[0070] The present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the voltage and line loss optimization method when seasonal loads are connected to a distribution network with a high photovoltaic penetration rate are executed.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] 1. The present invention accurately simulates the complex operating environment of the distribution network with high photovoltaic penetration rate by comprehensively considering multiple factors such as photovoltaic power generation output, seasonal load fluctuations, voltage deviation and line loss. It can optimize voltage and power loss in real time, ensure that the distribution network can still maintain stable operation under the condition of high photovoltaic penetration rate, avoid power outages or equipment damage caused by unstable voltage or excessive line loss, thereby significantly improving the operating safety of the distribution network.

[0073] 2. The present invention utilizes a particle swarm multi-objective optimization algorithm to perform global optimization on voltage and line loss, and by optimizing voltage deviation and line loss, effectively reduces power loss and voltage instability problems caused by photovoltaic power generation fluctuations and seasonal load changes, thereby enhancing the adaptability of the distribution network under complex operating conditions. Especially in areas with high penetration of photovoltaic access, the present invention provides reliable technical support for the intelligent scheduling and optimization of the distribution network, and has important practical application value and practical engineering significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0075] In this embodiment, a voltage and line loss optimization method for seasonal load access to a distribution network with high photovoltaic penetration is proposed specifically to address the complex operational challenges of the distribution network under seasonal load access. This method is achieved by establishing a comprehensive model that covers multiple factors such as photovoltaic power generation output, seasonal load timing, voltage deviation, line loss, and photovoltaic curtailment, to accurately simulate the dynamic behavior of the distribution network. Using a particle swarm multi-objective optimization algorithm, global optimization is performed for voltage stability and line loss minimization, thereby effectively solving the problems of voltage instability and excessive line loss faced by the distribution network under high photovoltaic penetration conditions, and significantly improving the operational safety and reliability of the distribution network. Specifically, the method includes the following steps:

[0076] Step 1: The active power of photovoltaic power generation depends on solar irradiance, ambient temperature and photovoltaic conversion efficiency. The seasonal load consists of two parts: the stable benchmark load and the load fluctuation part caused by seasonal changes. The fluctuation part is described in the form of a sine function. The mathematical model of the high photovoltaic penetration distribution network including photovoltaic power generation output and seasonal load sequence is established using equations (1) to (3):

[0077] (1)

[0078] (2)

[0079] (3)

[0080] In formula (1) to formula (3), represents the active power generated by the photovoltaic power generation at the i node at time t, represents the function of photovoltaic power generation, I is the solar irradiance, T is the ambient temperature, is the photovoltaic conversion efficiency; represents the actual power of the seasonal load on node i at time t, represents the base load of the seasonal load on node i at time t, represents the seasonal load fluctuation part at node i at time t, represents the fluctuation amplitude of seasonal load on node i at time t; represents the fluctuation frequency of seasonal load on node i at time t, represents the initial phase of seasonal load fluctuation at node i;

[0081] Step 2: In order to optimize the voltage and power loss, ensure that the distribution network can maintain stable operation under high photovoltaic penetration conditions, and avoid power outages or equipment damage caused by voltage instability or excessive line loss, the voltage deviation of the distribution network at time t is established using equations (4) to (6): , Active power loss of distribution network at time t and total reduction in PV Model:

[0082] (4)

[0083] (5)

[0084] (6)

[0085] In formula (4) to formula (6), represents the node set of the distribution network, represents the voltage on node i at time t; represents the active power on the branch connected to nodes i and j, represents the reactive power on the branch connected to nodes i and j, It represents the resistance value of the branch connected to the i-node and the j-node. Represents a set of branches of a distribution network; represents the total reduction of photovoltaic power generation at time t, Represents the set of distribution network nodes connected to photovoltaics, Indicates the maximum active power of photovoltaic power generation at node i;

[0086] Step 3: According to formula (7), the objective function J of the voltage and line loss optimization model of the distribution network with seasonal load access and high photovoltaic penetration is established:

[0087] (7)

[0088] In formula (7), represents the weight coefficient of node voltage deviation, represents the weight coefficient of the distribution network line loss, The weight coefficient representing the reduction in photovoltaic power generation;

[0089] Step 4: Establish the constraints of the objective function of the voltage and line loss optimization model for seasonal load access to the distribution network with high photovoltaic penetration:

[0090] Step 4.1: Use equations (8) to (12) to establish the power balance constraint of the distribution network:

[0091] (8)

[0092] (9)

[0093] (10)

[0094] (11)

[0095] (12)

[0096] In formulas (8)-(12), and They represent the active power and reactive power of node j at time t respectively; , They represent the active power and reactive power of the branches connected to nodes i and j at time t, respectively. , Respectively represent the active power and reactive power of the branches connected to the j node and the k node at time t; Indicates the reactance value of the branch connected to the i-node and the j-node; Represents the current value of the branch connected to node i and node j at time t; and They represent the active power and reactive power generated by the generator at node i at time t, represents the reactive power generated by the photovoltaic power generation at the i node at time t; represents the reactive load on node i at time t; represents the power factor of the load on node i;

[0097] Step 4.2: Establish node voltage equation constraints according to equations (13) and (14):

[0098] (13)

[0099] (14)

[0100] In formula (13)-formula (14), and Respectively represent the voltage values ​​at node i and node j at time t;

[0101] Step 4.3: Establish line power flow constraints according to formula (15):

[0102] (15)

[0103] In formula (15), Indicates the maximum capacity allowed by the branch connected to nodes i and j;

[0104] Step 4.4: Establish distributed photovoltaic power generation constraints according to equations (16) to (20):

[0105] (16)

[0106] (17)

[0107] (18)

[0108] (19)

[0109] (20)

[0110] In formula (16) to formula (20), It represents the reduction value of active power generated by photovoltaic power generation at node i at time t. represents the reactive power generated by the photovoltaic power generation at the i node at time t, Represents the power factor angle of photovoltaic power generation at node i; represents the photovoltaic power generation capacity at node i at time t, and They represent the minimum and maximum reactive power that can be generated by the photovoltaic power source at node i respectively;

[0111] Step 4.5: In order to optimize the grid operation more accurately, ensure the matching of photovoltaic power generation and load demand, improve the stability and safety of the grid, and reduce voltage fluctuations and line losses, the control constraints of seasonal load fluctuations are established according to formula (21):

[0112] (twenty one)

[0113] In formula (21), It represents the maximum amplitude of seasonal load fluctuation on node i;

[0114] Step 5: When dealing with optimization problems with multiple objectives and complex constraints, the particle swarm multi-objective optimization algorithm has the advantages of global search capability, strong adaptability, easy implementation, no need for gradient information, and the ability to handle nonlinear problems. Especially in application scenarios such as distribution network voltage and line loss optimization and photovoltaic power generation, the present invention uses a particle swarm multi-objective optimization algorithm to solve the distribution network voltage and line loss optimization model with seasonal load access to high photovoltaic penetration, and obtains the total voltage deviation of the distribution network at time t. , total active power loss and total reduction in PV The minimum value of

[0115] Step 5.1, define the number of iterations as k, and initialize k=0, define the maximum number of iterations as K max ;

[0116] Set the ζ particles in the kth iteration to be randomly generated, where the position of the ath particle in the kth iteration is , the velocity of the ath particle at the kth iteration is ,in, represents the active power generated by the photovoltaic power on node i at time t in the a-th particle in the k-th iteration;

[0117] Step 5.2: Substitute into formula (22) to calculate The fitness value of , the fitness value is another manifestation of the objective function:

[0118] (twenty two)

[0119] In formula (22), represents the total voltage deviation of the ath particle at time t in the kth iteration, represents the total active power loss of the distribution network of the ath particle at time t in the kth iteration, represents the total PV reduction of the ath particle at time t in the kth iteration;

[0120] Step 5.3: Update the velocity of the ath particle according to equations (23) and (24): and location , thus obtaining the velocity of the ath particle in the k+1th iteration and location :

[0121] (twenty three)

[0122] (twenty four)

[0123] In formula (23)-formula (24), and They represent the individual learning factor at the kth iteration and the group learning factor at the kth iteration respectively, and are obtained by formula (25); represents the inertia weight at the kth iteration and is obtained by formula (26); and Respectively represent two random numbers in the range [0,1], represents the optimal position of the ath particle in the kth iteration, represents the global optimal position of all particles at the kth iteration;

[0124] (25)

[0125] In formula (25), c 1min and c 1max are the minimum and maximum values ​​of the individual learning factor, c 2min and c 2max are the minimum and maximum values ​​of the group learning factor, respectively;

[0126] (26)

[0127] In formula (26), and are the minimum and maximum values ​​of the inertia weight, respectively;

[0128] Step 5.4: Update the optimal position of a single particle and the global optimal position:

[0129] Step 5.4.1: Use formula (22) to get the fitness value of the ath particle in the kth iteration: , if any < , then let the optimal position of the ath particle in the kth iteration be = , otherwise, let = ;

[0130] Step 5.4.2: Select the particle position with the best fitness from all particles in the kth iteration as the global optimal position in the kth iteration. , and then use formula (22) to calculate The corresponding global optimal fitness under the k-th iteration;

[0131] Step 5.5: If k=K max or , then the iteration is stopped and the optimal position of the ath particle in the kth iteration is obtained. , output the global optimal fitness under the kth iteration, which is the minimum value of the objective function J, so as to obtain the total voltage deviation of the distribution network at time t The minimum value of the total active power loss The minimum value and total photovoltaic reduction On the contrary, after assigning k+1 to k, return to step 5.3 and execute sequentially; To set the threshold.

[0132] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0133] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are executed.

Claims

1. A method for optimizing voltage and line loss when seasonal loads are connected to a distribution network with high photovoltaic penetration, characterized in that: The following steps are involved: Step 1: Use equations (1) to (3) to establish a high photovoltaic penetration distribution network model including photovoltaic power generation output and seasonal load time series: (1) (2) (3) In formula (1) to formula (3), represents the active power generated by the photovoltaic power plant at node i at time t, represents the function of photovoltaic power generation, I is the solar irradiance, T is the ambient temperature, is the photovoltaic conversion efficiency; represents the actual power of the seasonal load on node i at time t, represents the base load of the seasonal load on node i at time t, represents the seasonal load fluctuation part on node i at time t, represents the fluctuation amplitude of seasonal load on node i at time t; represents the fluctuation frequency of seasonal load on node i at time t, represents the initial phase of seasonal load fluctuation at node i; Step 2: Use equations (4) to (6) to establish the total voltage deviation of the distribution network at time t: , the total active power loss of the distribution network at time t and total reduction in PV : (4) (5) (6) In formula (4) to formula (6), represents the node set of the distribution network, represents the voltage on node i at time t; and Respectively represent the active power and reactive power on the branch connected to node i and node j, represents the resistance value of the branch connected to node i and node j, Represents a set of branches of a distribution network; represents the total reduction of photovoltaic power generation at time t, Represents the set of distribution network nodes connected to photovoltaics, represents the maximum active power of photovoltaic power generation at node i; Step 3: According to formula (7), the objective function J of the voltage and line loss optimization model of the distribution network with seasonal load access and high photovoltaic penetration is established: (7) In formula (7), represents the weight coefficient of node voltage deviation, represents the weight coefficient of the distribution network line loss, The weight coefficient representing the reduction in photovoltaic power generation; Step 4: Establish the constraints of the voltage and line loss optimization model for seasonal load access to the distribution network with high photovoltaic penetration: Step 5: Use the particle swarm multi-objective optimization algorithm to solve the voltage and line loss optimization model of the distribution network with seasonal load access and high photovoltaic penetration rate, and obtain the total voltage deviation of the distribution network at time t. The minimum value of the total active power loss The minimum value and total photovoltaic reduction The minimum value of .

2. The voltage and line loss optimization method for seasonal load access to a high photovoltaic penetration distribution network according to claim 1, characterized in that: Step 4 includes the following steps: Step 4.1: Use equations (8) to (12) to establish the power balance constraint of the distribution network: (8) (9) (10) (11) (12) In formula (8) to formula (12), and They represent the active power and reactive power of node j at time t respectively; , They represent the active power and reactive power of the branches connected to node i and node j at time t, respectively. , Respectively represent the active power and reactive power of the branches connected to node j and node k at time t; Represents the reactance value of the branch connected to node i and node j; Represents the current value of the branch connected to node i and node j at time t; and They represent the active power and reactive power generated by the generator at node i at time t, represents the reactive power generated by the photovoltaic power plant at node i at time t; represents the reactive load on node i at time t; represents the power factor of the load on node i; Step 4.2: Establish node voltage equation constraints according to equations (13) and (14): (13) (14) In formula (13)-formula (14), and Respectively represent the voltage values ​​at node i and node j at time t; Step 4.3: Establish line power flow constraints according to formula (15): (15) In formula (15), It represents the maximum capacity allowed by the branch connecting node i and node j; Step 4.4: Establish distributed photovoltaic power generation constraints according to equations (16) to (20): (16) (17) (18) (19) (20) In formula (16) to formula (20), represents the reduction value of active power generated by the photovoltaic power generation at node i at time t, represents the reactive power generated by the photovoltaic power generation at node i at time t, represents the power factor angle of photovoltaic power generation at node i; represents the photovoltaic power generation capacity at node i at time t, and They represent the minimum and maximum reactive power that the photovoltaic power source at node i can generate respectively; Step 4.5: Establish the control constraints for seasonal load fluctuations according to formula (21): (21) In formula (21), It represents the maximum amplitude of seasonal load fluctuation at node i.

3. The voltage and line loss optimization method for seasonal load access to a high photovoltaic penetration distribution network according to claim 2, characterized in that: Step 5 includes the following steps: Step 5.1, define the number of iterations as k, and initialize k=0, define the maximum number of iterations as K max ; Randomly generate ζ particles in the kth iteration, where the position of the ath particle in the kth iteration is , the velocity of the ath particle at the kth iteration is ,in, represents the active power generated by the photovoltaic power on node i at time t in the a-th particle in the k-th iteration; Step 5.2: Use formula (22) to calculate the fitness value of the ath particle in the kth iteration: : (22) In formula (22), represents the total voltage deviation of the ath particle at time t in the kth iteration, represents the total active power loss of the distribution network of the ath particle at time t in the kth iteration, represents the total PV reduction of the ath particle at time t in the kth iteration; Step 5.3: Update the velocity of the ath particle according to equations (23) and (24): and location , thus obtaining the velocity of the ath particle in the k+1th iteration and location : (23) (24) In formula (23)-formula (24), and They represent the individual learning factor at the kth iteration and the group learning factor at the kth iteration respectively, and are obtained by formula (25); represents the inertia weight at the kth iteration and is obtained by formula (26); and Respectively represent two random numbers in the range [0,1], represents the optimal position of the ath particle in the kth iteration, represents the global optimal position of all particles at the kth iteration; (25) In formula (25), c 1min and c 1max are the minimum and maximum values ​​of the individual learning factor, c 2min and c 2max are the minimum and maximum values ​​of the group learning factor, respectively; (26) In formula (26), and are the minimum and maximum values ​​of the inertia weight, respectively; Step 5.4: Update the optimal position of a single particle and the global optimal position at the kth iteration : Step 5.5: If k=K max or , then the iteration is stopped and the optimal position of the ath particle in the kth iteration is obtained. , and output the total voltage deviation of the distribution network at time t corresponding to the global optimal fitness under the kth iteration The minimum value of the total active power loss The minimum value and total photovoltaic reduction minimum value; otherwise, after assigning k+1 to k, return to step 5.3 and execute sequentially; where, To set the threshold.

4. The voltage and line loss optimization method for seasonal load access to a high photovoltaic penetration distribution network according to claim 3, characterized in that: Step 5.4 includes the following steps: Step 5.4.1: Use formula (22) to get the fitness value of the ath particle in the kth iteration: , if any < , then let the optimal position of the ath particle in the kth iteration be = , otherwise, let = ; Step 5.4.2: Select the particle position with the best fitness from all particles in the kth iteration as the global optimal position in the kth iteration. , and then use formula (22) to calculate The global optimal fitness corresponding to the kth iteration.

5. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the voltage and line loss optimization model and objective function under the seasonal load access to the high photovoltaic penetration distribution network as described in any one of claims 1-4, and the processor is configured to execute the program stored in the memory.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing voltage and line loss when seasonal loads are connected to a distribution network with a high photovoltaic penetration rate as described in any one of claims 1 to 4 are executed.

Images