Distributed photovoltaic optimal configuration multi-objective planning method for unbalanced power distribution network

Abstract

The invention discloses a distributed photovoltaic optimal configuration multi-objective planning method for an unbalanced power distribution network, and the method comprises the following steps: S1, constructing a photovoltaic output model based on a probability distribution function for a distributed photovoltaic power generation system in a power distribution network system; s2, aiming at each phase load growth coefficient in the unbalanced power distribution network system, constructing a load demand prediction model; s3, taking stable operation of a power distribution network system as a target, and constructing a distributed photovoltaic locating and sizing multi-target optimization model; and S4, based on a multi-target chaotic particle swarm optimization algorithm, solving the distributed photovoltaic locating and sizing multi-target optimization model to obtain a distributed photovoltaic configuration strategy. According to the invention, when the power distribution network enters three-phase imbalance, stable operation of the system can be maintained through distributed photovoltaic optimal configuration, so that the operation robustness of the power distribution network system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and in particular to a distributed photovoltaic optimization configuration multi-objective planning method for an unbalanced distribution network. Background Art

[0002] As residents and industrial users have increasing requirements for power system reliability, the traditional power system, which is mainly characterized by centralized power generation and long-distance transmission, has gradually exposed its vulnerability. Distributed power generation technology, with its advantages of small geographical restrictions, flexible power generation methods, high efficiency and economy, has become one of the key ways to solve the energy crisis and power system reliability problems.

[0004] With the large-scale access of distributed photovoltaic power to the distribution network, the three-phase voltage and current imbalance problem has become increasingly prominent, which has a significant impact on the power quality, network loss and system stability of the distribution network. Traditional research has focused on the optimal configuration of distributed photovoltaic power under the three-phase balanced conditions of the distribution network system, ignoring the asymmetric loads and asymmetric lines that are prevalent in the distribution network. With the increase in the number of distributed photovoltaic accesses, the three-phase imbalance problem has become more serious, which not only reduces the power supply quality, but also may cause risks such as equipment damage and system instability. Summary of the invention

[0005] Purpose of the invention: The purpose of the present invention is to provide a multi-objective planning method for distributed photovoltaic optimal configuration for unbalanced distribution networks, which can ensure that when the distribution network enters three-phase imbalance, the distributed photovoltaic optimal configuration maintains stable operation of the system, thereby improving the robustness of the distribution network system operation.

[0006] Technical solution: A multi-objective planning method for distributed photovoltaic optimal configuration for unbalanced distribution networks, including the following steps:

[0007] S1, for the distributed photovoltaic power generation system in the distribution network system, a photovoltaic output model is constructed based on the probability distribution function;

[0008] S2, construct a load demand prediction model for each phase load growth coefficient in the unbalanced distribution network system;

[0009] S3, with the goal of stable operation of the distribution network system, a multi-objective optimization model for distributed photovoltaic site selection and capacity determination is constructed;

[0010] S4, based on the multi-objective chaotic particle swarm optimization algorithm, solves the multi-objective optimization model of distributed photovoltaic site selection and capacity determination to obtain the distributed photovoltaic configuration strategy.

[0011] Furthermore, the output of distributed photovoltaic power generation at each moment is calculated based on the light intensity, and the distributed photovoltaic output model is constructed as follows:

[0012]

[0013] Where N s Represents the total number of discrete states of light intensity, is the g-th state of light intensity at time t, is the probability evaluation of light intensity in each state, PG PVg The output of the photovoltaic array during the average light intensity period;

[0014] in,

[0015] PG PGg (s ag )=N PV τV g I g

[0016] Where N PV is the total number of photovoltaic modules, τ is the duty cycle of distributed photovoltaic power generation, V g ,I g They are the voltage and current characteristic evaluation results of the photovoltaic modules;

[0017] Indicates light intensity, the calculation method is as follows:

[0018]

[0019] In the formula, s t represents the light intensity at time t, Γ() represents the γ distribution function; α t , β t are shape parameters respectively; are the mean and standard deviation of the light intensity at each moment, respectively.

[0020] Further, in step S2, based on the load growth coefficient of each phase of the unbalanced distribution network system, the load demand of each phase is calculated to obtain a load demand model:

[0021]

[0022] In the formula, is the total historical three-phase load demand at the same time t; k represents any phase in the three-phase system of the distribution network, and t represents any operating time in a day;

[0023] LMF 3-Φ is the unbalanced system load growth coefficient, and the calculation formula is as follows:

[0024]

[0025] in, is the synergy coefficient, LMF 1-Φ is the load growth factor in the three-phase balanced system of the distribution network, which is obtained by fitting historical data.

[0026] Furthermore, the multi-objective optimization model for distributed photovoltaic site selection and capacity determination is constructed as follows:

[0027] F=min{ω1VUF-ω2VSF+ω3APL}

[0028] In the formula, ω1, ω2, ω3 are weight coefficients, VUF, VSF, APL are voltage unbalance factor, voltage safety factor, and active power loss factor, respectively; F represents the objective function for site selection and capacity determination of distributed photovoltaics;

[0029] The specific expression of voltage unbalance factor VUF is as follows:

[0030]

[0031] In the formula, is the k-th phase voltage per unit value at the n-th node; is the per unit value of the three-phase average voltage on the nth node at time t; It represents the voltage unbalance degree of the kth phase at the nth node at time t;

[0032] The specific expression of voltage safety factor VSF is as follows:

[0033]

[0034] Where N B is the total number of nodes in the unbalanced distribution network system; is the voltage safety factor of the kth phase at the n+1th node at time t; represents the voltage of the kth phase at the nth node at time t;

[0035] The specific expression of active power loss factor APL is as follows:

[0036]

[0037] In the formula, are the per unit voltage values ​​at the beginning and end of the k-th phase line i at time t, is the per unit current value of line i, is the power factor, is the phase angle of the k-th phase line i at time t; APL t is the active power loss factor at time t;

[0038] The constraints include active power balance constraints, node power flow constraints, photovoltaic configuration constraints and voltage limit constraints, which are calculated as follows:

[0039] Active power balance constraints:

[0040]

[0041] In the formula, is the active power of other power sources in the distribution network; Provide active power for all photovoltaic power in the distribution network; Indicates that node n is configured with photovoltaic, Indicates that node n is not configured with PV; is the photovoltaic output of node n at time t;

[0042] Node power flow constraints:

[0043]

[0044] In the formula, are the active power of other power sources, photovoltaic active power, and load active power of distribution network node n, are the active power of other power sources and the active power of loads at the distribution network node n respectively; is the k-th phase voltage per unit value at the m-th node; G nm represents the conductance between nodes n and m, B nm represents the reactance between nodes n and m, δ nm represents the phase angle between nodes n and m;

[0045] PV configuration constraints:

[0046]

[0047] In the formula, represents the photovoltaic installation capacity configured for node n;

[0048] Voltage limit constraint:

[0049]

[0050] In the formula, They are the lower and upper limits of the distribution network voltage respectively.

[0051] Furthermore, the implementation steps for solving the multi-objective optimization model for distributed photovoltaic site selection and capacity determination are as follows:

[0052] S41, initialize the particle population, and use the chaotic sequence to initialize the position and velocity of the particle population, where the distributed power output is the position of each particle;

[0053] S42, calculate the values ​​of all objective functions according to the positions of each particle and the distributed photovoltaic site selection and capacity multi-objective optimization model, take the value of the objective function as the fitness value, and assign the maximum value as the global optimal solution to g based on the particle fitness size. bes,j , assign the maximum value of each particle as the optimal solution of the particle to p bes,j ;

[0054] S43, update the fitness value of each particle until the iteration converges and return the optimal solution g bes,j As the optimal solution of the multi-objective optimization model for distributed photovoltaic site selection and capacity determination.

[0055] Compared with the prior art, the present invention has the following significant effects:

[0056] The present invention constructs a photovoltaic output model based on a probability distribution function, and then establishes a distribution network load demand prediction model based on a load growth coefficient for each phase load in a three-phase unbalanced distribution network system. Finally, a multi-objective optimization model for distributed photovoltaic site selection and sizing in an unbalanced distribution network is established, and the model is solved by a multi-objective chaotic particle swarm optimization algorithm. This ensures that when the distribution network enters a three-phase imbalance, the distributed photovoltaic optimization configuration is used to maintain the stable operation of the system, thereby improving the robustness of the distribution network system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flow chart of the present invention;

[0058] Figure 2 This is a diagram of the IEEE 19-node distributed photovoltaic configuration result in Example 2 of the present invention;

[0059] Figure 3 It is a schematic diagram of the iterative results of the chaotic particle swarm optimization algorithm for various parameters of the distribution network in the present invention. DETAILED DESCRIPTION

[0060] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] like Figure 1 As shown, it is a flowchart of a multi-objective planning method for distributed photovoltaic optimal configuration for unbalanced distribution network, including the following steps:

[0064] Step 1: for the distributed photovoltaic power generation system in the distribution network system, a distributed photovoltaic output model is constructed based on the probability distribution function;

[0065] The calculation method of light intensity is as follows:

[0066]

[0067]

[0068] In the formula, Indicates light intensity; s t represents the light intensity at time t, Γ() represents the γ distribution function; α t , β t are shape parameters respectively; are the mean and standard deviation of the light intensity at each moment, respectively.

[0069] The output of distributed photovoltaic power generation at each moment is calculated based on the light intensity, and the distributed photovoltaic output model is constructed as follows:

[0070]

[0071] Where N s Represents the total number of discrete states of light intensity, is the g-th state of light intensity at time t, is the probability evaluation of light intensity in each state, PG PVg is the photovoltaic array output during the average light intensity period.

[0072] in, It can be expressed as:

[0073]

[0074] PG PVg It can be expressed as:

[0075] PG PGg (s ag )=N PV τV g I g (6)

[0076] Where N PV is the total number of photovoltaic modules, τ is the duty cycle of distributed photovoltaic power generation, V g ,I gThey are the voltage and current characteristic evaluation results of photovoltaic modules.

[0077] Step 2: For the three-phase unbalanced distribution network system, a load demand model based on load growth coefficient is constructed;

[0078] Based on the load growth coefficient of each phase of the unbalanced distribution network system, the load demand of each phase of the distribution network is predicted. The load growth coefficient of each phase of the three-phase unbalanced system is calculated in the following form:

[0079]

[0080] In the formula, k represents any phase in the three-phase system of the distribution network, and t represents any operating time in a day; is the synergy coefficient, LMF 1-Φ LMF is the load growth factor in the three-phase balanced system of the distribution network, which can be obtained based on historical data and the least squares method. 3-Φ is the load growth factor of the unbalanced system.

[0081] Based on the load growth coefficient of each phase, the load demand of each phase is calculated to obtain the load demand model:

[0082]

[0083] In the formula, is the total historical three-phase load demand at the same time t.

[0084] Step 3: To improve the stable operation of the distribution network system, a multi-objective optimization model for distributed photovoltaic site selection and capacity determination is constructed;

[0085] The multi-objective optimization model for distributed photovoltaic site selection and capacity determination is as follows:

[0086] F=min{ω1VUF-ω2VSF+ω3APL} (9)

[0087] Where ω1, ω2, and ω3 are weight coefficients; VUF, VSF, and APL are voltage unbalance factor, voltage safety factor, and active power loss factor, respectively; and F represents the objective function for site selection and capacity determination of distributed photovoltaic power generation.

[0088] The specific expression of voltage unbalance factor VUF is as follows:

[0089]

[0090] In the formula, is the k-th phase voltage per unit value at the n-th node; is the per unit value of the three-phase average voltage on the nth node at time t; It represents the voltage unbalance degree of the kth phase at the nth node at time t.

[0091] The specific expression of voltage safety factor VSF is as follows:

[0092]

[0093] Where N B is the total number of nodes in the unbalanced distribution network system; is the voltage safety factor of the kth phase at the n+1th node at time t; Represents the voltage of the kth phase at the nth node at time t.

[0094] The specific expression of active power loss factor APL is as follows:

[0095]

[0096] In the formula, are the per unit voltage values ​​at the beginning and end of the k-th phase line i at time t, is the per unit current value of line i, is the power factor, is the phase angle of the k-th phase line i at time t; APL t is the active power loss factor at time t.

[0097] The constraints of the distributed photovoltaic site selection and capacity multi-objective optimization model include active power balance constraints, node power flow constraints, photovoltaic configuration constraints and voltage limit constraints, which are calculated as follows:

[0098] Active power balance constraints:

[0099]

[0100] In the formula, is the active power of other power sources in the distribution network; Provide active power for all photovoltaic power in the distribution network; Indicates that node n is configured with photovoltaic, Indicates that node n is not configured with PV; is the photovoltaic output of node n at time t.

[0101] Node power flow constraints:

[0102]

[0103] In the formula, are the active power of other power sources, photovoltaic active power, and load active power of distribution network node n, are the reactive power of other power sources and the reactive power of load at distribution network node n respectively; is the k-th phase voltage per unit value at the m-th node; G nm represents the conductance between nodes n and m, Bnm represents the reactance between nodes n and m, δ nm Represents the phase angle between nodes n and m.

[0104] PV configuration constraints:

[0105]

[0106] In the formula, represents the photovoltaic installation capacity configured for node n.

[0107] Voltage limit constraint:

[0108]

[0109] In the formula, They are the lower and upper limits of the distribution network voltage respectively.

[0110] Step 4, using a multi-objective chaotic particle swarm optimization algorithm to optimize and solve the distributed photovoltaic site selection and capacity multi-objective optimization model;

[0111] The main steps include:

[0112] First, the particle population is initialized, and the position and velocity of the particle population are initialized using a chaotic sequence to increase the diversity and uniformity of the initial population. is the position of each particle;

[0113] Secondly, the values ​​of all objective functions are calculated according to the positions of each particle and the optimization model (i.e., formula (9)). The values ​​of the objective functions are used as fitness values. Based on the fitness values ​​of the particles, the maximum value is assigned to g as the global optimal solution. bes,j , assign the maximum value of each particle as the optimal solution of the particle to p bes,j ;

[0114] Furthermore, the particle speed and position are updated according to the following formula:

[0115]

[0116] In the formula, are the speed and position of the jth optimization target of the ith particle at the hth iteration, w is the iteration weight, r1 and r2 are random numbers between 0 and 1, is the learning factor, and the specific expression is as follows:

[0117]

[0118] In the formula, λ, c 1f 、c 1i are learning factor update parameters, h maxis the maximum number of iterations.

[0119] Finally, update the fitness value of each particle until the iteration converges and returns the optimal solution g bes,j As the optimal solution of the multi-objective optimization model for distributed photovoltaic site selection and capacity determination.

[0120] In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0121] like Figure 2 The IEEE 19-node system shown in FIG. 1 solves the optimal configuration of distributed photovoltaics in an unbalanced three-phase distribution network. The results show that when distributed photovoltaics are configured at nodes 14 and 19 respectively, the system configuration reaches the optimal value.

[0122] Table 1 is a diagram showing the parameters of the distribution network before and after the distributed photovoltaic optimization configuration in the present invention.

[0123]

[0124] At this time, as shown in Table 1, the voltage unbalance factor VUF, voltage safety factor VSF and active power loss factor APL of the distribution network have been significantly improved before and after the distributed photovoltaic optimization configuration, which further proves that the method proposed in the present invention can effectively improve the stable operation effect of the distribution network.

[0125] Figure 3 The figure shows the iterative results when using the multi-objective chaotic particle swarm optimization algorithm. It can be seen from the figure that after a short number of iterations, all indicators of the distribution network have converged, indicating that the adopted algorithm has a high degree of fit with the constructed planning model.

[0126] In summary, the present invention constructs a photovoltaic output model based on a probability distribution function, and then establishes a distribution network load demand prediction model based on the load growth coefficient for each phase load in the three-phase unbalanced distribution network system. Finally, a multi-objective optimization model for distributed photovoltaic site selection and sizing of unbalanced distribution networks is established, and the optimal configuration of photovoltaic site selection and sizing is obtained by solving the model through a chaotic particle swarm optimization algorithm. Compared with the distributed photovoltaic optimization configuration under three-phase balanced conditions, the present invention realizes a multi-objective planning method for distributed photovoltaic site selection and sizing when the distribution network is in three-phase unbalanced operation, which is of great significance for further improving the safe and stable operation of the distribution network.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the protection scope of the present invention.

Claims

1. A multi-objective planning method for distributed photovoltaic optimal configuration for unbalanced distribution networks, characterized in that: The steps include: S1, for the distributed photovoltaic power generation system in the distribution network system, a photovoltaic output model is constructed based on the probability distribution function; S2, construct a load demand prediction model for each phase load growth coefficient in the unbalanced distribution network system; S3, with the goal of stable operation of the distribution network system, a multi-objective optimization model for distributed photovoltaic site selection and capacity determination is constructed; S4, based on the multi-objective chaotic particle swarm optimization algorithm, solves the multi-objective optimization model of distributed photovoltaic site selection and capacity determination to obtain the distributed photovoltaic configuration strategy.

2. The multi-objective planning method for distributed photovoltaic optimization configuration for unbalanced distribution network according to claim 1 is characterized in that: The output of distributed photovoltaic power generation at each moment is calculated based on the light intensity, and the distributed photovoltaic output model is constructed as follows: Where N s Represents the total number of discrete states of light intensity, is the g-th state of light intensity at time t, is the probability evaluation of light intensity in each state, PG PVg The output of the photovoltaic array during the average light intensity period; in, PG PGg (s ag )=N PV tV g I g Where N PV is the total number of photovoltaic modules, τ is the duty cycle of distributed photovoltaic power generation, V g ,I g They are the voltage and current characteristic evaluation results of the photovoltaic modules; Indicates light intensity, the calculation method is as follows: In the formula, s t represents the light intensity at time t, Γ() represents the γ distribution function; α t , β t are shape parameters respectively; are the mean and standard deviation of the light intensity at each moment, respectively.

3. The multi-objective planning method for distributed photovoltaic optimization configuration for unbalanced distribution network according to claim 1 is characterized in that: In step S2, based on the load growth coefficient of each phase of the unbalanced distribution network system, the load demand of each phase is calculated to obtain a load demand model: In the formula, is the total historical three-phase load demand at the same time t; k represents any phase in the three-phase system of the distribution network, and t represents any operating time in a day; LMF 3-Φ is the unbalanced system load growth coefficient, and the calculation formula is as follows: in, is the synergy coefficient, LMF 1-Φ is the load growth factor in the three-phase balanced system of the distribution network, which is obtained by fitting historical data.

4. The multi-objective planning method for distributed photovoltaic optimization configuration for unbalanced distribution network according to claim 1 is characterized in that: The constructed distributed photovoltaic site selection and capacity multi-objective optimization model is as follows: F=min{ω1VUF-ω2VSF+ω3APL} In the formula, ω1, ω2, ω3 are weight coefficients, VUF, VSF, APL are voltage unbalance factor, voltage safety factor, and active power loss factor, respectively; F represents the objective function for site selection and capacity determination of distributed photovoltaics; The specific expression of voltage unbalance factor VUF is as follows: In the formula, is the k-th phase voltage per unit value at the n-th node; is the per unit value of the three-phase average voltage on the nth node at time t; It represents the voltage unbalance degree of the kth phase at the nth node at time t; The specific expression of voltage safety factor VSF is as follows: Where N B is the total number of nodes in the unbalanced distribution network system; is the voltage safety factor of the kth phase at the n+1th node at time t; represents the voltage of the kth phase at the nth node at time t; The specific expression of active power loss factor APL is as follows: In the formula, are the per unit voltage values ​​at the beginning and end of the k-th phase line i at time t, is the per unit current value of line i, is the power factor, is the phase angle of the k-th phase line i at time t; APL t is the active power loss factor at time t; The constraints include active power balance constraints, node power flow constraints, photovoltaic configuration constraints and voltage limit constraints, which are calculated as follows: Active power balance constraints: In the formula, is the active power of other power sources in the distribution network; Provide active power for all photovoltaic power in the distribution network; Indicates that node n is configured with photovoltaic, Indicates that node n is not configured with PV; is the photovoltaic output of node n at time t; Node power flow constraints: In the formula, are the active power of other power sources, photovoltaic active power, and load active power of distribution network node n, are the active power of other power sources and the active power of loads at the distribution network node n respectively; is the k-th phase voltage per unit value at the m-th node; G nm represents the conductance between nodes n and m, B nm represents the reactance between nodes n and m, δ nm represents the phase angle between nodes n and m; PV configuration constraints: In the formula, represents the photovoltaic installation capacity configured for node n; Voltage limit constraint: In the formula, They are the lower and upper limits of the distribution network voltage respectively.

5. The multi-objective planning method for distributed photovoltaic optimization configuration for unbalanced distribution network according to claim 4 is characterized in that: The implementation steps for solving the multi-objective optimization model for distributed photovoltaic site selection and capacity determination are as follows: S41, initialize the particle population, and use the chaotic sequence to initialize the position and velocity of the particle population, where the distributed power output is the position of each particle; S42, calculate the values ​​of all objective functions according to the positions of each particle and the distributed photovoltaic site selection and capacity multi-objective optimization model, take the value of the objective function as the fitness value, and assign the maximum value as the global optimal solution to g based on the particle fitness size. bes,j , assign the maximum value of each particle as the optimal solution of the particle to p bes,j ; S43, update the fitness value of each particle until the iteration converges and return the optimal solution g bes,j As the optimal solution of the multi-objective optimization model for distributed photovoltaic site selection and capacity determination.

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