Optimization control method for distributed photovoltaic participated reactive loss reduction voltage regulation

Abstract

The invention discloses an optimal control method for reactive loss reduction and voltage regulation participated by distributed photovoltaic, and the method comprises the steps: building a power distribution network model containing the distributed photovoltaic and a reactive power source, determining the reactive power regulation range of the distributed photovoltaic, carrying out the normalization of the severity of the voltage out-of-limit of each node, and carrying out the superposition, and forming an overall voltage out-of-limit severity degree, and punishing the overall voltage out-of-limit severity degree, thereby establishing an optimal control objective function for the distributed photovoltaic to participate in reactive power loss reduction and voltage regulation, determining constraint conditions of the power distribution network and various regulation and control resources, and solving the optimal control objective function for the distributed photovoltaic to participate in reactive power loss reduction and voltage regulation. And analyzing the optimal control effect of the distributed photovoltaic participating in reactive loss reduction and voltage regulation. According to the method, the effects of loss reduction and voltage regulation after distributed photovoltaic reactive power regulation are comprehensively considered, and the operation quality of the power distribution network is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy conservation and loss reduction of distribution networks, and particularly relates to an optimal control method for distributed photovoltaic (PV) participating in reactive power loss reduction and voltage regulation. Background Art

[0002] With the large-scale access of distributed PV in distribution networks, the operation of distribution networks faces many severe challenges. On the one hand, the output of distributed PV is affected by factors such as light intensity and time, and has strong randomness and volatility. This makes the power flow of the distribution network fluctuate greatly and be unreasonably distributed. In some areas with frequent light changes, the power flow of the distribution network may change significantly in a short time, resulting in a sharp fluctuation of the line current, and then significantly increasing the loss of the distribution network. The line loss rate in some areas even exceeds 20%, greatly affecting the economic operation efficiency of the distribution network.

[0003] On the other hand, before the access of distributed PV, the original reactive power regulation means of the distribution network are relatively limited. After the access of distributed PV, if its reactive power regulation ability is not effectively utilized, it will further exacerbate the problem of insufficient reactive power regulation ability of the distribution network. When the output of distributed PV changes, reactive power compensation and regulation cannot be carried out in a timely and effective manner, resulting in unstable voltage of the distribution network, sometimes too high and sometimes too low. In some areas with large load changes, the over-limit problems of voltage exceeding 1.3 p.u. or being lower than 0.8 p.u. frequently occur, seriously affecting the power consumption quality of power users, and even may damage electrical equipment, posing a threat to the safe and stable operation of the distribution network.

[0004] Current traditional methods for reactive power regulation of distribution networks often only focus on using traditional reactive power sources (such as capacitor banks, SVG devices, etc.) for regulation, and fail to fully exploit the reactive power regulation potential of distributed PV. These methods are difficult to achieve efficient loss reduction and stable voltage regulation of the distribution network when facing the complex operating conditions brought by the large-scale access of distributed PV, and cannot meet the requirements of economic and safe operation of the distribution network. Therefore, there is an urgent need for a new method to make full use of the reactive power regulation ability of distributed PV and cooperate with other reactive power sources to achieve optimal control of reactive power loss reduction and voltage regulation of the distribution network. Summary of the Invention

[0005] Aiming at the problems of high line loss, serious voltage over-limit and insufficient utilization of reactive power regulation ability of distributed PV in the prior art, the present invention provides an optimal control method for distributed PV participating in reactive power loss reduction and voltage regulation. The present invention comprehensively considers the roles of distributed PV in reactive power loss reduction and voltage regulation after reactive power regulation, and improves the operation quality of the distribution network.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An optimal control method for distributed PV participating in reactive power loss reduction and voltage regulation, comprising the following steps: Step 1: Establish a distribution network model with distributed photovoltaic and reactive power sources; Step 2: Determine the reactive power regulation range of the distributed photovoltaic; Step 3: Establish an optimal control objective function for the distributed photovoltaic to participate in reactive power loss reduction and voltage regulation; Step 4: Determine the constraint conditions of the distribution network and various types of regulation resources; Step 5: Solve the optimal control objective function for the distributed photovoltaic to participate in reactive power loss reduction and voltage regulation; Step 6: Analyze the optimal control effect of the distributed photovoltaic to participate in reactive power loss reduction and voltage regulation.

[0007] Furthermore, the said Step 1 includes the following sub-steps: Step 1.1: Determine the distribution network grid structure and set the line and load parameters in the MATLAB simulation software; Step 1.2: Determine the number, capacity and access location information of the distributed photovoltaic units in the distribution network grid structure; Step 1.3: Determine the access conditions of the capacitor banks and SVG devices (static var generators) in the distribution network grid structure; Step 1.4: Finally, form a simulation model of the distribution network with distributed photovoltaic and reactive power sources.

[0008] Furthermore, the said Step 2 determines the reactive power regulation range of the distributed photovoltaic as follows: (1); (2); In the formula: is the reactive power output of the i-th photovoltaic unit; is the rated capacity of the i-th photovoltaic unit; is the maximum power point tracking (MPPT) active power output of the i-th photovoltaic unit, is the active power output of the i-th photovoltaic unit.

[0009] Furthermore, compared with the traditional loss reduction optimization function, the present invention normalizes and superimposes the severity of voltage violation at each node to form the overall severity of voltage violation, and punishes it to achieve the goal of taking into account voltage regulation, thereby establishing an optimal control objective function for the distributed photovoltaic to participate in reactive power loss reduction and voltage regulation, as shown in formula (3): (3); (4); In the formula: F is the objective function, comprehensively considering the network loss and voltage violation conditions; is the network loss value at time t; is the penalty factor; is the total number of nodes; T is the total number of time instants; is the upper voltage limit of the i-th node, is the lower voltage limit of the i-th node, is the voltage of the i-th node, is the voltage deviation of the i-th node.

[0010] Further, the constraint conditions in step 4 include: Power balance constraint: (5); (6); In the formula: , are the active and reactive power losses in the system, respectively; , are the active and reactive power consumed by the i-th load, respectively; is the reactive power output of the i-th SVG device; is the reactive power output of the i-th capacitor bank.

[0011] Node voltage constraint: (7); Distributed photovoltaic reactive power output constraint: (8); In the formula: , are the minimum and maximum reactive power outputs of the i-th photovoltaic unit, respectively.

[0012] SVG device reactive power output constraint: (9); In the formula: , are the minimum and maximum reactive power outputs of the i-th SVG device, respectively, is the reactive power output of the i-th SVG device. As a fast dynamic reactive power compensation device, the output range of the SVG device needs to meet the equipment capacity limit to ensure safe operation.

[0013] Capacitor bank reactive power output constraint: The capacitor bank is switched on and off as a whole.

[0014] Compared with traditional regulation measures, not only the regulation of reactive power sources such as SVG devices and capacitor banks is retained, but also the regulation of distributed photovoltaic reactive power output is increased.

[0015] Further, in step 5, the parameters of the particle swarm optimization algorithm are set, including the inertia weight , two acceleration learning factors , , two random numbers between [0, 1] , , the current individual optimal position of the particle , the current population optimal position of the particle , with , and as the optimization variables, the optimal control objective function of distributed PV participating in reactive power loss reduction and voltage regulation is solved based on the particle swarm optimization algorithm.

[0016] Further, the specific steps of step 6 are as follows: Step 6.1: When the reactive power of the distributed PV converter does not participate in regulation, perform simulation calculations based on the distribution network model containing distributed PV and reactive power sources to solve the total network line loss before optimization and the number of voltage violation nodes X before optimization; Step 6.2: When the reactive power of the distributed PV converter participates in regulation, perform simulation calculations based on the distribution network model containing distributed PV and reactive power sources to solve the total network line loss after optimization and the number of voltage violation nodes of the entire network after optimization ; Step 6.3: Quantitatively analyze the loss reduction effect , indicates an increase in line loss, indicates a decrease in line loss, quantitatively analyze the voltage regulation effect , indicates an increase in the number of voltage violation nodes, indicates a decrease in the number of violation nodes.

[0017] The beneficial effects of the present invention: By establishing a distribution network model containing distributed PV and reactive power sources, the present invention accurately analyzes the influence of distributed PV reactive power regulation on the distribution network loss. On the basis of determining the distributed PV regulation range, establishing the optimal control objective function, and considering various constraint conditions, the optimal reactive power output scheme is obtained by using the particle swarm optimization algorithm. After the reactive power of the distributed PV converter participates in regulation, the total network line loss is effectively reduced. Compared with the situation where the distributed PV reactive power does not participate in regulation, the loss reduction effect is obvious, reducing the energy loss of the distribution network, improving the energy utilization efficiency, and reducing the operation cost of the distribution network.

[0018] The present invention normalizes the severity of the over-limit of each node voltage and superimposes it on the optimal control objective function, and penalizes it, thus achieving the goal of taking into account voltage regulation. Through the coordinated regulation of reactive power sources such as distributed photovoltaic, SVG devices, and capacitor banks, the voltage quality of the distribution network is effectively improved. After the distributed photovoltaic reactive power participates in the regulation, the number of over-limit voltages in the whole network is significantly reduced, the voltage stability is greatly improved, the normal power consumption of power users is guaranteed, the damage to electrical equipment caused by voltage problems is reduced, and the power supply reliability of the distribution network is improved.

[0019] Different from traditional control measures, the present invention not only retains the regulation of reactive power sources such as SVG devices and capacitor banks, but also fully exploits the reactive power regulation potential of distributed photovoltaics, increasing the regulation of the reactive power output of distributed photovoltaics. This enables distributed photovoltaics to better participate in the reactive power regulation of the distribution network while generating electricity, realizing the comprehensive utilization of various regulation resources in the distribution network and improving the overall operation performance of the distribution network.

[0020] The particle swarm optimization algorithm is used to solve the optimal control objective function. By reasonably setting the key parameters of the algorithm, the optimal reactive power output combination of distributed photovoltaics and other reactive power sources can be quickly and accurately found under the premise of meeting the constraints of the distribution network and various regulation resources. This precise optimal control method can better adapt to the complex and changeable operating conditions of the distribution network and provides a strong guarantee for the economic and safe operation of the distribution network. Description of the Drawings

[0021] Figure 1 is the step flow chart of the present invention; Figure 2 is the schematic diagram of the distribution network grid structure in the present invention. Detailed Embodiment

[0022] In order to make the advantages and technical solutions of the present invention clearer and more definite, the present invention will be described in detail below with specific embodiments.

[0023] An optimal control method for distributed photovoltaics to participate in reactive power loss reduction and voltage regulation, as Figure 1 shown, includes the following steps: Step 1: Establish a distribution network model including distributed photovoltaics and reactive power sources; Step 1.1: Determine the distribution network grid structure (as Figure 2 shown) in the MATLAB simulation software and set the line and load parameters; Step 1.2: Connect 2MVA distributed photovoltaic units (PV) at nodes 18, 21, and 33 in the distribution network grid structure as Figure 2 shown, where the power factor cos of the photovoltaic inverter ranges between [-0.95, 0.95]; Step 1.3: Connect SVG device equipment at nodes 12 and 24 in the distribution network grid structure, with a compensation capacity of 0.5 MVar, and connect 10 capacitor banks with a capacity of 50 kvar at node 30.

[0024] Step 1.4: Build a distribution network model containing distributed photovoltaic, capacitor banks, and SVG devices in MATLab as Figure 2 shown, where 1 - 33 are node numbers, C represents capacitor banks, SVG1 and SVG2 represent the first and second SVG devices respectively, and PV1, PV2, and PV3 represent the first, second, and third photovoltaic units respectively.

[0025] Step 2: Determine the reactive power regulation range of distributed photovoltaic. The rated capacity of the photovoltaic unit is 2 MVA, and the power factor determines the distribution relationship between active power and reactive power, which is an important basis for calculating reactive power. Substitute the power factor = 0.95 into formula (2) to determine the reactive power regulation range.

[0026] (1); (2); In the formula: is the reactive power output of the i-th photovoltaic unit; is the rated capacity of the i-th photovoltaic unit; is the maximum power point tracking (MPPT) active power output of the i-th photovoltaic unit, is the active power output of the i-th photovoltaic unit.

[0027] Step 3: Establish an optimization control objective function for distributed photovoltaic to participate in reactive power loss reduction and voltage regulation; (3); (4); In the formula: F is the objective function, comprehensively considering network loss and voltage violation conditions; is the network loss value at time t; is the penalty factor; is the total number of nodes; T is the total number of time instants; is the upper voltage limit of the i-th node, is the lower voltage limit of the i-th node, is the voltage of the i-th node, is the voltage deviation of the i-th node. Take = 100; = 0.95; = 33; T = 24; = 1.05.

[0028] Step 4: Determine the constraint conditions of the distribution network and various types of regulation resources.

[0029] Power balance constraint: (5); (6); In the formula: , are the active and reactive power losses in the system respectively; , are the active and reactive power consumed by the i-th load respectively; is the reactive power output of the i-th SVG device; is the reactive power output of the i-th capacitor bank.

[0030] Node voltage constraint: (7); In the formula: = 0.95, = 1.05.

[0031] Reactive power output constraint of distributed photovoltaic: (8); In the formula: The minimum reactive power output of the i-th photovoltaic unit = -620 kvar, the maximum reactive power output of the i-th photovoltaic unit = 620 kvar.

[0032] Reactive power output constraint of SVG device: (9); In the formula: The minimum reactive power output of the i-th SVG device = -500 kvar, the maximum reactive power output of the i-th SVG device = 500 kvar.

[0033] The reactive power output of the i-th capacitor bank is an integer multiple of 50 kvar.

[0034] Step 5: Solve the optimal control model for distributed photovoltaic to participate in reactive power reduction and voltage regulation; Set the parameters of the particle swarm optimization algorithm, the inertia weight = 0.5, the two acceleration learning factors , are equal to 1 and 2 respectively, two random numbers between [0,1] , , the current individual optimal position of the particle , the current population optimal position of the particle , taking , and as the optimization variables, the optimal control objective function of distributed PV participating in reactive power loss reduction and voltage regulation with constraints is solved based on the particle swarm optimization algorithm.

[0035] Step 6: Analyze the optimal control effect of distributed PV participating in reactive power loss reduction and voltage regulation.

[0036] Quantitatively analyze the loss reduction effect , and the line loss after optimization is reduced.

[0037] Quantitatively analyze the voltage regulation effect , and the number of voltage over-limit nodes after optimization is reduced.

[0038] The optimization results of the reactive power output of distributed PV are shown in Table 1 as follows: Table 1 Optimization data of each reactive power source in the distribution network:

[0039] In the table, is the voltage of the first node, , , are the reactive power outputs of the first, second, and third PV units respectively; , are the reactive power outputs of the first and second SVG devices respectively; is the reactive power output of the capacitor bank put into operation.

[0040] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions, or substitutions made by those skilled in the art within the scope of the essence of the present invention should also fall within the protection scope of the present invention.

Claims

1. An optimized control method for distributed photovoltaic to participate in reactive power loss reduction and voltage regulation, characterized in that, It includes the following steps: Step 1: Establish a distribution network model with distributed photovoltaic and reactive power sources; Step 2: Determine the reactive power regulation range of distributed photovoltaic; Step 3: Normalize and superimpose the severity of voltage violation at each node, form the overall severity of voltage violation, and impose penalties on it, so as to establish an optimal control objective function for distributed photovoltaic to participate in reactive power loss reduction and voltage regulation: ； ； Where: F is the objective function; is the network loss value at time t; is the penalty factor; is the total number of nodes; T is the total number of time instants; is the upper voltage limit of the i-th node, is the lower voltage limit of the i-th node, is the voltage of the i-th node, is the voltage deviation of the i-th node; Step 4: Determine the constraint conditions of the distribution network and various types of control resources; Step 5: Solve the optimal control objective function for distributed photovoltaic to participate in reactive power loss reduction and voltage regulation; Step 6: Analyze the optimal control effect of distributed photovoltaic to participate in reactive power loss reduction and voltage regulation.

2. The optimization control method according to claim 1, characterized in that, The said Step 1 includes the following sub-steps: Step 1.1: Determine the distribution network grid structure and set line and load parameters in the MATLAB simulation software; Step 1.2: Determine the number, capacity and access location information of distributed photovoltaic units in the distribution network grid structure; Step 1.3: Determine the access conditions of capacitor banks and SVG devices in the distribution network grid structure; Step 1.4: Finally, form a simulation model of the distribution network with distributed photovoltaic and reactive power sources.

3. The optimization control method according to claim 1, characterized in that The said Step 2 determines the reactive power regulation range of distributed photovoltaic as follows: ； ； Wherein: is the reactive power output of the i-th photovoltaic unit; is the rated capacity of the i-th photovoltaic unit; is the maximum power point tracking active power output of the i-th photovoltaic unit, is the active power output of the i-th photovoltaic unit.

4. The optimization control method according to claim 3, wherein The constraint conditions described in the said Step 4 include: Power balance constraint, node voltage constraint, reactive power output constraint of distributed photovoltaic, reactive power output constraint of SVG device and reactive power output constraint of capacitor bank, where the power balance constraint is: ； ； Wherein: and are the active and reactive power losses in the system respectively; and are the active and reactive power consumed by the i-th load respectively; is the reactive power output of the i-th SVG device; is the reactive power output of the i-th capacitor bank.

5. The optimization control method according to claim 1, characterized in that In step 5, set the parameters of the particle swarm optimization algorithm, including the inertia weight , two acceleration learning factors , , two random numbers between [0, 1] , , the current individual optimal position of the particle , the current population optimal position of the particle , taking , and as optimization variables, solve the optimal control objective function of distributed photovoltaic participating in reactive power reduction and voltage regulation with constraints based on the particle swarm optimization algorithm.

6. The optimization control method according to claim 1, wherein The said Step 6 is specifically as follows: Step 6.1: When the reactive power of the distributed PV inverter does not participate in regulation, perform simulation calculations based on the distribution network model containing distributed PV and reactive power sources to solve the overall network line loss before optimization and the number X of overall network voltage violations before optimization; Step 6.2: When the distributed PV converters participate in reactive power regulation, perform simulation calculations based on the distribution network model with distributed PV and reactive power sources to solve the optimized total network line loss and the optimized number of voltage violations in the whole network ; Step 6.3: Quantitative analysis of the loss reduction effect , indicates an increase in line loss, indicates a decrease in line loss, and quantitatively analyzes the voltage regulation effect , indicates an increase in the number of voltage violation nodes, indicates a decrease in the number of violation nodes.

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