Optimized operation method and system for active power distribution network containing photovoltaic power supply

Abstract

The invention relates to the field of power systems, and discloses an optimized operation method and system for an active power distribution network containing a photovoltaic power supply. The method comprises the steps that an SVG reactive power compensation device is additionally installed in a power distribution network, and the node voltage of the power distribution network is adjusted; (2) introducing an OLTC on-load tap-changing transformer at a power grid balance node, and changing the transformation ratio of the transformer by adjusting the position of a tap of the OLTC on-load tap-changing transformer so as to adjust the voltage; (3) a multi-target optimization idea is adopted to reduce the network loss, reduce the voltage fluctuation and improve the photovoltaic consumption rate as optimization targets; (4) introducing a particle swarm algorithm and improving the particle swarm algorithm into a multi-target particle swarm algorithm; and (5) establishing an IEEE33 node power distribution network model containing a photovoltaic power supply on an MATLAB platform, setting related parameters and constraint conditions for simulation, and verifying the feasibility of an optimization strategy. According to the technical scheme, the problems of stability, reliability, economical efficiency and the like caused by the fact that the photovoltaic power source is connected into the active power distribution network can be solved, and optimized operation of the power distribution network is achieved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and particularly to an optimal operation method and system for an active distribution network with photovoltaic power sources. Background Art

[0002] With the increasing global resource pressure and the growing severity of environmental energy problems, renewable new energy sources have been widely used. Among them, photovoltaic power sources have attracted much attention due to their advantages such as clean environmental protection, safety and reliability. However, the access of photovoltaic power sources to the active distribution network will cause a series of problems. For example, photovoltaics are greatly affected by time and weather, and the output power changes periodically and fluctuates in the short term, resulting in variable power flows and voltage distributions in the system, and problems such as reverse power flow and voltage over-limit are likely to occur, making it difficult for the distribution network to absorb and causing resource waste; photovoltaic power generation systems need to be equipped with power electronic inverters, which have weak anti-interference and overload capabilities and are prone to problems such as harmonics and three-phase current imbalance; the large-scale access of photovoltaics will also cause frequent misoperations of discrete voltage regulating devices, affecting the sensitivity and selectivity of relay protection devices. At the same time, the existing distribution network operation control methods are mostly designed based on the characteristics of traditional distribution networks and do not consider the impact of photovoltaic access. The access of photovoltaics changes the structure of the distribution network, increasing the control difficulty, and the relay protection also needs to be adjusted. In addition, the acceptance of photovoltaics by the distribution network will increase the network loss and reduce the equipment utilization rate in some cases, affecting the operation economy. Summary of the Invention

[0003] The object of the present invention is to provide an optimal operation method and system for an active distribution network with photovoltaic power sources to solve problems such as stability, reliability, and economy brought by the access of photovoltaic power sources to the active distribution network and realize the optimal operation of the distribution network.

[0004] To achieve the above object, on the one hand, the present invention provides an optimal operation method for an active distribution network with photovoltaic power sources, including the following steps:

[0005] (1) Install an SVG reactive power compensation device in the distribution network. According to the node voltage situation, when the node voltage is lower than the lower limit, the SVG emits reactive power, and when the node voltage is higher than the upper limit, the SVG absorbs reactive power to adjust the node voltage of the distribution network;

[0006] (2) Introduce an OLTC on-load tap-changing transformer at the grid balance node, and adjust the transformer turns ratio by adjusting the tap position of the transformer to further adjust the voltage;

[0007] (3) Adopt the multi-objective optimization idea, with the reduction of network loss, the reduction of voltage fluctuation, and the improvement of photovoltaic accommodation rate as the optimization objectives;

[0008] (4) Introduce the particle swarm algorithm and improve it into a multi-objective particle swarm algorithm, and use this algorithm to perform iterative simulation calculations on the distribution network model with photovoltaic power sources to obtain the optimal solution;

[0009] (5) Establish an IEEE 33-node distribution network model with a photovoltaic power source on the MATLAB platform, set relevant parameters and constraint conditions for simulation, and verify the feasibility of the optimization strategy.

[0010] Preferably, the SVG reactive power compensation device detects the grid voltage and current signals, calculates the reactive power and voltage deviation of the grid, and then generates reactive power opposite to the grid current through power electronic devices to achieve reactive power compensation. At the same time, it adjusts the grid voltage by controlling the phase of the current.

[0011] Preferably, the tap position of the OLTC on-load tap-changing transformer is represented by a gear, and the change between adjacent gears will produce a 1.25% change in the standard voltage. By adjusting the tap position, the number of turns of the secondary coil is changed, thereby changing the output voltage magnitude and the transformer turns ratio.

[0012] Preferably, in the multi-objective optimization, each objective adopts a separate weighting method, and weights are assigned for reducing network losses, reducing voltage fluctuations, and improving the photovoltaic accommodation rate according to actual requirements.

[0013] Preferably, in the multi-objective particle swarm optimization algorithm, the particles update their velocities and positions according to their own historical optimal solutions and the global optimal solution, according to the following formula:

[0014]

[0015] where, t represents the current iteration number; w is the weight coefficient, c1 and c2 are acceleration coefficients, r1 and r2 are random numbers uniformly distributed on [0,1], p i is the individual optimal solution of the i-th particle, and g p is the global optimal solution of the entire population.

[0016] Preferably, in the IEEE 33-node distribution network model, node 1 is set as the balanced node, and the initial voltage value is set to 1.05; the reactive power compensation device svg is selected to be connected between node 15 and node 31, and the constraint upper limit is taken as 0.08 mva; the photovoltaic access is set at node 21, and the power factor is taken as 0.8; the on-load tap-changing transformer is placed at the 1 balanced node, and the constraint lower limit is taken as 1 and the upper limit is 1.1; the photovoltaic power input is selected from a reasonable day in the historical data of normal weather, and each hour is taken as a data selection point; in the multi-objective optimization, weighted averaging is taken, and the maximum number of iterations in the particle swarm optimization algorithm is taken as 150.

[0017] The second aspect of the present invention provides an optimized operation system for an active distribution network with a photovoltaic power source, including:

[0018] An SVG reactive power compensation device, which is used to emit or absorb reactive power according to the node voltage situation and adjust the node voltage of the distribution network;

[0019] The on-load tap-changer (OLTC) transformer is installed at the grid balancing node and adjusts the voltage by regulating the tap position.

[0020] The multi-objective optimization module takes reducing network losses, reducing voltage fluctuations, and increasing the PV accommodation rate as the optimization objectives and uses the method of separate weighting to handle each objective.

[0021] The multi-objective particle swarm optimization algorithm module is improved based on the particle swarm optimization algorithm and is used to perform iterative simulation calculations on the distribution network model with PV power sources to obtain the optimal solution.

[0022] The simulation verification module establishes an IEEE 33-node distribution network model with PV power sources on the MATLAB platform, sets relevant parameters and constraint conditions for simulation, and verifies the feasibility of the optimization strategy.

[0023] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0024] (1) Improve voltage stability: Through the coordinated action of the SVG reactive power compensation device and the OLTC transformer, the node voltage is effectively controlled, the voltage over-limit situation is reduced, the voltage fluctuation is decreased, the voltage becomes more stable, and the safe operation of power equipment is ensured.

[0025] (2) Reduce network losses: The application of the optimization strategy and algorithm improves the power transmission efficiency in the power grid, reduces the losses of power equipment such as transmission lines and transformers, as well as non-technical losses, and reduces the energy cost.

[0026] (3) Increase the PV accommodation rate: Taking increasing the PV accommodation rate as one of the optimization objectives, the optimal solution is found through multi-objective optimization and the particle swarm optimization algorithm, which promotes the utilization of renewable energy, reduces the consumption of fossil energy, and ensures the security of energy supply.

[0027] (4) Enhance the operation stability of the distribution network: The optimized distribution network is improved in aspects such as power flow distribution and voltage control, reduces the impact of PV power source access on the distribution network stability, and improves the operation stability of the entire distribution network. Specific implementation manners

[0028] The following details the specific implementation manners of the present invention. It should be understood that the specific implementation manners described herein are only for explaining and illustrating the present invention and are not used to limit the present invention.

[0029] The method for optimizing the operation of the active distribution network with PV power sources according to the present invention includes the following steps:

[0030] (1) Install an SVG reactive power compensation device in the distribution network. According to the node voltage conditions, when the node voltage is lower than the lower limit, the SVG emits reactive power; when the node voltage is higher than the upper limit, the SVG absorbs reactive power to regulate the node voltage of the distribution network.

[0031] (2) Introduce an OLTC on-load tap-changer transformer at the grid balance node. By adjusting the tap position of the transformer, the transformer turns ratio is changed, thereby regulating the voltage.

[0032] (3) Adopt the idea of multi-objective optimization, with the goals of reducing network loss, reducing voltage fluctuation, and increasing the PV accommodation rate.

[0033] (4) Introduce the particle swarm optimization algorithm and improve it into a multi-objective particle swarm optimization algorithm. Use this algorithm to perform iterative simulation calculations on the distribution network model with PV power sources to obtain the optimal solution.

[0034] (5) Establish an IEEE 33-node distribution network model with PV power sources on the MATLAB platform, set relevant parameters and constraint conditions for simulation, and verify the feasibility of the optimization strategy.

[0035] In some embodiments, the SVG reactive power compensation device detects the grid voltage and current signals, calculates the reactive power and voltage deviation of the grid, and then generates reactive power opposite to the grid current through power electronic devices to achieve reactive power compensation. At the same time, the grid voltage is regulated by controlling the phase of the current.

[0036] In some embodiments, the tap position of the OLTC on-load tap-changer transformer is represented by a gear position. The change between adjacent gear positions will result in a 1.25% change in the standard voltage. By adjusting the tap position, the number of turns of the secondary coil is changed, thereby changing the magnitude of the output voltage and the transformer turns ratio.

[0037] In some embodiments, in the multi-objective optimization, each objective adopts a separate weighting method, and weights are assigned to reducing network loss, reducing voltage fluctuation, and increasing the PV accommodation rate according to actual requirements.

[0038] In some embodiments, in the multi-objective particle swarm optimization algorithm, the particles update their velocities and positions according to their own historical optimal solutions and the global optimal solution according to the following formula:

[0039]

[0040] where t represents the current iteration number; w is the weight coefficient, c1 and c2 are acceleration coefficients, r1 and r2 are random numbers uniformly distributed in [0,1], p i is the individual optimal solution of the i-th particle, and g p is the global optimal solution of the entire population.

[0041] In some embodiments, in the IEEE 33-node distribution network model, node 1 is set as the balanced node, and the initial voltage value is set to 1.05; the reactive power compensation device SVG is selected to be connected between node 15 and node 31, and the upper limit of the constraint is taken as 0.08 MVA; the photovoltaic access is set at node 21, and the power factor is taken as 0.8; the on-load tap-changing transformer is placed at the balanced node 1, and the lower limit of the constraint is taken as 1 and the upper limit is 1.1; the photovoltaic power input is selected from a reasonable day in the historical data of normal weather, and each hour is taken as a data selection point; in the multi-objective optimization, weighted averages are taken, and the maximum number of iterations in the particle swarm algorithm is taken as 150.

[0042] The method for optimizing the operation of an active distribution network with a photovoltaic power source according to the present invention specifically includes:

[0043] Optimization strategy: Install an SVG reactive power compensation device in the distribution network. Utilize its characteristic of being able to both generate and absorb reactive power to perform reactive power compensation and voltage regulation for the problem that the node voltage may exceed the upper limit or lower limit after the photovoltaic power source is connected. When the node voltage is lower than the lower limit, the SVG generates reactive power to reduce the line voltage drop; when the node voltage exceeds the upper limit, the SVG absorbs reactive power to increase the line voltage drop. Introduce an OLTC on-load tap-changing transformer at the grid balanced node. By adjusting the tap position of the transformer, the transformer turns ratio is changed, and thus the voltage is regulated. When the system voltage level is too high, the tap position is adjusted upward to reduce the secondary side voltage; when the system voltage level is too low, the tap position is adjusted downward to increase the secondary side voltage.

[0044] Optimization algorithm: Adopt the idea of multi-objective optimization to determine the optimization objectives centered on reducing network loss, reducing voltage fluctuation, and improving the photovoltaic accommodation rate. Introduce the particle swarm algorithm and improve it for multi-objective optimization to form a multi-objective particle swarm algorithm, which is used as the calculation method for iterative simulation of the mathematical model. The multi-objective particle swarm algorithm simulates the movement of particles in the solution space, uses non-dominated sorting to divide the solutions into different levels, calculates the particle velocity and position to update the current solution, and thus searches for the optimal solution in the solution space.

[0045] Model establishment and simulation verification: An IEEE 33-node distribution network model with a photovoltaic power source is established on the MATLAB platform, and relevant parameters and constraint conditions are set. For example, the No. 1 node is set as the balanced node with an initial voltage value of 1.05; the reactive power compensation device SVG is connected to nodes 15 and 31, and the upper limit of the constraint is taken as 0.08 mva; the photovoltaic access is set at node 21 with a power factor of 0.8; the on-load tap-changing transformer (OLTC) is placed at the 1 balanced node, and the lower limit of the constraint is taken as 1 and the upper limit is 1.1; the photovoltaic power input is selected from a reasonable day in the historical data of normal weather (one data selection point per hour); in multi-objective optimization, weighted averages are taken, and the maximum number of iterations in the particle swarm algorithm is taken as 150. Through simulation, the results such as the change curve of the reactive power compensator, the change curve of the tap ratio, the voltage change, the photovoltaic accommodation, and the network loss change are obtained. By comparing the situation before and after optimization, the feasibility of the optimization strategy is verified.

[0046] The active distribution network optimization operation system with a photovoltaic power source described in the present invention includes:

[0047] An SVG reactive power compensation device for emitting or absorbing reactive power according to the node voltage situation to adjust the node voltage of the distribution network;

[0048] An OLTC on-load tap-changing transformer is set at the grid balanced node to adjust the voltage by adjusting the tap position;

[0049] A multi-objective optimization module with the optimization objectives of reducing network loss, reducing voltage fluctuation, and increasing the photovoltaic accommodation rate, and using the method of weighted averaging for each objective;

[0050] A multi-objective particle swarm algorithm module, improved based on the particle swarm algorithm, for performing iterative simulation calculations on the distribution network model with a photovoltaic power source to obtain the optimal solution;

[0051] A simulation verification module that establishes an IEEE 33-node distribution network model with a photovoltaic power source on the MATLAB platform, sets relevant parameters and constraint conditions for simulation, and verifies the feasibility of the optimization strategy.

[0052] The specific application process of the active distribution network optimization operation system with a photovoltaic power source described in the present invention includes:

[0053] (1) Data collection and preparation: Collect the historical output data of the photovoltaic power source, select a representative day's data under normal weather, with one data point per hour, for the input of photovoltaic power in subsequent simulations. Obtain the line parameters and load parameters of the IEEE 33-node distribution network model, such as line resistance, reactance, active power and reactive power requirements of each node, etc.

[0054] (2) Model construction: On the MATLAB platform, a distribution network model with photovoltaic power sources is constructed according to the structure and parameters of the IEEE 33-node distribution network model. The 1st node is set as the balanced node, and its initial voltage value is set to 1.05. Reactive power compensation devices SVG are connected to the 15th node and the 31st node, and their constraint upper limit is set to 0.08 mva; a photovoltaic power source is connected to the 21st node, and the power factor is set to 0.8; a on-load tap-changer (OLTC) is placed at the 1 balanced node, and its constraint lower limit is set to 1 and the upper limit is 1.1.

[0055] (3) Algorithm setting and optimization calculation: In the multi-objective optimization algorithm, it is determined to aim at reducing network loss, reducing voltage fluctuation, and increasing the photovoltaic accommodation rate, and weights are assigned to each objective according to actual requirements. In the particle swarm algorithm, the maximum number of iterations is set to 150, and parameters such as weight coefficients and acceleration coefficients are determined. The multi-objective particle swarm algorithm is run to perform iterative calculations on the model. In each iteration, the particles update their velocities and positions according to their own historical optimal solutions and the global optimal solution, and the optimal solution is found through continuous adjustment.

[0056] (4) Result analysis and verification: After the simulation is completed, results such as the change curve of the reactive power compensator, the change curve of the transformation ratio, voltage variation, photovoltaic accommodation, and network loss change are obtained. Analyze the compensation power fluctuation of the reactive power compensator and judge its compensation effect on the photovoltaic input power and power consumption in different time periods; observe the change of the OLTC voltage transformation ratio and evaluate its voltage regulation effect; compare the voltage fluctuation range and amplitude change before and after optimization to verify the improvement effect on voltage stability; calculate the photovoltaic accommodation ratio and determine the improvement degree of the photovoltaic accommodation rate; analyze the network loss change and verify the effectiveness of the optimization strategy in reducing network loss. Through the above analysis, the feasibility and superiority of the optimization operation method and system of the present invention are comprehensively verified.

[0057] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited thereto. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, including any other suitable combination of each technical feature. These simple modifications and combinations should also be regarded as the content disclosed by the present invention and fall within the protection scope of the present invention.

Claims

1. A method for optimizing the operation of an active distribution network containing a photovoltaic power source, characterized in that: The following steps are involved: (1) Install SVG reactive power compensation device in the distribution network. According to the node voltage, when the node voltage is lower than the lower limit, SVG generates reactive power. When the node voltage is higher than the upper limit, SVG absorbs reactive power to adjust the node voltage of the distribution network. (2) Introducing an OLTC on-load tap-changing transformer at the grid balancing node, changing the transformer ratio by adjusting its tap position, and thus adjusting the voltage; (3) Adopting the multi-objective optimization concept to reduce network losses, reduce voltage fluctuations, and increase photovoltaic consumption rate as the optimization goals; (4) Introducing the particle swarm algorithm and improving it to a multi-objective particle swarm algorithm, the algorithm is used to perform iterative simulation calculations on the distribution network model containing photovoltaic power sources to obtain the optimal solution; (5) An IEEE33-node distribution network model containing photovoltaic power sources was established on the MATLAB platform, and relevant parameters and constraints were set for simulation to verify the feasibility of the optimization strategy.

2. The method for optimizing the operation of an active distribution network containing a photovoltaic power source according to claim 1, characterized in that: The SVG reactive power compensation device calculates the reactive power and voltage deviation of the grid by detecting the grid voltage and current signals, and then generates reactive power opposite to the grid current through power electronic devices to achieve reactive power compensation, while adjusting the grid voltage by controlling the current phase.

3. The method for optimizing operation of an active distribution network containing a photovoltaic power source according to claim 1 or 2, characterized in that: The tap position of the OLTC on-load voltage regulating transformer is represented by gears, and the change between adjacent gears will produce a 1.25% change in the standard voltage. By adjusting the tap position, the number of turns of the secondary coil is changed, thereby changing the output voltage and the transformer ratio.

4. The method for optimizing operation of an active distribution network containing a photovoltaic power source according to any one of claims 1 to 3, characterized in that: In the multi-objective optimization, each objective is weighted separately, and weights are allocated according to actual needs to reduce network losses, reduce voltage fluctuations, and increase photovoltaic consumption rate.

5. The method for optimizing operation of an active distribution network containing a photovoltaic power source according to any one of claims 1 to 4, characterized in that: In the multi-objective particle swarm algorithm, particles update their speed and position according to the following formula based on their own historical optimal solution and the global optimal solution: Among them, t represents the current number of iterations; w is the weight coefficient, c1 and c2 are acceleration coefficients, r1 and r2 are random numbers uniformly distributed on [0,1], and p i is the individual optimal solution of the i-th particle, g p It is the global optimal solution for the entire group.

6. The method for optimizing operation of an active distribution network containing a photovoltaic power source according to any one of claims 1 to 5, characterized in that: In the IEEE 33-node distribution network model, node 1 is set as a balancing node, and the initial voltage value is set to 1.05; the reactive compensation device svg is selected to be connected to nodes 15 and 31, and the upper limit of the constraint is 0.08mva; the photovoltaic access is set at node 21, and the power factor is 0.8; the on-load tap-changing transformer is placed at 1 balancing node, and the lower limit of the constraint is 1 and the upper limit is 1.1; the photovoltaic power input selects a reasonable day in the normal weather historical data, with one data selection point per hour; the weighted average is taken in the multi-objective optimization, and the maximum number of iterations in the particle swarm algorithm is 150.

7. An active distribution network optimization operation system containing photovoltaic power sources, characterized in that: include: SVG reactive power compensation device, used to generate or absorb reactive power according to the node voltage conditions and adjust the node voltage of the distribution network; OLTC on-load tap-changing transformer is set at the grid balancing node and adjusts the voltage by adjusting the tap position; The multi-objective optimization module takes reducing network losses, reducing voltage fluctuations, and increasing photovoltaic consumption rate as the optimization objectives, and uses a weighted method to process each objective; The multi-objective particle swarm algorithm module is improved based on the particle swarm algorithm and is used to perform iterative simulation calculations on the distribution network model containing photovoltaic power sources to obtain the optimal solution; The simulation verification module establishes an IEEE33-node distribution network model containing photovoltaic power sources on the MATLAB platform, sets relevant parameters and constraints for simulation, and verifies the feasibility of the optimization strategy.

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