Power distribution area energy storage and reactive compensation coordination optimization method based on particle swarm optimization

Abstract

The invention discloses a particle swarm optimization-based power distribution area energy storage and reactive compensation coordinated optimization method, which comprises the following steps of: constructing a power distribution area power flow model, and setting operation constraint strips of an energy storage device, a reactive compensation device and a photovoltaic power supply; according to the method, the problem of maximizing the photovoltaic consumption capacity of the power distribution area is converted into the problem of minimizing the photovoltaic overflow power when light is not abandoned, constraint conditions and safe operation boundaries of elements of the power distribution area are considered, the minimum total network loss of a system and the minimum total photovoltaic overflow amount in a period of time are taken as objective functions, a coordinated optimization model is established, and the optimal power consumption of the power distribution area is reduced. The particle swarm optimization algorithm is adopted to optimize the charging and discharging amount of the energy storage device and the compensation amount of the reactive power compensation device in the time period, solving of the optimal operation scheme of the power distribution area is achieved, and on the premise that safe and stable operation of the power distribution area is guaranteed, the network loss can be reduced, the photovoltaic permeability is improved, and the energy consumption is reduced. And the operation safety and economy of the power distribution area are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of power system optimization, and particularly relates to a coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on a particle swarm algorithm. Background Art

[0002] After the access of distributed power sources, the distribution substation area changes from the original passive network to a more complex active network. Since the output of distributed power sources is closely related to meteorological factors such as irradiation intensity and temperature, and its intermittency, volatility and uncertainty are relatively significant, the access of distributed power sources will have a great impact on the traditional distribution substation area. If the substation area lacks reasonable and effective control of distributed power sources, the power quality of the substation area will be greatly affected, voltage fluctuations will intensify, network operation losses will increase, and overvoltage and undervoltage problems will become more serious, threatening the safe and stable operation of the distribution substation area;

[0003] Due to the large ratio of resistance to reactance R / X of the lines in the low-voltage distribution substation area, the active power and reactive power in the lines cannot be decoupled, and the voltage amplitude of the nodes will also be affected by both at the same time. Therefore, it is necessary to coordinate and optimize the active and reactive power of the substation area. As an important part of the end of the power system, the operation status of the distribution substation area will directly affect the economic benefits of local enterprises and the living quality of residents. Therefore, it is of great significance to deeply study the optimization control technology of distribution substation areas containing renewable energy. Summary of the Invention

[0004] The purpose of the invention is to provide a coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on a particle swarm algorithm to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the invention provides the following technical solution: A coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on a particle swarm algorithm;

[0006] The purpose of the invention can be realized by the following technical solutions:

[0007] An active and reactive power coordinated optimization method for a distribution substation area with high photovoltaic penetration rate, comprising the following steps:

[0008] Step 1, construct a power flow model of the distribution substation area to calculate the node voltage and line loss, and set the two-way power exchange limit of the transformer between the distribution substation area and the power distribution network;

[0009] Step 2, set the operation constraint conditions of the energy storage device, reactive power compensation device and photovoltaic power source. Among them, the constraint conditions of the energy storage device can be expressed as:

[0010]

[0011] where SOC(s) is the state of charge of the energy storage device in the s-th time period, SOC min and SOC max are the lower and upper limits of the state of charge respectively, ΔW max is the maximum charge and discharge amount of the energy storage device per unit time period, S ess is the total capacity of the energy storage device, SOC(0) and SOC end are the initial and final states of charge of the energy storage device respectively;

[0012] Step 3: Transform the problem of maximizing the photovoltaic accommodation capacity of the distribution transformer area into the problem of minimizing the photovoltaic spillage of the distribution transformer area, and establish an optimization model with the goal of minimizing network loss and minimizing photovoltaic spillage power;

[0013] Step 4: Use the particle swarm optimization algorithm to optimize the charge and discharge amount of the energy storage and the reactive power compensation amount, and obtain the optimal configuration plan.

[0014] Preferably, in the said Step 1, a power flow model of the distribution transformer area is constructed based on Kirchhoff's law, the node injection power and line loss are calculated, and the voltage amplitude of each node and the two-way power flow of the distribution transformer are restricted.

[0015] Preferably, in the said Step 2, the state of charge of the energy storage device reflects the remaining capacity of the storage. The state of charge and the charge and discharge amount per unit time period need to be restricted within a safe range, and the initial and final states of charge need to be kept consistent. Therefore, its operation constraint conditions include the constraint of its state of charge range, the charge and discharge power constraint per unit time period, and the initial and final state of charge constraint. The discrete reactive power compensation device controls the reactive power compensation amount by switching to different gears, and its constraint conditions are the reactive power compensation amount per unit gear and the gear constraint. When there is no light curtailment phenomenon for the photovoltaic power source, its active power output value is set to the predicted value. When the output value is less than the capacity of the grid-connected inverter, the remaining capacity of the grid-connected inverter can be used as a reactive power resource.

[0016] Preferably, in the said Step 3, first assume that there is no light curtailment phenomenon, set the grid-connected power of the photovoltaic power generation to the predicted power value under this meteorological condition, monitor the magnitude of the power fed back from the distribution transformer area to the distribution network. If it exceeds the power limit, reduce the grid-connected power of the photovoltaic at this moment by light curtailment to meet the requirements. Thus, the problem of maximizing the photovoltaic accommodation capacity of the distribution transformer area is transformed into the problem of minimizing the photovoltaic spillage of the distribution transformer area. The magnitude of the photovoltaic spillage is measured by the reverse power flow of the distribution transformer. The objective function of the optimization model is to minimize the total network loss of the distribution transformer area and minimize the total photovoltaic spillage. The optimization of the dual objectives is achieved by introducing a network loss term coefficient. A penalty term determined by the difference between the initial and final states of charge of the energy storage device during the optimization time period is added to the optimization function to prompt the energy storage device to return its final state of charge to its initial state during the optimization time period. The optimization objective function is expressed as:

[0017]

[0018] Among them, p loss (s) and p of (s) are the network loss and PV spillage of the s-th sub-time period of the distribution transformer area respectively, λ is the network loss item coefficient, G(x) is the penalty function, and ΔSOC is the difference between the initial and final state of charge of the energy storage device during the optimization time period.

[0019] Preferably, in the fourth step, the charging and discharging power of the energy storage device in the distribution transformer area and the reactive power compensation amount of the discrete reactive power compensation device are optimized by the particle swarm optimization algorithm. The specific particle swarm optimization algorithm iteration process is as follows:

[0020] The first step is to set parameters such as the total number of particles and dimensions, and initialize the position and velocity of each particle. The position information of each particle in two dimensions stores the state arrays of the energy storage device and the reactive power compensation device in each sub-time period within the optimization time interval.

[0021] The second step is to substitute the charging and discharging power of the energy storage, the output of the reactive power compensation device, the PV power, and the load power in each time period into the distribution transformer area model, calculate the power flow of the transformer area in each sub-time period, obtain the network loss and PV spillage in each sub-time period, and calculate the initial value of the optimization objective function.

[0022] The third step is to update the charging and discharging power of the energy storage device and the compensation amount of the reactive power compensation device in each sub-time period represented by each particle according to the particle swarm optimization algorithm, and judge whether the updated energy storage and reactive power compensation meet the constraint conditions. If they do not meet, select the closest state to make them meet.

[0023] The fourth step is to update the injection power of each node in the distribution transformer area system in each sub-time period, calculate the power flow, obtain the network loss and PV spillage, monitor the voltage amplitude of each node. If there is a voltage violation at any node in any time period, directly discard this optimization scheme. If the voltage amplitude meets the constraint conditions, calculate the optimization objective function value, and update the individual optimal solution and the population optimal solution.

[0024] The fifth step is to end the algorithm if the stop requirement is met, otherwise return to the third step.

[0025] Compared with the prior art, the beneficial effects of the present invention are:

[0026] (1) The present invention uses the particle swarm optimization algorithm to coordinately optimize the output of the energy storage device and the output of the reactive power compensation device in the distribution transformer area, effectively solving the problem that active and reactive power in the low-voltage distribution transformer area cannot be decoupled due to the large line impedance ratio.

[0027] (2) By coordinating and optimizing the active and reactive power in the distribution substation area, the present invention significantly reduces the network loss of the distribution substation area, improves the in-situ consumption capacity of photovoltaic power generation, effectively suppresses the node voltage fluctuation, and ensures the security and reliability of the system.

[0028] (3) By adding a penalty function of the difference between the initial and final state of charge of the energy storage device to the optimization objective function, the present invention effectively promotes the final state of charge of the energy storage device to return to its initial state during the optimization period, facilitating the normal optimization of multiple consecutive time periods and having high practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the coordinated optimization of energy storage and reactive power compensation in the distribution substation area of the present invention;

[0030] Figure 2 It is a schematic diagram of the improved IEEE9-node distribution substation area system of an example of the present invention;

[0031] Figure 3 It is a flowchart of the particle swarm optimization in the distribution substation area of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0032] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] The present invention provides a coordinated optimization method for energy storage and reactive power compensation in the distribution substation area based on the particle swarm algorithm, and the present invention is applicable to low-voltage distribution substation areas. The distribution substation area system and the particle swarm optimization process adopted are respectively as Figure 2 AND Figure 3 SHOWN AS:

[0034] I. Establishing a system model: The distribution substation area includes n nodes and m branches. Distributed photovoltaic power generation, energy storage devices and reactive power compensation devices are connected to the nodes to form an active distribution substation area network. The calculation formulas for the node injection power and the line power flow can be expressed as:

[0035]

[0036] Wherein, g ij AND b ij ARE RESPECTIVELY THE CONDUCTANCE AND SUSCEPTANCE BETWEEN NODE I AND NODE J, u i IS THE VOLTAGE AMPLITUDE AT NODE I, AND δ ij IS THE DIFFERENCE IN VOLTAGE PHASE ANGLES BETWEEN NODE I AND NODE J;

[0037] The line loss calculation formula for the distribution transformer area is as follows:

[0038]

[0039] The node voltage amplitude constraint condition and the two-way power constraint condition at the distribution transformer of the area are respectively expressed as:

[0040] u min ≤u i ≤u max

[0041] -P rmax ≤p grid ≤P fmax

[0042] The constraint condition of the energy storage device is expressed as:

[0043]

[0044] Among them, SOC(s) is the state of charge of the energy storage device in the s-th time period, SOC min and SOC max are respectively the lower limit and the upper limit of the state of charge, ΔW max is the maximum charge and discharge amount of the energy storage device in a unit time period, S ess is the total capacity of the energy storage device, SOC(0) and SOC end are respectively the initial and final states of charge of the energy storage device;

[0045] The constraint conditions of the discrete reactive power compensation device and the photovoltaic power source are respectively expressed as:

[0046]

[0047] Among them, q CB (s) is the reactive power compensation amount of the discrete reactive power compensation device in the s-th time period, t(s) is the gear position of the discrete reactive power compensation device in the s-th time period, is the compensation amount of each gear of the discrete reactive power compensation device, t max is the maximum gear position; p PV (s) and q PV (s) are respectively the active power and reactive power of the photovoltaic power source feeding into the grid in the s-th time period, s PV is the capacity of the photovoltaic grid-connected converter.

[0048] II. Establishment of the optimization objective function: The present invention aims to minimize the total network loss and the minimum photovoltaic spillover power of the distribution transformer area, and the optimization objective function is defined as:

[0049]

[0050] Among them, ploss (s) and p of (s) are the network loss and the PV spillage of the s-th sub-time period transformer area respectively, λ is the network loss item coefficient, G(x) is the penalty function, and ΔSOC is the difference between the initial and final state of charge of the energy storage device during the optimization period.

[0051] III. Steps for implementing the particle swarm optimization algorithm:

[0052] First step, set the parameters of the particle swarm optimization algorithm, including the number of particles, the maximum number of iterations, the initial position and velocity. The position of each particle represents the state array of energy storage and reactive power compensation;

[0053] Second step, for each particle, calculate the power flow and obtain the network loss p ch and the reactive power compensation q CB according to its energy storage charge and discharge power p loss and PV spillage p of , check the initial and final state of charge of the energy storage device. If the constraint conditions are not met, set a high penalty value for processing to obtain the fitness value;

[0054] Third step, update the velocity and position of the particle, which can be expressed by the following formula:

[0055]

[0056] where k is the iterative optimization number of the particle swarm optimization, v i (k) and x i (k) are the velocity and position of the i-th particle at the k-th optimization respectively, and are the individual optimal position and the global optimal position of the particle respectively, c1 and c2 are the cognitive coefficients, r1 and r2 are random numbers uniformly distributed as the convergence factors, φ is a contraction factor used to control the velocity range, ω is the inertia weight. To enhance the convergence, its value decreases with the increase of the number of iterations:

[0057]

[0058] where ω max and ω min are the maximum and minimum values of the inertia weight respectively, and k max is the maximum number of iterations.

[0059] Fourth step, update the individual optimal solution and the population optimal solution. If the maximum number of iterations is reached or the objective function converges, output the global optimal solution. The optimal solution includes the energy storage charge and discharge strategy and the reactive power compensation device compensation strategy.

[0060] IV. Optimization results: For Figure 2For the improved IEEE 9-node distribution substation area system shown, the total capacity of the energy storage device is 300 kWh, the initial state of charge is 0.6, and the maximum charge and discharge power is 120 kW; the discrete gears of the reactive power compensation device are 6 gears, and the compensation amount for each gear is 10 kVar; the optimization time interval is 24 hours of a typical day, and the data sampling interval is 1 hour. The optimization results of the embodiments of the present invention verify the effectiveness of the method of the present invention: after optimization, the total network loss of the distribution substation area is reduced by more than 25%, the total spillage of photovoltaic power generation is reduced by nearly 40%, the voltages of all nodes do not exceed the limit within 24 hours, and the final state of charge of the energy storage device reliably returns to its initial state of charge.

[0061] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on the particle swarm algorithm, characterized in that, It includes the following steps: Step 1: Construct a power flow model for the distribution substation area to calculate the node voltage and line loss, and set the two-way power exchange limit of the transformer between the distribution substation area and the distribution network; Step 2: Set the operation constraints of the energy storage device, reactive power compensation device, and photovoltaic power source. Among them, the constraint conditions of the energy storage device are expressed as: Step 3: Convert the problem of photovoltaic accommodation capacity in the distribution substation area into the problem of photovoltaic spillage in the distribution substation area, and establish an optimization model with network loss and photovoltaic spillage power as the objectives. Among them, the objective function of the optimization model can be expressed as: Step 4: Use the particle swarm optimization algorithm to optimize the charge and discharge amount of the energy storage and the reactive power compensation amount, and obtain the optimal configuration scheme.

2. The coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on the particle swarm algorithm according to claim 1, characterized in that: In Step 1, the distribution substation area conducts two-way power exchange with the distribution network through a transformer, that is, it allows the distribution substation area to send a certain amount of power back to the distribution network. On the premise of meeting the reverse power flow limit, the distribution substation area can send the spillage of the internally unaffordable photovoltaic power generation back to the distribution network.

3. A coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on the particle swarm algorithm according to claim 1, characterized in that: In Step 2, the energy storage device optimizes the active power of the distribution substation area. And to ensure the normal optimization of multiple consecutive time periods, the final state of charge of the energy storage device within the optimization time period is set to the same value as the initial state of charge.

4. A coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on the particle swarm algorithm according to claim 1, characterized in that: In step 2, where SOC(s) is the state of charge of the energy storage device in the s-th time period, SOC min and SOC max are the lower and upper limits of the state of charge respectively, and ΔW max is the maximum charge and discharge amount of the energy storage device per unit time period, S ess is the total capacity of the energy storage device, and SOC(0) and SOC end are the initial and final states of charge of the energy storage device respectively.

5. A coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on the particle swarm algorithm according to claim 1, characterized in that: In Step 3, first assume that there is no light curtailment phenomenon, set the grid-connected power of the photovoltaic power generation to the predicted power value under this meteorological condition, monitor the magnitude of the power sent back from the distribution substation area to the distribution network. If it exceeds the power limit, then reduce the grid-connected power of the photovoltaic at this moment through light curtailment to make it meet the requirements. Thus, the problem of maximizing the photovoltaic accommodation capacity in the distribution substation area is converted into the problem of photovoltaic spillage in the distribution substation area. The magnitude of the photovoltaic spillage is measured by the reverse power flow of the substation area transformer.

6. The coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on the particle swarm algorithm according to claim 1, wherein: In Step 3, the objective function of the optimization model is the total network loss and the total photovoltaic spillage in the distribution substation area. The optimization of the dual objectives is achieved by introducing a network loss term coefficient, and a penalty term determined by the difference between the initial and final states of charge of the energy storage device within the optimization time period is added to the optimization function to prompt the final state of charge of the energy storage device to return to its initial state within the optimization time period.

7. A coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on the particle swarm algorithm according to claim 1, characterized in that: In step 3, where p loss (s) and p of (s) are the network loss and the PV spillage of the s-th sub-time period sub-region respectively, λ is the network loss item coefficient, G(x) is the penalty function, and ΔSOC is the difference between the initial and final state of charge of the energy storage device during the optimization time period.

8. A coordinated optimization method for energy storage and reactive power compensation in a distribution substation area based on the particle swarm algorithm according to claim 1, characterized in that: In Step 4, the optimization variables are the charge and discharge amount of the energy storage device and the compensation amount of the reactive power compensation device in the distribution substation area within a time period. An array is established to store the two-dimensional variable information, and the two-dimensional variables are optimized through the particle swarm optimization algorithm to obtain the optimal energy storage charge and discharge scheme and reactive power compensation scheme for the distribution substation area.

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