Voltage adjusting method, device and equipment for voltage out-of-limit node in power distribution network

Abstract

The invention discloses a voltage regulation method for a voltage out-of-limit node in a power distribution network, and relates to the technical field of voltage regulation, and the method comprises the steps: obtaining a power utilization node of which the actual voltage exceeds a limit voltage in the power distribution network after a distributed energy storage system and renewable energy are merged into the power distribution network; using a dung beetle optimization algorithm to find a group of most distributed energy storage system charging and discharging power; and adjusting the charging and discharging power of the distributed energy storage system based on the optimal charging and discharging power of the distributed energy storage system so as to adjust the voltage of the power utilization node where the voltage is out of limit at the moment when the voltage of the power distribution network is out of limit. The method not only improves the moderate value, but also accelerates the convergence efficiency, stabilizes the voltage fluctuation by regulating and controlling the distributed energy storage charging and discharging, improves the electric energy quality, reduces the operation cost, and provides effective support for the stable operation of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage regulation, and specifically provides a method for adjusting the voltage of voltage - over - limit nodes in a distribution network. Background Art

[0002] A distributed energy storage system is a solution to the voltage fluctuation problem caused by the grid connection of renewable energy and the growth of power demand in a distribution network. When a distribution network realizes distributed power generation through new energy sources such as a photovoltaic power generation system and is equipped with a distributed energy storage system to balance supply and demand, when a certain power consumption node is monitored to have a voltage exceeding the specified range, the discharge power of the distributed energy storage system is adjusted to provide additional load to reduce the voltage of the node.

[0003] However, how to effectively reduce the node voltage deviation on the basis of promoting the economic operation of the distribution network by the distributed energy storage system in the distribution network is an urgent problem to be studied. Summary of the Invention

[0004] In view of the above - mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for adjusting the voltage of voltage - over - limit nodes in a distribution network, which can solve the problems mentioned in the background art.

[0006] To solve the above - mentioned technical problems, the present invention provides the following technical solution: A method for adjusting the voltage of voltage - over - limit nodes in a distribution network, including: adjusting the charge - discharge power of the energy storage system in the distribution network to adjust the voltage of the nodes with voltage over - limit in the distribution network to a non - over - limit state. It is characterized by including: obtaining the power consumption nodes in the distribution network whose actual voltage exceeds the limit voltage after the distributed energy storage system and renewable energy are incorporated into the distribution network; using the dung beetle optimization algorithm to find a set of optimal charge - discharge powers of the distributed energy storage system. The optimal charge - discharge powers of the distributed energy storage system are the charge - discharge powers of the distributed energy storage system that minimize the sum of the deviation between the actual voltage and the limit voltage of the power consumption nodes and the power purchase cost of the power consumption nodes under multiple constraints of power flow, node voltage, line power, renewable energy output power, and energy storage system operation; adjusting the charge - discharge power of the distributed energy storage system based on the optimal charge - discharge powers of the distributed energy storage system to adjust the voltage of the power consumption nodes with voltage over - limit at the moment when the voltage over - limit occurs in the distribution network.

[0007] As a preferred solution of the voltage adjustment method for voltage - over - limit nodes in the distribution network described in the present invention, wherein: using the dung beetle optimization algorithm to find a set of optimal charging and discharging powers of distributed energy storage systems, the optimal charging and discharging powers of the distributed energy storage systems are those that minimize the sum of the deviation between the actual voltage and the limit voltage of the power - consuming nodes and the power - purchase cost of the power - consuming nodes under multiple constraints of power flow, node voltage, line power, renewable energy output power, and energy storage system operation, specifically including:

[0008] Construct a voltage optimization model, which takes the sum of the deviation between the actual voltage and the limit voltage of the power - consuming nodes and the power - purchase cost of the power - consuming nodes as the objective function, and takes power - flow constraint, node - voltage constraint, line - power constraint, photovoltaic - output - power constraint, and energy - storage operation constraint as the constraint conditions;

[0009] Use the dung beetle optimization algorithm to solve the voltage optimization model to obtain the charging and discharging powers of the distributed energy storage systems that minimize the objective function.

[0010] As a preferred solution of the voltage adjustment method for voltage - over - limit nodes in the distribution network described in the present invention, wherein: taking the sum of the deviation between the actual voltage and the limit voltage of the power - consuming nodes and the power - purchase cost of the power - consuming nodes as the objective function, specifically including:

[0011] f = w 1 f 1 +w 2 f 2

[0012]

[0013]

[0014] wherein, f is the objective function, f 1 is the deviation between the actual voltage and the limit voltage of the power - consuming nodes, W 1 is the voltage - deviation weight coefficient, f 2 is the power - purchase cost of the power - consuming nodes, W 2 is the power - purchase - cost weight coefficient; T is 24 hours, n is the number of nodes, U i, t is the voltage of the i - th node at time t, U N is the reference voltage of the distribution network; C u (t) is the unit price of purchasing electricity from the upper - layer power grid at time t; P Grid (t) is the power exchanged between the distribution network and the upper - layer power grid at time t.

[0015] As a preferred solution of the voltage adjustment method for voltage over-limit nodes in the distribution network described in the present invention, the following are included: taking power flow constraints, node voltage constraints, line power constraints, photovoltaic output power constraints, and energy storage operation constraints as constraint conditions, specifically including:

[0016] Set power flow constraints:

[0017]

[0018] Among them, Ω(i) represents all adjacent nodes of node i, and respectively represent the active power and reactive power of node i at time t; U i and U j respectively represent the i-th and j-th nodes; G ij represents the conductance of branch ij; B ij represents the susceptance of branch ij; θ ij represents the phase angle difference between node i and node j;

[0019] Set node voltage constraints:

[0020] U min ≤U i,t ≤U max

[0021] Among them, U i,t is the voltage magnitude of node i at time t; U min is the minimum allowable node voltage; U max is the maximum allowable node voltage;

[0022] Set line power constraints:

[0023] l ij,t ≤l ij,max

[0024] Among them, Iij,t is the branch current amplitude at time t; Iij,max is the upper limit of the branch current amplitude.

[0025] As a preferred solution of the voltage adjustment method for voltage over-limit nodes in the distribution network described in the present invention, the following is included: Set photovoltaic output power constraints:

[0026]

[0027] Among them, PG,i,t is the active power output of the photovoltaic at node i at time t; QG,i,t is the reactive power output of the photovoltaic at node i at time t; and are respectively the upper and lower limits of the active power output of the photovoltaic at node i at time t; and are the upper and lower limits of the reactive power output of the photovoltaic at node i at time t, respectively;

[0028] Set the energy storage operation state constraint:

[0029] S(t + ΔT) = S(t) + K c,t P ESS,c ΔT - K d,t P ESS,d ΔT

[0030]

[0031] where S(t) is the state of charge at time t; ΔT represents the time change; K c,t and K d,t are the charge and discharge coefficients of the energy storage respectively; P ESS,c and P ESS,d are the charge and discharge powers of the energy storage respectively; and are the maximum charge and discharge powers of the energy storage; D c,i,t and D c,i,t are 0 - 1 variables respectively, used to ensure that charging and discharging do not occur simultaneously;

[0032] Set the energy storage state of charge constraint:

[0033] S min ≤ S(t) ≤ S max

[0034] where S min is the lower limit of the state of charge; S max is the upper limit of the state of charge;

[0035] Set the energy storage power constraint:

[0036] -P ESS,N ≤ P ESS,i,t ≤ P ESS,N

[0037] where PESS,N is the rated power of the energy storage; PESS,i,t is the real - time power of the energy storage at node i at time t.

[0038] As a preferred scheme of the voltage adjustment method for voltage - over - limit nodes in the distribution network described in the present invention, wherein: solving the voltage optimization model using the dung beetle optimization algorithm to obtain the charge and discharge powers of the distributed energy storage system that minimize the objective function specifically includes:

[0039] Input daily load, photovoltaic data, energy storage data, time - of - use electricity price periods;

[0040] Control the energy storage to charge during the low - price period of the electricity price according to the time - of - use electricity price period to achieve economic optimization;

[0041] Perform a global power flow calculation to find the moment when voltage violation occurs, and the node starts to control.

[0042] Set the basic parameters of the dung beetle optimization algorithm, including the number of dung beetles, the maximum number of iterations, and the position limit.

[0043] As a preferred solution of the voltage adjustment method for voltage - violating nodes in the distribution network described in the present invention, wherein: using the dung beetle optimization algorithm to solve the voltage optimization model to obtain the charging and discharging power of the distributed energy storage system that minimizes the objective function further includes:

[0044] Calculate the initial fitness value and obtain the initial optimal solution.

[0045] Update the positions of dung beetle individuals in different groups, as well as the optimal spawning position and the best foraging position, calculate the fitness and update the optimal solution.

[0046] Judge whether the maximum number of iterations is reached. If not, return to the step "Calculate the initial fitness value and obtain the initial optimal solution"; if so, output the time - series output of the distributed energy storage.

[0047] To further solve the above - mentioned technical problems, the present invention provides the following technical solution: A voltage adjustment device for voltage - violating nodes in a distribution network, including: an adjustment object acquisition module for acquiring the power - consuming nodes in the distribution network where the actual voltage exceeds the limit voltage after the distributed energy storage system and renewable energy are incorporated into the distribution network; an optimization module for using the dung beetle optimization algorithm to find a set of optimal charging and discharging powers of the distributed energy storage system, where the optimal charging and discharging powers of the distributed energy storage system are those that minimize the sum of the deviation between the actual voltage and the limit voltage of the power - consuming nodes and the power purchase cost of the power - consuming nodes under multiple constraints of power flow, node voltage, line power, renewable energy output power, and energy storage system operation; a voltage adjustment module for adjusting the charging and discharging power of the distributed energy storage system based on the optimal charging and discharging powers of the distributed energy storage system to adjust the voltage of the power - consuming nodes where voltage violation occurs at the moment when voltage violation occurs in the distribution network.

[0048] A computer device includes a memory and a processor. The memory stores a computer program. The feature is that when the processor executes the computer program, it implements the steps of the voltage adjustment method for voltage - violating nodes in the distribution network as described above.

[0049] A computer - readable storage medium stores a computer program. The feature is that when the computer program is executed by a processor, it implements the steps of the voltage adjustment method for voltage - violating nodes in the distribution network as described above.

[0050] Advantages of the present invention: By establishing a comprehensive optimal scheduling model considering time-of-use electricity price and distributed energy storage, with the minimum voltage deviation and power purchase cost as the objective function and voltage non-exceedance as the constraint, a comprehensive voltage regulation strategy for energy storage with voltage-exceeding nodes is designed. The dung beetle optimization algorithm is used to solve the model. The simulation results show that the dung beetle optimization algorithm can quickly and accurately solve the comprehensive optimal scheduling model. It not only improves the fitness value and speeds up the convergence efficiency, but also suppresses voltage fluctuations by regulating the charging and discharging of distributed energy storage, improves power quality, reduces operating costs, and provides effective support for the stable operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0052] Figure 1 It is a schematic flowchart of a method for adjusting the voltage of voltage-exceeding nodes in a distribution network provided in this specification;

[0053] Figure 2 It is a schematic diagram of a device for adjusting the voltage of voltage-exceeding nodes in a distribution network provided in this specification;

[0054] Figure 3 It is a schematic diagram of a computer device for implementing a method for adjusting the voltage of voltage-exceeding nodes in a distribution network provided in this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0057] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides a method for adjusting the voltage of voltage-exceeding nodes in a distribution network.

[0058] This application provides a solution that can effectively address the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail on how to implement the voltage adjustment method for voltage-limit nodes in the distribution network.

[0059] Figure 1 Fig. shows the overall flowchart of a voltage adjustment method for voltage-limit nodes in a distribution network, including:

[0060] With the large-scale grid connection of renewable energy and the continuous growth of power demand, the distribution network faces unprecedented challenges. These challenges mainly include voltage stability problems and power quality problems. To address these problems, researchers have proposed various solutions. Among them, distributed energy storage systems (DESS) have become one of the effective means to solve power quality problems due to their fast response speed and ability to provide rapid power support. In addition, as an economic incentive mechanism, time-of-use electricity price strategies can guide users to reasonably adjust their electricity consumption behaviors by adjusting electricity prices at different time periods, thereby reducing power demand during peak hours and improving the operating efficiency of the power grid. However, how to combine distributed energy storage systems with time-of-use electricity price strategies to achieve optimal dispatching of the distribution network remains an issue worthy of in-depth study.

[0061] In this context, the present invention combines time-of-use electricity prices and distributed energy storage to establish an integrated optimal dispatching model considering time-of-use electricity prices and distributed energy storage. With the voltage deviation and the minimum purchase cost as the objective function and considering various constraints, an integrated voltage regulation control strategy based on the dung beetle optimization algorithm is established. Finally, the proposed algorithm is tested using the IEEE 33-node standard example to verify the effectiveness of the proposed model and method.

[0062] S1: Obtain the power consumption nodes in the distribution network where the actual voltage exceeds the limit voltage after the distributed energy storage system and renewable energy are integrated into the distribution network.

[0063] S2: Use the dung beetle optimization algorithm to find a set of optimal charging and discharging powers of the distributed energy storage system. The optimal charging and discharging powers of the distributed energy storage system are those that minimize the sum of the deviation between the actual voltage and the limit voltage of the power consumption nodes and the purchase cost of the power consumption nodes under multiple constraints of power flow, node voltage, line power, renewable energy output power, and energy storage system operation.

[0064] S201: Construct a user time-of-use electricity price model.

[0065] The load transfer rate is a key indicator to measure the degree of load transfer among different electricity price periods for users in the power system. It is defined as the ratio of the electricity load transferred by users from high - price periods to low - price periods after implementing the time - of - use electricity price system to the original load in high - price periods. By constructing a user response model based on the load transfer rate, we can approximate the user's behavior as a piece - wise linear function. Taking the valley - peak, peak - flat, and flat - valley periods as examples, their load transfer rates are given by formulas (1), (2), and (3) respectively:

[0066]

[0067] where Δp fg = p f - p g p g and p f are the electricity prices in the valley period and peak period respectively. a fg and b fg are the upper limit values of the user for the electricity price difference between peak - valley periods in the dead zone and linear zone respectively, K fg is the slope of the linear zone. is the maximum value of the load transfer rate in the peak - valley period.

[0068]

[0069] where Δp fp = p f - p p p p is the electricity price in the flat period. a fp and b fp are the upper limit values of the user for the electricity price difference between peak - flat periods in the dead zone and linear zone respectively, K fp is the slope of the linear zone. is the maximum value of the load transfer rate in the peak - flat period.

[0070]

[0071] where Δp pg = p p - p g . a pg and b pg are the upper limit values of the user for the electricity price difference between flat - valley periods in the dead zone and linear zone respectively, K pg is the slope of the linear zone. is the maximum value of the load transfer rate in the flat - valley period.

[0072] S202: Construct a comprehensive voltage regulation control strategy for the distribution network.

[0073] (1) Set the objective function.

[0074] Taking the minimum of voltage deviation and power purchase quantity as the objective function, the specific formula is as follows:

[0075]

[0076] In the formula, T is 24 hours, n is the number of nodes, Ui,t is the voltage of the i-th node at time t, and UN is the reference voltage of the system.

[0077]

[0078] In the formula: Cu(t) is the unit price of purchasing electricity from the upper-level power grid at time t; PGrid(t) is the power exchanged between the distribution network and the upper-level power grid at time t.

[0079] In order to comprehensively consider the influence of voltage deviation and power purchase cost, this paper uses the method of weighted summation to solve the multi-objective function and establishes a comprehensive objective function. The specific calculation formula is as follows:

[0080] f = w 1 f 1 + w 2 f 2 (6)

[0081] In the formula: W 1 is the weight coefficient of voltage deviation, and W 2 is the weight coefficient of power purchase cost.

[0082] (2) Set the constraint conditions.

[0083] To ensure the continuous and stable operation of the distribution network, the following constraints need to be strictly followed: power flow constraint, node voltage constraint, line power constraint, photovoltaic output power constraint, energy storage operation constraint.

[0084] ① Power flow constraint:

[0085]

[0086]

[0087] Among them, Ω(i) represents all adjacent nodes of node i, P i t and Q i t respectively represent the active power and reactive power of node i at time t; U i and U j respectively represent the i-th and j-th nodes; G ij represents the conductance of branch ij; B ij represents the susceptance of branch ij; θ ijDenote the phase angle difference between node i and node j;

[0088] ② Node voltage constraint:

[0089] U min ≤U i,t ≤U max (9)

[0090] where U i,t is the magnitude of the voltage at node i at time t; U min is the minimum allowable value of the node voltage; U max is the maximum allowable value of the node voltage, taking 1.05U N .

[0091] ③ Line power constraint:

[0092] l ij,t ≤l ij,max (8)

[0093] where I ij,t is the magnitude of the branch current at time t; I ij,max is the upper limit of the branch current magnitude;

[0094] ④ Photovoltaic output power constraint:

[0095]

[0096]

[0097] where P G,i,t is the active power output of the photovoltaic at node i at time t; Q G,i,t is the reactive power output of the photovoltaic at node i at time t; and are the upper and lower limits of the active power output of the photovoltaic at node i at time t respectively; and are the upper and lower limits of the reactive power output of the photovoltaic at node i at time t respectively.

[0098] ⑤ Energy storage operation state constraint:

[0099] S(t + ΔT) = S(t) + K c,t P ESS,c ΔT - K d,t P ESS,d ΔT (13)

[0100]

[0101] where S(t) is the state of charge at time t; ·ΔT represents the time change; K c,t and K d,t are the charge and discharge coefficients of the energy storage respectively; PESS,c and P ESS,d are the charging and discharging powers of the energy storage, respectively; and are the maximum charging and discharging powers of the energy storage; D c,i,t and D c,i,t are 0-1 variables respectively, used to ensure that charging and discharging do not occur simultaneously.

[0102] ⑥ Energy storage state of charge constraint:

[0103] To prevent overcharging and discharging, the state of charge of the energy storage should not exceed the upper and lower limits, increasing the service life of the energy storage:

[0104] S min ≤S(t)≤S max (15)

[0105] where S min is the lower limit of the state of charge; S max is the upper limit of the state of charge.

[0106] ⑦ Energy storage power constraint:

[0107] -P ESS,N ≤P ESS,i,t ≤P ESS,N (16)

[0108] where P ESS,N is the rated power of the energy storage; P ESS,i,t is the real-time power of the energy storage at node i at time t.

[0109] S203: Construct a comprehensive control strategy.

[0110] First, determine the voltage over-limit time and over-limit nodes according to the accessed distributed power sources. Secondly, at the voltage over-limit time, connect the distributed energy storage and select the over-limit nodes as the adjustment objects. Use the improved dung beetle optimization algorithm to adjust the voltage of the distributed energy storage charging and discharging, so that the voltage returns to a reasonable range. To balance economy and make the energy storage charging and discharging balanced. Control the energy storage to charge during the valley period of the time-of-use electricity price. The specific steps are as Figure 2 shown, including:

[0111] (1) Input the daily load, photovoltaic data, energy storage data, etc. of a certain field area.

[0112] (2) Conduct a power flow calculation, find the nodes with voltage over-limit, count the over-limit time, and control the over-limit nodes and over-limit time

[0113] (3) Refer to the time-of-use electricity price period, control the energy storage to charge during the electricity price valley to achieve economic optimization

[0114] (4) Select the node with the most serious over-limit as the regulation object, and use the dung beetle optimization algorithm to regulate the charge and discharge power of distributed energy storage with the minimum voltage deviation as the objective function.

[0115] (5) Output the time-series output of energy storage.

[0116] S204: Solve the model using the dung beetle optimization algorithm.

[0117] Introduce the dung beetle optimization algorithm to solve the model. The dung beetle optimization algorithm forms an efficient method for exploring the solution space by simulating its natural behaviors of using skylight sources for direction positioning and rolling dung balls, combined with random steering and adaptive adjustment, while traditional algorithms are prone to falling into local optimal solutions.

[0118] (1) The principle of the DBO algorithm is inspired by the foraging behavior of dung beetles in nature and is described as follows:

[0119] Randomly initialize the population positions to divide the dung beetle population and calculate the fitness values.

[0120] The position update formula for the rolling dung beetle is as follows:

[0121] Unobstructed update mode:

[0122] x o (m + 1) = x o (m) + α × k × x o (m - 1) + b × Δx,

[0123] Δx = |x o (m) - X w | (17)

[0124] In the formula: x o (m + 1) is the position information of the o-th dung beetle at the (m + 1)-th iteration, x o (m) represents the position information of the o-th dung beetle at the m-th iteration, k ∈ (0, 0.2] is a constant representing the deflection coefficient, b is a fixed value in (0, 1), α is the natural coefficient taking 1 or -1, -1 indicates deviation from the original direction, 1 indicates no deviation, Xw represents the global worst position, and Δx simulates the change in light intensity.

[0125] Obstructed update mode:

[0126] x o (m + 1) = x o (m) + tan(θ)|x o (m) - x o (t - m)| (18)

[0127] In the formula: x o(m + 1) is the position information x of the m + 1-th iteration of the o-th dung beetle o (m) represents the position information of the m-th iteration of the o-th dung beetle, θ is the deflection angle, belonging to [0, π]. |x o (m) - x o (t - m)| represents the difference between the position of the o-th dung beetle at the m-th iteration and its position at the m - 1-th iteration. Therefore, the update of the dung beetle is related to the current and historical information. If θ = 0, π / 2, π, the position of the dung beetle is not updated.

[0128] Reproductive dung beetles will roll the dung ball to a safe place to hide and reproduce. The specific formula is as follows:

[0129] Ub * = min(X * ×(1 + R), Ub) (19)

[0130] In the formula: X* represents the current local best position, Lb* and Ub* respectively represent the lower and upper limits of the ovipositor, R = 1 - m / Mmax, Mmax represents the maximum number of iterations, and Lb and Ub respectively represent the lower and upper limits of the optimization problem.

[0131] X o (m + 1) = X * + b 1 ×(X o (m) - Lb * ) + b 2 ×(X o (m) - Ub * ) (20)

[0132] In the formula: x o (m + 1) is the position information of the o-th brood ball at the m + 1-th iteration, x o (m) is the position information of the o-th brood ball at the m-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D represents the dimension of the optimization problem.

[0133] The young dung beetles will forage in the best foraging area. The specific formula is as follows:

[0134] Lb b = max(X b ×(1 - R), Lb),

[0135] Ub b = min(X b ×(1 + R), Ub) (21)

[0136] In the formula: X b represents the current local best position, and Lb and Ub respectively represent the upper and lower limits of the best foraging area.

[0137] x o (m + 1) = x o (m) + C 1 ×(x o (m) - Lb b ) + C 2 ×(x o (m) - Ub b ) (22)

[0138] Where: x o (m + 1) is the position information of the o-th dung beetle at the (m + 1)-th iteration, x o (m) is the position information of the o-th dung beetle at the m-th iteration, C1 represents a random number following a normal distribution, and C2 ∈ (0, 1) represents a random vector.

[0139] The small dung beetles will also search for food based on the positions of other dung beetles and the optimal foraging area. The specific formula is as follows:

[0140] x o (m + 1) = X b + S × g × (∣x o (m) - X * ∣ + ∣x o (m) - X b ∣) (23)

[0141] Where: x o (m + 1) is the position information of the o-th small dung beetle at the (m + 1)-th iteration, x o (m) is the position information of the o-th small dung beetle at the m-th iteration, g represents a random vector of size 1 × D following a normal distribution, and S represents a constant value.

[0142] (2) Comprehensive voltage regulation control strategy for distribution network based on dung beetle optimization algorithm.

[0143] The specific process of the comprehensive voltage regulation control strategy for distribution network based on dung beetle optimization algorithm is as Figure 3 shown, including the following steps:

[0144] Step 1: Input the daily load, photovoltaic data, energy storage data, etc. of a certain field area.

[0145] Step 2: According to the time-of-use electricity price period, control the energy storage to charge during the low electricity price period to achieve economic optimization.

[0146] Step 3: Conduct global power flow calculation, find the moments when voltage exceeds the limit, and start control at the nodes.

[0147] Step 4: Configure the parameters of the dung beetle optimization algorithm, including the number of dung beetles, the maximum number of iterations, position limits, etc.

[0148] Step 5: Calculate the fitness value of each dung beetle individual through Equation (6) to determine the initial optimal solution.

[0149] Step 6: Update the positions of dung beetle individuals in different groups, as well as the optimal spawning position and the best foraging position, through Equations (17)-(23), calculate the fitness, and update the solution.

[0150] Step 7: Determine whether the number of iterations is the same as the set value. If so, output the time-series output of the distributed energy storage; otherwise, return to Step 5.

[0151] S3: Adjust the charging and discharging power of the distributed energy storage system based on the optimal charging and discharging power of the distributed energy storage system to adjust the voltage of the power consumption nodes with voltage over-limit at the moment when the voltage over-limit occurs in the distribution network.

[0152] In summary, the present invention designs a comprehensive voltage regulation strategy for energy storage with voltage over-limit nodes by establishing a comprehensive optimal scheduling model considering time-of-use electricity price and distributed energy storage, with the minimum voltage deviation and electricity purchase cost as the objective function and voltage non-over-limit as the constraint. The dung beetle optimization algorithm is used to solve the model. The simulation results show that the dung beetle optimization algorithm can quickly and accurately solve the comprehensive optimal scheduling model. It not only improves the fitness value and speeds up the convergence efficiency, but also suppresses voltage fluctuations by regulating the charging and discharging of distributed energy storage, improves power quality, reduces operating costs, and provides effective support for the stable operation of the distribution network.

[0153] Embodiment 2 is an embodiment of the present invention, which provides a voltage adjustment device for voltage over-limit nodes in a distribution network, including:

[0154] An adjustment object acquisition module, configured to acquire the power consumption nodes in the distribution network where the actual voltage exceeds the limit voltage after the distributed energy storage system and renewable energy are incorporated into the distribution network;

[0155] An optimization module, configured to use the dung beetle optimization algorithm to find a set of optimal charging and discharging powers of the distributed energy storage system, where the optimal charging and discharging powers of the distributed energy storage system are the charging and discharging powers of the distributed energy storage system that minimize the sum of the deviation between the actual voltage and the limit voltage of the power consumption nodes and the electricity purchase cost of the power consumption nodes under multiple constraints of power flow, node voltage, line power, renewable energy output power, and energy storage system operation;

[0156] A voltage regulation module, configured to adjust the charging and discharging power of the distributed energy storage system based on the optimal charging and discharging power of the distributed energy storage system to adjust the voltage of the power consumption nodes with voltage over-limit at the moment when the voltage over-limit occurs in the distribution network.

[0157] Example 3. Refer to Figure 2 , which is an embodiment of the present invention. The difference from the previous embodiment is that when the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes of various types.

[0158] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0159] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part with one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as necessary, and then storing it in a computer memory.

[0160] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0161] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A voltage adjustment method for a voltage-over-limit node in a distribution network, which is used to adjust the voltage of a node in the distribution network where the voltage exceeds the limit to a non-over-limit state by adjusting the charging and discharging power of an energy storage system in the distribution network, and is characterized in that: include: Obtain the power consumption nodes where the actual voltage in the distribution network exceeds the limit voltage after the distributed energy storage system and renewable energy are integrated into the distribution network; Use the dung beetle optimization algorithm to find a set of optimal distributed energy storage system charging and discharging powers, wherein the optimal distributed energy storage system charging and discharging power is the distributed energy storage system charging and discharging power that minimizes the sum of the deviation between the actual voltage of the power consumption node and the limit voltage and the power purchase cost of the power consumption node under the multiple constraints of power flow, node voltage, line power, renewable energy output power and energy storage system operation; The charging and discharging power of the distributed energy storage system is adjusted based on the optimal charging and discharging power of the distributed energy storage system, so as to adjust the voltage of the power consumption node where the voltage exceeds the limit when the voltage of the distribution network exceeds the limit.

2. The voltage adjustment method for a voltage-limited node in a distribution network according to claim 1, characterized in that: The method uses the dung beetle optimization algorithm to find a set of optimal distributed energy storage system charging and discharging powers. The optimal distributed energy storage system charging and discharging powers are distributed energy storage system charging and discharging powers that minimize the sum of the deviation between the actual voltage of the power consumption node and the limit voltage and the power purchase cost of the power consumption node under the multiple constraints of power flow, node voltage, line power, renewable energy output power and energy storage system operation, and specifically include: Constructing a voltage optimization model, wherein the voltage optimization model takes the deviation between the actual voltage of the power consumption node and the limit voltage and the sum of the power purchase cost of the power consumption node as the objective function, and takes power flow constraint, node voltage constraint, line power constraint, photovoltaic output power constraint, and energy storage operation constraint as constraint conditions; The voltage optimization model is solved using the dung beetle optimization algorithm to obtain the charging and discharging power of the distributed energy storage system that minimizes the objective function.

3. The voltage adjustment method for a voltage-limited node in a distribution network according to claim 2, characterized in that: The objective function is the sum of the deviation between the actual voltage of the power consumption node and the limit voltage and the power purchase cost of the power consumption node, which specifically includes: f=w1f1+w2f2 Among them, f is the objective function, f1 is the deviation between the actual voltage of the power node and the limit voltage, W1 is the voltage deviation weight coefficient, f2 is the power purchase cost of the power node, W2 is the power purchase cost weight coefficient; T is 24 hours, n is the number of nodes, U i, t is the voltage of the ith node at time t, U N is the reference voltage of the distribution network; C u (t) is the unit price of electricity purchased from the upper power grid at time t; P Grid (t) is the power exchanged between the distribution network and the upper power grid at time t.

4. The voltage adjustment method for a voltage-limited node in a distribution network according to claim 3, characterized in that: The constraints are power flow constraints, node voltage constraints, line power constraints, photovoltaic output power constraints, and energy storage operation constraints, which specifically include: Set power flow constraints: Among them, Ω(i) represents all the neighboring nodes of node i, and They are respectively represented as the active power and reactive power of node i at time t; U i and U j Represent the i-th and j-th nodes respectively; G ij represents the conductance of branch ij; B ij represents the susceptance of branch ij; θ ij represents the phase angle difference between node i and node j; Set node voltage constraints: IN min ≤U i,t ≤U max Among them, U i,t is the voltage of node i at time t; U min is the minimum allowed value of the node voltage; U max is the maximum value allowed for the node voltage; To set line power constraints: l ij,t ≤l ij,max Among them, I ij,t is the branch current amplitude at time t; I ij,max is the upper limit of branch current amplitude.

5. The voltage adjustment method for a voltage-limited node in a distribution network according to claim 4, characterized in that: Set the photovoltaic output power constraint: Among them, PG,i,t is the active power output of photovoltaic at node i at time t; QG,i,t is the reactive power output of photovoltaic at node i at time t; and They are the upper and lower limits of the active power output of the photovoltaic at the i-node at the time t; and They are the upper and lower limits of the reactive power output of PV at node i at time t; Set energy storage operation state constraints: S(t+ΔT)=S(t)+K c,t P ESS,c ΔT-K d,t P ESS,d ΔT Where S(t) is the state of charge at time t; ΔT represents the time change; K c,t and K d,t are the charging and discharging coefficients of energy storage respectively; P ESS,c and P ESS,d are the charging and discharging power of energy storage respectively; and is the maximum charging and discharging power of energy storage; D c,i,t and D c,i,t They are 0 and 1 variables respectively, which are used to ensure that charging and discharging are not performed simultaneously; Set the energy storage state of charge constraints: S min ≤S(t)≤S max Among them, S min is the lower limit of the charge state; S max is the upper limit of the state of charge; Set energy storage power constraints: -P ESS,N ≤P ESS,i,t ≤P ESS,N Among them, P ESS,N is the energy storage rated power; P ESS,i,t is the real-time power of energy storage at node i at time t.

6. The voltage adjustment method for a voltage-limited node in a distribution network according to claim 5, characterized in that: The method of using the dung beetle optimization algorithm to solve the voltage optimization model and obtain the charging and discharging power of the distributed energy storage system that minimizes the objective function specifically includes: Input daily load, photovoltaic data, energy storage data, and time-of-use electricity price period; According to the time-of-use electricity price period, control the energy storage to supplement the electricity at the low electricity price to achieve economic optimization; Perform global power flow calculations to find the moment when voltage exceeds the limit, and then start controlling the nodes; Set the basic parameters of the dung beetle optimization algorithm, including the number of dung beetles, the maximum number of iterations, and the position limit.

7. The voltage adjustment method for a voltage-limited node in a distribution network according to claim 6, characterized in that: The method of using the dung beetle optimization algorithm to solve the voltage optimization model to obtain the charging and discharging power of the distributed energy storage system that minimizes the objective function also includes: Calculate the initial fitness value and obtain the initial optimal solution; Update the positions of dung beetle individuals from different groups, as well as the optimal egg-laying position and the optimal foraging position, calculate the fitness and update the optimal solution; Determine whether the maximum number of iterations has been reached. If not, return to step "calculate the initial fitness value and obtain the initial optimal solution"; if yes, output the distributed energy storage time series output.

8. A device using the voltage adjustment method for a voltage-limiting node in a distribution network as claimed in any one of claims 1 to 7, characterized in that: include: A regulation object acquisition module is used to obtain power consumption nodes in the distribution network where the actual voltage exceeds the limit voltage after the distributed energy storage system and renewable energy are integrated into the distribution network; An optimization module, used to use a dung beetle optimization algorithm to find a set of optimal distributed energy storage system charging and discharging powers, wherein the optimal distributed energy storage system charging and discharging powers are distributed energy storage system charging and discharging powers that minimize the sum of the deviation between the actual voltage of the power consumption node and the limit voltage and the power purchase cost of the power consumption node under the multiple constraints of power flow, node voltage, line power, renewable energy output power and energy storage system operation; The voltage regulation module is used to adjust the charging and discharging power of the distributed energy storage system based on the optimal charging and discharging power of the distributed energy storage system, so as to adjust the voltage of the power consumption node where the voltage exceeds the limit when the voltage of the distribution network exceeds the limit.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for adjusting the voltage of a voltage-exceeding node in a distribution network according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for adjusting voltage at a voltage-exceeding node in a distribution network according to any one of claims 1 to 7 are implemented.

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