Reactive compensation adjusting method and system for low-voltage transformer area

Abstract

The invention discloses a reactive compensation adjustment method and system for a low-voltage transformer area. The method comprises the following steps: acquiring related parameters of the low-voltage transformer area of a power distribution network; based on the related parameters of the low-voltage transformer area of the power distribution network, constructing an objective function and constraint conditions of reactive compensation adjustment of the low-voltage transformer area, and establishing a reactive optimization model of the low-voltage transformer area; a variable replacement and phase angle relaxation technology is utilized to convert the low-voltage transformer area reactive power optimization model into a second-order cone programming model; and solving the second-order cone programming model by using an improved Harris eagle algorithm to obtain an optimal reactive compensation regulation scheme. According to the invention, global search and local optimization are carried out by using the improved Harris eagle algorithm, so that the algorithm is effectively prevented from falling into local optimum, the adjustment efficiency and precision of reactive compensation are remarkably improved, the node voltage is effectively prevented from being too high, and the loss of the power distribution network is also remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of reactive power optimization of power distribution networks, and in particular to a reactive power compensation and regulation method and system for low-voltage substations. Background Art

[0002] With the global emphasis on environmental protection and sustainable development, the optimization and transformation of energy structure has become an international consensus. In recent years, my country has actively responded to the "dual carbon" goals (carbon peak and carbon neutrality), committed to promoting a revolution in energy production and consumption, and placing energy conservation and resource conservation at the top of the national development strategy. Against this background, clean energy, especially distributed photovoltaic power generation, has developed rapidly. Through reasonable layout and optimized design, distributed photovoltaic power generation systems can not only efficiently utilize solar energy resources, but also reduce dependence on traditional fossil energy, which is of great significance for alleviating the energy crisis and improving environmental quality.

[0003] Although distributed photovoltaic power generation systems have achieved remarkable results in promoting the use of clean energy, their large-scale access to distribution networks has also brought a series of technical challenges. First, after distributed photovoltaics are connected to the grid, the output power of photovoltaic power sources is affected by natural conditions such as temperature and light, showing strong volatility and uncertainty, which makes the power flow distribution of the distribution network more complicated. Especially when the sun is sufficient and the photovoltaic output reaches the highest level, while the grid load is at a low point, the voltage of nodes close to distributed photovoltaics will be high, and may even exceed the safety threshold, posing a threat to the stable operation of the grid. Secondly, traditional distribution networks usually adopt a radial structure, and their voltage regulation and reactive power compensation mainly rely on the reactive power compensation equipment of the central substation. However, with the access of distributed photovoltaics, traditional reactive power compensation methods have been unable to meet the grid's needs for voltage stability and reactive power balance. Especially in low-voltage areas, due to the relatively weak grid structure and insufficient reactive power compensation capacity, it is more likely to have problems such as high voltage or excessive reactive power, affecting the power quality of users and the overall operation efficiency of the grid. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a low-voltage area reactive power compensation adjustment method to solve the problem of serious high voltage at low-voltage area nodes caused by large-scale access of distributed photovoltaic power generation to the distribution network.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for reactive power compensation and adjustment in a low voltage area, comprising:

[0008] Obtain relevant parameters of low-voltage areas in the distribution network;

[0009] Based on the relevant parameters of the low-voltage area of ​​the distribution network, the objective function and constraint conditions of reactive power compensation regulation in the low-voltage area are constructed, and a reactive power optimization model of the low-voltage area is established;

[0010] The low voltage area reactive power optimization model is transformed into a second-order cone programming model by using variable substitution and phase angle relaxation technology;

[0011] The improved Harris Eagle algorithm is used to solve the second-order cone programming model to obtain the optimal reactive power compensation regulation scheme.

[0012] As a preferred solution of the low voltage area reactive power compensation regulation method described in the present invention, the objective function of constructing the low voltage area reactive power compensation regulation includes:

[0013] Based on the voltage quality constraint of the low-voltage area, an objective function is established with the goal of minimizing the power loss in the low-voltage area and minimizing the amount of electricity purchased by the substation node. The objective function is expressed as:

[0014] minf(p,q,P,Q,V,I)

[0015] Where p, q are the node injection powers of the optimization variables of the optimal power flow, P, Q are the branch power flows of the optimization variables of the optimal power flow, V is the node voltage, I is the branch current, and f is the objective function value.

[0016] As a preferred solution of the reactive power compensation and regulation method for low voltage substations described in the present invention, the second-order cone programming model is solved by using the improved Harris Eagle algorithm, including:

[0017] The initial reactive power compensation adjustment scheme is generated by using Tent chaotic mapping initialization;

[0018] According to the objective function and the constraint conditions, the initial reactive compensation adjustment scheme is updated, and the updated reactive compensation adjustment scheme is subjected to constraint processing. If the reactive compensation adjustment scheme does not meet the constraint conditions, the reactive compensation adjustment scheme is adjusted to obtain a reactive compensation adjustment scheme set;

[0019] Using adaptive grids to divide and organize the reactive compensation adjustment scheme set, mapping each reactive compensation adjustment scheme to a nearest grid point, and marking the grid point as having been visited;

[0020] If the reactive power compensation adjustment scheme does not meet the constraint conditions, the corresponding grid point will be marked as invalid;

[0021] The size and resolution of the adaptive grid are dynamically adjusted according to the distribution of reactive compensation regulation schemes.

[0022] As a preferred solution of the reactive power compensation adjustment method for low voltage substations described in the present invention, the expression for generating the reactive power compensation adjustment solution by initializing the Tent chaotic map is:

[0023]

[0024] Among them, u is the chaotic degree of the mapping, x i It is the initial reactive power compensation adjustment scheme.

[0025] As a preferred solution of the low voltage area reactive power compensation adjustment method described in the present invention, updating the initial reactive power compensation adjustment solution includes:

[0026] In the first stage of updating, each reactive compensation adjustment scheme exchanges information with each other to update and optimize its own reactive compensation adjustment scheme. The update expression of the reactive compensation adjustment scheme in the first stage is:

[0027] X(t+1)=X rand (t)-r1|X rand (t)-2r2X(t)|1

[0028] q≥0.5

[0029] X(t+1)=X rabbit (t)-X m (t)-r3(lb+r4(ub-lb))q≤0.5

[0030] Among them, X(t+1) and X(t) are the reactive power compensation adjustment schemes at the t+1th and tth iterations, respectively. rand (t) is the reactive power compensation adjustment scheme randomly selected at the tth iteration, r1, r2, r4, r4 and q are uniformly distributed random numbers in the interval [0,1], X rabbit (t+1) and X m (t) is the current iterative optimal reactive power compensation adjustment scheme and the center position of the Harris Hawk group, ub and lb are the upper and lower bounds of the reactive power compensation adjustment scheme parameter value range;

[0031] In the second updating stage, among the reactive compensation adjustment schemes obtained in the first updating stage, a reactive compensation adjustment scheme with a smaller objective function value is identified and obtained, and other reactive compensation adjustment schemes update and adjust the parameters of their own schemes according to the reactive compensation adjustment scheme with a smaller objective function value.

[0032] As a preferred solution of the low voltage area reactive power compensation adjustment method described in the present invention, obtaining the optimal reactive power compensation adjustment solution includes:

[0033] After dividing each reactive power compensation regulation scheme into the adaptive grid, based on the constraints of reactive power compensation regulation in the low voltage area, the average objective function value of the current reactive power compensation regulation scheme set in the adaptive grid is calculated, and the adaptive grid with the optimal average objective function value is selected as the optimal reactive power compensation regulation scheme set of the current adaptive grid;

[0034] Compare the current adaptive grid optimal reactive power compensation adjustment scheme with the previous iterative optimal reactive power compensation adjustment scheme set, and update the optimal reactive power compensation adjustment scheme set;

[0035] When the update reaches the number of iterations of the improved Harris Eagle algorithm, the iteration is stopped, the objective function corresponding to the optimal reactive power compensation adjustment scheme set is calculated, and the optimal reactive power compensation adjustment scheme is obtained.

[0036] As a preferred scheme of the low-voltage area reactive power compensation adjustment method described in the present invention, the reactive power compensation adjustment constraints of the low-voltage area include the forward and backward substitution power flow equation constraints of the low-voltage area, the upper and lower limit constraints of the node voltage, the upper and lower limit constraints of the road current, the reactive power constraints of the static reactive compensation device, the number of discrete reactive compensation groups and the number of adjustments, the upper and lower limit constraints of the photovoltaic output and the energy storage constraints.

[0037] In a second aspect, the present invention provides a system for reactive power compensation and regulation in a low voltage area, comprising:

[0038] Data acquisition module, used to obtain relevant parameters of low-voltage areas of distribution network;

[0039] A reactive power optimization model establishment module is used to construct the objective function and constraint conditions of reactive power compensation adjustment in the low-voltage area based on the relevant parameters of the low-voltage area of ​​the distribution network, and establish a reactive power optimization model in the low-voltage area;

[0040] A model optimization module, used to transform the low voltage area reactive power optimization model into a second-order cone programming model by using variable substitution and phase angle relaxation technology;

[0041] The scheme acquisition module is used to solve the second-order cone programming model using the improved Harris Eagle algorithm to obtain the optimal reactive power compensation adjustment scheme.

[0042] In a third aspect, the present invention provides a computing device, comprising:

[0043] Memory and processor;

[0044] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the low-voltage area reactive power compensation adjustment method are implemented.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the low-voltage area reactive power compensation adjustment method.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention is based on a low-voltage substation reactive power compensation regulation method based on the improved Harris Eagle algorithm. By constructing precise objective functions and constraints, and cleverly converting the reactive power optimization problem into a second-order cone programming model, the improved Harris Eagle algorithm is combined for global search and local optimization, which effectively avoids the algorithm from falling into local optimality, significantly improves the regulation efficiency and accuracy of reactive power compensation, and effectively prevents the node voltage from being too high, and significantly reduces the loss of the distribution network, improves the operating efficiency and economy of the distribution network, and provides an efficient and intelligent solution for reactive power compensation regulation in low-voltage substations. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0048] Figure 1 It is a schematic diagram of the overall process of a reactive power compensation adjustment method for a low voltage area according to an embodiment of the present invention;

[0049] Figure 2 A schematic diagram of a network of an improved Harris Eagle algorithm for a reactive power compensation adjustment method for a low voltage area according to an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of an adaptive grid division process of a reactive power compensation adjustment method for a low voltage area according to an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of an adaptive grid division network of a reactive power compensation adjustment method for a low voltage area according to an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of a low-voltage area simulation environment of a low-voltage area reactive power compensation adjustment method according to an embodiment of the present invention;

[0053] Figure 6A schematic diagram of voltage amplitude calculated by an improved Harris Eagle algorithm of a reactive power compensation adjustment method for a low voltage station area according to an embodiment of the present invention;

[0054] Figure 7 A schematic diagram of network loss calculated by an improved Harris Eagle algorithm for a reactive power compensation adjustment method for a low voltage station area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0058] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0059] At the same time, in the description of the present invention, it should be noted that the orientations or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0060] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] Example 1

[0062] Reference Figure 1-4 , is an embodiment of the present invention, and provides a method for reactive power compensation and adjustment in a low voltage area, comprising:

[0063] S100: Obtaining parameters related to low voltage areas of the distribution network;

[0064] In the embodiment of the present application, the parameters related to the low-voltage area of ​​the distribution network include the injected power of each node, the initial state of the branch flow, the voltage level of each node and its upper and lower limits, the upper and lower limits of the branch current, the reactive power adjustment range and current state of the static VAR compensation device (SVG) at each node, the number of groups of discrete VAR compensation devices (CB), the compensation power of each group and the limit of its adjustment times, the output power range of distributed photovoltaic power sources, the charge and discharge state of the energy storage system (ESS), the power limit, the capacity limit and its charge and discharge efficiency coefficient, etc.;

[0065] After obtaining the relevant parameters of the low-voltage area of ​​the distribution network, these parameters are preprocessed. The main purpose of preprocessing is to ensure the accuracy, completeness and consistency of the data so that the reactive power optimization model can be efficiently constructed and solved later; through preprocessing, erroneous data points can be corrected or eliminated, missing data can be filled, and data from different sources or formats can be unified, so as to avoid deviations or errors caused by data problems in the construction of the reactive power optimization model of the low-voltage area.

[0066] S102: Based on the relevant parameters of the low-voltage area of ​​the distribution network, construct the objective function and constraint conditions of reactive power compensation regulation in the low-voltage area, and establish a reactive power optimization model for the low-voltage area;

[0067] Preferably, based on the voltage quality constraint of the low-voltage area, an objective function is established with the goal of minimizing the power loss in the low-voltage area and minimizing the power purchase amount of the substation node. The objective function is expressed as:

[0068] minf(p,q,P,Q,V,I)

[0069] Where p, q are the node injection powers of the optimization variables of the optimal power flow, P, Q are the branch power flows of the optimization variables of the optimal power flow, V is the node voltage, I is the branch current, and f is the objective function value;

[0070] Preferably, the reactive power compensation regulation constraint conditions of the low voltage area include the forward and backward substitution power flow equation constraint of the low voltage area, the upper and lower limit constraint of the node voltage, the upper and lower limit constraint of the road current, the reactive power constraint of the static reactive power compensation device, the number of discrete reactive power compensation groups and the number of adjustment times, the upper and lower limit constraint of the photovoltaic output and the energy storage constraint;

[0071] The constraints are expressed as follows:

[0072] ① The forward-pushing back power flow equation constraint of the low-voltage area is expressed as:

[0073]

[0074] Among them, (p, q) is the reactive power injected into each node, (P, Q) is the power flow of each branch, node voltage (V), and road current (I), line impedance (r, x), and line admittance (g, b);

[0075] ② Node voltage upper and lower limit constraints:

[0076]

[0077] Among them, V j , are the lower and upper limits of the voltage at node j, E and B respectively. + is the set of network nodes;

[0078] ③Road current upper and lower limit constraints:

[0079]

[0080] in, I ij , are the lower and upper limits of the branch (i, j) current, and E is the set of network nodes;

[0081] ④ The expression of reactive power constraint of static VAR compensation device (SVG) is:

[0082]

[0083] Among them, Q i.SVG ,Q i.SVG , They are the minimum allowable value, actual value and maximum allowable value of reactive power of static VAR compensation device of node i respectively;

[0084] ⑤Discrete reactive compensation (CB) group number and adjustment times constraints:

[0085]

[0086] Among them, B CBis the set of CB nodes;. is the number of operational groups and is a discrete variable value; For node j, the upper limit of the number of connected CB groups; is the compensation power of each group of CBs. Considering factors such as equipment life or economy, discrete reactive power compensation is often limited by the number of adjustments, so it generally includes the total number of operations in multiple time periods; Double is the upper limit of the number of operations;

[0087] ⑥ Photovoltaic output upper and lower limits:

[0088]

[0089] Among them, P i.DG , They are respectively the actual value and the maximum allowable value of the active power output of the distributed power source;

[0090] ⑦Energy storage (ESS) constraints:

[0091] 1) Charge and discharge status restrictions:

[0092]

[0093] 2) Power limit:

[0094]

[0095] 3) Capacity limitation:

[0096]

[0097] Among them, B ESS is the node set containing ESS; Formula 1) indicates that ESS cannot be charged or discharged at the same time; and The charging and discharging states of the ESS; and They are the upper and lower limits of ESS charging and discharging power respectively; is the power of ESS in period t, and The upper and lower limits are taken into account for factors such as the life of the ESS; and are the charge and discharge efficiency coefficients respectively. In general,

[0098] It should be noted that, based on the above-constructed low-voltage area reactive power compensation regulation objective function and constraints, the established reactive power optimization model can comprehensively consider the various operating characteristics and constraints of the low-voltage area of ​​the distribution network, and achieve minimization of power loss and minimization of power purchase by substation nodes by optimizing node injection power and branch currents, while ensuring voltage quality and safety of equipment operation. The present invention not only effectively improves the economy and operating efficiency of the distribution network, but also improves the voltage quality of the low-voltage area through refined reactive power compensation regulation, reduces power loss, and extends the service life of equipment, providing strong support for the intelligent management and optimized operation of the distribution network.

[0099] S104: using variable substitution and phase angle relaxation technology, converting the low voltage area reactive power optimization model into a second-order cone programming model;

[0100] In the embodiment of the present application, after the nonlinear objective function and constraint conditions are transformed into a linear transformation by introducing variable substitution, the low voltage area reactive power optimization model is transformed into a second-order cone programming model;

[0101] Specifically, introduce the variable and Then the original objective function and constraints are all linear functions, which is the general form of second-order cone programming. The model introduces new variables and After phase angle relaxation, the feasible solution domain is a second-order cone;

[0102] The objective function is transformed into:

[0103]

[0104] The power flow equation is transformed into:

[0105]

[0106] The voltage upper and lower limit constraints are transformed into:

[0107]

[0108] The upper and lower limit constraints of the road current are transformed into:

[0109]

[0110] Among them, ||·||2 is the Euclidean norm;

[0111] The above formula constitutes the basic form of the relaxed static optimal power flow;

[0112] It should be noted that the present invention successfully transforms the original problem into a second-order cone programming model by cleverly using variable substitution and phase angle relaxation technology to transform the nonlinear objective function and constraints in the reactive power optimization model of the low-voltage substation. This transformation not only simplifies the complexity of the problem, but also retains the key information of the original problem. The transformed model has achieved linearization in both the objective function and the constraints, making it easy to use efficient mathematical algorithms to solve it, such as the interior point method. At the same time, the phase angle relaxation technology further expands the feasible solution domain, shaping it into the shape of a second-order cone, thereby improving the flexibility and accuracy of the model in the solution process.

[0113] S106: Using the improved Harris Eagle algorithm to solve the second-order cone programming model to obtain the optimal reactive power compensation adjustment scheme; the network diagram of the improved Harris Eagle algorithm of the present invention is as follows Figure 2 As shown;

[0114] It should be noted that the Harris Hawk algorithm is an optimization algorithm based on bionics. It is inspired by simulating the behavior of Harris Hawk hunting prey to find the optimal solution to the optimization problem. The problem to be optimized is converted into a group of individuals, each of which represents a possible solution in the solution space. The algorithm simulates the collaborative behavior of Harris Hawk hunting prey and divides the individuals into two categories: explorers and developers.

[0115] The prey escape energy is shown as follows, where E decreases with iteration:

[0116]

[0117] Among them, E is the prey escape energy; t is the current iteration position; T is the maximum number of iterations; E0 is the initial escape energy of the prey, which is a uniformly distributed random number between [-1,1].

[0118] The improved Harris Eagle algorithm of the present invention mainly introduces Tent chaos mapping initialization to generate an initial reactive power compensation adjustment scheme when initializing algorithm parameters;

[0119] Preferably, the expression for generating the reactive power compensation adjustment scheme by using Tent chaotic mapping initialization is:

[0120]

[0121] Among them, u is the chaotic degree of the mapping, x i It is the initial reactive power compensation adjustment scheme;

[0122] It should be noted that the Tent chaotic map, with its efficient traversal and randomness, can generate an initial solution set with a more uniform distribution and wide coverage. This feature enables the exploration of multiple potential high-quality areas in the solution space at the beginning of the algorithm, effectively avoiding the local optimal trap problem that may be caused by traditional random initialization methods, thereby improving the global search ability of the algorithm; the chaotic initialization method enhances the diversity of the algorithm. In the reactive power compensation regulation problem, the diverse solution set means that different compensation strategies can be considered more comprehensively, which is conducive to discovering the optimal or suboptimal solution that is more in line with the actual situation and economic benefits. This is particularly important for the optimization problems of complex systems such as power systems, because it requires balancing multiple constraints and objectives; using the Tent chaotic map for initialization can also improve the convergence speed of the algorithm. Due to the higher quality of the initial solution set, the algorithm can converge to the vicinity of the optimal solution faster during the iteration process, reducing unnecessary computational overhead and improving the execution efficiency of the algorithm.

[0123] Preferably, according to the objective function and the constraint conditions, the initial reactive compensation adjustment scheme is updated, and the updated reactive compensation adjustment scheme is subjected to constraint processing. If the reactive compensation adjustment scheme does not meet the constraint conditions, the reactive compensation adjustment scheme is adjusted to obtain a reactive compensation adjustment scheme set;

[0124] Preferably, in the first stage of updating, each reactive compensation adjustment scheme exchanges information with each other to update and optimize its own reactive compensation adjustment scheme, and the update expression of the reactive compensation adjustment scheme in the first stage is:

[0125] X(t+1)=X rand (t)-r1|X rand (t)-2r2X(t)|1

[0126] q≥0.5

[0127] X(t+1)=X rabbit (t)-X m (t)-r3(lb+r4(ub-lb))q≤0.5

[0128] Among them, X(t+1) and X(t) are the reactive power compensation adjustment schemes at the t+1th and tth iterations, respectively. rand (t) is the reactive power compensation adjustment scheme randomly selected at the tth iteration, r1, r2, r4, r4 and q are uniformly distributed random numbers in the interval [0,1], X rabbit (t+1) and X m (t) is the current iterative optimal reactive power compensation adjustment scheme and the center position of the Harris Hawk group, ub and lb are the upper and lower bounds of the reactive power compensation adjustment scheme parameter value range;

[0129] Preferably, in the second stage of updating, among the reactive compensation adjustment schemes obtained in the first stage of updating, a reactive compensation adjustment scheme with a smaller objective function value is identified and obtained, and other reactive compensation adjustment schemes update and adjust parameters of their own schemes according to the reactive compensation adjustment scheme with a smaller objective function value;

[0130] In the embodiment of the present application, in the exploration phase of the algorithm, the explorers are responsible for exploring potential optimal solutions in the solution space. These individuals try to discover unknown optimization directions and possible solutions in the solution space through a variety of search strategies. Once a potential optimization direction or solution is found, the explorer introduces it into the next phase of the algorithm, that is, the development phase. During the development process, the Harris Eagle algorithm uses 4 different strategies for search, namely:

[0131] Strategy 1: Reconnaissance, or soft encirclement; reconnaissance refers to randomly generating new solutions and calculating their fitness to find new potential solutions;

[0132] Strategy 2: Transfer, or hard encirclement; Transfer refers to the strategy of improving the current solution by learning and drawing lessons from excellent solutions;

[0133] Strategy 3: Random walk, i.e., progressive fast dive soft encirclement; Random walk refers to a strategy of exploring the solution space through randomly generated step sizes;

[0134] Strategy 4: Local search, that is, progressive rapid dive hard encirclement; The local search of the Harris Eagle algorithm refers to the strategy of searching in the vicinity of the current solution.

[0135] Preferably, adaptive grid division and organization of reactive compensation adjustment scheme sets are used to map each reactive compensation adjustment scheme to the nearest grid point, and mark the grid point as visited; the adaptive grid division flow chart and network diagram of the present invention are as follows: Figure 3 , Figure 4 As shown;

[0136] Preferably, if the reactive power compensation adjustment scheme does not meet the constraint condition, the corresponding grid point is marked as invalid;

[0137] Preferably, the size and resolution of the adaptive grid are dynamically adjusted according to the distribution of reactive compensation adjustment schemes;

[0138] Preferably, obtaining the optimal reactive compensation adjustment scheme includes, after dividing each reactive compensation adjustment scheme into adaptive grids, calculating the average objective function value of the current reactive compensation adjustment scheme set in the adaptive grid based on the constraint conditions of reactive compensation adjustment in the low voltage station area, and selecting the adaptive grid with the optimal average objective function value as the optimal reactive compensation adjustment scheme set of the current adaptive grid;

[0139] Preferably, the current adaptive grid optimal reactive power compensation adjustment scheme is compared with the optimal reactive power compensation adjustment scheme set of the previous iteration, and the optimal reactive power compensation adjustment scheme set is updated;

[0140] Preferably, when the update reaches the number of iterations of the improved Harris Eagle algorithm, the iteration is stopped, the objective function corresponding to the optimal reactive power compensation adjustment scheme set is calculated, and the optimal reactive power compensation adjustment scheme is obtained;

[0141] In the embodiment of the present application, the reactive power compensation adjustment scheme with the smallest objective function value is taken as the optimal reactive power compensation adjustment scheme;

[0142] It should be noted that the present invention solves the second-order cone programming model by using the improved Harris Hawk algorithm. The algorithm simulates the predation behavior of the Harris Hawk and divides the search process into two stages: exploration and development. It not only enhances the global search capability, enabling the algorithm to find potential optimal solutions in a wide solution space, but also accelerates the convergence speed and reduces the computational overhead by introducing a variety of search strategies and optimization mechanisms. At the same time, the algorithm can flexibly handle various constraints to ensure that all solutions meet the actual operation requirements of the power system. Through multiple iterations and optimizations, the algorithm can find the optimal reactive power compensation and regulation scheme that meets the needs of the power system and takes into account economic benefits, providing strong support for the stable operation and efficiency improvement of the power system.

[0143] The above is a schematic scheme of a low-voltage area reactive power compensation adjustment method of this embodiment. It should be noted that the technical scheme of the low-voltage area reactive power compensation adjustment system and the technical scheme of the low-voltage area reactive power compensation adjustment method mentioned above belong to the same concept. The details not described in detail in the technical scheme of the low-voltage area reactive power compensation adjustment system in this embodiment can be found in the description of the technical scheme of the low-voltage area reactive power compensation adjustment method mentioned above.

[0144] The low voltage area reactive power compensation and regulation system in this embodiment includes:

[0145] Data acquisition module, used to obtain relevant parameters of low-voltage areas of distribution network;

[0146] The reactive power optimization model establishment module is used to construct the objective function and constraint conditions of reactive power compensation regulation in the low-voltage area based on the relevant parameters of the low-voltage area of ​​the distribution network, and to establish the reactive power optimization model of the low-voltage area;

[0147] Model optimization module, used to transform the low voltage area reactive power optimization model into a second-order cone programming model by using variable substitution and phase angle relaxation technology;

[0148] The scheme acquisition module is used to solve the second-order cone programming model using the improved Harris Eagle algorithm to obtain the optimal reactive power compensation regulation scheme.

[0149] This embodiment further provides a computing device, which is applicable to reactive power compensation and regulation in low voltage areas, and includes:

[0150] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the reactive power compensation adjustment method for low voltage substations as proposed in the above embodiment.

[0151] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for reactive power compensation and adjustment in a low-voltage area is implemented as proposed in the above embodiment.

[0152] The storage medium proposed in this embodiment and the method for implementing reactive power compensation and regulation in low-voltage substations proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0153] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0154] Example 2

[0155] Reference Figure 5-7 , which is an embodiment of the present invention, provides a low-voltage area reactive power compensation adjustment method. In order to verify its beneficial effects, comparison results of several schemes are provided.

[0156] In the field of reactive power optimization of distribution networks, especially in the face of the complex technical problem of high node voltage in low-voltage areas caused by large-scale distributed photovoltaic access, the proposal of this invention undoubtedly brings an innovative solution to the power industry. In order to fully verify the superiority of this solution, the IEEE33 node system was selected as the simulation environment. The IEEE33 node system is widely used in power system research due to its typicality and complexity.

[0157] exist Figure 5In the IEEE33-node system diagram shown, three different operating scenarios are designed for comparative analysis:

[0158] The first operating scenario is the baseline scenario where no nodes are connected to distributed photovoltaics. The voltage distribution in this scenario reflects the normal operating status of the traditional distribution network.

[0159] The second operating scenario is when a large number of distributed photovoltaics are connected. At this time, due to the uncertainty of photovoltaic output and the volatility of load, the node voltage is generally high, especially the nodes close to the photovoltaic access points, where the voltage problem is particularly serious.

[0160] The third operating scenario is the scenario after utilizing the reactive compensation adjustment method based on the improved Harris Hawk algorithm (AG-MOHHO) proposed in the present invention. The algorithm uses an intelligent optimization strategy to accurately control the reactive compensation equipment in the distribution network in order to achieve the goals of voltage stability and power loss reduction.

[0161] Figure 6 The voltage distribution of each node in the above three scenarios is intuitively displayed. When not connected to distributed photovoltaics, the node voltage is within a relatively stable range, but after connecting to photovoltaics, the voltage is significantly high, especially the voltage of some nodes has approached or exceeded the safety threshold, posing a threat to the safe operation of the power grid. However, after applying the improved Harris Eagle algorithm of the present invention for reactive power compensation adjustment, the node voltage is effectively controlled, which not only avoids the problem of high voltage, but also makes the voltage distribution of the entire system more uniform and stable, effectively improving the voltage quality and stability of the power grid.

[0162] Further, Figure 7 By comparing the network loss before and after optimization, the superiority of the method of the present invention is verified again. After the distributed photovoltaic access, the network loss increased significantly due to the high voltage and unreasonable flow of reactive power. After the improved Harris Eagle algorithm is used for reactive power optimization, the transmission and conversion losses of reactive power in the power grid are reduced by rationally allocating reactive compensation resources, thereby achieving a significant reduction in network losses. This result not only improves the economy of the distribution network, but also provides strong support for the realization of green and low-carbon energy utilization.

[0163] In summary, the low-voltage area reactive power compensation and regulation method based on the improved Harris Eagle algorithm proposed in the present invention has shown excellent performance in solving the problem of high node voltage caused by large-scale access of distributed photovoltaic power to the distribution network. Through intelligent optimization strategies, this method can not only effectively control node voltage and improve the voltage quality and stability of the power grid, but also significantly reduce network losses and improve the operating efficiency and economy of the distribution network. This innovative achievement is of great significance for promoting the development of the power industry in a cleaner, more efficient and sustainable direction.

[0164] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for reactive power compensation and adjustment in a low voltage area, characterized in that: include: Obtain relevant parameters of low-voltage areas in the distribution network; Based on the relevant parameters of the low-voltage area of ​​the distribution network, the objective function and constraint conditions of reactive power compensation regulation in the low-voltage area are constructed, and a reactive power optimization model of the low-voltage area is established; The low voltage area reactive power optimization model is transformed into a second-order cone programming model by using variable substitution and phase angle relaxation technology; The improved Harris Eagle algorithm is used to solve the second-order cone programming model to obtain the optimal reactive power compensation regulation scheme.

2. The low voltage area reactive power compensation adjustment method according to claim 1, characterized in that: The objective function for constructing reactive power compensation regulation in low voltage areas includes: Based on the voltage quality constraint of the low-voltage area, an objective function is established with the goal of minimizing the power loss in the low-voltage area and minimizing the amount of electricity purchased by the substation node. The objective function is expressed as: minf(p,q,P,Q,V,I) Where p, q are the node injection powers of the optimization variables of the optimal power flow, P, Q are the branch power flows of the optimization variables of the optimal power flow, V is the node voltage, I is the branch current, and f is the objective function value.

3. The low voltage area reactive power compensation adjustment method according to claim 1, characterized in that: Solving the second-order cone programming model using the improved Harris Eagle algorithm includes: The initial reactive power compensation adjustment scheme is generated by using Tent chaotic mapping initialization; According to the objective function and the constraint conditions, the initial reactive compensation adjustment scheme is updated, and the updated reactive compensation adjustment scheme is subjected to constraint processing. If the reactive compensation adjustment scheme does not meet the constraint conditions, the reactive compensation adjustment scheme is adjusted to obtain a reactive compensation adjustment scheme set; Using adaptive grids to divide and organize the reactive compensation adjustment scheme set, mapping each reactive compensation adjustment scheme to a nearest grid point, and marking the grid point as having been visited; If the reactive power compensation adjustment scheme does not meet the constraint conditions, the corresponding grid point will be marked as invalid; The size and resolution of the adaptive grid are dynamically adjusted according to the distribution of reactive compensation regulation schemes.

4. The low voltage area reactive power compensation adjustment method according to claim 3, characterized in that: The expression of the reactive compensation adjustment scheme generated by using Tent chaotic map initialization is: Among them, u is the chaotic degree of the mapping, x i It is the initial reactive power compensation adjustment scheme.

5. The low voltage area reactive power compensation adjustment method according to claim 3, characterized in that: Updating the initial reactive power compensation adjustment scheme includes: In the first stage of updating, each reactive compensation adjustment scheme exchanges information with each other to update and optimize its own reactive compensation adjustment scheme. The update expression of the reactive compensation adjustment scheme in the first stage is: X(t+1)=X rand (t)-r1|X rand (t)-2r2X(t)1 q≥0.5 X(t+1)=X rabbit (t)-X m (t)-r3(lb+r4(ub-lb))q≤0.5 Among them, X(t+1) and X(t) are the reactive power compensation adjustment schemes at the t+1th and tth iterations, respectively. rand (t) is the reactive power compensation adjustment scheme randomly selected at the tth iteration, r1, r2, r4, r4 and q are uniformly distributed random numbers in the interval [0,1], X rabbit (t+1) and X m (t) is the current iterative optimal reactive power compensation adjustment scheme and the center position of the Harris Hawk group, ub and lb are the upper and lower bounds of the reactive power compensation adjustment scheme parameter value range; In the second updating stage, among the reactive compensation adjustment schemes obtained in the first updating stage, a reactive compensation adjustment scheme with a smaller objective function value is identified and obtained, and other reactive compensation adjustment schemes update and adjust the parameters of their own schemes according to the reactive compensation adjustment scheme with a smaller objective function value.

6. The low voltage area reactive power compensation adjustment method according to claim 1 or 3, characterized in that: Obtaining the optimal reactive power compensation adjustment solution includes: After dividing each reactive power compensation regulation scheme into the adaptive grid, based on the constraints of reactive power compensation regulation in the low voltage area, the average objective function value of the current reactive power compensation regulation scheme set in the adaptive grid is calculated, and the adaptive grid with the optimal average objective function value is selected as the optimal reactive power compensation regulation scheme set of the current adaptive grid; Compare the current adaptive grid optimal reactive power compensation adjustment scheme with the previous iterative optimal reactive power compensation adjustment scheme set, and update the optimal reactive power compensation adjustment scheme set; When the update reaches the number of iterations of the improved Harris Eagle algorithm, the iteration is stopped, the objective function corresponding to the optimal reactive power compensation adjustment scheme set is calculated, and the optimal reactive power compensation adjustment scheme is obtained.

7. The low voltage area reactive power compensation adjustment method according to claim 1, characterized in that: The reactive power compensation regulation constraints of low-voltage substations include the forward and backward substitution constraints of the low-voltage substation power flow equation, the upper and lower limit constraints of the node voltage, the upper and lower limit constraints of the road current, the reactive power constraints of the static reactive compensation device, the number of discrete reactive compensation groups and the number of adjustments, the upper and lower limit constraints of the photovoltaic output and the energy storage constraints.

8. A system for reactive power compensation and regulation in low voltage areas, characterized in that: include, Data acquisition module, used to obtain relevant parameters of low-voltage areas of distribution network; A reactive power optimization model establishment module is used to construct the objective function and constraint conditions of reactive power compensation adjustment in the low-voltage area based on the relevant parameters of the low-voltage area of ​​the distribution network, and establish a reactive power optimization model in the low-voltage area; A model optimization module, used to transform the low voltage area reactive power optimization model into a second-order cone programming model by using variable substitution and phase angle relaxation technology; The scheme acquisition module is used to solve the second-order cone programming model using the improved Harris Eagle algorithm to obtain the optimal reactive power compensation adjustment scheme.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the low-voltage area reactive power compensation adjustment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the low-voltage area reactive power compensation adjustment method as described in any one of claims 1 to 7.

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