Method and system for active-reactive collaborative voltage optimization of distributed power supply of power distribution network

Abstract

The invention discloses an active-reactive collaborative voltage optimization method and system for a distributed power supply of a multiphase power distribution network, and relates to the technical field of energy system and power distribution system planning, and the method comprises the steps: constructing a multiphase linear model of node voltage, branch current and active power exchanged between a relaxation node and an upstream power grid about the active power and reactive power of the distributed power supply; constructing a day-ahead voltage optimization model; on the basis of the day-ahead optimization model, rolling optimization is carried out by adopting day-mid ultra-short-term prediction data and a day-ahead optimization model prediction control method, and iteration is carried out for multiple times; and solving the day-ahead optimization model by using a Gurobi solver to obtain active-reactive scheduling data of voltage optimization of the distributed power supply. According to the method, a linear model of voltage, current and exchange active power with an upstream power grid about active power and reactive power of the distributed power supply is constructed, and the solving speed is greatly improved. And meanwhile, a rolling optimization and multi-iteration method is adopted, so that the solution speed is ensured, and the understanding precision is also improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system and power distribution system planning, and in particular to a method and system for active-reactive coordinated voltage optimization of distributed power sources in a distribution network. Background Art

[0002] With the proposal of the "dual carbon" goal and the continuous development of new energy, an increasing proportion of photovoltaics are connected to the grid as distributed energy, and the reverse flow is significant, which brings about problems such as voltage exceeding the limit. However, the distribution network is different from the traditional high-voltage transmission line. The structure is more complex and the impedance ratio is diverse, which makes the power decoupling difference. Therefore, a distributed photovoltaic voltage active support method based on the impedance characteristics of the distribution network is urgently needed.

[0003] The traditional distribution network is relatively simple because it has no distributed energy, only loads. However, with people's attention to renewable energy, more and more photovoltaic power stations and wind power plants have been put into use, especially in recent years, they have been directly connected to the distribution network, making the distribution network problem gradually complicated and bringing many new problems. It is obvious that a high proportion of distributed energy is connected to the distribution network, and there is an obvious reverse flow, and the voltage exceeds the limit obviously, which brings serious challenges to the power quality of users. Therefore, the previous research methods on the distribution network are no longer applicable, so it is necessary to propose new methods for active distribution networks. However, at this stage, most of the methods for distributed power sources, especially photovoltaics, to support the voltage of the distribution network are still in the framework of reactive voltage regulation used by high-voltage transmission lines, ignoring the impact of active power on voltage.

[0004] That is to say, at the current stage, when a high proportion of new energy such as photovoltaics is connected to the distribution network, problems such as voltage exceeding the limit and reduced power quality are faced. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: optimizing the voltage of the distribution network, avoiding voltage exceeding the limit and maximizing the consumption of new energy.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network, which comprises the following steps:

[0008] Construct a multiphase linear model of node voltage, branch current and active power exchanged between the relaxed node and the upstream power grid regarding the active and reactive power of distributed generation;

[0009] The day-ahead voltage optimization model is constructed using the day-ahead forecast data. Based on the day-ahead optimization model, the ultra-short-term forecast data of the day is used, and rolling optimization and multiple iterations are performed through the predictive control method of the day-ahead optimization model. The day-ahead optimization model is solved using the Gurobi solver to obtain the active-reactive dispatch data of distributed power sources for voltage optimization.

[0010] As a preferred solution of the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network described in the present invention, the multi-phase distribution network includes a relaxation node and N PQ nodes, where N is a positive integer greater than 1.

[0011] The multi-phase linear model for active and reactive power of distributed power sources includes a voltage linear model, a current linear model and a relaxed node three-phase power linear model.

[0012] The voltage linear model is expressed as,

[0013]

[0014] in, is the three-phase voltage of PQ node, is the node voltage amplitude, For the coefficient matrix M Y ,M Δ ∈C 3N×6N , K Y ,K Δ ∈R 3N×6N , Y LL is the three-phase admittance matrix, diag is the diagonal function, x Y is the injected three-phase power vector ((p Y ) T ,(q Y ) T ) T , x Y ∈C 6N , x Δ is the injected three-phase power vector of the △ connection node ((p Δ ) T ,(q Δ ) T ) T , x Δ ∈C 6N , is the real part function, is the initial voltage, is the initial power, H is the Y-△ conversion matrix, a, b are the steady-state values ​​of the voltage vector and amplitude before the node is injected with power, a∈C 3N , b∈R 3N, C represents the complex number field, and R represents the real number field.

[0015] The current linear model is expressed as,

[0016]

[0017] in, and i is the line current injected into the node, i∈C 3N , coefficient matrix J Y ,J Δ ∈R 3N×6N , is the initial voltage, c is the steady-state value of the current amplitude before power is injected into the node, c∈R 3N .

[0018] The relaxed node three-phase power linear model is expressed as,

[0019]

[0020] Among them, P import , P export are the active power purchased from the upstream power grid and the active power sold, respectively, and the coefficient matrix G Y ,G Δ ∈R 3×6N , v0 is the relaxed node voltage, Y 0L is the three-phase admittance matrix, d is the steady-state value of the active power exchanged between the relaxed node and the upstream power grid before the node injects power, d∈R 3 , are the initial values ​​of active power purchased from the upstream power grid and sold, respectively.

[0021] As a preferred solution of the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network described in the present invention, the day-ahead voltage optimization model takes operating cost, voltage deviation and abandoned light penalty as objective functions, which is expressed as:

[0022]

[0023] Among them, C import , C export are the purchase price and sale price of active power from the upstream power grid, P i MPPT , P i pv are the maximum active output and actual active output of the ith photovoltaic power plant, respectively; λ is the penalty coefficient for abandoned photovoltaic power generation; V, V N are the node voltage amplitude and rated value respectively.

[0024] As a preferred solution of the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network described in the present invention, wherein: the day-ahead voltage optimization model considers power constraints, voltage constraints, current constraints and photovoltaic capacity constraints, and the power constraints are expressed as:

[0025]

[0026] in, is the maximum active power purchased from the upstream power grid, is the maximum active power sold to the upstream grid, It is the active power value exchanged with the upstream grid before the PV power changes.

[0027] The voltage constraint is expressed as,

[0028]

[0029] in, The three-phase power vector ((Δp Y ) T ,(Δq Y ) T ) T , Δx Δ is the injected three-phase power vector of the △-connected photovoltaic ((Δp Δ ) T ,(Δq Δ ) T ) T , Δx Δ ∈C 6N , is the voltage amplitude before the PV power changes, | v |, are the lower and upper limits of the voltage amplitude respectively.

[0030] As a preferred solution of the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network according to the present invention, the current constraint is expressed as:

[0031]

[0032] in, is the coefficient matrix, is the current amplitude between nodes l and m before the photovoltaic power changes, is the upper limit of the current amplitude.

[0033] The photovoltaic capacity constraint is expressed as,

[0034] α i P iMPPT ≤P i pv ≤P i MPPT

[0035]

[0036] Among them, α i is the photovoltaic reduction factor, is the reactive power of the ith photovoltaic, is the capacity of the i-th photovoltaic, is the minimum power factor angle.

[0037] As a preferred solution of the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network described in the present invention, the method for predictive control of the day-ahead optimization model includes optimizing the voltage of four time nodes 1-4 with the day-ahead voltage optimization model for the first time, and obtaining a voltage linear model by only using load data for flow calculation, which is expressed as:

[0038]

[0039] in, is the voltage linear model after the jth optimization at time node i, is the optimized PV active and reactive power result for the jth time at time node i, f is the linear model calculation, and g is the voltage optimization model calculation.

[0040] Roll back one time node, substitute the photovoltaic active and reactive power obtained by the day-ahead voltage optimization model back into the distribution network at the corresponding time node, and recalculate the power flow to obtain a new voltage linear model, which is expressed as:

[0041]

[0042] The voltage at time nodes 2-5 is optimized. At this time, the three time nodes 2-4 are equivalent to two iterative calculations, and time node 5 starts the first optimization iteration.

[0043] As a preferred solution of the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network described in the present invention, wherein: the solving of the day-ahead optimization model includes defining operating variables in Gurobi, setting a multi-objective optimization function using Gurobi according to the operating cost, voltage deviation and abandonment penalty of the power grid, adding constraints using the addConstr method according to the constraints of voltage, current, power and photovoltaic capacity, calling Gurobi's optimize method to start the solving process, and using Gurobi's getVarByName method to obtain the optimized active and reactive scheduling data.

[0044] Another object of the present invention is to provide a system for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network, which can solve the problems of large voltage fluctuations, slow regulation response, and low optimization efficiency in existing power systems in a multi-source heterogeneous environment through real-time data-driven dynamic optimization and advanced linear model predictive control methods.

[0045] To solve the above technical problems, the present invention provides the following technical solutions: a system for active-reactive collaborative optimization of voltage of distributed power sources in a multi-phase distribution network, comprising a model building module, a data preprocessing and input module, and an optimization model building module.

[0046] The model building module is responsible for building a multi-phase linear model of node voltage, branch current and active power exchanged between the relaxed node and the upstream power grid regarding the active and reactive power of the distributed power source.

[0047] The data preprocessing and input module is responsible for processing the day-ahead forecast data and the day-to-day ultra-short-term forecast data, and providing necessary input for the day-ahead optimization model.

[0048] The optimization model building module is responsible for building a mathematical model for day-ahead voltage optimization.

[0049] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network are implemented as described above.

[0050] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network as described above.

[0051] Beneficial effects of the present invention: The present invention constructs a linear model of the voltage, current, and active power exchanged with the upstream power grid regarding the active and reactive power of the distributed power source, which greatly improves the speed of solution. At the same time, the rolling optimization and multiple iteration methods are adopted to improve the accuracy of the solution while ensuring the speed of solution. This method can be applied to multi-phase asymmetric networks and conforms to the actual operation of existing distribution networks. Through the day-ahead forecast data and the ultra-short-term forecast data during the day, the voltage can be optimized from different time scales to meet the needs of different time scales. The active-reactive collaborative optimization method is adopted, which is coupled in time and space. The objective function includes operating cost and voltage deviation, taking into account both economy and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0053] Figure 1 An overall flow chart of a method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in the first embodiment of the present invention.

[0054] Figure 2 A framework diagram of a day-ahead voltage optimization model in a method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in the first embodiment of the present invention.

[0055] Figure 3 A photovoltaic control flow chart of voltage optimization in the middle of the day in the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in the first embodiment of the present invention.

[0056] Figure 4 A schematic diagram of a rolling optimization multiple iteration method in a method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in a first embodiment of the present invention.

[0057] Figure 5 The overall framework diagram of the system for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in the second embodiment of the present invention.

[0058] Figure 6 A method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in the third embodiment of the present invention is an IEEE 13-node system topology connection diagram.

[0059] Figure 7 A photovoltaic active power curve diagram of a day-ahead voltage optimization model in a method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in a third embodiment of the present invention.

[0060] Figure 8 A photovoltaic reactive power curve diagram of a day-ahead voltage optimization model in a method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in a third embodiment of the present invention.

[0061] Fig. 9 A curve diagram of the voltage maximum value of the day-ahead voltage optimization model in the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in the third embodiment of the present invention.

[0062] Fig.10The photovoltaic active power curve of the Japanese-Chinese voltage rolling optimization model in the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in the third embodiment of the present invention.

[0063] Fig.11 A photovoltaic reactive power curve diagram of the Japanese-Chinese voltage rolling optimization model in the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in the third embodiment of the present invention.

[0064] Fig.12 A curve diagram of the maximum voltage of the Japanese-Chinese voltage rolling optimization model in the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0065] 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.

[0066] Example 1, reference Figure 1 to Figure 3 , is an embodiment of the present invention, and provides a method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network, characterized in that:

[0067] S1 constructs a multiphase linear model of node voltage, branch current, and active power exchanged between the relaxed node and the upstream power grid regarding the active and reactive power of distributed generation.

[0068] The multiphase distribution network includes a slack node and N PQ nodes, where N is a positive integer greater than 1;

[0069] The multiphase linear model of active and reactive power of distributed generation includes voltage linear model, current linear model and relaxed node three-phase power linear model.

[0070] The voltage linear model is expressed as,

[0071]

[0072] in, is the three-phase voltage of PQ node, is the node voltage amplitude, For the coefficient matrix M Y ,M Δ ∈C 3N×6N , K Y ,K Δ ∈R3N×6N , Y LL is the three-phase admittance matrix, diag is the diagonal function, x Y is the injected three-phase power vector ((p Y ) T ,(q Y ) T ) T , x Y ∈C 6N , x Δ is the injected three-phase power vector of the △ connection node ((p Δ ) T ,(q Δ ) T ) T , x Δ ∈C 6N , To take the real part function, is the initial voltage, is the initial power, H is the Y-△ conversion matrix, a, b are the steady-state values ​​of the voltage vector and amplitude before the node is injected with power, a∈C 3N , b∈R 3N , C represents the complex number field, and R represents the real number field.

[0073] The current linear model is expressed as,

[0074]

[0075] in, and i is the line current injected into the node, i∈C 3N , coefficient matrix J Y ,J Δ ∈R 3N×6N , is the initial voltage, c is the steady-state value of the current amplitude before power is injected into the node, c∈R 3N .

[0076] The relaxed node three-phase power linear model is expressed as,

[0077]

[0078] Among them, P import , P export are the active power purchased from the upstream power grid and the active power sold, respectively, and the coefficient matrix G Y ,G Δ ∈R 3×6N , d is the steady-state value of the active power exchanged between the relaxed node and the upstream power grid before the node is injected with power, d∈R 3 .

[0079] Furthermore, through step S1, the present invention constructs a multiphase linear model of node voltage, branch current and power exchange between a relaxed node and an upstream power grid. The model includes a voltage linear model, a current linear model and a relaxed node power linear model.

[0080] This model provides a basic framework for future voltage optimization and can accurately describe the voltage of each node in the distribution network, branch current, and power interaction between slack nodes and upstream power grids through simple linear relationships.

[0081] Through the multi-phase linear model, the key parameters in the multi-phase distribution network can be accurately characterized with lower computational complexity, achieving high computational efficiency and model accuracy, providing a reliable mathematical basis for subsequent optimization, and reducing the errors caused by the introduction of nonlinearity in the model.

[0082] S2: Use the day-ahead forecast data to build a day-ahead voltage optimization model.

[0083] like Figure 2 The figure shows the framework diagram of the day-China voltage optimization model.

[0084] The day-ahead voltage optimization model takes operating cost, voltage deviation and abandoned light penalty as the objective function, which is expressed as:

[0085]

[0086] Among them, C import , C export are the purchase price and sale price of active power from the upstream power grid, P i MPPT , P i pv are the maximum active output and actual active output of the ith photovoltaic power plant, respectively; λ is the penalty coefficient for abandoned photovoltaic power generation; V, V N are the node voltage amplitude and rated value respectively.

[0087] The day-ahead voltage optimization model considers power constraints, voltage constraints, current constraints, and photovoltaic capacity constraints. The power constraint is expressed as:

[0088]

[0089] in, is the maximum active power purchased from the upstream power grid, is the maximum active power sold to the upstream grid, It is the active power value exchanged with the upstream grid before the PV power changes.

[0090] The voltage constraint is expressed as,

[0091]

[0092] in, The three-phase power vector ((Δp Y ) T ,(Δq Y ) T ) T , Δx Δ is the injected three-phase power vector of the △-connected photovoltaic ((Δp Δ ) T ,(Δq Δ ) T ) T , Δx Δ ∈C 6N , is the voltage amplitude before the PV power changes, | v |, are the lower and upper limits of the voltage amplitude respectively.

[0093] The current constraint is expressed as,

[0094]

[0095] in, is the coefficient matrix, is the current amplitude between nodes l and m before the photovoltaic power changes, is the upper limit of the current amplitude.

[0096] The PV capacity constraint is expressed as,

[0097] α i P i MPPT ≤P i pv ≤P i MPPT

[0098]

[0099]

[0100] Among them, α i is the photovoltaic reduction factor, is the reactive power of the ith photovoltaic, is the capacity of the i-th photovoltaic, is the minimum power factor angle.

[0101] Furthermore, S2 constructed a voltage optimization model considering power constraints, voltage constraints, current constraints and photovoltaic capacity constraints based on the day-ahead forecast data.

[0102] This step constructs an adaptive day-ahead voltage optimization model by using day-ahead forecast data and taking operating cost, voltage deviation and power abandonment penalty as objective functions, effectively balancing voltage fluctuations and the volatility of photovoltaic power output.

[0103] By optimizing the voltage of the distribution network in advance, the inventors can optimize various voltage and power dispatches through the prediction model before the grid is put into operation, reducing the voltage deviation and instability in the actual operation of the grid. In particular, the introduction of the penalty for abandoned light effectively reduces the waste of photovoltaic power, improves the utilization efficiency of clean energy, and reduces operating costs.

[0104] S3: Based on the day-ahead optimization model, the ultra-short-term forecast data of the day and the middle of the day are used, and rolling optimization and multiple iterations are performed through the predictive control method of the day-ahead optimization model.

[0105] S31: The method of predictive control of the day-ahead optimization model includes: firstly, the day-ahead voltage optimization model optimizes the voltages of four time nodes 1-4, and only uses the load data for flow calculation to obtain a voltage linear model, which is expressed as:

[0106]

[0107] in, is the voltage linear model after the jth optimization at time node i, is the optimized PV active and reactive power result for the jth time at time node i, f is the linear model calculation, and g is the voltage optimization model calculation.

[0108] S32: Scroll back one time node, substitute the photovoltaic active and reactive power obtained by the day-ahead voltage optimization model back into the distribution network at the corresponding time node, and recalculate the power flow to obtain a new voltage linear model, expressed as:

[0109]

[0110] The voltage at time nodes 2-5 is optimized. At this time, the three time nodes 2-4 are equivalent to two iterative calculations, and time node 5 starts the first optimization iteration.

[0111] S33: Repeat the process in S32 until the last time node is optimized. Because the time length of each optimization is 4, except for the three time nodes at the beginning and the end, the voltages of the remaining time nodes are calculated four times as they scroll backward.

[0112] The voltage optimization process in Japan and China is as follows Figure 3 ,,The schematic diagram of rolling optimization is as follows Figure 4 The length of rolling optimization can be adjusted according to the data scale and model accuracy.

[0113] The model calculation speed is improved by rolling optimization, the accuracy of the model solution is improved by multiple iterations, and the error caused by the linear model in step S1 is reduced.

[0114] Furthermore, through step S3, the inventors combined the ultra-short-term forecast data between Japan and China, and further performed rolling optimization based on the day-ahead optimization model, and performed optimization step by step at each time node.

[0115] Rolling optimization optimizes voltage through multiple iterations, making the voltage adjustment during operation more refined and dynamic. In particular, during the operation of the power grid, real-time adjustments can be made based on actual voltage change data.

[0116] Through this step, the inventor's solution can significantly improve the real-time performance and accuracy of voltage optimization. Rolling optimization combines short-term forecast data to make the model solution closer to the actual situation and reduce errors. At the same time, multiple iterative calculations can effectively compensate for the errors caused by the multi-phase linear model in the S1 step, improving the accuracy of the overall optimization. In addition, rolling optimization not only improves the model calculation speed, but also can flexibly adjust the time length according to the data scale, improving the adaptability of the system.

[0117] S4: Use the Gurobi solver to solve the day-ahead optimization model and obtain the active-reactive scheduling data of distributed generation for voltage optimization.

[0118] Solving the day-ahead optimization model includes defining operating variables in Gurobi, using Gurobi to set a multi-objective optimization function based on the grid’s operating costs, voltage deviation, and curtailment penalties, using the addConstr method to add constraints based on voltage, current, power, and PV capacity constraints, calling Gurobi’s optimize method to start the solution process, and using Gurobi’s getVarByName method to obtain optimized active and reactive scheduling data.

[0119] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides a system for a method for active-reactive collaborative voltage optimization of distributed power sources in a multi-phase distribution network, characterized in that it includes a model building module 100, a data preprocessing and input module 200, and an optimization model building module 300.

[0120] The model building module 100 is responsible for building a multi-phase linear model of node voltage, branch current and active power exchanged between the relaxed node and the upstream power grid regarding the active and reactive power of the distributed power source.

[0121] The data preprocessing and input module 200 is responsible for processing the day-ahead forecast data and the day-to-day ultra-short-term forecast data, and providing the necessary input for the day-ahead optimization model.

[0122] The optimization model building module 300 is responsible for building a mathematical model for day-ahead voltage optimization.

[0123] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0125] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0126] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of 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, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0127] Example 3: In this example, in order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. Figure 5 , the day-ahead forecast data of PV and load and the day-to-day ultra-short-term forecast data are known, and PV is integrated into five nodes on a large scale. The node types are shown in Table 1. Tables 2 and 4 are line data. Tables 3 and 4 are overhead line data and transformer data, respectively.

[0128] Table 1 Node types of IEEE 13-node distribution network

[0129]

[0130] Table 2 Line data

[0131]

[0132] Table 3 Overhead line data

[0133]

[0134]

[0135] Substituting the line data and the photovoltaic and load forecast data into the model, the photovoltaic day-ahead and day-midday power dispatch data and the maximum voltage can be obtained, such as Figure 6-11 , PV means photovoltaic, Phase means phase, Upper limit means upper limit, and Lower limit means lower limit.

[0136] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network, characterized in that: include: Construct a multiphase linear model of node voltage, branch current and active power exchanged between the relaxed node and the upstream power grid regarding the active and reactive power of distributed generation; The day-ahead voltage optimization model is constructed using day-ahead forecast data; Based on the day-ahead optimization model, the ultra-short-term forecast data of the day and the middle of the day are used, and rolling optimization and multiple iterations are performed through the predictive control method of the day-ahead optimization model; The Gurobi solver is used to solve the day-ahead optimization model to obtain the active-reactive scheduling data of distributed generation for voltage optimization.

2. The method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network according to claim 1, characterized in that: The multiphase distribution network includes a slack node and N PQ nodes, where N is a positive integer greater than 1; The multi-phase linear model for active and reactive power of distributed power sources includes a voltage linear model, a current linear model and a relaxed node three-phase power linear model; The voltage linear model is expressed as, in, is the three-phase voltage of PQ node, is the node voltage amplitude, For the coefficient matrix M Y ,M Δ ∈C 3N×6N , K Y ,K Δ ∈R 3N×6N , Y LL is the three-phase admittance matrix, diag is the diagonal function, x Y is the injected three-phase power vector ((p Y ) T ,(q Y ) T ) T , x Y ∈C 6N , x Δ is the injected three-phase power vector of the △ connection node ((p Δ ) T ,(q Δ ) T ) T , x Δ ∈C 6N , To take the real part function, is the initial voltage, is the initial power, H is the Y-△ conversion matrix, a, b are the steady-state values ​​of the voltage vector and amplitude before the node is injected with power, a∈C 3N , b∈R 3N , C represents the complex number field, R represents the real number field; The current linear model is expressed as, in, and i is the line current injected into the node, i∈C 3N , coefficient matrix J Y ,J Δ ∈R 3N×6N , is the initial voltage, c is the steady-state value of the current amplitude before power is injected into the node, c∈R 3N ; The relaxed node three-phase power linear model is expressed as, Among them, P import , P export are the active power purchased from the upstream power grid and the active power sold, respectively, and the coefficient matrix G Y ,G Δ ∈R 3×6N , v0 is the relaxed node voltage, Y 0L is the three-phase admittance matrix, d is the steady-state value of the active power exchanged between the relaxed node and the upstream power grid before the node injects power, d∈R 3 , are the initial values ​​of active power purchased from the upstream power grid and sold, respectively.

3. The method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network according to claim 2, characterized in that: The day-ahead voltage optimization model takes operating cost, voltage deviation and abandoned light penalty as the objective function, which is expressed as: Among them, C import , C export are the purchase price and sale price of active power from the upstream power grid, P i MPPT , P i pv are the maximum active output and actual active output of the ith photovoltaic power plant, respectively; λ is the penalty coefficient for abandoned photovoltaic power generation; V, V N are the node voltage amplitude and rated value respectively.

4. The method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network according to claim 3, characterized in that: The day-ahead voltage optimization model considers power constraints, voltage constraints, current constraints, and photovoltaic capacity constraints. The power constraint is expressed as: in, is the maximum active power purchased from the upstream power grid, is the maximum active power sold to the upstream grid, is the active power value exchanged with the upstream power grid before the PV power changes; The voltage constraint is expressed as, in, The three-phase power vector ((Δp Y ) T ,(Δq Y ) T ) T , Δx Δ is the injected three-phase power vector of the △-connected photovoltaic ((Δp Δ ) T ,(Δq Δ ) T ) T , Δx Δ ∈C 6N , is the voltage amplitude before the PV power changes, | v |, are the lower and upper limits of the voltage amplitude respectively.

5. The method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network according to claim 4, characterized in that: The current constraint is expressed as, in, is the coefficient matrix, is the current amplitude between nodes l and m before the photovoltaic power changes, is the upper limit of the current amplitude; The photovoltaic capacity constraint is expressed as, a i P i MPPT ≤P i pv ≤P i MPPT Among them, α i is the photovoltaic reduction factor, is the reactive power of the ith photovoltaic, is the capacity of the i-th photovoltaic, is the minimum power factor angle.

6. The method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network according to claim 5, characterized in that: The method of predictive control of the day-ahead optimization model includes: firstly, the day-ahead voltage optimization model optimizes the voltages of four time nodes 1-4, and only uses the load data for flow calculation to obtain a voltage linear model, which is expressed as: in, is the voltage linear model after the jth optimization at time node i, is the optimized photovoltaic active and reactive power result of the jth time node i, f is the linear model calculation, and g is the voltage optimization model calculation; Roll back one time node, substitute the photovoltaic active and reactive power obtained by the day-ahead voltage optimization model back into the distribution network at the corresponding time node, and recalculate the power flow to obtain a new voltage linear model, which is expressed as: The voltage at time nodes 2-5 is optimized. At this time, the three time nodes 2-4 are equivalent to two iterative calculations, and time node 5 starts the first optimization iteration.

7. The method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network according to claim 6, characterized in that: The solving of the day-ahead optimization model includes defining operating variables in Gurobi, setting a multi-objective optimization function using Gurobi according to the operation cost of the power grid, voltage deviation and abandonment penalty, adding constraints using the addConstr method according to the constraints of voltage, current, power and photovoltaic capacity, calling Gurobi's optimize method to start the solving process, and using Gurobi's getVarByName method to obtain the optimized active and reactive scheduling data.

8. A system using the method for active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network as claimed in any one of claims 1 to 7, characterized in that: It includes a model building module (100), a data preprocessing and input module (200), and an optimization model building module (300); The model building module (100) is responsible for building a multiphase linear model of node voltage, branch current and active power exchanged between the relaxed node and the upstream power grid regarding the active and reactive power of the distributed power source; The data preprocessing and input module (200) is responsible for processing the day-ahead forecast data and the day-to-day ultra-short-term forecast data, and providing necessary input for the day-ahead optimization model; The optimization model building module (300) is responsible for building a mathematical model for day-ahead voltage optimization.

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 active-reactive coordinated voltage optimization of distributed power sources in a multi-phase 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 active-reactive coordinated voltage optimization of distributed power sources in a multi-phase distribution network as described in any one of claims 1 to 7 are implemented.

Images