A reactive power optimization configuration method and terminal for active distribution network

Abstract

The invention discloses a reactive power optimization configuration method for an active distribution network, which establishes a reactive power configuration planning layer model and a reactive power configuration operation layer model for the active distribution network; determines the minimum comprehensive cost and carbon emission as the first objective function of the reactive power configuration planning layer model, and determines the first constraint condition corresponding thereto; determines the minimum operation cost as the second objective function of the reactive power configuration operation layer model, and determines the second constraint condition corresponding thereto; solves the reactive power configuration planning layer model based on the first objective function and the first constraint condition to obtain the site selection and capacity determination of the reactive power source, and solves the reactive power configuration operation layer model based on the second objective function and the second constraint condition to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator and the tap position of the on-load tap-changing transformer, thereby effectively realizing the reactive power optimization of the active distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of active power distribution network, and in particular to a reactive power optimization configuration method and a terminal for an active power distribution network. Background Art

[0002] Reactive power optimization of distribution network helps to improve the system voltage level and reduce the network loss of distribution network. The reactive equipment of traditional distribution network includes capacitor banks (CB) and static var compensator (SVC), which generally adopt local balancing method to compensate the reactive power of the system. In the future, the large-scale access of distributed clean energy is an inevitable trend in the development of distribution network. Distributed generators (DG) have powerful reactive power output and regulation capabilities and are an important reactive power source. With the continuous development of power system, traditional distribution network is gradually evolving into active distribution network. Active management capability is the core technology of active distribution network. Reactive power source is one of the important resources of active management. Traditional reactive power source planning cannot adapt to the development needs of active distribution network. Therefore, studying the reactive power source optimization configuration method under the background of active distribution network is of vital practical significance to the development of distribution network. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a reactive power optimization configuration method and terminal for an active power distribution network, which can effectively realize reactive power optimization of the active power distribution network.

[0004] In order to solve the above technical problems, a technical solution adopted by the present invention is:

[0005] A method for optimizing reactive power configuration of an active power distribution network comprises the following steps:

[0006] Establish reactive power configuration planning layer model and reactive power configuration operation layer model of active distribution network;

[0007] Determine the minimum comprehensive cost and carbon emission as the first objective function of the reactive power configuration planning layer model, and determine a first constraint condition corresponding to the first objective function;

[0008] Determine the minimum operating cost as the second objective function of the reactive power configuration operating layer model, and determine a second constraint condition corresponding to the second objective function;

[0009] The reactive power configuration planning layer model is solved based on the first objective function and the first constraint condition to obtain the site selection and sizing of the reactive power source, and the reactive power configuration operation layer model is solved based on the second objective function and the second constraint condition to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator and the tap position of the on-load tap-changing transformer.

[0010] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0011] A reactive power optimization configuration terminal for an active power distribution network includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0012] Establish reactive power configuration planning layer model and reactive power configuration operation layer model of active distribution network;

[0013] Determine the minimum comprehensive cost and carbon emission as the first objective function of the reactive power configuration planning layer model, and determine a first constraint condition corresponding to the first objective function;

[0014] Determine the minimum operating cost as the second objective function of the reactive power configuration operating layer model, and determine a second constraint condition corresponding to the second objective function;

[0015] The reactive power configuration planning layer model is solved based on the first objective function and the first constraint condition to obtain the site selection and sizing of the reactive power source, and the reactive power configuration operation layer model is solved based on the second objective function and the second constraint condition to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator and the tap position of the on-load tap-changing transformer.

[0016] The beneficial effects of the present invention are as follows: minimizing comprehensive costs and carbon emissions is determined as the first objective function of the reactive power configuration planning layer model, and the first constraint condition corresponding to the first objective function is determined; minimizing operating costs is determined as the second objective function of the reactive power configuration operating layer model, and the second constraint condition corresponding to the second objective function is determined, thereby establishing a two-layer model of reactive power configuration, and solving based on the respective objective functions and constraints, realizing reactive power site selection and capacity determination through the upper planning layer, realizing reactive power optimization scheduling through the lower operating layer, making full use of reactive power, minimizing comprehensive costs and carbon emissions as the first objective function, and minimizing operating costs as the second objective function, which is beneficial to reducing the carbon emission level of the active distribution network and improving investment efficiency, solving the upper and lower layers respectively, and obtaining the final reactive power configuration result, thereby effectively realizing reactive power optimization of the active distribution network, and being beneficial to the safe and stable operation of the active distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for optimizing configuration of reactive power sources in an active power distribution network according to an embodiment of the present invention;

[0018] Figure 2 A schematic diagram of the structure of a reactive power optimization configuration terminal for an active power distribution network according to an embodiment of the present invention;

[0019] Figure 3 A schematic diagram of the topology of a distribution network system in a reactive power source optimization configuration method for an active distribution network according to an embodiment of the present invention;

[0020] Figure 4 It is a schematic diagram of active output results of the upper power grid, WTG and PVG in the reactive power optimization configuration method of the active power distribution network according to an embodiment of the present invention;

[0021] Figure 5 It is a schematic diagram of reactive power output results of the upper power grid, WTG and PVG in the reactive power optimization configuration method of the active power distribution network according to an embodiment of the present invention;

[0022] Figure 6 It is a schematic diagram of reactive power output results of CB in the reactive power optimization configuration method of the active power distribution network according to an embodiment of the present invention;

[0023] Figure 7 It is a schematic diagram of reactive power output results of SVC in the reactive power optimization configuration method of active power distribution network according to an embodiment of the present invention;

[0024] Figure 8 It is a schematic diagram of comparing system voltage levels before and after optimization in the reactive power source optimization configuration method of the active power distribution network according to an embodiment of the present invention;

[0025] Fig. 9It is a schematic diagram of the relaxation error scatter points of each branch in the reactive power source optimization configuration method of the active power distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0027] Please refer to Figure 1 The embodiment of the present invention provides a method for optimizing reactive power configuration of an active power distribution network, comprising the steps of:

[0028] Establish reactive power configuration planning layer model and reactive power configuration operation layer model of active distribution network;

[0029] Determine the minimum comprehensive cost and carbon emission as the first objective function of the reactive power configuration planning layer model, and determine a first constraint condition corresponding to the first objective function;

[0030] Determine the minimum operating cost as the second objective function of the reactive power configuration operating layer model, and determine a second constraint condition corresponding to the second objective function;

[0031] The reactive power configuration planning layer model is solved based on the first objective function and the first constraint condition to obtain the site selection and sizing of the reactive power source, and the reactive power configuration operation layer model is solved based on the second objective function and the second constraint condition to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator and the tap position of the on-load tap-changing transformer.

[0032] From the above description, it can be seen that the beneficial effects of the present invention are: the comprehensive cost and carbon emission are minimized as the first objective function of the reactive power configuration planning layer model, and the first constraint corresponding to the first objective function is determined, and the operating cost is minimized as the second objective function of the reactive power configuration operating layer model, and the second constraint corresponding to the second objective function is determined. In this way, a two-layer model of reactive power configuration is established, and the solution is performed based on the respective objective functions and constraints. The reactive power site selection and capacity determination are realized through the upper planning layer, and the reactive power optimization scheduling is realized through the lower operating layer, and the reactive power is fully utilized. The comprehensive cost and carbon emission are minimized as the first objective function, and the operating cost is minimized as the second objective function, which is conducive to reducing the carbon emission level of the active distribution network and improving the investment efficiency. The upper and lower layers are solved separately to obtain the final reactive power configuration result, thereby effectively realizing the reactive optimization of the active distribution network, which is conducive to the safe and stable operation of the active distribution network.

[0033] Further, the first objective function includes a first sub-objective function and a second sub-objective function;

[0034] The step of minimizing the comprehensive cost and carbon emission as the first objective function of the reactive power configuration planning layer model comprises:

[0035] Minimizing the comprehensive cost is determined as the first sub-objective function of the reactive power configuration planning layer model;

[0036] Minimizing carbon emissions is determined as the second sub-objective function of the reactive power configuration planning layer model;

[0037] The first sub-objective function F1 is:

[0038] F1=min(C I +C O +C P );

[0039]

[0040] In the formula, C I represents the investment cost, C O represents the operating cost, C P represents the voltage crossing penalty cost, represents the construction cost of the wind turbine, represents the construction cost of photovoltaic power generation, represents the construction cost of the switchable capacitor bank, represents the construction cost of the static VAR compensator, T represents the scheduling time set, C O,t represents the operating cost at time t, K P Indicates the low voltage single penalty fee, N LV Indicates the number of low voltage occurrences;

[0041] The second sub-objective function F2 is:

[0042]

[0043] In the formula, represents the total carbon dioxide emissions of the upper power grid at time t, Ω gen Represents the set of upper-level power grid nodes, represents the carbon dioxide emission factor, Represents the power input of the upper power grid at time t.

[0044] From the above description, it can be seen that minimizing the comprehensive cost is determined as the first sub-objective function of the reactive power supply configuration planning layer model, and minimizing carbon emissions is determined as the second sub-objective function of the reactive power supply configuration planning layer model, thereby achieving the minimization of comprehensive costs and carbon emissions when performing reactive power optimization of active distribution networks.

[0045] Furthermore, the first constraint condition is:

[0046]

[0047] In the formula, represents the actual installed number of wind turbines at node j, represents the maximum number of wind turbines installed at node j, represents the actual installed quantity of the photovoltaic power generation at node j, represents the maximum number of photovoltaic power generation installations at node j, represents the actual installed number of the switchable capacitor bank at node j, Represents the maximum installed number of the switchable capacitor banks at node j.

[0048] From the above description, it can be seen that the first constraint condition corresponding to the first objective function is determined, and the reactive power supply configuration planning layer model is solved under the constraint of the maximum installed number of wind turbines, switchable capacitor groups and photovoltaic power generation, so as to realize the site selection and capacity determination of reactive power supply.

[0049] Further, the minimizing the operating cost as the second objective function of the reactive power configuration operating layer model comprises:

[0050] The second objective function f is:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] In the formula, Indicates the electricity input cost of the upper power grid, represents the operating cost of distributed generation, represents the operating cost of the switchable capacitor bank, represents the operating cost of the static VAR compensator, K gen represents the transmission price of the upper power grid, P t gen Represents the power input from the upper grid, K WTG represents the electricity price of the wind turbine, Indicates the active output of the fan, K WTG,cut represents the unit cost of abandoned electricity of the wind turbine, represents the amount of power wasted by the fan, K PVG represents the electricity price of the photovoltaic power generation, represents the active output of the photovoltaic power generation, K PVG,cut represents the unit cost of abandoned electricity of the photovoltaic power generation, Indicates the amount of abandoned photovoltaic power generation, K CB represents the operation cost of the switchable capacitor bank per unit capacity, Indicates the reactive power generated by the switchable capacitor bank, K SVC represents the commissioning cost of the static VAR compensator per unit capacity, Represents the reactive power generated by the static VAR compensator, Ω WTG represents the wind turbine node set, Ω PVG represents the photovoltaic power generation node set, Ω CB represents the node set of the switchable capacitor bank, Ω SVC represents the static VAR compensator node set.

[0057] From the above description, it can be seen that minimizing the operating cost is determined as the second objective function of the reactive power configuration operating layer model. The second objective function includes various operating costs, so that the operating layer can be solved later with the goal of minimizing the operating cost.

[0058] Further, the second constraint condition includes a power flow constraint condition;

[0059] The power flow constraint condition is:

[0060]

[0061]

[0062]

[0063] Where P j,t represents the active power injected by node j, δ(j) represents the branch with node j as the first node, i.e. branch jk, P jk,t represents the active power flow of the branch jk, π(j) represents the branch with node j as the terminal node, i.e., branch ij, P ij,t represents the active power flow of the branch ij, represents the square of the current in the branch ij, R ij represents the resistance of the branch ij, Q j,t represents the injected reactive power of the node j, Q jk,t represents the reactive power flow of the branch jk, Q ij,t represents the reactive power flow of the branch ij, X ij represents the reactance of the branch ij, Indicates the active power of the load, represents the reactive power generated by the wind turbine, represents the reactive power generated by the photovoltaic power generation, Represents the reactive power of the load, Represents the square of the voltage on the low voltage side, represents the square of the voltage at the node i.

[0064] Furthermore, the second constraint condition also includes a low-voltage side voltage constraint condition of the on-load tap-changing transformer;

[0065] The voltage constraint condition of the low voltage side of the on-load tap-changing transformer is:

[0066]

[0067]

[0068] In the formula, represents a binary auxiliary determinant variable, Indicates the adjustable voltage sequence of the on-load tap-changing transformer, Ω OLTC Represents the on-load tap-changing transformer node set.

[0069] Furthermore, the second constraint condition also includes a distributed power supply constraint condition;

[0070] The distributed power supply constraints are:

[0071]

[0072] In the formula, S DG Represents the apparent power of the distributed power source, P t DG represents the active output of the distributed generation at time t, represents the maximum reactive power output of the distributed generation at time t, represents the maximum active output of the distributed generation at time t, Q t DG Represents the reactive output of the distributed generation at time t.

[0073] Furthermore, the second constraint condition also includes a switchable capacitor bank constraint and a static VAR compensator constraint condition;

[0074] The switchable capacitor bank constraint is:

[0075]

[0076]

[0077] In the formula, represents the number of switchable capacitors in node j at time t, Indicates the rated capacity of a single capacitor of the switchable capacitor bank, represents the first binary auxiliary variable, represents the second binary auxiliary variable, represents the maximum adjustment times of the switchable capacitor bank at node j, represents the number of switchable capacitor banks at node j at time t-1; represents the actual installed number of the switchable capacitor bank at node j;

[0078] The static VAR compensator constraint condition is:

[0079]

[0080] In the formula, represents the minimum value of the reactive power generated by the static VAR compensator at node j, represents the maximum value of the reactive power generated by the static VAR compensator at node j.

[0081] From the above description, it can be seen that the power flow constraints corresponding to the second objective function, the low-voltage side voltage constraints of the on-load tap-changing transformer, the distributed power constraints, the switchable capacitor group constraints and the static VAR compensator constraints are determined, so that the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator and the tap position of the on-load tap-changing transformer are used as decision variables of the reactive power configuration operation layer model. Solving the objective function under these constraints can achieve reactive power optimization scheduling, deeply tap the potential of the power grid, and meet the access needs of loads and distributed power sources.

[0082] Further, the reactive power configuration planning layer model is solved based on the first objective function and the first constraint condition to obtain the location and capacity of the reactive power source, and the reactive power configuration operation layer model is solved based on the second objective function and the second constraint condition to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator, and the tap position of the on-load tap changer transformer, including:

[0083] Using a non-dominated sorting genetic algorithm to solve the reactive power configuration planning layer model based on the first objective function and the first constraint condition to obtain a location and capacity determination of the reactive power source;

[0084] The reactive power configuration operation layer model is solved using a CPLEX solver based on the second objective function and the second constraint condition to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator, and the tap position of the on-load tap changing transformer.

[0085] From the above description, it can be seen that the use of non-dominated sorting genetic algorithm to solve the reactive power configuration planning layer model and the use of CPLEX solver to solve the reactive power configuration operation layer model greatly improves the computational efficiency.

[0086] Please refer to Figure 2 Another embodiment of the present invention provides a reactive power configuration terminal for an active power distribution network, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements each step of the reactive power configuration method for the active power distribution network when executing the computer program.

[0087] The reactive power optimization configuration method and terminal of the active power distribution network of the present invention can be applied to the active power distribution network, and the following is described through specific implementation methods:

[0088] Embodiment 1

[0089] Please refer to Figure 1 , Figure 3-Figure 9 , a reactive power optimization configuration method of an active power distribution network of this embodiment includes the steps of:

[0090] S1. Establish a reactive power configuration planning layer model and a reactive power configuration operation layer model of the active distribution network;

[0091] S2, determining the minimum comprehensive cost and carbon emission as the first objective function of the reactive power configuration planning layer model, and determining the first constraint condition corresponding to the first objective function, specifically including:

[0092] Wherein, the first objective function includes a first sub-objective function and a second sub-objective function;

[0093] S21, determining the minimum comprehensive cost as the first sub-objective function of the reactive power configuration planning layer model;

[0094] S22, determining the minimum carbon emission as the second sub-objective function of the reactive power configuration planning layer model;

[0095] Specifically, the comprehensive cost is composed of investment cost, operation cost and voltage over-line penalty cost, wherein the first sub-objective function F1 is:

[0096] F1=min(C I +C O +CP );

[0097]

[0098] In the formula, C I represents the investment cost, C O represents the operating cost, C P represents the voltage crossing penalty cost, represents the construction cost of the wind turbine, represents the construction cost of photovoltaic power generation, represents the construction cost of the switchable capacitor bank, represents the construction cost of the static VAR compensator, T represents the scheduling time set, C O,t represents the operating cost at time t, K P Indicates the low voltage single penalty fee, N LV Indicates the number of low voltage occurrences;

[0099] in,

[0100] In the formula, represents the unit capacity cost of the wind turbine, represents the rated capacity of the fan, represents the fixed cost of the wind turbine, represents the unit capacity cost of the photovoltaic power generation, represents the rated capacity of the photovoltaic power generation, represents the fixed cost of photovoltaic power generation, represents the unit capacity cost of the switchable capacitor bank, Indicates the rated capacity of the switchable capacitor bank, represents the fixed cost of the switchable capacitor bank, represents the unit capacity cost of the static VAR compensator, represents the rated capacity of the static VAR compensator, represents the fixed cost of the static VAR compensator;

[0101] The second sub-objective function F2 is:

[0102]

[0103] In the formula, represents the total carbon dioxide emissions of the upper power grid at time t, Ω gen Represents the set of upper-level power grid nodes, represents the carbon dioxide emission factor, represents the power input of the upper power grid at time t;

[0104] S23, determining a first constraint condition corresponding to the first objective function;

[0105] The first constraint condition is:

[0106]

[0107] In the formula, represents the actual installed number of wind turbines at node j, represents the maximum number of wind turbines installed at node j, represents the actual installed quantity of the photovoltaic power generation at node j, represents the maximum number of photovoltaic power generation installations at node j, represents the actual installed number of the switchable capacitor bank at node j, represents the maximum installed number of the switchable capacitor bank at node j;

[0108] S3, determining the minimum operating cost as the second objective function of the reactive power configuration operating layer model, and determining a second constraint condition corresponding to the second objective function;

[0109] Wherein, the second objective function f is:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] In the formula, Indicates the electricity input cost of the upper power grid, represents the operating cost of distributed generation, represents the operating cost of the switchable capacitor bank, represents the operating cost of the static VAR compensator, K gen represents the transmission price of the upper power grid, Represents the power input from the upper grid, K WTG represents the electricity price of the wind turbine, Indicates the active output of the fan, K WTG,cut represents the unit cost of abandoned electricity of the wind turbine, represents the amount of power wasted by the fan, K PVG represents the electricity price of the photovoltaic power generation, represents the active output of the photovoltaic power generation, KPVG,cut represents the unit cost of abandoned electricity of the photovoltaic power generation, Indicates the amount of abandoned photovoltaic power generation, K CB represents the operation cost of the switchable capacitor bank per unit capacity, Indicates the reactive power generated by the switchable capacitor bank, K SVC represents the commissioning cost of the static VAR compensator per unit capacity, Represents the reactive power generated by the static VAR compensator, Ω WTG represents the wind turbine node set, Ω PVG represents the photovoltaic power generation node set, Ω CB represents the node set of the switchable capacitor bank, Ω SVC represents the static VAR compensator node set;

[0116] The second constraint condition includes a power flow constraint condition, a low voltage side voltage constraint condition of an on-load tap-changing transformer, a distributed power source constraint condition, a switchable capacitor bank constraint condition and a static VAR compensator constraint condition;

[0117] The power flow constraint condition is:

[0118]

[0119]

[0120]

[0121] Where P j,t represents the active power injected by node j, δ(j) represents the branch with node j as the first node, i.e. branch jk, P jk,t represents the active power flow of the branch jk, π(j) represents the branch with node j as the terminal node, i.e., branch ij, P ij,t represents the active power flow of the branch ij, represents the square of the current in the branch ij, R ij represents the resistance of the branch ij, Q j,t represents the injected reactive power of the node j, Q jk,t represents the reactive power flow of the branch jk, Q ij,t represents the reactive power flow of the branch ij, X ij represents the reactance of the branch ij, Indicates the active power of the load, represents the reactive power generated by the wind turbine, represents the reactive power generated by the photovoltaic power generation, Represents the reactive power of the load, Represents the square of the voltage on the low voltage side, represents the square of the voltage at the node i;

[0122] The voltage constraint condition of the low voltage side of the on-load tap-changing transformer is:

[0123]

[0124]

[0125] In the formula, represents a binary auxiliary determinant variable, Indicates the adjustable voltage sequence of the on-load tap-changing transformer, Ω OLTC represents the on-load tap-changing transformer node set;

[0126] in,

[0127] The distributed power source can only output active power, and the active power cannot be adjusted. However, under the RPC (Railway Static Power Conditioner) control strategy, the maximum reactive power output of the distributed power source can be adjusted within a certain range. The distributed power source constraint conditions are:

[0128]

[0129] In the formula, S DG Represents the apparent power of the distributed power source, P t DG represents the active output of the distributed generation at time t, represents the maximum reactive power output of the distributed generation at time t, represents the maximum active output of the distributed generation at time t, represents the reactive power output of the distributed power source at time t;

[0130] Wherein, the distributed power source includes the wind turbine and the photovoltaic power generation; the active power is related to environmental factors and is uncontrollable;

[0131] The switchable capacitor bank constraint is:

[0132]

[0133]

[0134] In the formula, represents the number of switchable capacitors in node j at time t, Indicates the rated capacity of a single capacitor of the switchable capacitor bank, represents the first binary auxiliary variable, represents the second binary auxiliary variable, represents the maximum adjustment times of the switchable capacitor bank at node j, represents the number of switchable capacitor banks at node j at time t-1; represents the actual installed number of the switchable capacitor bank at node j;

[0135] The static VAR compensator constraint condition is:

[0136]

[0137] In the formula, represents the minimum value of the reactive power generated by the static VAR compensator at node j, represents the maximum value of the reactive power generated by the static VAR compensator at node j;

[0138] S4, based on the first objective function and the first constraint condition, the reactive power configuration planning layer model is solved to obtain the location and capacity of the reactive power source, and based on the second objective function and the second constraint condition, the reactive power configuration operation layer model is solved to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator and the tap position of the on-load tap changer transformer, specifically including:

[0139] S41, using a non-dominated sorting genetic algorithm to solve the reactive power configuration planning layer model based on the first objective function and the first constraint condition to obtain a location and capacity of a reactive power source;

[0140] The binary crossover operation in the non-dominated sorting genetic algorithm (NSGA-II algorithm) is:

[0141]

[0142]

[0143] In the formula, γ j represents the distribution factor, x 1j represents the first parent population, x 2j represents the second parent population, u j represents a random number from 0 to 1, and η1 represents the crossover operator;

[0144] The polynomial mutation operator in the non-dominated sorting genetic algorithm (NSGA-II algorithm) is:

[0145]

[0146] In the formula, B jrepresents the mutation factor, η2 represents the mutation operator;

[0147] The non-dominated sorting genetic algorithm is used to solve the reactive power configuration planning layer model based on the first objective function and the first constraint condition, and the value of the constrained parameter in the corresponding first constraint condition (such as the actual number of wind turbines installed, the actual number of photovoltaic power generation installed, etc.) can be obtained when the comprehensive cost and carbon emissions are minimized, so as to obtain the location and capacity of the reactive power source;

[0148] S42, using a CPLEX solver to solve the reactive power configuration operation layer model based on the second objective function and the second constraint condition, to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator, and the tap position of the on-load tap changer transformer;

[0149] The reactive power configuration operation layer model combines the optimal power flow algorithm to relax the distflow branch power flow equation, linearize the nonlinear constraints such as the switchable capacitor bank and the position of the on-load tap-changing transformer, and convert it into a mixed integer second-order cone optimization problem, which is easy to solve using the CPLEX commercial solver;

[0150] The obtained location and capacity of the reactive power source, the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator and the tap position of the on-load tap-changing transformer, i.e., the reactive power source optimization configuration result;

[0151] The CPLEX solver is used to solve the reactive power configuration operation layer model based on the second objective function and the second constraint condition, and the values ​​of the constrained parameters in the corresponding second constraint condition when the operating cost is minimized can be obtained, namely, the output of the distributed generation, the output of the capacitor, the output of the static VAR compensator, and the tap position of the on-load tap-changing transformer, thereby realizing reactive power optimization scheduling;

[0152] This embodiment is based on Figure 3 The distribution network system shown is simulated and verified by using the above-mentioned reactive power optimization configuration method of the active distribution network, wherein node 1 is the access location of the upper power grid point, and the remaining nodes 2 to 33 are load nodes, and 96 time periods are formed by using 24h data of typical days in four seasons; in this embodiment, the residential load nodes are nodes 2 to 18; the commercial load nodes are nodes 26 to 33; the industrial load nodes are nodes 19 to 25; the system load is twice the benchmark value of the IEEE 33-node distribution system;

[0153] The unit capacity cost of the wind turbine, photovoltaic power generation, switchable capacitor bank and static VAR compensator (i.e., K WTG , K PVG , K CB and K SVC) are 200,000 yuan / MVA, 120,000 yuan / MVA, 7,000 yuan / group and 14,000 yuan / group respectively; the upper grid on-grid electricity price (i.e. K gen ) is 0.3 yuan / kWh; the power generation price of the distributed power source (including wind turbines and photovoltaic power generation) is 0.5 / kWh; the unit cost of abandoned power of the distributed power source is 2 yuan / kWh, and the penalty cost of voltage crossing is 50 yuan / time; the commissioning cost of the switchable capacitor bank per unit capacity is 0.1 yuan / kVar; the commissioning cost of the static VAR compensator per unit capacity is 0.1 yuan / kVar; the carbon dioxide emission factor is 0.8647 tons / MWh; the rated power factor P / Q of the wind turbine is 0.9; the rated power factor of the photovoltaic power generation is 0.9; the on-load tap-changing transformer has 9 adjustment gears, and the voltage adjustment step is 0.0125pu; the voltage adjustable range is 0.95pu~1.05pu; the system allows a maximum voltage deviation of ±7%; the maximum number of wind turbines installed at the node is 5 groups, with a unit capacity of 0.2MVA, and can be connected to a maximum of 2 nodes; the maximum number of photovoltaic power generation installed at the node is 5 groups, with a unit capacity of 0.3MVA, and can be connected to a maximum of 2 nodes; the maximum number of switchable capacitor groups installed at the node is 10 groups, each with a capacity of 0.1Mvar, and can be connected to a maximum of 2 nodes; the maximum number of adjustments per day for the switchable capacitor group does not exceed 30 groups; the capacity of the static VAR compensator is 0.3Mvar, and can be connected to a maximum of 1 node;

[0154] The NSGA-II algorithm uses binary crossover operation and polynomial mutation operator to iterate the population. The parent population size is 100, the maximum number of iterations is 50, the crossover rate is 0.9, the mutation rate is 0.1, η1=1, η2=5;

[0155] The reactive power optimization configuration results obtained in this embodiment are shown in Table 1;

[0156] Table 1 Reactive power supply optimization configuration results

[0157]

[0158] In Table 1, 3 WTGs are installed at node 22, and the installed capacity of a single WTG is 0.2MVA, so the installed capacity of the 3 WTGs is 0.6MVA. 5 WTGs are installed at node 32, and the installed capacity of the 5 WTGs is 1MVA. 5 PVGs are installed at node 11, and the installed capacity of a single PVG is 0.3MVA, so the installed capacity of the 5 PVGs is 1.5MVA. 5 PVGs are installed at node 26, and the installed capacity of the 5 PVGs is 1.5MVA. CB is installed at node 25, with a total of 10 groups installed, and the total installed capacity is 1Mvar. SVC is installed at node 24, with an installed capacity of 0.3Mvar.

[0159] As shown in Table 2, Table 2 shows the comparison results of the simulation results of the network optimized by the above method in this embodiment and the initial network;

[0160] Table 2 Comparison of simulation results of optimized network and initial network

[0161]

[0162]

[0163] As can be seen from Table 2, the total cost of the initial network is 15.327 million yuan, and the total CO2 emissions are 30,400 tons. After optimization by the above method of the present invention, the total cost is 13.18 million yuan, a decrease of 14%, and the CO2 emissions are 18,300 tons, a decrease of 39.8%; the initial network does not install any clean power facilities. After optimization by the present invention, the installed capacity of clean power is 4.6MVA. Since the current clean power generation price is relatively high, the installation of clean power will increase the operating cost. After optimization, the voltage level of the distribution network is within the allowable range, while the initial network has 95,340 low voltages, and the low voltage penalty fee is 4.767 million yuan. Therefore, although the increase in clean power output will increase the operating cost, after comprehensively considering its reliability cost, the total cost is still reduced; in terms of CO2 emissions, since clean power does not emit CO2, the CO2 emission level is significantly reduced; in summary, after optimization by the above-mentioned method for optimizing the configuration of reactive power sources of the active distribution network of the present invention, the economy, reliability and low carbon of the distribution network are improved;

[0164] The active and reactive outputs of the upper power grid (upper power source), wind turbines, and photovoltaic power generation are shown in Figure 4 and Figure 5 As shown, it can be seen that the output characteristics of wind turbines and photovoltaic power generation are opposite. Photovoltaic power generation has more output in summer and less output in winter, while wind turbines have more output in winter and less output in summer. Relatively speaking, the output curve of wind turbines and the load characteristic curve are basically consistent. After installing wind turbines and photovoltaic power generation, there is a more obvious inhibitory effect on the midday peak of power output, but the inhibitory effect on the evening peak is not significant. The main reason is that the output of wind turbines and photovoltaic power generation is relatively small during the evening peak, but combined with Figure 5 It can be seen that during the evening peak period, although the active output of wind turbines and photovoltaic power generation is relatively small, their reactive output capacity is fully utilized to support the system voltage;

[0165] The input conditions of switchable capacitor banks and static VAR compensators in four seasons are as follows: Figure 6 and Figure 7As shown, it can be seen that the reactive output of the switchable capacitor bank and the static VAR compensator is mainly concentrated between 8:00-21:00. The load is large in summer. Until 23:00 at night, the reactive output is still large. The reactive output level and load curve of the switchable capacitor bank and the static VAR compensator are almost completely consistent.

[0166] from Figures 4 to 7 It can be seen that the demand for reactive power is greater at night. This is because the active power output of clean power is small at night, resulting in a heavier load at night. In order to maintain the stability of the system voltage, the reactive power output level must be increased. The data at the peak load time of the whole year, that is, 20:00 in summer, are selected for simulation, and the voltage levels of the network optimized by the method of the present invention and the original network are compared. The comparison results are as follows: Figure 8 As shown, it can be seen that the voltage level is significantly improved after optimization. At the peak load time of 20:00 in summer, there are 19 nodes in the original network that have low voltage problems. From Table 2, it can be seen that low voltage occurs 95,340 times in the whole year, and the voltage level of the distribution network is poor. However, through the optimization of the method of the present invention, the low voltage control effect is significant, and low voltage problems do not occur throughout the year;

[0167] The relaxation error of the present invention is as follows Fig. 9 As shown, it can be seen that the relaxation error is in the order of 10-7, and the relaxation accuracy is relatively high. The present invention makes full use of reactive power sources such as on-load tap-changing transformers, fans, photovoltaic power generation, static VAR compensators, and switchable capacitor banks, deeply taps the potential of the power grid, meets the access requirements of loads and distributed power sources, and realizes voltage regulation, thereby effectively realizing reactive optimization of the active distribution network.

[0168] Embodiment 2

[0169] Please refer to Figure 2 , a reactive power optimization configuration terminal for an active distribution network in this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the reactive power optimization configuration method for an active distribution network in Embodiment 1 is implemented.

[0170] In summary, the present invention provides a reactive power optimization configuration method and terminal for an active distribution network, which establishes a reactive power configuration planning layer model and a reactive power configuration operation layer model for the active distribution network; minimizes comprehensive costs and carbon emissions as the first objective function of the reactive power configuration planning layer model, and determines a first constraint condition corresponding to the first objective function; minimizes operating costs as the second objective function of the reactive power configuration operation layer model, and determines a second constraint condition corresponding to the second objective function; uses a CPLEX solver to solve the reactive power configuration operation layer model based on the second objective function and the second constraint condition , the output of distributed power sources, the output of capacitors, the output of static VAR compensators and the tap position of on-load tap-changing transformers are obtained, which greatly improves the calculation efficiency. The reactive power site selection and capacity determination are realized through the upper planning layer, and the reactive power optimization scheduling is realized through the lower operation layer. The reactive power source is fully utilized, and the minimum comprehensive cost and carbon emissions are taken as the first objective function, and the minimum operating cost is taken as the second objective function, which is conducive to reducing the carbon emission level of the active distribution network and improving the investment efficiency. The upper and lower layers are solved respectively to obtain the final reactive power configuration result, thereby effectively realizing the reactive power optimization of the active distribution network, which is conducive to the safe and stable operation of the active distribution network.

[0171] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing reactive power configuration of an active distribution network, characterized in that: Includes steps: Establish reactive power configuration planning layer model and reactive power configuration operation layer model of active distribution network; Determine the minimum comprehensive cost and carbon emission as the first objective function of the reactive power configuration planning layer model, and determine a first constraint condition corresponding to the first objective function; Determine the minimum operating cost as the second objective function of the reactive power configuration operating layer model, and determine a second constraint condition corresponding to the second objective function; Solving the reactive power configuration planning layer model based on the first objective function and the first constraint condition to obtain the location and capacity of the reactive power source, and solving the reactive power configuration operation layer model based on the second objective function and the second constraint condition to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator, and the tap position of the on-load tap changer transformer; The first objective function includes a first sub-objective function and a second sub-objective function; The step of minimizing the comprehensive cost and carbon emission as the first objective function of the reactive power configuration planning layer model comprises: Minimizing the comprehensive cost is determined as the first sub-objective function of the reactive power configuration planning layer model; Minimizing carbon emissions is determined as the second sub-objective function of the reactive power configuration planning layer model; The first sub-objective function F1 is: ； ； In the formula, C I represents the investment cost, C O represents the operating cost, C P represents the voltage crossing penalty cost, represents the construction cost of the wind turbine, represents the construction cost of photovoltaic power generation, represents the construction cost of the switchable capacitor bank, represents the construction cost of the static VAR compensator, T represents the scheduling time set, represents the operating cost at time t, K P Indicates the low voltage single penalty fee, N LV Indicates the number of low voltage occurrences; The second sub-objective function F2 is: ； In the formula, represents the total carbon dioxide emissions of the upper power grid at time t, Represents the set of upper-level power grid nodes, represents the carbon dioxide emission factor, represents the power input of the upper power grid at time t; The first constraint condition is: ； In the formula, represents the actual installed number of wind turbines at node j, represents the maximum number of wind turbines installed at node j, represents the actual installed quantity of the photovoltaic power generation at node j, represents the maximum number of photovoltaic power generation installations at node j, represents the actual installed number of the switchable capacitor bank at node j, represents the maximum installed number of the switchable capacitor bank at node j; The second objective function of the reactive power configuration operation layer model is to determine the minimum operating cost as the second objective function of the reactive power configuration operation layer model, including: The second objective function f is: ； ； ； ； ； In the formula, Indicates the electricity input cost of the upper power grid, represents the operating cost of distributed generation, represents the operating cost of the switchable capacitor bank, represents the operating cost of the static VAR compensator, K gen represents the transmission price of the upper power grid, Indicates the power input from the upper grid. K WTG represents the electricity price of the wind turbine, represents the active output of the fan, K WTG,cut represents the unit cost of abandoned electricity of the wind turbine, represents the amount of power wasted by the fan, K PVG represents the electricity price of the photovoltaic power generation, represents the active output of the photovoltaic power generation, K PVG,cut represents the unit cost of abandoned electricity of the photovoltaic power generation, represents the amount of abandoned photovoltaic power generation, K CB represents the operation cost of the switchable capacitor bank per unit capacity, represents the reactive power generated by the switchable capacitor bank, K SVC represents the commissioning cost of the static VAR compensator per unit capacity, represents the reactive power generated by the static VAR compensator, represents the wind turbine node set, represents the photovoltaic power generation node set, represents the set of nodes of the switchable capacitor bank, represents the static VAR compensator node set; The second constraint conditions include power flow constraint conditions, low voltage side voltage constraint conditions of the on-load tap-changing transformer, distributed power source constraint conditions, switchable capacitor bank constraint conditions and static VAR compensator constraint conditions.

2. The method for optimizing reactive power configuration of an active power distribution network according to claim 1, characterized in that: The power flow constraint condition is: ； ； ； In the formula, represents the active power injected by node j, represents the branch with node j as the first node, that is, branch jk, P jk,t represents the active power flow of the branch jk, represents the branch with node j as the end node, that is, branch ij, P ij,t represents the active power flow of the branch ij, represents the square of the current in the branch ij, represents the resistance of the branch ij, represents the injected reactive power of the node j, Q jk,t represents the reactive power flow of the branch jk, Q ij,t represents the reactive power flow of the branch ij, represents the reactance of the branch ij, Indicates the active power of the load, represents the reactive power generated by the wind turbine, represents the reactive power generated by the photovoltaic power generation, Represents the reactive power of the load, Represents the square of the voltage on the low voltage side, represents the square of the voltage at the node i.

3. The method for optimizing reactive power configuration of an active power distribution network according to claim 2, characterized in that: The voltage constraint condition of the low voltage side of the on-load tap-changing transformer is: ； ； In the formula, represents a binary auxiliary determinant variable, Indicates the adjustable voltage sequence of the on-load tap-changing transformer. Represents the on-load tap-changing transformer node set.

4. The method for optimizing reactive power configuration of an active power distribution network according to claim 1, characterized in that: The distributed power supply constraints are: ； In the formula, S DG represents the apparent power of the distributed generation, represents the active output of the distributed generation at time t, represents the maximum reactive power output of the distributed generation at time t, represents the maximum active output of the distributed generation at time t, Represents the reactive output of the distributed generation at time t.

5. The method for optimizing reactive power configuration of an active power distribution network according to claim 3, characterized in that: The switchable capacitor bank constraint is: ； ； In the formula, represents the number of switchable capacitors in node j at time t, Indicates the rated capacity of a single capacitor of the switchable capacitor bank, represents the first binary auxiliary variable, represents the second binary auxiliary variable, represents the maximum adjustment times of the switchable capacitor bank at node j, represents the number of switchable capacitor banks at node j at time t-1; represents the actual installed number of the switchable capacitor bank at node j; The static VAR compensator constraint condition is: ； In the formula, represents the minimum value of the reactive power generated by the static VAR compensator at node j, represents the maximum value of the reactive power generated by the static VAR compensator at node j.

6. The method for optimizing reactive power configuration of an active power distribution network according to claim 1, characterized in that: The reactive power configuration planning layer model is solved based on the first objective function and the first constraint condition to obtain the location and capacity of the reactive power source, and the reactive power configuration operation layer model is solved based on the second objective function and the second constraint condition to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator, and the tap position of the on-load tap changer transformer, including: Using a non-dominated sorting genetic algorithm to solve the reactive power configuration planning layer model based on the first objective function and the first constraint condition to obtain a location and capacity determination of the reactive power source; The reactive power configuration operation layer model is solved using a CPLEX solver based on the second objective function and the second constraint condition to obtain the output of the distributed power source, the output of the capacitor, the output of the static VAR compensator, and the tap position of the on-load tap changing transformer.

7. A reactive power optimization configuration terminal for an active power distribution network, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the method for optimizing reactive power configuration of an active power distribution network according to any one of claims 1 to 6 is implemented.

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