Multi-source and load site selection method and system for distribution network facing distributed photovoltaic

Abstract

The application discloses a kind of distributed photovoltaic-oriented distribution network multi-source load site selection and system, and wherein method includes: establishing the first mathematical model for determining the access location and boundary capacity of distributed power;Establish the second mathematical model for determining the access location and boundary capacity of electric vehicle;Based on the first mathematical model and the second mathematical model, determine the access location and boundary capacity of distribution network multi-source load;Establish the comprehensive objective function of distribution network active power loss and distributed photovoltaic active reduction;Determine grid operation safety constraint and power flow constraint;Establish the mathematical model of distributed photovoltaic power generation;Determine the electric vehicle aggregation power constraint and electric vehicle SOC constraint;Based on the comprehensive objective function, the grid operation safety constraint, the power flow constraint, the mathematical model of distributed photovoltaic power generation, the electric vehicle aggregation power constraint and the electric vehicle SOC constraint, the operation of distribution network multi-source load is regulated.

Description

Technical Field

[0001] This invention relates to the field of distribution network planning technology, and more specifically, to a method and system for multi-source load location and capacity determination in distribution networks for distributed photovoltaic systems. Background Technology

[0002] With the depletion of resources such as oil and coal, clean energy is playing an increasingly important role. Fully utilizing renewable energy generation is an inevitable choice for improving my country's energy security, alleviating energy shortages, and improving the social environment. Large-scale photovoltaic (PV) integration has become a trend, but the PV absorption capacity of distribution networks is limited. Exceeding this capacity may threaten the safe and economical operation of the distribution network, thus requiring an improvement in the distributed PV absorption capacity. Reasonable site selection and capacity determination of multi-source loads can enhance the PV absorption capacity of the distribution network. The site selection and capacity determination of distributed power sources affect the short-circuit current, node voltage, line power flow, network reliability, and grid operation safety of the distribution network. Electric vehicles, as a clean mode of transportation, have been widely promoted in recent years. However, the charging and discharging of electric vehicles are random in time and space, and their integration into the distribution network may impact the grid's safety and stability. A large number of electric vehicle charging loads may be concentrated during peak load periods, increasing the load demand and potentially overloading the power system, leading to local grid overload. The construction of charging piles and other infrastructure will change the distribution network topology, increase network nodes, increase the difficulty of line modification, and increase grid losses, bringing a series of negative impacts and difficulties to grid planning and operation. By rationally planning the location and capacity of multiple source loads in the distribution network, the impact of multiple source loads on grid connection can be effectively reduced, and the photovoltaic absorption capacity of the distribution network can be improved.

[0003] Currently, scholars both domestically and internationally have researched methods for determining the location and capacity of distributed generation sources (DG sources). Some scholars have proposed definitions for the access capacity of DG sources that allow access at arbitrary locations and with arbitrary capacities, and have studied mathematical models for the maximum access capacity of DG sources considering load uncertainties. However, these methods do not consider power flow constraints, voltage and power constraints, and rely on the distribution of loads. For electric vehicles (EVs) connecting to the distribution network, this is mainly achieved through EV charging stations. However, some methods only consider charging demand and service range, neglecting power flow, voltage, and power constraints. When a large number of EVs are connected, voltage fluctuations may occur.

[0004] Therefore, there is an urgent need to develop a multi-source load location and capacity determination method and system for distribution networks that fully considers the operational constraints of distributed photovoltaics, electric vehicles, and the safe operation constraints of distribution networks, and is geared towards improving the absorption capacity of distributed photovoltaics. Summary of the Invention

[0005] The present invention provides a method and system for multi-source load location and capacity determination in distribution networks for distributed photovoltaic (PV) systems, in order to solve the problem of multi-source load location and capacity determination in order to improve the PV absorption capacity of distribution networks.

[0006] To address the aforementioned problems, this invention provides a method for multi-source load location and capacity determination in distribution networks for distributed photovoltaic systems, the method comprising:

[0007] A first mathematical model is established to determine the access location and boundary capacity of distributed power sources; a second mathematical model is established to determine the access location and boundary capacity of electric vehicles; based on the first and second mathematical models, the access locations and boundary capacities of multiple sources and loads in the distribution network are determined.

[0008] A comprehensive objective function is established to determine the active power loss of the distribution network and the active power reduction of distributed photovoltaic power generation; power grid operation safety constraints and power flow constraints are determined; a mathematical model of distributed photovoltaic power generation is established; electric vehicle aggregate power constraints and electric vehicle SOC constraints are determined; based on the comprehensive objective function, the power grid operation safety constraints, the power flow constraints, the mathematical model of distributed photovoltaic power generation, the electric vehicle aggregate power constraints, and the electric vehicle SOC constraints, the operation of the multi-source load of the distribution network is regulated.

[0009] Preferably, it further includes:

[0010] An optimal objective function is established to maximize the access capacity of multiple source loads in the distribution network and the technical indicators of the distribution network. Based on the power grid operation safety constraints, the power flow constraints, the mathematical model of distributed photovoltaic power generation, the aggregated power constraints of electric vehicles, and the SOC constraints of electric vehicles, the optimal objective function is solved to determine the location and capacity determination method for multiple source loads in the distribution network.

[0011] Preferably, the first mathematical model includes:

[0012] max P DGk

[0013]

[0014]

[0015] η DG,t P DGk,max -η Load,t P Load,max ≤γP Load,max

[0016]

[0017] S Lm ≤S Lm,max

[0018] Among them, P s,i Q s,i G represents the active and reactive power injected at node i, respectively. ij B ij U represents the real and imaginary parts of the network admittance matrix, respectively. i U j Let θ represent the voltage magnitudes at nodes i and j, respectively. ij P represents the voltage phase difference between nodes i and j. DGk Q DGk S represents the active and reactive power generated by the photovoltaic system at node k, respectively. DGk P represents the maximum photovoltaic capacity that can be connected at node k. DGk,max P Load,max η represents the maximum active power of photovoltaic power and the maximum load that the current distribution network can withstand, respectively. DG,t η represents the ratio of the active power generated at time t to its maximum power. Load,t U represents the ratio of the load in the network at time t to the maximum load that the distribution network can withstand, γ represents the backflow coefficient, and U represents the load in the distribution network at time t. N Indicates the rated voltage of the network, ΔU%. min ΔU% max S represents the upper and lower limits of the voltage deviation, respectively. Lm S Lm,max These represent the current apparent power and maximum allowable capacity of line m, respectively, and N is the total number of nodes.

[0019] Preferably, the second mathematical model includes:

[0020] max P Lk

[0021]

[0022]

[0023]

[0024] S Lm ≤S Lm,max

[0025] Among them, P Lk This represents the active power connected to the load characteristic device at node k.

[0026] Preferably, the comprehensive objective function includes:

[0027]

[0028] Among them, c Loss c PVr represents the system network loss and the active power reduction loss coefficient of distributed photovoltaic power generation, respectively. ij I represents the impedance between node i and node j. ij,t This represents the current between node i and node j at time t. This represents the maximum active power of distributed photovoltaic injection node i at time t. Let represent the active power injected into node i by the distributed photovoltaic system at time t, where t represents time t and T represents the total connection time.

[0029] Preferably, the mathematical model for distributed photovoltaic power generation includes:

[0030]

[0031] In the formula: Let N represent the active power, reactive power, and capacity provided by the photovoltaic system connected to node i at time t, respectively. PV Let i be the set of PVs connection points, where i represents a node.

[0032] Preferably, the aggregated power constraint of the electric vehicle includes:

[0033]

[0034] Among them, P ev,i (t) represents the active power of electric vehicle aggregation node i at time t. This represents the maximum active power of electric vehicle aggregation node i at time t.

[0035] Preferably, the electric vehicle SOC constraint includes:

[0036]

[0037] in, Let SOC represent the minimum state of charge (SOC) of the electric vehicle battery at node i at time t. ev,i (t) represents the state of charge of the electric vehicle battery at node i at time t. This represents the maximum state of charge of the electric vehicle battery at node i at time t.

[0038] Preferably, the optimal objective function includes:

[0039]

[0040]

[0041] in, This represents the distributed photovoltaic capacity connected to node i at time t. This represents the energy storage capacity connected to node i at time t. This represents the electric vehicle energy storage capacity connected to node i at time t. This indicates the network loss of each line. The voltage deviation at each node is represented by t, where t represents time t and T represents the total connection time.

[0042] Based on another aspect of the present invention, the present invention provides a multi-source load location and capacity determination system for distributed photovoltaic power grids, the system comprising:

[0043] An initial unit is used to establish a first mathematical model to determine the access location and boundary capacity of distributed power sources; to establish a second mathematical model to determine the access location and boundary capacity of electric vehicles; and to determine the access location and boundary capacity of multiple sources and loads in the distribution network based on the first and second mathematical models.

[0044] The execution unit is used to establish a comprehensive objective function for the active power loss of the distribution network and the active power reduction of distributed photovoltaic power; determine the power grid operation safety constraints and power flow constraints; establish a mathematical model for distributed photovoltaic power generation; determine the aggregate power constraints and SOC constraints of electric vehicles; and regulate the operation of the multi-source load of the distribution network based on the comprehensive objective function, the power grid operation safety constraints, the power flow constraints, the mathematical model of distributed photovoltaic power generation, the aggregate power constraints of electric vehicles, and the SOC constraints of electric vehicles.

[0045] This invention provides a method and system for determining the location and capacity of multiple sources of loads in a distribution network for distributed photovoltaic (PV) power generation. The method includes: establishing a first mathematical model to determine the access location and boundary capacity of distributed power sources; establishing a second mathematical model to determine the access location and boundary capacity of electric vehicles (EVs); determining the access location and boundary capacity of multiple sources of loads in the distribution network based on the first and second mathematical models; establishing a comprehensive objective function for the active power loss of the distribution network and the active power reduction of distributed PV power generation; determining the grid operation safety constraints and power flow constraints; establishing a mathematical model for distributed PV power generation; determining the aggregated power constraints and state of charge (SOC) constraints of EVs; and regulating the operation of multiple sources of loads in the distribution network based on the comprehensive objective function, grid operation safety constraints, power flow constraints, the mathematical model for distributed PV power generation, the aggregated power constraints of EVs, and the SOC constraints of EVs. The proposed multi-source load location and capacity determination method firstly models and analyzes the access location and boundary capacity of multi-source loads to provide power grid companies with alternative solutions for multi-source load access; secondly, based on the research on the operation and control modes of multi-source loads, it proposes a multi-source load location and capacity determination method, which effectively improves the distributed photovoltaic absorption capacity of the distribution network, ensures the safe and stable operation of the distribution network, and gives full play to the photovoltaic absorption potential of the distribution network. Attached Figure Description

[0046] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0047] Figure 1 This is a flowchart of a multi-source load location and capacity determination method for a distribution network oriented towards distributed photovoltaic power, according to a preferred embodiment of the present invention.

[0048] Figure 2 This is a flowchart of a multi-source load location and capacity determination method according to a preferred embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of a 33-node system structure according to a preferred embodiment of the present invention;

[0050] Figure 4 A schematic diagram of the spatial distribution of the Pareto front according to a preferred embodiment of the present invention; and

[0051] Figure 5 This is a structural diagram of a multi-source load location and capacity determination system for a distribution network oriented towards distributed photovoltaic power, according to a preferred embodiment of the present invention. Detailed Implementation

[0052] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0053] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0054] Figure 1 This is a flowchart illustrating a method for multi-source load location and capacity determination in a distribution network for distributed photovoltaic (PV) systems, according to a preferred embodiment of the present invention. With the increasing penetration rate of distributed PV in distribution networks, there is an urgent need to improve the PV absorption capacity of the distribution network to ensure its safe and stable operation. This invention proposes a method for multi-source load location and capacity determination in a distribution network to improve the absorption capacity of distributed PV. First, the access locations and boundary capacities of multi-source loads are modeled and analyzed. Second, based on research into the operation and control modes of multi-source loads, a method for multi-source load location and capacity determination is proposed.

[0055] like Figure 1As shown, this invention provides a method for multi-source load location and capacity determination in a distribution network for distributed photovoltaic power generation. The method includes:

[0056] Step 101: Establish a first mathematical model to determine the access location and boundary capacity of distributed power sources; establish a second mathematical model to determine the access location and boundary capacity of electric vehicles; based on the first and second mathematical models, determine the access location and boundary capacity of multiple sources and loads in the distribution network;

[0057] Preferably, the first mathematical model includes:

[0058] max P DGk

[0059]

[0060]

[0061] η DG,t P DGk,max -η Load,t P Load,max ≤γP Load,max

[0062]

[0063] S Lm ≤S Lm,max

[0064] Among them, P s,i Q s,i G represents the active and reactive power injected at node i, respectively. ij B ij U represents the real and imaginary parts of the network admittance matrix, respectively. i U j Let θ represent the voltage magnitudes at nodes i and j, respectively. ij P represents the voltage phase difference between nodes i and j. DGk Q DGk S represents the active and reactive power generated by the photovoltaic system at node k, respectively. DGk P represents the maximum photovoltaic capacity that can be connected at node k. DGk,max P Load,max η represents the maximum active power of photovoltaic power and the maximum load that the current distribution network can withstand, respectively. DG,t η represents the ratio of the active power of photovoltaic power generation to its maximum power at time t. Load,t U represents the ratio of the load in the network at time t to the maximum load that the distribution network can withstand, γ represents the backflow coefficient, and U represents the load in the distribution network at time t. N Indicates the rated voltage of the network, ΔU%. min ΔU% maxS represents the upper and lower limits of the voltage deviation, respectively. Lm S Lm,max These represent the current apparent power and maximum allowable capacity of line m, respectively, and N is the total number of nodes.

[0065] Preferably, the second mathematical model includes:

[0066] max P Lk

[0067]

[0068]

[0069]

[0070] S Lm ≤S Lm,max

[0071] Among them, P Lk This represents the active power connected to the load characteristic device at node k.

[0072] This invention determines the access locations and boundary capacities of multiple source loads in a distribution network.

[0073] 1) Mathematical model of the access location and boundary capacity of distributed power sources

[0074] When a high proportion of distributed generation (DG) sources are connected to the distribution network, it can cause phenomena such as increased node voltage and line overload, and sometimes even severe backflow of power. If the current network's automation level is insufficient to cope with the backflow of power, it may lead to increased network losses, malfunctions of relay protection devices, and may even threaten the normal operation of existing equipment in the network. Taking DG as an example, when calculating the boundary conditions for grid connection of power source characteristic devices, backflow constraints need to be taken into account. The guidelines stipulate the maximum allowable capacity of PVs (Power Generation Units) to address backflow: the total capacity of PVs connected to the grid should be less than 25% of the maximum load within the power supply range of the upstream transformer. The provisions in the guidelines provide a certain reference for the selection of PV access capacity, but the specific network should be analyzed according to the actual situation. When the automation level and relay protection configuration of the network to be calculated cannot meet the requirements, backflow of power should be prevented; when the network to be calculated has a certain degree of automatic regulation and protection configuration capability, a certain degree of backflow of power can be allowed according to the actual situation.

[0075] In summary, the mathematical model of boundary conditions for PVs grid connection established in this invention is shown in formulas (1) to (7). It mainly considers the maximum power that PVs can access at node k under constraints such as load and PVs power changes, power flow back, node voltage deviation, PVs power factor, and line capacity.

[0076] max P DGk (1)

[0077]

[0078]

[0079] η DG,t P DGk,max -η Load,t P Load,max ≤γP Load,max (5)

[0080]

[0081] S Lm ≤S Lm,max (7)

[0082] Equations (2) to (3) are the power flow equilibrium equations for node i, P s,i Q s,i G represents the active and reactive power injected at node i, respectively; ij B ij U represents the real and imaginary parts of the network admittance matrix, respectively; i U j θ represents the voltage magnitudes at nodes i and j, respectively; ij This represents the voltage phase difference between nodes i and j.

[0083] Equation (4) represents the reactive power constraint of PVs, S DGk P represents the maximum photovoltaic capacity that can be connected at node k; DGk Q DGk These represent the active and reactive power generated by the photovoltaic system at node k, respectively.

[0084] Equation (5) is the backflow constraint, P DGk,max P Load,max These represent the maximum active power of photovoltaic power and the maximum load that the current distribution network can withstand, respectively; η DG,t η represents the ratio of the active power of photovoltaic power generation to its maximum power at time t. Load,t γ represents the ratio of the load in the network at time t to the maximum load that the distribution network can withstand; γ represents the power flow backflow coefficient, which can be calculated by the ratio of the maximum allowable power flow backflow of the network to the maximum load that the distribution network can withstand. Generally, γ≥0. When γ=0, it means that the current network does not allow power flow backflow.

[0085] Equation (6) is the node voltage deviation constraint, U N Indicates the rated voltage of the network; ΔU% min ΔU%max Indicates the upper and lower limits of voltage deviation;

[0086] Equation (7) represents the line capacity constraint, S Lm S Lm,max These represent the current apparent power and maximum allowable capacity of line m, respectively.

[0087] 2) Mathematical model of electric vehicle access location and boundary capacity

[0088] The boundary conditions for electric vehicles connected to the grid mainly consider the power flow constraints, voltage constraints and line capacity constraints of the network. Its mathematical model can be expressed by formulas (8) to (12).

[0089] max P Lk (8)

[0090]

[0091]

[0092]

[0093] S Lm ≤S Lm,max (12)

[0094] In the formula: P Lk This represents the active power connected to the load characteristic device at node k.

[0095] Step 102: Establish a comprehensive objective function for the active power loss of the distribution network and the active power reduction of distributed photovoltaic power; determine the power grid operation safety constraints and power flow constraints; establish a mathematical model for distributed photovoltaic power generation; determine the aggregated power constraints and SOC constraints of electric vehicles; based on the comprehensive objective function, power grid operation safety constraints, power flow constraints, mathematical model of distributed photovoltaic power generation, aggregated power constraints of electric vehicles, and SOC constraints of electric vehicles, regulate the operation of multi-source loads in the distribution network.

[0096] Preferably, the comprehensive objective function includes:

[0097]

[0098] Among them, c Loss c PV r represents the system network loss and the active power reduction loss coefficient of distributed photovoltaic power generation, respectively. ij I represents the impedance between node i and node j. ij,t This represents the current between node i and node j at time t. This represents the maximum active power of distributed photovoltaic injection node i at time t. Let represent the active power injected into node i by the distributed photovoltaic system at time t, where t represents time t and T represents the total connection time.

[0099] Preferably, the mathematical model for distributed photovoltaic power generation includes:

[0100]

[0101] In the formula: Let N represent the active power, reactive power, and capacity provided by the photovoltaic system connected to node i at time t, respectively. PV Let i be the set of PVs connection points, where i represents a node.

[0102] Preferably, the aggregate power constraint for electric vehicles includes:

[0103]

[0104] Among them, P ev,i (t) represents the active power of electric vehicle aggregation node i at time t. This represents the maximum active power of electric vehicle aggregation node i at time t.

[0105] Preferably, the SOC constraint for electric vehicles includes:

[0106]

[0107] in, Let SOC represent the minimum state of charge (SOC) of the electric vehicle battery at node i at time t. ev,i (t) represents the state of charge of the electric vehicle battery at node i at time t. This represents the maximum state of charge of the electric vehicle battery at node i at time t.

[0108] (2) Operation and control methods of multi-source loads

[0109] Taking full account of the operational control capabilities of multi-source loads under economic operating scenarios, an operation and scheduling model for multi-source loads connected to the distribution network is established with the goal of minimizing system active power loss and active power reduction of distributed photovoltaic power. The model is then solved using a genetic algorithm with an elite retention strategy, as detailed below:

[0110] 1) Objective function

[0111]

[0112] In the formula: c Loss c PV These are the system network loss and the active power reduction loss coefficient of distributed photovoltaic power, respectively. Equation (13) represents the comprehensive objective function of active power network loss and active power reduction of distributed photovoltaic power.

[0113] 2) Constraints

[0114] Power grid operation safety constraints

[0115] The operation of the distribution network should always meet voltage and current safety constraints, as shown below:

[0116]

[0117] S Lm ≤S Lm,max (15)

[0118] Equation (14) is the node voltage deviation constraint, U N Indicates the rated voltage of the network; ΔU% min ΔU% max Indicates the upper and lower limits of voltage deviation;

[0119] Equation (15) represents the line capacity constraint, S Lm S Lm,max These represent the current apparent power and maximum allowable capacity of line m, respectively.

[0120] Current constraints

[0121]

[0122]

[0123] Equations (16)-(17) are the power flow equilibrium equations for node i, P s,i Q s,i G represents the active and reactive power injected at node i, respectively; ij B ij U represents the real and imaginary parts of the network admittance matrix, respectively; i U j θ represents the voltage magnitudes at nodes i and j, respectively; ij This represents the voltage phase difference between nodes i and j.

[0124] 3) PV model

[0125] PV (Photovoltaic Power Generation) is a new type of power generation system that converts solar energy into electrical energy. Solar irradiance typically follows a beta distribution. It provides active and reactive power support to the distribution network through inverters. Its mathematical model is as follows:

[0126]

[0127] In the formula: Let N represent the active power, reactive power, and capacity provided by the photovoltaic system connected to node i at time t; PV This is the set of PVs grid connection points.

[0128] 4) Electric vehicle model

[0129] Electric vehicle aggregate power constraint

[0130] The charging power of electric vehicles should not exceed the maximum charging power at any given time.

[0131]

[0132] Electric vehicle SOC constraints

[0133] To meet users' travel needs during peak hours and to extend the lifespan of electric vehicle batteries, the state of charge (SOC) of the battery should meet certain requirements:

[0134]

[0135] Preferably, the method further includes:

[0136] An optimal objective function is established to maximize the access capacity of multiple source loads in the distribution network and the optimal technical indicators of the distribution network. Based on the mathematical model of grid operation safety constraints, power flow constraints, distributed photovoltaic power generation, electric vehicle aggregate power constraints, and electric vehicle SOC constraints, the optimal objective function is solved to determine the location and capacity determination method of multiple source loads in the distribution network.

[0137] Preferably, the optimal objective function includes:

[0138]

[0139]

[0140] in, This represents the distributed photovoltaic capacity connected to node i at time t. This represents the energy storage capacity connected to node i at time t. This represents the electric vehicle energy storage capacity connected to node i at time t. Indicates the network loss of each line, ΔU i , represent the voltage deviation of each node, t represents time t, and T represents the total connection time.

[0141] (3) A method for multi-source load location and capacity determination in distribution networks aimed at improving the absorption capacity of distributed photovoltaic power.

[0142] This invention aims to maximize the capacity of multi-source load access and optimize the distribution network operation indicators, fully considering the operational constraints of distributed photovoltaic power, electric vehicles, and the safety constraints of the distribution network. It generates the access locations and capacities of multi-source loads and employs a genetic algorithm with an elite retention strategy and a Pareto algorithm to solve the multi-objective function.

[0143] 1) Objective function:

[0144] With the objectives of maximizing the access capacity of multi-source loads and optimizing the technical indicators of the distribution network, the specific function is as follows:

[0145]

[0146]

[0147] In equation (21), This represents the distributed photovoltaic capacity connected to node i at time t; This represents the energy storage capacity connected to node i at time t; This represents the electric vehicle energy storage capacity connected to node i at time t.

[0148] In equation (22), This indicates the network loss of each line; This indicates the voltage deviation at each node.

[0149] Constraints:

[0150] Power grid operation safety constraints

[0151] The operation of the distribution network should always meet voltage and current safety constraints, as shown below:

[0152]

[0153] S Lm ≤S Lm,max (twenty four)

[0154] Equation (23) is the node voltage deviation constraint, U N Indicates the rated voltage of the network; ΔU% min ΔU% max Indicates the upper and lower limits of voltage deviation;

[0155] Equation (24) represents the line capacity constraint, S Lm S Lm,max These represent the current apparent power and maximum allowable capacity of line m, respectively.

[0156] Current constraints

[0157]

[0158]

[0159] Equations (25)-(26) are the power flow equilibrium equations for node i, P s,i Q s,i G represents the active and reactive power injected at node i, respectively; ij B ijU represents the real and imaginary parts of the network admittance matrix, respectively; i U j θ represents the voltage magnitudes at nodes i and j, respectively; ij This represents the voltage phase difference between nodes i and j.

[0160] 3) PV model

[0161] PV is a new type of power generation system that converts solar energy into electrical energy. It provides active and reactive power support to the distribution network through an inverter. Its mathematical model is as follows:

[0162]

[0163] In the formula: Let N represent the active power, reactive power, and capacity provided by the photovoltaic system connected to node i at time t; PV This is the set of PV grid connection points.

[0164] 4) Electric vehicle model

[0165] Electric vehicle aggregate power constraint

[0166] The charging power of electric vehicles should not exceed the maximum charging power at any given time.

[0167]

[0168] Electric vehicle SOC constraints

[0169] To meet users' travel needs during peak hours and to extend the lifespan of electric vehicle batteries, the state of charge (SOC) of the battery should meet certain requirements:

[0170]

[0171] (1) This invention fully considers the power distribution network’s ability to absorb multi-source loads and establishes a mathematical model of the access boundary conditions and access capacity of multi-source load equipment, providing a certain reference for power grid companies;

[0172] (2) Based on the analysis of the distribution network's operational control capabilities for multi-source loads, a method for selecting and determining the location and capacity of multi-source loads in the distribution network to improve the distributed photovoltaic (PV) absorption capacity is proposed. With the goal of maximizing the access capacity of multi-source loads and optimizing the distribution network's operational indicators, and considering the operational constraints of distributed PV, electric vehicles, and the safe operation constraints of the distribution network, the method ultimately obtains the access location and capacity of multi-source loads, maximizing the PV absorption capacity of the distribution network. For example... Figure 2 As shown.

[0173] The computational example used in this invention is the IEEE 33-node computational system shown in the figure. For example... Figure 3 As shown.

[0174] The example assumes the following:

[0175] 1) Electric vehicles require 800kW of electricity.

[0176] 2) The photovoltaic capacity installed at the specified nodes is 200kW, and the charging station capacity is 200kW.

[0177] 3) The equipment investment payback period is T = 20 years, r0 = 0.1, and the annual maximum load utilization hours T max =4500h, the proportion of photovoltaic power generation does not exceed 30% of the maximum load, the confidence level of node voltage and branch power is taken as 0.9, the unit electricity price is 0.5 yuan / kWh, the photovoltaic power generation node and the charging station are regarded as PQ nodes, and the power factor is set to 0.9. The irradiance follows a beta distribution, with parameters α taken as 0.45 and β taken as 9.41; the load distribution follows a normal distribution. The investment and operating cost parameters of photovoltaic power generation and electric vehicle charging station are shown in Table 1.

[0178] Table 1 Investment Costs and Operation & Maintenance Costs

[0179]

[0180] 4) In the process of solving the problem using the NSGA-II algorithm, the objective function is 3, the population size is 60, the number of iterations is 100, and the encoding format is [ABCD], where A represents whether a PV is installed, B represents the PV capacity, C represents whether a node has a charging station installed, and D represents the charging station capacity.

[0181] The results of the calculation examples of this invention are analyzed as follows:

[0182] The optimization results of the example yield a Pareto front, which represents the distribution of the non-dominated solution set in the solution space, such as... Figure 4 As shown.

[0183] from Figure 4 As can be seen, with the increase in investment costs, both network losses and environmental costs decrease significantly. That is, with the increase in photovoltaic power generation and electric vehicle charging station capacity, grid losses decrease, and environmental costs also decrease significantly; conversely, both grid loss costs and environmental costs increase. Figure 4 In the solution set of the distribution, all solutions are optimal, so the final planning scheme is determined according to the specific focus of the investor.

[0184] Here, four schemes are selected for analysis. For one scheme, the expected membership value of the objective function is set to 0.9. For the other two schemes, the expected membership value of the objective function is set to 0.9. For the other two schemes, the expected membership value is set to 0.65. This forms three schemes for comparison.

[0185] Table 2 Planning Configuration Results

[0186]

[0187]

[0188] The planning and configuration results are shown in Table 2: Scheme 1 has a high expectation for investment cost, so its investment cost is the lowest among the four schemes; Scheme 2 has a high expectation for network loss, so its network loss is the lowest among the four schemes; Scheme 3 has the lowest environmental cost, which shows that it also has the largest photovoltaic power capacity, so the integration of photovoltaic power is the fundamental solution to environmental problems. In Scheme 4, all three objective functions have high expectations, so the final total investment cost is the lowest.

[0189] (1) Modeling the entry location and boundary capacity of multiple source loads.

[0190] (2) A method for selecting and determining the location and capacity of multiple sources of loads in the distribution network to improve the absorption capacity of distributed photovoltaic power.

[0191] Figure 5 This is a structural diagram of a multi-source load location and capacity determination system for a distribution network oriented towards distributed photovoltaic power, according to a preferred embodiment of the present invention.

[0192] like Figure 5 As shown, this invention provides a multi-source load location and capacity determination system for distributed photovoltaic power distribution networks, the system comprising:

[0193] Initial unit 501 is used to establish a first mathematical model to determine the access location and boundary capacity of distributed power sources; establish a second mathematical model to determine the access location and boundary capacity of electric vehicles; and determine the access location and boundary capacity of multiple sources and loads in the distribution network based on the first and second mathematical models.

[0194] Preferably, the first mathematical model includes:

[0195] max P DGk

[0196]

[0197]

[0198] η DGt P DGkmax -η Loadt P Loadmax ≤γP Loadmax

[0199]

[0200] S Lm ≤S Lm,max

[0201] Among them, P s,i Q s,i G represents the active and reactive power injected at node i, respectively. ij B ij U represents the real and imaginary parts of the network admittance matrix, respectively. i U j Let θ represent the voltage magnitudes at nodes i and j, respectively. ij P represents the voltage phase difference between nodes i and j. DGk Q DGk S represents the active and reactive power generated by the photovoltaic system at node k, respectively. DGk P represents the maximum photovoltaic capacity that can be connected at node k. DGk,max P Load,max η represents the maximum active power of photovoltaic power and the maximum load that the current distribution network can withstand, respectively. DG,t η represents the ratio of the active power of photovoltaic power generation to its maximum power at time t. Load,t U represents the ratio of the load in the network at time t to the maximum load that the distribution network can withstand, γ represents the backflow coefficient, and U represents the load in the distribution network at time t. N Indicates the rated voltage of the network, ΔU%. min ΔU% max S represents the upper and lower limits of the voltage deviation, respectively. Lm S Lm,max These represent the current apparent power and maximum allowable capacity of line m, respectively, and N is the total number of nodes.

[0202] Preferably, the second mathematical model includes:

[0203] max P Lk

[0204]

[0205]

[0206]

[0207] S Lm ≤S Lm,max

[0208] Among them, P Lk This represents the active power connected to the load characteristic device at node k.

[0209] The execution unit 502 is used to establish a comprehensive objective function for the active power loss of the distribution network and the active power reduction of distributed photovoltaic power; determine the power grid operation safety constraints and power flow constraints; establish a mathematical model for distributed photovoltaic power generation; determine the aggregate power constraints and SOC constraints of electric vehicles; and regulate the operation of the multi-source load of the distribution network based on the comprehensive objective function, power grid operation safety constraints, power flow constraints, mathematical model of distributed photovoltaic power generation, aggregate power constraints of electric vehicles, and SOC constraints of electric vehicles.

[0210] Preferably, the comprehensive objective function includes:

[0211]

[0212] Among them, c Loss c PV r represents the system network loss and the active power reduction loss coefficient of distributed photovoltaic power generation, respectively. ij I represents the impedance between node i and node j. ij,t This represents the current between node i and node j at time t. This represents the maximum active power of distributed photovoltaic injection node i at time t. Let represent the active power injected into node i by the distributed photovoltaic system at time t, where t represents time t and T represents the total connection time.

[0213] Preferably, the mathematical model for distributed photovoltaic power generation includes:

[0214]

[0215] In the formula: Let N represent the active power, reactive power, and capacity provided by the photovoltaic system connected to node i at time t, respectively. PV Let i be the set of PVs connection points, where i represents a node.

[0216] Preferably, the aggregate power constraint for electric vehicles includes:

[0217]

[0218] Among them, P ev,i (t) represents the active power of electric vehicle aggregation node i at time t. This represents the maximum active power of electric vehicle aggregation node i at time t.

[0219] Preferably, the SOC constraint for electric vehicles includes:

[0220]

[0221] in, Let SOC represent the minimum state of charge (SOC) of the electric vehicle battery at node i at time t. ev,i (t) represents the state of charge of the electric vehicle battery at node i at time t. This represents the maximum state of charge of the electric vehicle battery at node i at time t.

[0222] Preferably, the execution unit 502 is further configured to:

[0223] An optimal objective function is established to maximize the access capacity of multiple source loads in the distribution network and the optimal technical indicators of the distribution network. Based on the mathematical model of grid operation safety constraints, power flow constraints, distributed photovoltaic power generation, electric vehicle aggregate power constraints, and electric vehicle SOC constraints, the optimal objective function is solved to determine the location and capacity determination method of multiple source loads in the distribution network.

[0224] Preferably, the optimal objective function includes:

[0225]

[0226]

[0227] in, This represents the distributed photovoltaic capacity connected to node i at time t. This represents the energy storage capacity connected to node i at time t. This represents the electric vehicle energy storage capacity connected to node i at time t. This indicates the network loss of each line. The voltage deviation at each node is represented by t, where t represents time t and T represents the total connection time.

[0228] The preferred embodiment of the present invention provides a multi-source load location and capacity determination system for a distribution network oriented towards distributed photovoltaic power, which corresponds to the preferred embodiment of the present invention provides a multi-source load location and capacity determination method for a distribution network oriented towards distributed photovoltaic power, and will not be described in detail here.

[0229] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0230] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0231] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0232] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0233] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0234] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0235] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0236] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless otherwise expressly defined herein. All references to “a / / the [device, component, etc.]” ​​are openly interpreted as at least one instance of the device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein are not necessarily to be performed in the exact order disclosed, unless explicitly stated otherwise.

Claims

1. A method for multi-source load location and capacity determination in a distribution network for distributed photovoltaic power generation, the method comprising: Establish a first mathematical model to determine the access location and boundary capacity of distributed power sources; Establish a second mathematical model to determine the access location and boundary capacity of electric vehicles; Based on the first mathematical model and the second mathematical model, the access locations and boundary capacities of multiple sources of loads in the distribution network are determined. The first mathematical model includes: in, , Representing nodes respectively i Injected active and reactive power, , Let represent the real and imaginary parts of the network admittance matrix, respectively. , Representing nodes respectively i and j voltage amplitude, Represents a node i and j voltage phase difference, , Representing nodes respectively k The active and reactive power generated by the photovoltaic system. Represents a node k The maximum capacity that can be connected to the photovoltaic system. , These represent the maximum active power of the photovoltaic system and the maximum load that the current power distribution network can withstand, respectively. express t The ratio of the active power of photovoltaic power generation to its maximum power at any given time. express t The ratio of the load in the network at any given time to the maximum load that the distribution network can handle. Indicates the current backflow coefficient. Indicates the rated voltage of the network. These represent the upper and lower limits of the voltage deviation, respectively. These represent the current apparent power and maximum allowable capacity of line m, respectively. N The total number of nodes; A comprehensive objective function is established to determine the active power loss of the distribution network and the active power reduction of distributed photovoltaic power generation; power grid operation safety constraints and power flow constraints are determined; a mathematical model of distributed photovoltaic power generation is established; electric vehicle aggregate power constraints and electric vehicle SOC constraints are determined; based on the comprehensive objective function, the power grid operation safety constraints, the power flow constraints, the mathematical model of distributed photovoltaic power generation, the electric vehicle aggregate power constraints, and the electric vehicle SOC constraints, the operation of the multi-source load of the distribution network is regulated. The comprehensive objective function includes: in, , These represent the system network loss and the active power reduction loss coefficients of distributed photovoltaic power, respectively. Represents a node i and nodes j The impedance between them Indicates in Time Node i and nodes j The current between, Indicates in t Distributed photovoltaic injection node i Maximum active power, This indicates the distributed photovoltaic injection node at time t. i active power, express time, This indicates the typical total duration of a day; The method further includes: establishing an optimal objective function for maximizing the access capacity of multiple sources and loads in the distribution network and the technical indicators of the distribution network; solving the optimal objective function based on the power grid operation safety constraints, the power flow constraints, the mathematical model of distributed photovoltaic power generation, the aggregated power constraints of electric vehicles, and the SOC constraints of electric vehicles; and determining the multi-source load location and capacity determination method for the distribution network. The optimal objective function includes: in, Represents a node At the moment The capacity of the distributed photovoltaic system connected to the grid, Represents a node At the moment The connected energy storage capacity, Represents a node At the moment The connected energy storage capacity of electric vehicles, This indicates the network loss of each line. This indicates the voltage deviation at each node. t express t time, T This indicates the total duration of a typical day.

2. The method according to claim 1, wherein the second mathematical model comprises: in, This represents the active power connected to the load characteristic device at node k.

3. The method according to claim 1, wherein the mathematical model for distributed photovoltaic power generation includes: In the formula: , , Represented as nodes The connected photovoltaic system is in constant motion The active power, reactive power, and its own capacity provided. A collection of photovoltaic grid connection points. i Represents a node.

4. The method according to claim 1, wherein the aggregated power constraint of the electric vehicle comprises: in, Indicates in t Electric vehicle aggregation node i active power, Indicates in t Electric vehicle aggregation node i The maximum active power.

5. The method according to claim 1, wherein the electric vehicle SOC constraint includes: in, Indicates in t Time, node i The minimum state of charge of an electric vehicle battery. Indicates in t Time, node i The state of charge of electric vehicle batteries. Indicates in t Time, node i The maximum state of charge of an electric vehicle battery.

6. A multi-source load location and capacity determination system for distributed photovoltaic power distribution networks, the system comprising: The initial unit is used to establish the first mathematical model for determining the access location and boundary capacity of distributed power sources. A second mathematical model is established to determine the access location and boundary capacity of electric vehicles; based on the first and second mathematical models, the access location and boundary capacity of multiple sources of load in the distribution network are determined. The first mathematical model includes: in, , Representing nodes respectively i Injected active and reactive power, , Let represent the real and imaginary parts of the network admittance matrix, respectively. , Representing nodes respectively i and j voltage amplitude, Represents a node i and j voltage phase difference, , Representing nodes respectively k The active and reactive power generated by the photovoltaic system. Represents a node k The maximum capacity that can be connected to the photovoltaic system. , These represent the maximum active power of the photovoltaic system and the maximum load that the current power distribution network can withstand, respectively. express t The ratio of the active power of photovoltaic power generation to its maximum power at any given time. express t The ratio of the load in the network at any given time to the maximum load that the distribution network can handle. Indicates the current backflow coefficient. Indicates the rated voltage of the network. These represent the upper and lower limits of the voltage deviation, respectively. These represent the current apparent power and maximum allowable capacity of line m, respectively. N The total number of nodes; The execution unit is used to establish a comprehensive objective function for the active power loss of the distribution network and the active power reduction of distributed photovoltaic power; determine the power grid operation safety constraints and power flow constraints; establish a mathematical model for distributed photovoltaic power generation; determine the aggregated power constraints and SOC constraints of electric vehicles; and regulate the operation of the multi-source load of the distribution network based on the comprehensive objective function, the power grid operation safety constraints, the power flow constraints, the mathematical model of distributed photovoltaic power generation, the aggregated power constraints of electric vehicles, and the SOC constraints of electric vehicles. The comprehensive objective function includes: in, , These represent the system network loss and the active power reduction loss coefficients of distributed photovoltaic power, respectively. Represents a node i and nodes j The impedance between them Indicates in t Time Node i and nodes j The current between, Indicates in t Distributed photovoltaic injection node i Maximum active power, This indicates the distributed photovoltaic injection node at time t. i active power, express t time, This indicates the typical total duration of a day; The system is also used to: establish the optimal objective function for maximizing the access capacity of multiple sources of loads in the distribution network and the technical indicators of the distribution network; solve the optimal objective function based on the power grid operation safety constraints, the power flow constraints, the mathematical model of distributed photovoltaic power generation, the aggregated power constraints of electric vehicles, and the SOC constraints of electric vehicles; and determine the method for determining the location and capacity of multiple sources of loads in the distribution network. The optimal objective function includes: in, Represents a node At the moment The capacity of the distributed photovoltaic system connected to the grid, Represents a node At the moment The connected energy storage capacity, Represents a node At the moment The connected energy storage capacity of electric vehicles, This indicates the network loss of each line. This indicates the voltage deviation at each node. t express t time, T This indicates the total duration of a typical day.

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