Distributed power supply-energy storage-charging station-capacitor combined configuration method for power distribution network

Abstract

The invention relates to a distributed power supply-energy storage-charging station-capacitor combined configuration method for a power distribution network, which comprises the following steps of: firstly, configuring addresses and capacities of a distributed power supply, battery energy storage and a charging station in stages, and judging whether a configuration result in the first stage meets a power flow requirement or not; when it is judged that the configuration result in the first stage meets the power flow requirement, the address and capacity of the capacitor are configured in the second stage, so that comprehensive planning of the distributed power supply, the energy storage power station, the charging station and the capacitor is considered at the same time, and the problems of limitation and low efficiency caused by single equipment planning are solved. Meanwhile, the planning process is more detailed and scientific by stages, optimization can be carried out according to different technical requirements, and the accuracy and effect of overall planning are improved. Moreover, compared with a traditional method only paying attention to the technical indexes, the method has the advantages that economic benefits can be remarkably improved while the technical indexes are guaranteed to reach the standard, and the planning scheme has higher practical value and market competitiveness.

Description

Technical Field

[0001] The present invention belongs to the technical field of active power distribution networks, and in particular relates to a method, system and electronic equipment for jointly configuring a distributed power source-energy storage-charging station-capacitor in a power distribution network. Background Art

[0002] The rapid development of electric vehicles and wind, solar and energy storage technologies has played a key role in distribution network planning. With the popularization of electric vehicles, the electricity demand pattern has changed significantly, increasing the peak and volatility of loads. As representatives of renewable energy, the instability and intermittency of wind and solar power generation have also posed new challenges to distribution networks. In order to adapt to these changes, distribution networks need to be comprehensively planned and upgraded to ensure the reliability and stability of power supply. This includes the application of smart grid technologies, the expansion and modernization of grid infrastructure, and the integration of energy storage systems, aiming to optimize the distribution and management of electricity and achieve efficient and sustainable energy utilization.

[0003] At present, with the rapid development of distributed generation (DG) and electric vehicles (EV), the theoretical results related to the site selection and capacity determination of distributed generation and the planning of charging stations are constantly being updated and the factors to be considered are increasing. Integrating charging stations into the power system may lead to problems such as increased voltage fluctuations and increased power losses. Connecting capacitors to radial distribution networks can effectively reduce energy losses and improve power factors. However, in existing charging station planning studies, capacitors are rarely planned together with charging stations to play the role of capacitors in reducing losses and improving voltage distribution. Some other studies integrate energy storage systems with the power grid because they have the ability to improve voltage curves, load balancing, reduce power losses, adjust frequency, quickly respond to reserves, and provide auxiliary services such as reactive power compensation.

[0004] Although the current research results on the planning and configuration of distributed power sources, energy storage power stations, charging stations, and capacitors are quite rich, most of the research has only conducted single optimization configuration research on the above objects, or only conducted joint configuration research on two or three of the above objects. There are still few studies on the joint planning of distribution networks that consider the four objects of distributed power sources, energy storage power stations, electric vehicle charging stations, and capacitors at the same time. Summary of the invention

[0005] In view of this, in order to overcome the problem that the equipment planning and configuration method in the existing distribution network planning research cannot simultaneously consider the four objects of distributed power supply, energy storage power station, electric vehicle charging station and capacitor, the present invention provides a distribution network distributed power supply-energy storage power station-charging station-capacitor joint configuration method, in order to solve the three key technical problems: 1) How to simultaneously consider distributed power supply, energy storage power station, electric vehicle charging station, capacitor, and conduct joint planning of multiple objects to improve the overall operation efficiency and reliability of the distribution network; 2) How to fully consider the operation stage when planning the distribution network; 3) In the process of distribution network planning, how to significantly improve the economic benefits while ensuring that the technical indicators are met.

[0006] The first aspect of the present invention discloses a method for jointly configuring a distributed power supply-energy storage-charging station-capacitor in a distribution network, comprising the following steps:

[0007] Corresponding to distributed power sources, battery energy storage power stations, electric vehicle charging stations and capacitors, build their own basic models respectively;

[0008] Based on the basic model, a first-stage target site selection and sizing model is constructed for site selection and sizing of distributed power sources, battery energy storage power stations, and electric vehicle charging stations, with cost minimization as the first-stage objective function; and based on the basic model, a second-stage target site selection and sizing model is constructed for site selection and sizing of capacitors, with minimization of voltage deviation and maximization of configuration benefits as the second-stage objective functions;

[0009] At least the power balance constraint, branch flow constraint and branch current capacity constraint are used as the first-stage constraint conditions, and the first-stage target site selection and sizing model is solved based on the adaptive simulated annealing-particle swarm algorithm to obtain the first-stage configuration results reflecting the site selection and sizing information of distributed generation, battery energy storage power station and electric vehicle charging station;

[0010] Determine whether the configuration result of the first stage meets the power flow requirements. If not, reset the optimization parameters of the first stage and solve the first stage target location and capacity model again to obtain the configuration result of the first stage again.

[0011] Conduct electric vehicle charging demand analysis and simulate the time series data of distributed power sources and distribution network loads based on weather, season, and working days / holidays to generate typical daily operating curves of electric vehicle charging stations, distributed power sources, and distribution network loads;

[0012] Based on the generated typical daily operation curve, the voltage conditions of each node in the distribution network are analyzed to obtain the voltage limit conditions of all nodes in the network;

[0013] When it is judged that the configuration result of the first stage meets the power flow requirements, at least the maximum reactive power compensation constraint of the capacitor is used as the constraint condition of the second stage. Based on the voltage over-limit situation of the whole network nodes, the second stage target site selection and sizing model is solved by adaptive simulated annealing-particle swarm algorithm to obtain the second stage configuration result reflecting the capacitor site selection and sizing information;

[0014] Based on the configuration results of the first stage and the second stage, the joint configuration results of distributed power sources, battery energy storage power stations, electric vehicle charging stations and capacitors are output.

[0015] In the present invention, as a whole, the comprehensive planning of distributed power sources, battery energy storage power stations, electric vehicle charging stations and capacitors is taken into account at the same time. This multi-object joint planning method can better coordinate the operation of various equipment, improve the overall efficiency and reliability of the distribution network, and avoid the limitations and inefficiency caused by single equipment planning. In addition, typical operating scenarios are fully considered during planning, based on the load curve characteristics of distributed power sources, energy storage power stations, charging stations, etc., as well as representative days of the year. This method can more accurately reflect the actual operating conditions, optimize the planning results, and improve the performance and adaptability of the system in different operating scenarios.

[0016] In addition, in the overall scheme of the present invention, it is particularly important that a two-stage planning method is proposed. The first stage mainly considers capacity constraints, active power balance and flow control to ensure the basic operation requirements of the system. The second stage plans reactive power compensation to achieve voltage and power quality standards. This staged planning method makes the planning process more detailed and scientific, and can be optimized according to different technical requirements, thereby improving the accuracy and effectiveness of the overall planning.

[0017] In summary, in the scheme of the present invention, economic benefits are fully taken into consideration, and the investment cost, operation and maintenance cost and possible benefits of the equipment are comprehensively analyzed. Compared with the traditional method that only focuses on technical indicators, this method can significantly improve the economic benefits while ensuring that the technical indicators are met, making the planning scheme more practical and competitive in the market.

[0018] Among them, the distributed power supply of the present invention includes a wind power station and a photovoltaic power station. In the step of constructing the basic model, the wind power station, photovoltaic power station, battery energy storage power station, electric vehicle charging station load, and capacitor are modeled separately.

[0019] Specifically, in the step of building the basic model, the wind power model is first built. When the power generation of the wind turbine is higher than the load demand of the system, the excess power will be transferred to the battery energy storage station, also known as the energy storage system (BESS). In the scheme of the present invention, the maximum output power of the wind turbine at its rated wind speed is set to 0.5MW. The wind speed data at the hub height of 50 meters is used as the benchmark, and the output power of each wind turbine for 24 hours is calculated by the following hub height related formula:

[0020]

[0021] Where: v γ is the actual corrected wind speed; v ref is the reference wind speed; h actual is the actual height of the fan; h ref is the reference height of the wind turbine; γ is the atmospheric stability coefficient, and γ = 0.143 is regarded as a natural stability condition.

[0022] Then, the expression of wind power generation (i.e. wind power model) is as follows:

[0023]

[0024] Where: W p is the wind power output power; P r is the rated wind power; ν ci is the cut-in wind speed; v co To cut out wind speed.

[0025] Furthermore, a photovoltaic power generation model is constructed. The daily output power calculation formula based on the actual average solar radiation is as follows:

[0026] S P =S ηP ×(G SP / G stc )×(1+κ(T SP -T stc ));

[0027] Where: S ηP is the rated power of the solar panel; G SP is the solar irradiance; G stc is the solar irradiance of the satellite test center; κ is the temperature coefficient; T SP is the temperature of the solar panel; T stc The temperature of the solar panels at the satellite test center.

[0028] The direct current generated by photovoltaics needs to be converted into alternating current to be transmitted to the grid to power the load. Therefore, the smart photovoltaic inverter is an important part of the photovoltaic power generation system. The output active power and capacity constraint model of the photovoltaic inverter is as follows:

[0029] 0≤P PV ≤P MPPT ;

[0030] P PV，i ≤S PV，i ;

[0031] Where: P MPPT is the inverter maximum power point tracking (MPPT) power; P PV，i is the active power of the photovoltaic inverter; S PV，i is the rated capacity of the PV inverter.

[0032] Furthermore, a battery energy storage power station model is constructed, namely, a battery energy storage system (BSS) model. According to the capacity and discharge rate of the battery, the application modes of the energy storage system can be roughly divided into four categories: long-term storage with frequent discharge, long-term storage with infrequent discharge, short-term storage with frequent discharge, and short-term storage with infrequent discharge. The above classification can be further divided according to the number of hours of stored electrical energy, usage time / duty cycle, and application mode. The relevant cost of the battery energy storage system is determined by its unit cost and the cost of the power conversion equipment. Among them, the cost of the power conversion equipment is proportional to the rated power of the system, and the expression is as follows:

[0033] C BSS =C PCE +C Bat ;

[0034] C PCE =C PCE，unit ×P BSS ;

[0035] C Bat =C Bat，unit ×E BSS ;

[0036] Where: C BSS is the total cost related to the energy storage system; C PCE is the cost of electric energy conversion equipment; C Bat is the battery price; C PCE，unit is the unit price of electric energy conversion equipment; P BSS is the output power of the battery energy storage system; C Bat，unit is the battery unit price; E BSS is the stored electrical energy of the battery energy storage system, E BSS It is calculated by the battery state of charge (SOC) of the energy storage system, that is, the maximum possible value of SOC determines the size of the battery energy storage system.

[0037] In addition, the economic feasibility of BSS is also crucial. When optimizing BSS operation and scheduling, the optimal charging and discharging parameters of BSS are used as design variables. SOC and its related formulas are as follows:

[0038]

[0039] Where: P ch Charging power for BSS; P g is the power generation of the power grid; P l P is the load demand power in the power grid at time t; dch Discharge power to BSS.

[0040]

[0041] Where: SOC(t) is the BSS charge state at time t; η ch is the BSS charging efficiency; η dch is the BSS discharge efficiency.

[0042] The above formula shows that when the total power generation exceeds the load demand, the BESS starts to operate in charging mode, so the SOC increases. On the contrary, when the load demand is greater than the power supply, the BESS operates as a generator in the discharge mode.

[0043] Furthermore, the load model of the electric vehicle charging station (EVCS) is constructed. As an additional load on the distribution network, the calculation formula of the total load of the distribution network after integrating the electric vehicle charging station is as follows:

[0044]

[0045] Where: P load is the total load in the distribution network; N bus is the total number of nodes in the distribution network; is the existing load on node i; is the load of the EV charging station connected to node i.

[0046] Many vehicles operate in network-to-vehicle (G2V) and vehicle-to-network (V2G) modes, and both the discharge rate (DCR) and charge rate (CR) affect the load of the electric vehicle charging station. The capacity calculation formula of the electric vehicle charging station is as follows:

[0047]

[0048] Where: is the capacity of the electric vehicle charging station set up at node i; N EV (G2V) is the number of vehicles working in network-to-vehicle mode; N EV (V2G) is the number of vehicles working in vehicle-to-network mode; Rch is the charging rate; R dch is the discharge rate.

[0049] Furthermore, the capacitor model is constructed. Installing capacitors at appropriate locations in the distribution network can improve the power factor, enhance voltage distribution, reduce line losses, etc. After inserting a capacitor at node n, the net reactive power of node n can be expressed as:

[0050]

[0051] Where: is the net injected reactive power of the node; Q n is the reactive power at node n before reactive power compensation; Q cap Reactive power provided to the capacitor.

[0052] The active power loss after inserting a capacitor at node n is calculated as follows:

[0053]

[0054] Where: is the active power loss of line mn; P n is the active power at node n; V n is the voltage at the capacitor terminals; R j is the line resistance of line mn.

[0055] The calculation formula for the change in active power loss before and after the capacitor is connected is as follows:

[0056]

[0057] The total active power loss can be calculated by summing up the individual power losses of each line:

[0058]

[0059] Where: is the total reactive power loss of the distribution network after the capacitor is connected; P loss,j (m,n) is the power loss of the jth line; N line The power loss of the network can be effectively reduced by coordinating the configuration of capacitor banks.

[0060] According to the distribution network distributed power supply-energy storage-charging station-capacitor joint configuration method disclosed in the first aspect of the present invention, the second stage objective function includes two objective functions, one objective function is to minimize the voltage deviation, and the other objective function is to maximize the configuration benefit, and the two expressions are:

[0061]

[0062] V dev =V i,j -V ref ;

[0063] Among them, V dev is the deviation voltage; V ref =1 is the reference voltage; i∈I, j∈J, I and J are the daily voltage distribution matrices V ij The row and column indices of ;

[0064] in,

[0065] Where: C save To reduce the power loss, K ep P is the cost of producing 1 kWh of electricity; loss,total is the total active power loss; T is the time period in hours, is the total active power loss after integrating the capacitor;

[0066] in,

[0067] Where: C cap is the capacitor equipment installation cost, β is the depreciation coefficient; is the construction cost of the i-th capacitor; N cap is the number of capacitors; K cp is the purchase cost coefficient of capacitor per KVAR; Compensate the reactive power of the capacitor at the i-th node;

[0068] in,

[0069] Where: is the total operating cost of the capacitor equipment, is the operating cost of the capacitor.

[0070] According to the distribution network distributed power supply-energy storage-charging station-capacitor joint configuration method disclosed in the first aspect of the present invention, the expression of the first stage objective function is:

[0071] F1=C BSS +C PV +C WP +C EVCS +C E-loss +C PeakP -C Sellp

[0072] C BSS =C PCE +C Bat

[0073]

[0074] Where: C BSS is the total construction cost of the battery energy storage power station; C PV is the total cost of photovoltaic power station construction; C WP is the total construction cost of the wind farm; C EVCS The total cost of building and operating an electric vehicle charging station; C E-loss is the total cost of power loss; is the annual cost per kilowatt of peak power; is the total cost of electricity sales; is the cost of the inverter and photovoltaic modules in the i-th photovoltaic power station; β is the depreciation coefficient; is the construction cost of the i-th electric vehicle charging station; N EVCS the number of charging stations for electric vehicles; The operating cost of the electric vehicle charging station; E is the electricity price; P i is the power loss under load level i; T i is the i-th time period in hours; κ P is the price coefficient; P Peak is the peak power; A is the power generation of a power plant at a unit price, in kW; B is the return on investment; and m is the lifespan in years. The cost of a wind farm and the cost of a concentrated photovoltaic farm are obtained by multiplying the optimal number of wind turbines and solar panels by their unit costs, respectively.

[0075] According to the method for jointly configuring a distributed power source-energy storage-charging station-capacitor in a distribution network disclosed in the first aspect of the present invention, in the step of determining whether the configuration result of the first stage meets the power flow requirement, the determination formula for the power flow requirement is:

[0076]

[0077] Where: PF j is the flow on the jth branch; is the maximum value of the power flow of the jth branch.

[0078] According to the method for jointly configuring a distributed power source-energy storage-charging station-capacitor in a distribution network disclosed in the first aspect of the present invention, the distributed power source includes a wind power station and a photovoltaic power station; in the step of solving the first-stage target site selection and capacity determination model based on the adaptive simulated annealing-particle swarm algorithm, the variables to be solved include the configuration nodes and capacities of the wind power station, the photovoltaic power station, the battery energy storage power station, and the electric vehicle charging station, wherein a vector with four dimensions equal to the total number of distribution network nodes is introduced to represent the installation status of the above four types of equipment, and the following vector represents the equipment installation status represented by the g-th particle in the k-th iteration process:

[0079]

[0080] Where: k represents the number of iterations; g represents the particle number; represents the equipment capacity vector represented by the g-th particle in the k-th iteration; Represents the equipment installation status of node i represented by the g-th particle in the k-th iteration.

[0081] According to the method for jointly configuring a distributed power source-energy storage-charging station-capacitor in a distribution network disclosed in the first aspect of the present invention, the first-stage constraint conditions also include a constraint on the number of wind turbines, a constraint on the number of photovoltaic panels, and a constraint on battery energy storage; wherein, in the power balance constraint, the sum of the complex power at each node is a nonlinear equality constraint, so that the expression of the power balance constraint condition is:

[0082]

[0083] Where: and are the active and reactive powers generated by the power supply at the i-th node respectively; and are respectively the active and reactive power requirements of the grid-connected load at the i-th node; V i and V j are the voltage amplitudes of nodes i and j respectively; δ i and δ j are the voltage phase angles of nodes i and j respectively; Y ij is the admittance of branch ij; α ij is the admittance phase angle of branch ij;

[0084] The expression of branch power flow constraint is:

[0085]

[0086] Where: PF j is the flow on the j-th branch; is the maximum value of the power flow of the jth branch;

[0087] The expression of branch current capacity constraint is:

[0088]

[0089] Where: I ij is the current flowing from node i to node j; is the upper limit of branch current;

[0090] The expression of the fan quantity constraint is:

[0091]

[0092] Where: is the optimal number of fans; is the maximum number of fans;

[0093] The expression for the PV panel quantity constraint is:

[0094]

[0095] Where: is the optimal number of photovoltaic panels; is the maximum number of photovoltaic panels;

[0096] The expression of battery energy storage constraint is:

[0097]

[0098] Where: and are the lower and upper limits of the battery energy storage charge capacity respectively; and They are the lower and upper limits of the battery energy storage discharge capacity; SOC min and SOC max are the lower and upper limits of SOC respectively.

[0099] According to the distribution network distributed power supply-energy storage-charging station-capacitor joint configuration method disclosed in the first aspect of the present invention, the second stage constraint conditions also include power flow constraints, node voltage constraints, branch power flow constraints, capacitor quantity and capacity constraints; wherein the power flow constraint expression is:

[0100]

[0101] Where: P sub and Q sub are the active and reactive power of the substation bus, respectively; and are the active and reactive power losses on the j-th branch respectively; and The existing active and reactive loads on node i respectively; is the load of the electric vehicle charging station connected to node i; Supplement reactive power for the capacitor at node i; N bus and N line are the number of nodes and branches in the distribution network, respectively;

[0102] The expression of node voltage constraint is:

[0103] |V imin |≤|V i |≤|V i max |;

[0104] Where: and are the lower and upper limits of the node voltage amplitude respectively; i is the node index, i=1,2,3...N bus ;

[0105] The expression of branch power flow constraint is:

[0106]

[0107] Where: PF j is the flow on the j-th branch; is the maximum value of the power flow of the jth branch;

[0108] The expressions for the number and capacity constraints of capacitors are:

[0109]

[0110] Where: is the maximum number of capacitors; and They are the minimum and maximum values ​​of reactive power compensated by a single capacitor respectively;

[0111] Among them, the expression of the maximum total reactive power compensation constraint of the capacitor is:

[0112]

[0113] Where: is the reactive power compensated by the pth capacitor; is the existing reactive load on node i.

[0114] According to the method for joint configuration of distributed power supply-energy storage-charging station-capacitor in a distribution network disclosed in the first aspect of the present invention, in the step of solving the second-stage target site selection and capacity determination model based on the adaptive simulated annealing-particle swarm algorithm, the variables to be solved are the access nodes and capacity of the capacitors, and a vector with a dimension equal to the total number of distribution network nodes is introduced to represent the installation status of the capacitors. The access status of the capacitor represented by the g-th particle in the k-th iteration process is represented by the following vector:

[0115]

[0116] Where: k represents the number of iterations; g represents the particle number; The vector representing the capacitor access situation of the g-th particle in the k-th iteration; Represents the capacitor access installation status of node i represented by the g-th particle in the k-th iteration.

[0117] The second aspect of the present invention discloses a distribution network distributed power supply-energy storage-charging station-capacitor joint configuration system, comprising:

[0118] A basic model building unit is constructed to respectively build respective basic models corresponding to distributed power sources, battery energy storage power stations, electric vehicle charging stations and capacitors;

[0119] A first-stage target location and capacity model generation unit is constructed to construct a first-stage target location and capacity model for the location and capacity of distributed power sources, battery energy storage power stations, and electric vehicle charging stations based on the basic model, with cost minimization as the first-stage objective function;

[0120] A second-stage target location and capacity determination model generation unit is constructed to construct a second-stage target location and capacity determination model for capacitor location and capacity determination based on the basic model, which takes minimizing voltage deviation and maximizing configuration benefit as the second-stage objective function;

[0121] The first-stage solving unit is constructed by solving the first-stage target site selection and sizing model based on the adaptive simulated annealing-particle swarm algorithm with at least power balance constraint, branch flow constraint and branch current capacity constraint as the first-stage constraint conditions, so as to obtain the first-stage configuration result reflecting the site selection and sizing information of the distributed power source, the battery energy storage power station and the electric vehicle charging station;

[0122] The first-stage configuration result judgment unit is constructed to judge whether the first-stage configuration result meets the power flow requirements. If it does not meet the power flow requirements, the first-stage optimization parameters are reset and the first-stage target location and capacity model is solved and calculated again to obtain the first-stage configuration result again.

[0123] A typical daily operation curve generating unit is constructed to analyze the charging demand of electric vehicles and simulate the time series data of distributed power sources and distribution network loads based on weather, season, working day / holiday as the basis for division to generate a typical daily operation curve of electric vehicle charging stations, distributed power sources and distribution network loads;

[0124] A voltage over-limit analysis unit is constructed to perform scenario analysis on the voltage conditions of each node in the distribution network based on the generated typical daily operation curve to obtain the voltage over-limit conditions of nodes in the entire network;

[0125] The second-stage solving unit is constructed such that, when it is judged that the configuration result of the first stage meets the power flow requirements, at least the maximum reactive power compensation constraint of the capacitor is used as the constraint condition of the second stage, and based on the obtained voltage over-limit situation of the whole network node, the second-stage target site selection and sizing model is solved by adaptive simulated annealing-particle swarm algorithm to obtain the second-stage configuration result reflecting the capacitor site selection and sizing information;

[0126] The configuration result output unit is constructed to output the joint configuration result of the distributed power source, the battery energy storage power station, the electric vehicle charging station and the capacitor based on the first stage configuration result and the second stage configuration result.

[0127] The third aspect of the present invention discloses an electronic device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the distribution network distributed power supply-energy storage-charging station-capacitor joint configuration method disclosed in the first aspect of the present invention.

[0128] Compared with the prior art, the method and system for joint configuration of distributed power supply-energy storage-charging station-capacitor in the distribution network of the present invention have the following beneficial effects: 1) Considering the comprehensive planning of distributed power supply, energy storage power station, charging station and capacitor at the same time, this multi-object joint planning method can better coordinate the operation of various equipment, improve the overall efficiency and reliability of the distribution network, and avoid the limitations and inefficiency caused by the planning of single equipment. 2) In the planning, the typical operation scenario is fully considered, and the load curve characteristics of distributed power supply, energy storage power station, charging station, etc., as well as the representative days of the year are jointly considered. This method can more accurately reflect the actual operation situation, optimize the planning results, and improve the performance and adaptability of the system in different operation scenarios. 3) A two-stage planning method is proposed. The first stage mainly considers capacity constraints, active power balance and power flow without exceeding the limit to ensure the basic operation requirements of the system. The second stage plans reactive compensation to achieve voltage and power quality standards. This staged planning method makes the planning process more detailed and scientific, and can be optimized for different technical requirements respectively, thereby improving the accuracy and effect of the overall planning. 4) The planning scheme fully considers economic benefits and comprehensively analyzes the investment cost, operation and maintenance cost and possible benefits of the equipment. Compared with the traditional method that only focuses on technical indicators, this method can not only ensure that the technical indicators are met, but also significantly improve the economic benefits, making the planning scheme more practical and competitive in the market.

[0129] The distribution network distributed power supply-energy storage-charging station-capacitor joint configuration method and system of the present invention are disclosed in detail below in conjunction with the embodiments shown in the accompanying drawings and the accompanying drawing numbers. BRIEF DESCRIPTION OF THE DRAWINGS

[0130] Figure 1 A flow chart showing the steps of the method for jointly configuring a distributed power source-energy storage-charging station-capacitor in a distribution network of the present invention is shown.

[0131] Figure 2 A schematic diagram of the optimization process of the adaptive simulated annealing-particle swarm algorithm is shown. DETAILED DESCRIPTION

[0132] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0133] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

[0134] Figure 1 The flowchart of the steps of the method for joint configuration of the distributed power supply-energy storage-charging station-capacitor of the distribution network of the present invention is shown. Figure 1 As shown, the present invention discloses a method for jointly configuring a distributed power supply-energy storage-charging station-capacitor in a distribution network, comprising the following steps:

[0135] S1, corresponding to distributed power sources, battery energy storage power stations, electric vehicle charging stations and capacitors, builds their own basic models respectively;

[0136] S2, based on the basic model, construct a first-stage target site selection and sizing model for site selection and sizing of distributed power sources, battery energy storage power stations, and electric vehicle charging stations, with cost minimization as the first-stage objective function; and based on the basic model, construct a second-stage target site selection and sizing model for site selection and sizing of capacitors, with minimizing voltage deviation and maximizing configuration benefits as the second-stage objective functions;

[0137] S3, setting the first-stage optimization parameters, and taking at least the power balance constraint, the branch flow constraint and the branch current capacity constraint as the first-stage constraint conditions, solving the first-stage target site selection and sizing model based on the adaptive simulated annealing-particle swarm algorithm to obtain the first-stage configuration results reflecting the site selection and sizing information of the distributed power source, the battery energy storage power station and the electric vehicle charging station; specifically, the first-stage constraint conditions include power balance constraint, branch flow constraint, branch current capacity constraint, wind turbine quantity constraint, PV panel quantity constraint and battery energy storage constraint;

[0138] S4, judging whether the configuration result of the first stage meets the power flow requirements. If so, the next step is executed. If not, the optimization parameters of the first stage are reset and the target location and capacity model of the first stage is solved and calculated again to obtain the configuration result of the first stage again.

[0139] S5, analyzing the charging demand of electric vehicles, and simulating the time series data of distributed power sources and distribution network loads based on weather, season, and working days / holidays, so as to generate typical daily operation curves of electric vehicle charging stations, distributed power sources, and distribution network loads;

[0140] S6, based on the generated typical daily operation curve, a scenario analysis is performed on the voltage conditions of each node in the distribution network to obtain the voltage over-limit conditions of the nodes in the entire network;

[0141] S7, setting the second-stage optimization parameters. When it is determined that the first-stage configuration result meets the power flow requirements, at least the maximum reactive power compensation constraint of the capacitor is used as the second-stage constraint condition. Based on the obtained voltage over-limit situation of the whole network nodes, the second-stage target site selection and sizing model is solved by adaptive simulated annealing-particle swarm algorithm to obtain the second-stage configuration result reflecting the capacitor site selection and sizing information.

[0142] S8, based on the first stage configuration results and the second stage configuration results, output the joint configuration results of the distributed power source, the battery energy storage power station, the electric vehicle charging station and the capacitor.

[0143] Among them, the construction and execution of the first-stage target site selection and capacity determination model is used to select the site and capacity of wind power, photovoltaic, battery energy storage system, and electric vehicle charging station; while the construction and execution of the second-stage target site selection and capacity determination model is used to select the site and capacity of capacitors. Moreover, it is particularly important that the execution of the second-stage target site selection and capacity determination model is carried out on the basis that the output results of the first-stage target site selection and capacity determination model meet the flow requirements, that is, the first-stage target site selection and capacity determination model and the second-stage target site selection and capacity determination model form a close mutual synergy, making the planning process more detailed and scientific, and being able to optimize for different technical requirements respectively, thereby improving the accuracy and effect of the overall planning.

[0144] Moreover, it is particularly important that the impact on future operation is taken into account in the second stage of planning the site selection and sizing of capacitors. Specifically, based on the generated typical daily operating curves of electric vehicle charging stations, distributed power sources and distribution network loads, a scenario analysis is performed on the voltage conditions of each node in the distribution network to obtain the voltage over-limit situation of the entire network node. Then, in the specific solution process, based on the obtained voltage over-limit situation of the entire network node, an adaptive simulated annealing-particle swarm algorithm is used to solve the reactive compensation capacitor, so as to select the site and size it, thereby ensuring that the node does not exceed the limit after reactive compensation.

[0145] The distributed power source of the present invention includes a wind power station and a photovoltaic power station. In the step of constructing a basic model, the wind power station, photovoltaic power station, battery energy storage power station, electric vehicle charging station load, and capacitor are modeled separately.

[0146] In addition, the optimization parameters in steps S3 and S7 refer to the configuration parameters of the solution algorithm (adaptive simulated annealing-particle swarm algorithm), such as particle swarm size, particle dimension, maximum number of iterations, inertia weight, learning factor, etc.

[0147] Specifically, in the step of building the basic model, the wind power model is first built. When the power generation of the wind turbine is higher than the load demand of the system, the excess power will be transferred to the battery energy storage station, also known as the energy storage system (BESS). In the scheme of the present invention, the maximum output power of the wind turbine at its rated wind speed is set to 0.5MW. The wind speed data at the hub height of 50 meters is used as the benchmark, and the output power of each wind turbine for 24 hours is calculated by the following hub height related formula:

[0148]

[0149] Where: v γ is the actual corrected wind speed; v ref is the reference wind speed; h actual is the actual height of the fan; h ref is the reference height of the wind turbine; γ is the atmospheric stability coefficient, and γ = 0.143 is regarded as a natural stability condition.

[0150] Then, the expression of wind power generation (i.e. wind power model) is as follows:

[0151]

[0152] Where: W p is the wind power output power; P r is the rated wind power; v ci is the cut-in wind speed; v co To cut out wind speed.

[0153] In the method embodiment of the present invention, a photovoltaic power generation model is further constructed. Specifically, the daily output power calculation formula based on the actual average solar radiation is as follows:

[0154] S P =S ηP ×(G SP / G stc )×(1+κ(T SP -T stc ));

[0155] Where: S P For; S ηP is the rated power of the solar panel; G SP is the solar irradiance; G stc is the solar irradiance of the satellite test center; κ is the temperature coefficient; T SP is the temperature of the solar panel; T stc The temperature of the solar panels at the satellite test center.

[0156] The direct current generated by photovoltaics needs to be converted into alternating current to be transmitted to the grid to power the load. Therefore, the smart photovoltaic inverter is an important part of the photovoltaic power generation system. The output active power and capacity constraint model of the photovoltaic inverter is as follows:

[0157] 0≤P PV ≤P MPPT ;

[0158] P PV，i <S PV，i ;

[0159] Where: P MPPT is the inverter maximum power point tracking (MPPT) power; P PV，i is the active power of the photovoltaic inverter; S PV，i is the rated capacity of the PV inverter.

[0160] In the method embodiment of the present invention, a battery energy storage power station model is further constructed, that is, a battery energy storage system (BSS) model. According to the capacity and discharge rate of the battery, the application modes of the energy storage system can be roughly divided into four categories: long-term storage with frequent discharge, long-term storage with infrequent discharge, short-term storage with frequent discharge, and short-term storage with infrequent discharge. The above classification can be further divided according to the number of hours of stored electrical energy, usage time / duty cycle, and application mode. The relevant cost of the battery energy storage system is determined by its unit cost and the cost of the power conversion equipment. Among them, the cost of the power conversion equipment is proportional to the rated power of the system, and the expression is as follows:

[0161] C BSS =C PCE +C Bat ;

[0162] C PCE =C PCE，unit ×P BSS ;

[0163] C Bat =C Bat，unit ×E BSS ;

[0164] Where: C BSS is the total cost related to the energy storage system; C PCE is the cost of electric energy conversion equipment; C Bat is the battery price; C PCE，unit is the unit price of electric energy conversion equipment; P BSS is the output power of the battery energy storage system; C Bat，unit is the battery unit price; E BSS is the stored electrical energy of the battery energy storage system, E BSS It is calculated by the battery state of charge (SOC) of the energy storage system, that is, the maximum possible value of SOC determines the size of the battery energy storage system.

[0165] In addition, the economic feasibility of BSS is also crucial. When optimizing BSS operation and scheduling, the optimal charging and discharging parameters of BSS are used as design variables. SOC and its related formulas are as follows:

[0166]

[0167] Where: P ch Charging power for BSS; P g is the power generation of the power grid; P l P is the load demand power in the power grid at time t; dch Discharge power to BSS.

[0168]

[0169] Where: SOC(t) is the BSS charge state at time t; η ch is the BSS charging efficiency; η dch is the BSS discharge efficiency.

[0170] The above formula shows that when the total power generation exceeds the load demand, the BESS starts to operate in charging mode, so the SOC increases. On the contrary, when the load demand is greater than the power supply, the BESS operates as a generator in the discharge mode.

[0171] In the method embodiment of the present invention, an electric vehicle charging station (EVCS) load model is further constructed. EVCS is an additional load on the distribution network, and the calculation formula of the total load of the distribution network after integrating the electric vehicle charging station is as follows:

[0172]

[0173] Where: P load is the total load in the distribution network; N bus is the total number of nodes in the distribution network; is the existing load on node i; is the load of the EV charging station connected to node i.

[0174] Many vehicles operate in network-to-vehicle (G2V) and vehicle-to-network (V2G) modes, and both the discharge rate (DCR) and charge rate (CR) affect the load of the electric vehicle charging station. The capacity calculation formula of the electric vehicle charging station is as follows:

[0175]

[0176] Where: is the capacity of the electric vehicle charging station set up at node i; N EV (G2V) is the number of vehicles working in network-to-vehicle mode; N EV (V2G) is the number of vehicles working in vehicle-to-network mode; R ch is the charging rate; R dch is the discharge rate.

[0177] In the method embodiment of the present invention, a capacitor model is further constructed. Specifically, capacitors are installed at appropriate locations in the distribution network to improve power factor, enhance voltage distribution, reduce line losses, etc. After a capacitor is inserted at node n, the net reactive power of node n can be expressed as:

[0178]

[0179] Where: is the net injected reactive power of the node; Q n is the reactive power at node n before reactive power compensation; Q cap Reactive power provided to the capacitor.

[0180] The active power loss after inserting a capacitor at node n is calculated as follows:

[0181]

[0182] Where: is the active power loss of line mn; P n is the active power at node n; V n is the voltage at the capacitor terminals; R j is the line resistance of line mn.

[0183] The calculation formula for the change in active power loss before and after the capacitor is connected is as follows:

[0184]

[0185] The total active power loss can be calculated by summing up the individual power losses of each line:

[0186]

[0187] Where: is the total reactive power loss of the distribution network after the capacitor is connected; P loss，j (m,n) is the power loss of the jth line; N line The power loss of the network can be effectively reduced by coordinating the configuration of capacitor banks.

[0188] In the method embodiment of the present invention, the second stage objective function includes two objective functions, one of which is to minimize the voltage deviation, and the other is to maximize the configuration benefit, and the two expressions are:

[0189]

[0190] V dev =V i,j -V ref ;

[0191] Among them, V dev is the deviation voltage; V ref =1 is the reference voltage; i∈I, j∈J, I and J are the daily voltage distribution matrices V ij The row and column indices of ;

[0192] in,

[0193] Where: C save To reduce the power loss, K ep P is the cost of producing 1 kWh of electricity; loss,total is the total active power loss; T is the time period in hours, is the total active power loss after integrating the capacitor;

[0194] in,

[0195] Where: C cap is the capacitor equipment installation cost, β is the depreciation coefficient; is the construction cost of the i-th capacitor; N cap is the number of capacitors; K cp is the purchase cost coefficient of capacitor per KVAR; Compensate the reactive power of the capacitor at the i-th node;

[0196] in,

[0197] Where: is the total operating cost of the capacitor equipment, is the operating cost of the capacitor.

[0198] In the method embodiment of the present invention, the goal of the first stage distribution network optimization configuration is cost minimization, wherein the configuration cost is mainly composed of 7 parts. Specifically, the expression of the first stage objective function is:

[0199]

[0200] C BSS =C PCE +C Bat

[0201]

[0202] Where: C BSS is the total construction cost of the battery energy storage power station; C PV is the total cost of photovoltaic power station construction; C WP is the total construction cost of the wind farm; C EVCS The total cost of building and operating an electric vehicle charging station; C E-loss is the total cost of power loss; is the annual cost per kilowatt of peak power; is the total cost of electricity sales; is the cost of the inverter and photovoltaic modules in the i-th photovoltaic power station; β is the depreciation coefficient; is the construction cost of the i-th electric vehicle charging station; N EVCS the number of charging stations for electric vehicles; The operating cost of the electric vehicle charging station; K E is the electricity price; P i is the power loss under load level i; T i is the i-th time period in hours; κ P is the price coefficient; P Peak is the peak power; A is the power generation of a power plant at a unit price, in kW; B is the return on investment; and m is the lifespan in years. The cost of a wind farm and the cost of a concentrated photovoltaic farm are obtained by multiplying the optimal number of wind turbines and solar panels by their unit costs, respectively.

[0203] According to the method for jointly configuring a distributed power source-energy storage-charging station-capacitor in a distribution network disclosed in the first aspect of the present invention, in the step of determining whether the configuration result of the first stage meets the power flow requirement, the determination formula for the power flow requirement is:

[0204]

[0205] Where: PF j is the flow on the jth branch; is the maximum value of the power flow of the jth branch.

[0206] In the method embodiment of the present invention, the distributed power source includes a wind power station and a photovoltaic power station; in the step of solving the first-stage target site selection and capacity determination model based on the adaptive simulated annealing-particle swarm algorithm, the solution variables include the configuration nodes and capacities of the wind power station, the photovoltaic power station, the battery energy storage power station, and the electric vehicle charging station, wherein a vector with four dimensions equal to the total number of distribution network nodes is introduced to represent the installation status of the above four types of equipment, and the equipment installation status represented by the g-th particle in the k-th iteration process is represented by the following vector:

[0207]

[0208] Where: k represents the number of iterations; g represents the particle number; represents the equipment capacity vector represented by the g-th particle in the k-th iteration; Represents the equipment installation status of node i represented by the g-th particle in the k-th iteration.

[0209] Among them, the optimization process of the particle swarm optimization (PSO) algorithm is completed by the flight of particles in the search space. The movement of particles in each iteration consists of three parts, namely, the inheritance of the previous speed, self-learning, and information interaction of the population. The update expressions of speed and position in the PSO iteration process are as follows:

[0210] V g (k+1)=ωV g (k)+c1·r1·(P best,g (k)-X g (k))+c2·r2·(G best (k)-X g (k));

[0211] X g (k+1)=X g (k)+V g (k);

[0212] Where: ω is the inertia weight; c1 and c2 are learning factors; r1 = rand() and r2 = rand() are random numbers in [0,1]; X g(k) is the current position of particle g; P best,g (k) is the individual historical optimal position of particle g; G best (k) is the historical optimal position of the group.

[0213] ω, c1 and c2 are among the most important parameters in particle swarm optimization. Their specific values ​​are usually determined through empirical judgment or a large number of simulation experiments, and the values ​​remain unchanged. If the above parameters can be continuously adaptively adjusted as the optimization proceeds, the PSO algorithm will show better performance. The inertia weight coefficient ω directly affects the strength of the algorithm's search ability. Therefore, a larger ω should be set in the early stage of the search to ensure a global search as wide as possible. In the later stage of the search, ω should be reduced to accurately determine the global optimal solution. Select a negative hyperbolic tangent curve between [-4,4] to control the change of the inertia weight coefficient. The inertia weight update function is as follows:

[0214]

[0215] Where: max ,ω min is the maximum and minimum value of the inertia weight coefficient, taking ω max ,ω min ; k is the current iteration number, k max is the maximum number of iterations.

[0216] The learning factors c1 and c2 determine the weight of particle self-learning and population information interaction in the particle search direction. In the early stage of the search, the self-cognition ability of the particles should be emphasized; in the later stage of the search, it is necessary to strengthen the population information interaction and focus on local search near the optimal solution of the population. Therefore, as the number of iterations increases, c1 gradually decreases and c2 gradually increases. The update functions of c1 and c2 are as follows:

[0217]

[0218] Where: c 1min 、c 1max are the minimum and maximum values ​​of c1 respectively; c 2min 、c 2max are the maximum and minimum values ​​of c2 respectively. 1min =c 2min =1.25, c 1max =c 2max =2.5.

[0219] In order to solve the problem that the particles initialized by the PSO algorithm are difficult to distribute near the global optimum and are prone to premature convergence, the simulated annealing operator is introduced in the process of updating the group optimum, so that the algorithm can accept new solutions with a certain probability and have the ability to jump, thereby avoiding falling into the local optimum. The Metropolis criterion in the simulated annealing algorithm is introduced into the BPSO iteration, and the temperature calculation formula of the iterative process is as follows:

[0220]

[0221] Where: T(k) is the temperature in the kth iteration; μ (μ = 0.95) is the temperature reduction coefficient; f(G best (k)) is the historical optimal fitness value of the group at the kth iteration. The probability of accepting the difference solution is calculated based on the temperature:

[0222]

[0223] Where: f(P best,g (k)) is the individual historical optimal fitness value of particle g. Finally, according to the Metropolis criterion, it is determined whether the generated new solution replaces the global optimal solution for annealing operation. The update formula of the global optimal solution is as follows:

[0224]

[0225] like Figure 2 As shown, in a specific embodiment, the main steps of solving the first-stage distribution network optimization configuration model based on the adaptive simulated annealing-particle swarm algorithm are as follows:

[0226] 1) Input data;

[0227] 2) Initialize the particle swarm parameters, including particle swarm size G, particle dimension D, maximum number of iterations K, inertia weight ω, and learning factors c1 and c2;

[0228] 3) Randomly initialize the position of each particle and speed

[0229] 4) Calculate the initial fitness value of each particle;

[0230] 5) The initial fitness value of each particle is used as the individual historical optimal fitness value of each particle at present, and the position corresponding to each fitness value is used as the individual historical optimal position of each particle;

[0231] 6) The best initial fitness value is taken as the best fitness value in the current group history, and the position corresponding to the best fitness value is taken as the best position in the group history;

[0232] 7) Update the current position and current flying speed of each particle, and limit the flying speed of each particle so that it does not exceed the set maximum flying speed;

[0233] 8) Calculate the fitness value of each particle;

[0234] 9) Compare whether the current fitness value of each particle is better than the individual historical optimal fitness value. If it is better, the current particle fitness value is used as the individual historical optimal fitness value of the particle, and its corresponding position is used as the individual historical optimal position of each particle;

[0235] 10) Compare whether the current group optimal fitness value is better than the group historical optimal fitness value. If it is better, the current group optimal fitness value is used as the group historical optimal fitness value, and its corresponding position is used as the group historical optimal position;

[0236] 11) Repeat 7)-10) until the set termination condition is met;

[0237] 12) Output the historical optimal fitness value and historical optimal position H of the particle swarm 最优 =[h WP ,h PV ,h BSS ,h EVCS ] T .

[0238] In the method embodiment of the present invention, in the first stage constraint condition, in the power balance constraint, the sum of the complex power at each node is a nonlinear equality constraint, so that the expression of the power balance constraint condition is:

[0239]

[0240] Where: and are the active and reactive powers generated by the power supply at the i-th node respectively; and are respectively the active and reactive power requirements of the grid-connected load at the i-th node; V i and V j are the voltage amplitudes of nodes i and j respectively; δ i and δ j are the voltage phase angles of nodes i and j respectively; Y ij is the admittance of branch ij; α ij is the admittance phase angle of branch ij;

[0241] The expression of branch power flow constraint is:

[0242]

[0243] Where: PF j is the flow on the jth branch; is the maximum value of the power flow of the jth branch;

[0244] In the branch current capacity constraint, to ensure that the branch does not exceed the temperature limit, add the branch current capacity constraint, the expression is:

[0245]

[0246] Where: I ij is the current flowing from node i to node j; is the upper limit of branch current;

[0247] The expression of the fan quantity constraint is:

[0248]

[0249] Where: is the optimal number of fans; is the maximum number of fans;

[0250] The expression for the PV panel quantity constraint is:

[0251]

[0252] Where: is the optimal number of photovoltaic panels; is the maximum number of photovoltaic panels;

[0253] In the battery energy storage constraints, the charging and discharging power and battery state of charge of each energy storage unit have upper and lower limit constraints, so that the expression is:

[0254]

[0255] Where: and are the lower and upper limits of the battery energy storage charge capacity respectively; and They are the lower and upper limits of the battery energy storage discharge capacity; SOC min and SOC max are the lower and upper limits of SOC respectively.

[0256] In the method embodiment of the present invention, the second stage constraint conditions also include power flow constraint, node voltage constraint, branch power flow constraint, capacitor quantity and capacity constraint; wherein the power flow constraint is expressed as:

[0257]

[0258] Where: P sub and Qsub are the active and reactive power of the substation bus, respectively; and are the active and reactive power losses on the j-th branch respectively; and The existing active and reactive loads on node i respectively; is the load of the electric vehicle charging station connected to node i; Supplement reactive power for the capacitor at node i; N bus and N line are the number of nodes and branches in the distribution network, respectively;

[0259] Among them, in the node voltage constraint condition, the voltage amplitude of each node needs to be limited between the set minimum value and the maximum value. The expression of the voltage amplitude constraint condition is:

[0260] |V i min |≤|V i |≤|V i max |;

[0261] Where: and are the lower and upper limits of the node voltage amplitude respectively; i is the node index, i=1,2,3...N bus ;

[0262] The expression of branch power flow constraint is:

[0263]

[0264] Where: PF j is the flow on the jth branch; is the maximum value of the power flow of the jth branch;

[0265] The expressions for the number and capacity constraints of capacitors are:

[0266]

[0267] Where: is the maximum number of capacitors; and They are the minimum and maximum values ​​of reactive power compensation of a single capacitor respectively;

[0268] Among them, the expression of the maximum total reactive power compensation constraint of the capacitor is:

[0269]

[0270] Where: is the reactive power compensated by the pth capacitor; is the existing reactive load on node i.

[0271] In the method embodiment of the present invention, in the step of solving the second-stage target site selection and capacity determination model based on the adaptive simulated annealing-particle swarm algorithm, the variables to be solved are the access nodes and capacity of the capacitor, and a vector with a dimension equal to the total number of distribution network nodes is introduced to represent the installation status of the capacitor. The following vector represents the capacitor access status represented by the g-th particle in the k-th iteration process:

[0272]

[0273] Where: k represents the number of iterations; g represents the particle number; The vector representing the capacitor access situation of the g-th particle in the k-th iteration; Represents the capacitor access installation status of node i represented by the g-th particle in the k-th iteration.

[0274] The present invention also discloses a distribution network distributed power supply-energy storage-charging station-capacitor joint configuration system, comprising:

[0275] A basic model building unit is constructed to respectively build respective basic models corresponding to distributed power sources, battery energy storage power stations, electric vehicle charging stations and capacitors;

[0276] A first-stage target location and capacity model generation unit is constructed to construct a first-stage target location and capacity model for the location and capacity of distributed power sources, battery energy storage power stations, and electric vehicle charging stations based on the basic model, with cost minimization as the first-stage objective function;

[0277] A second-stage target location and capacity determination model generation unit is constructed to construct a second-stage target location and capacity determination model for capacitor location and capacity determination based on the basic model, which takes minimizing voltage deviation and maximizing configuration benefit as the second-stage objective function;

[0278] The first-stage solving unit is constructed by solving the first-stage target site selection and sizing model based on the adaptive simulated annealing-particle swarm algorithm with at least power balance constraint, branch flow constraint and branch current capacity constraint as the first-stage constraint conditions, so as to obtain the first-stage configuration result reflecting the site selection and sizing information of the distributed power source, the battery energy storage power station and the electric vehicle charging station;

[0279] The first-stage configuration result judgment unit is constructed to judge whether the first-stage configuration result meets the power flow requirements. If it does not meet the power flow requirements, the first-stage optimization parameters are reset and the first-stage target location and capacity model is solved and calculated again to obtain the first-stage configuration result again.

[0280] A typical daily operation curve generating unit is constructed to analyze the charging demand of electric vehicles and simulate the time series data of distributed power sources and distribution network loads based on weather, season, working day / holiday as the basis for division to generate a typical daily operation curve of electric vehicle charging stations, distributed power sources and distribution network loads;

[0281] A voltage over-limit analysis unit is constructed to perform scenario analysis on the voltage conditions of each node in the distribution network based on the generated typical daily operation curve to obtain the voltage over-limit conditions of nodes in the entire network;

[0282] The second-stage solving unit is constructed such that, when it is judged that the configuration result of the first stage meets the power flow requirement, at least the maximum reactive power compensation constraint of the capacitor is used as the constraint condition of the second stage, and the second-stage target site selection and sizing model is solved based on the adaptive simulated annealing-particle swarm algorithm to obtain the second-stage configuration result reflecting the site selection and sizing information of the capacitor;

[0283] The configuration result output unit is constructed to output the joint configuration result of the distributed power source, the battery energy storage power station, the electric vehicle charging station and the capacitor based on the first stage configuration result and the second stage configuration result.

[0284] The present invention also discloses an electronic device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the distribution network distributed power supply-energy storage-charging station-capacitor joint configuration method disclosed according to the first aspect of the present invention.

[0285] Existing methods mainly focus on the matching of distribution networks and loads in the planning stage of distributed power sources to meet the power supply capacity, and the access of distributed power sources (DG), energy storage power stations and charging stations is usually planned one by one according to the carrying capacity of the distribution network. The present invention considers the comprehensive planning of distributed power sources, energy storage power stations, charging stations and capacitors at the same time. This multi-object joint planning method can better coordinate the operation of various equipment, improve the overall efficiency and reliability of the distribution network, and avoid the limitations and inefficiencies caused by single equipment planning.

[0286] In addition, traditional planning methods often only focus on the planning stage, with less consideration of the operation stage, and usually only plan based on a single load curve. However, the present invention fully considers typical operation scenarios during planning, and jointly considers the load curve characteristics of distributed power sources, energy storage power stations, charging stations, etc., as well as representative days of the year. This method can more accurately reflect the actual operation situation, optimize the planning results, and improve the performance and adaptability of the system in different operation scenarios.

[0287] The present invention proposes a two-stage planning method. The first stage mainly considers capacity constraints, active power balance and power flow within the limit to ensure the basic operation requirements of the system. The second stage plans reactive power compensation to achieve voltage and power quality standards. This phased planning method makes the planning process more detailed and scientific, and can be optimized for different technical requirements, thereby improving the accuracy and effect of the overall planning.

[0288] The present invention fully considers economic benefits in the planning scheme, and comprehensively analyzes the investment cost, operation and maintenance cost and possible benefits of the equipment. By optimizing the configuration of distributed power sources, energy storage power stations, charging stations and capacitors, the overall cost of the system can be reduced to the maximum extent and the economic benefits can be improved. Compared with the traditional method that only focuses on technical indicators, this method can significantly improve economic benefits while ensuring that technical indicators meet the standards, making the planning scheme more practical and more competitive in the market.

[0289] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used here to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0290] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0291] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0292] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0293] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for joint configuration of distributed power supply-energy storage-charging station-capacitor in a distribution network, characterized in that: The following steps are involved: Corresponding to distributed power sources, battery energy storage power stations, electric vehicle charging stations and capacitors, build their own basic models respectively; Based on the basic model, a first-stage target site selection and sizing model is constructed for site selection and sizing of distributed power sources, battery energy storage power stations, and electric vehicle charging stations, with cost minimization as the first-stage objective function; and based on the basic model, a second-stage target site selection and sizing model is constructed for site selection and sizing of capacitors, with minimization of voltage deviation and maximization of configuration benefits as the second-stage objective functions; At least the power balance constraint, branch flow constraint and branch current capacity constraint are used as the first-stage constraint conditions, and the first-stage target site selection and sizing model is solved based on the adaptive simulated annealing-particle swarm algorithm to obtain the first-stage configuration results reflecting the site selection and sizing information of distributed generation, battery energy storage power station and electric vehicle charging station; Determine whether the configuration result of the first stage meets the power flow requirements. If not, reset the optimization parameters of the first stage and solve the first stage target location and capacity model again to obtain the configuration result of the first stage again. Conduct electric vehicle charging demand analysis and simulate the time series data of distributed power sources and distribution network loads based on weather, season, and working days / holidays to generate typical daily operating curves of electric vehicle charging stations, distributed power sources, and distribution network loads; Based on the generated typical daily operation curve, the voltage conditions of each node in the distribution network are analyzed to obtain the voltage limit conditions of all nodes in the network; When it is judged that the configuration result of the first stage meets the power flow requirements, at least the maximum reactive power compensation constraint of the capacitor is used as the constraint condition of the second stage. Based on the voltage over-limit situation of the whole network nodes, the second stage target site selection and sizing model is solved by adaptive simulated annealing-particle swarm algorithm to obtain the second stage configuration result reflecting the capacitor site selection and sizing information; Based on the configuration results of the first stage and the second stage, the joint configuration results of distributed power sources, battery energy storage power stations, electric vehicle charging stations and capacitors are output.

2. The method for jointly configuring distributed power supply-energy storage-charging station-capacitor in distribution network according to claim 1, characterized in that: The objective function of the second stage includes two objective functions, one of which is to minimize the voltage deviation and the other is to maximize the configuration benefit. The two expressions are: V dev =V i,j -V ref ; Among them, V dev is the deviation voltage; V ref =1 is the reference voltage; i∈I, j∈J, I and J are the daily voltage distribution matrices V ij The row and column indices of ; in, Where: C save To reduce the power loss, K ep P is the cost of producing 1 kWh of electricity; loss,total is the total active power loss; T is the time period in hours, is the total active power loss after integrating the capacitor; in, Where: C cap is the capacitor equipment installation cost, β is the depreciation coefficient; is the construction cost of the i-th capacitor; N cap is the number of capacitors; K cp is the purchase cost coefficient of capacitor per KVAR; Compensate the reactive power of the capacitor at the i-th node; in, Where: is the total operating cost of the capacitor equipment, is the operating cost of a single capacitor.

3. The method for jointly configuring a distributed power supply-energy storage-charging station-capacitor in a distribution network according to claim 1, characterized in that: The expression of the objective function of the first stage is: C BSS =C PCE +C Bat Where: C BSS is the total construction cost of the battery energy storage power station; C PV is the total cost of photovoltaic power station construction; C WP is the total construction cost of the wind farm; C EVCS The total cost of building and operating an electric vehicle charging station; C E-loss is the total cost of power loss; is the annual cost per kilowatt of peak power; is the total cost of electricity sales; is the cost of the inverter and photovoltaic modules in the i-th photovoltaic power station; β is the depreciation coefficient; is the construction cost of the i-th electric vehicle charging station; N EVCS the number of charging stations for electric vehicles; The operating cost of the electric vehicle charging station; K E is the electricity price; P i is the power loss under load level i; T i is the i-th time period in hours; κ P is the price coefficient; P Peak is the peak power; A is the power generation of a power plant at a unit price, in kW; B is the return on investment; m is the lifespan in years.

4. The method for jointly configuring a distributed power supply-energy storage-charging station-capacitor in a distribution network according to claim 1, characterized in that: In the step of judging whether the configuration result of the first stage meets the power flow requirement, the judgment formula of the power flow requirement is: Where: PF j is the flow on the j-th branch; is the maximum value of the power flow of the jth branch.

5. The method for joint configuration of distributed power supply-energy storage-charging station-capacitor in distribution network according to claim 1, characterized in that: The distributed power source includes a wind power station and a photovoltaic power station; In the step of solving the first-stage target site selection and capacity determination model based on the adaptive simulated annealing-particle swarm algorithm, the solution variables include the configuration nodes and capacities of wind power stations, photovoltaic power stations, battery energy storage power stations, and electric vehicle charging stations. A vector with four dimensions equal to the total number of distribution network nodes is introduced to represent the installation status of the above four types of equipment. The following vector represents the equipment installation status represented by the g-th particle in the k-th iteration process: Where: k represents the number of iterations; g represents the particle number; represents the equipment capacity vector represented by the g-th particle in the k-th iteration; Represents the equipment installation status of node i represented by the g-th particle in the k-th iteration.

6. The method for jointly configuring distributed power supply-energy storage-charging station-capacitor in distribution network according to claim 1, characterized in that: The first stage constraints also include wind turbine quantity constraints, photovoltaic panel quantity constraints and battery energy storage constraints; among them, in the power balance constraint, the sum of the complex power at each node is a nonlinear equality constraint, so that the expression of the power balance constraint is: Where: and are the active and reactive powers generated by the power supply at the i-th node respectively; and are respectively the active and reactive power requirements of the grid-connected load at the i-th node; V i and V j are the voltage amplitudes of nodes i and j respectively; δ i and δ j are the voltage phase angles of nodes i and j respectively; Y ij is the admittance of branch ij; α ij is the admittance phase angle of branch ij; The expression of branch power flow constraint is: Where: PF j is the flow on the j-th branch; is the maximum value of the power flow of the jth branch; The expression of branch current capacity constraint is: Where: I ij is the current flowing from node i to node j; is the upper limit of branch current; The expression of the fan quantity constraint is: Where: is the optimal number of fans; is the maximum number of fans; The expression for the PV panel quantity constraint is: Where: is the optimal number of photovoltaic panels; is the maximum number of photovoltaic panels; The expression of battery energy storage constraint is: Where: and are the lower and upper limits of the battery energy storage charge capacity respectively; and They are the lower and upper limits of the battery energy storage discharge capacity; SOC min and SOC max are the lower and upper limits of SOC respectively.

7. The method for joint configuration of distributed power supply-energy storage-charging station-capacitor in distribution network according to claim 1, characterized in that: The second stage constraints also include power flow constraints, node voltage constraints, branch power flow constraints, capacitor quantity and capacity constraints; The expression of power flow constraint is: Where: P sub and Q sub are the active and reactive power of the substation bus, respectively; and are the active and reactive power losses on the j-th branch respectively; and The existing active and reactive loads on node i respectively; is the load of the electric vehicle charging station connected to node i; Supplement reactive power for the capacitor at node i; N bus and N line are the number of nodes and branches in the distribution network, respectively; The expression of node voltage constraint is: |V i min |≤|V i |≤|V i max |; Where: and are the lower and upper limits of the node voltage amplitude respectively; i is the node index, i=1,2,3...N bus ; The expression of branch power flow constraint is: Where: PF j is the flow on the j-th branch; is the maximum value of the power flow of the jth branch; The expressions for the number and capacity constraints of capacitors are: Where: is the maximum number of capacitors; and They are the minimum and maximum values ​​of reactive power compensated by a single capacitor respectively; Among them, the expression of the maximum total reactive power compensation constraint of the capacitor is: Where: is the reactive power compensated by the pth capacitor; is the existing reactive load on node i.

8. The method for joint configuration of distributed power supply-energy storage-charging station-capacitor in distribution network according to claim 1, characterized in that: In the step of solving the second-stage target location and capacity model based on the adaptive simulated annealing-particle swarm algorithm, the variables to be solved are the access nodes and capacity of the capacitors. A vector with a dimension equal to the total number of distribution network nodes is introduced to represent the installation status of the capacitors. The following vector represents the access status of the capacitor represented by the g-th particle in the k-th iteration process: Where: k represents the number of iterations; g represents the particle number; The vector representing the capacitor access situation of the g-th particle in the k-th iteration; Represents the capacitor access installation status of node i represented by the g-th particle in the k-th iteration.

9. A distribution network distributed power supply-energy storage-charging station-capacitor joint configuration system, characterized in that: include: A basic model building unit is constructed to respectively build respective basic models corresponding to distributed power sources, battery energy storage power stations, electric vehicle charging stations and capacitors; A first-stage target location and capacity model generation unit is constructed to construct a first-stage target location and capacity model for the location and capacity of distributed power sources, battery energy storage power stations, and electric vehicle charging stations based on the basic model, with cost minimization as the first-stage objective function; A second-stage target location and capacity determination model generation unit is constructed to construct a second-stage target location and capacity determination model for capacitor location and capacity determination based on the basic model, which takes minimizing voltage deviation and maximizing configuration benefit as the second-stage objective function; The first-stage solving unit is constructed by solving the first-stage target site selection and sizing model based on the adaptive simulated annealing-particle swarm algorithm with at least power balance constraint, branch flow constraint and branch current capacity constraint as the first-stage constraint conditions, so as to obtain the first-stage configuration result reflecting the site selection and sizing information of the distributed power source, the battery energy storage power station and the electric vehicle charging station; The first-stage configuration result judgment unit is constructed to judge whether the first-stage configuration result meets the power flow requirements. If it does not meet the power flow requirements, the first-stage optimization parameters are reset and the first-stage target location and capacity model is solved and calculated again to obtain the first-stage configuration result again. A typical daily operation curve generating unit is constructed to analyze the charging demand of electric vehicles and simulate the time series data of distributed power sources and distribution network loads based on weather, season, working day / holiday as the basis for division to generate a typical daily operation curve of electric vehicle charging stations, distributed power sources and distribution network loads; A voltage over-limit analysis unit is constructed to perform scenario analysis on the voltage conditions of each node in the distribution network based on the generated typical daily operation curve to obtain the voltage over-limit conditions of nodes in the entire network; The second-stage solving unit is constructed such that, when it is judged that the configuration result of the first stage meets the power flow requirement, at least the maximum reactive power compensation constraint of the capacitor is used as the constraint condition of the second stage, and the second-stage target site selection and sizing model is solved based on the adaptive simulated annealing-particle swarm algorithm to obtain the second-stage configuration result reflecting the site selection and sizing information of the capacitor; The configuration result output unit is constructed to output the joint configuration result of the distributed power source, the battery energy storage power station, the electric vehicle charging station and the capacitor based on the first stage configuration result and the second stage configuration result.

10. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the distribution network distributed power supply-energy storage-charging station-capacitor joint configuration method according to any one of claims 1-8.

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