A joint site selection and capacity determination method for distributed power sources and electric vehicle charging stations

Abstract

The present invention discloses a joint site selection and capacity determination method for distributed power sources and electric vehicle charging stations, which includes: establishing a site selection and capacity determination model with the minimum of the investment and operation and maintenance costs of the charging station, network loss costs, government subsidies, environmental costs, and the travel costs of users as the objective function, and the expression of the objective function is: minC = C1 + C2 - C3 - C4 + C5; where: C1 is the annual investment and operation and maintenance cost of the charging station; C2 is the network loss cost; C3 is the government subsidy; C4 is the environmental cost; C5 is the user travel cost; (S2) determining the constraint conditions of the site selection and capacity determination model; (S3) using an improved whale optimization algorithm to solve the site selection and capacity determination model along with the constraint conditions. An improved whale optimization algorithm is designed, and it is proposed to add an adaptive weight to the algorithm, which solves the problems such as slow convergence speed and easy entrapment in local optimum that occur when the whale optimization algorithm is applied to the site selection and capacity determination problem.

Description

Technical Field

[0001] The present invention relates to the field of motor planning, and in particular to a method for joint site selection and capacity determination of a distributed power source and an electric vehicle charging station. Background Art

[0002] At present, there are relatively few studies on the coordinated planning of distributed power sources and charging stations. However, the joint research and application of electric vehicle charging stations considering distributed power sources is an important research direction at present. The effective and reasonable use of electric vehicle charging stations and distributed power sources can not only maximize environmental and energy benefits, but also increase the stability and security of the power grid. Therefore, it is of great significance to jointly plan distributed power sources and electric vehicle charging stations and conduct more detailed and in-depth research.

[0003] Distributed power generation technology and electric vehicle technology are comprehensive studies involving multiple disciplines in many fields such as energy, control and materials. The flow of traditional distribution networks is unidirectional, but when large-scale distributed power sources are connected, the unidirectionality will change. At the same time, due to the uncertainty and volatility of DG, the distribution network will have a large power flow. This complexity depends on the location and capacity of the distributed power sources connected to the distribution network. Therefore, a scientific and reasonable distributed power configuration scheme can effectively improve the reliability of the distribution network, reduce network losses, and improve voltage quality. Similarly, the impact of large-scale electric vehicles connected to the distribution network also depends on the access location and capacity of electric vehicles. Therefore, the site selection and capacity determination of electric vehicle charging stations are also particularly important. If the site selection and capacity determination are improper, it will harm the reactive power balance and power quality in the distribution network, and may lead to serious consequences such as increased network losses and node voltage deviation. It can be seen that it is necessary to fully study the characteristics of distributed power sources and electric vehicles, analyze the impact of both on the distribution network after they are connected to the distribution network, and at the same time, coordinate the planning of various influencing factors.

[0004] At the same time, the site selection and capacity determination of electric vehicle charging stations not only affects the interests of charging station operators, but also greatly affects the charging convenience of electric vehicle users. Electric vehicle charging stations are essentially public facilities. Their existence is to serve the public, so it is necessary to consider user satisfaction when conducting site selection and capacity determination research. For car owners, the convenience and economy of charging are the main factors to improve user satisfaction. The travel cost of users to charging stations is used to reflect the convenience and economy of charging. The specific cost is closely related to the electric vehicle's kilometer power consumption, charging price and traffic conditions. Therefore, it is proposed to consider the user's travel cost when conducting site selection and capacity determination.

[0005] Scientific and reasonable site selection and sizing of distributed power sources and electric vehicle charging stations are the basis for the stable development of distributed power sources and electric vehicles, and user satisfaction is also related to the long-term development of electric vehicles. The present invention aims to study the joint site selection and sizing of distributed power sources and electric vehicle charging stations considering the user's travel cost, while ensuring the normal operation of the charging station and reducing the user's travel cost, thereby improving the local consumption level of distributed power sources, improving the stability and reliability of distribution network operation, and promoting the development and application of distributed power sources and electric vehicles.

[0006] Building an efficient charging network has become an important task for countries to increase the penetration rate of electric vehicles. A well-planned charging station can serve more electric vehicle users at a low cost, thereby improving user satisfaction. At the same time, distributed power sources have been vigorously promoted due to their clean and efficient characteristics. However, although electric vehicles and distributed power sources can effectively alleviate the energy crisis and reduce pollutant gas emissions, they still have the following shortcomings:

[0007] (1) If the planning of the two in the distribution network is unreasonable, it will inevitably affect the economic, safe and stable operation of the distribution network. Therefore, the present invention considers combining distributed power sources and electric vehicle charging stations to conduct site selection and capacity determination research.

[0008] (2) Previous literature only considered the interests of investors or environmental costs when constructing site selection and capacity determination models, and rarely considered customer satisfaction. Therefore, the present invention considers user satisfaction and reflects this satisfaction through user travel costs. Summary of the invention

[0009] The purpose of the present invention is to provide a method for joint site selection and capacity determination of distributed power sources and electric vehicle charging stations based on the above-mentioned deficiencies in the prior art, so as to solve the above-mentioned technical problems.

[0010] The purpose of the present invention is achieved by the following technical solutions:

[0011] A method for joint site selection and capacity determination of a distributed power source and an electric vehicle charging station, comprising:

[0012] (S1) A location selection and capacity determination model is established with the objective function of minimizing the investment and operation and maintenance costs of charging stations, network loss costs, government subsidies, environmental costs, and user travel costs. The expression of the objective function is:

[0013] minC=C1+C2-C3-C4+C5

[0014] Among them: C1 is the annual investment and operation and maintenance cost of the charging station; C2 is the network loss cost; C3 is the government subsidy; C4 is the environmental cost; C5 is the user's travel cost;

[0015] (S2) determining the constraint conditions of the site selection and capacity determination model;

[0016] (S3) An improved whale optimization algorithm is used to solve the site selection and capacity determination model based on constraints.

[0017] A further improvement of the present invention is that the annual investment and operation cost of the charging station includes the annual investment cost of the distributed power source and the electric vehicle charging station, and the mathematical expression is as follows:

[0018] C1=C INV +C OM

[0019]

[0020]

[0021] Where: C1 is the annual investment and maintenance cost of the charging station; C INV is the annual investment and construction cost of the charging station; C OM is the annual operation and maintenance cost of the charging station; n DG is the total number of nodes to be installed for distributed generation; P i,WG is the installed capacity of the wind power station at the i-th node; c t,WG P is the investment cost per unit capacity of the wind power station; i,PV is the installed capacity of the solar power station at the i-th node; c t,PV is the investment cost per unit capacity of the solar power station; r is the discount rate; n1 is the economic service life of the distributed power source; n EVCS is the total number of nodes to be installed in the electric vehicle charging station; c g is the fixed construction investment cost of the electric vehicle charging station; P i,EVCS is the installed capacity of the electric vehicle charging station at the i-th node; c t,EVCS is the investment cost per unit capacity of the electric vehicle charging station; n2 is the economic service life of the distributed power source and the electric vehicle charging station; N m is the number of seasons, take 4; d m is the number of days corresponding to the mth season; N s is the number of typical daily scenes after scene reduction, which is 4; P s is the probability of the sth scenario occurring, which is 0.25; c n,WG is the operation and maintenance cost per unit capacity of the wind power station; c n,PV is the operation and maintenance cost per unit capacity of the solar power station; c t,EVCS P is the operation and maintenance cost per unit capacity of the electric vehicle charging station; i,s,t,WG is the actual power generation of the i-th wind power station at the s-th scenario at time t; P i,s,t,PVis the actual power generation of the i-th node solar power station at the s-th scenario at time t.

[0022] A further improvement of the present invention is that in step S1, the network loss is converted into an economic indicator, and its mathematical formula is as follows:

[0023]

[0024] Where: C2 is the network loss cost; T is the number of days in a year, which is 365; I k(t) is the current of the kth line in period t; R k is the resistance of the kth circuit; C e For electricity price.

[0025] A further improvement of the present invention is that in step S1, the expression of government subsidy is:

[0026]

[0027] Where: C3 is the government subsidy; c r,WG is the government subsidy per unit capacity of the wind power station; c r,PV It is the government subsidy cost per unit capacity of solar power station.

[0028] A further improvement of the present invention is that in step S1, the expression of environmental cost is:

[0029]

[0030] Where: C4 is environmental cost; M is the type of power generation technology; N is the type of pollution; X n The environmental value generated by the nth type of pollution; Y n The unit pollution fine for the nth type of pollution; Q nm P is the emission of the nth type of pollution when the mth type of power generation technology is used to produce unit electricity; n is the annual power generation of the nth type of power generation technology.

[0031] A further improvement of the present invention is that in step S1, the cost of the journey to the charging station is converted into the product of the distance and the power consumption for calculation, and the expression of the user journey cost is obtained as follows:

[0032]

[0033] a∈A,b∈B

[0034] Where: C5 is the user's travel cost; P is the amount of electricity consumed by the electric vehicle per kilometer; C r is the charging electricity price of electric vehicle charging station; d abis the actual distance from charging demand point a to charging station b; A is the set of charging demand points, {A|a=1,2,…,l}; B is the set of alternative charging stations, {B|b=1,2,…,m}; N i is the traffic volume; y ab It is the decision variable for whether the vehicle at charging demand point a goes to charging station b to charge. It is 1 if it goes to charge, otherwise it is 0.

[0035] A further improvement of the present invention is that the constraint conditions in step S2 include equality constraint conditions and inequality constraint conditions; wherein:

[0036] The equality constraints include system power flow constraints;

[0037] The inequality constraints include node voltage constraints, branch power flow constraints, distributed generation installation capacity constraints, electric vehicle charging station capacity constraints, and node installation capacity constraints.

[0038] A further improvement of the present invention is that in step S3, the improvement of the whale optimization algorithm includes:

[0039] Adopt nonlinear adjustment strategy;

[0040] Adaptive weights are added during the position update process.

[0041] The advantages of the present invention are as follows: the present method takes the minimum sum of the investment in station construction and operation and maintenance costs, network loss costs, environmental costs, government subsidies and the costs incurred by the user's journey to the charging station as the objective function, and reasonably selects the address for building the distributed power source and the charging station under the constraints of satisfying the power flow, node voltage, distributed power source installation capacity and distributed power source output, and sets the corresponding capacity to ensure the service level of the electric vehicle charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of the joint site selection and sizing method for distributed generation and electric vehicle charging stations;

[0043] Figure 2 This is a road network diagram;

[0044] Figure 3 is the traffic network diagram;

[0045] Figure 4 This is a schematic diagram of the IEEE33 node diagram. DETAILED DESCRIPTION

[0046] like Figure 1 As shown, an embodiment of the present invention includes a method for joint site selection and capacity determination of a distributed power source and an electric vehicle charging station, the main features of which include:

[0047] (1) Taking the location and capacity of distributed power sources and electric vehicle charging stations as decision variables, and minimizing the investment and operation and maintenance costs of charging stations, network loss costs, government subsidies, environmental costs, and user travel costs as the objective function, a joint location and capacity determination model of distributed power sources and electric vehicle charging stations considering user travel costs is constructed;

[0048] (2) Design an improved whale optimization algorithm and verify its superiority, and use the Floyd shortest path method to calculate the travel cost. On this basis, solve the joint site selection and sizing model of distributed power sources and electric vehicle charging stations considering the user's travel cost.

[0049] (3) The traffic network diagram is simulated using IEEE33 nodes, and the site selection and capacity determination model is solved by combining the model and improved algorithm proposed in the present invention. A comparative analysis is also conducted between the traditional algorithm and the improved algorithm of the present invention.

[0050] The solution flow chart of the present invention is as follows Figure 1 The present invention is further described in detail below in conjunction with the accompanying drawings.

[0051] This method takes the minimum sum of investment in station construction and operation and maintenance costs, network loss costs, environmental costs, government subsidies, and the costs incurred by users traveling to charging stations as the objective function. Under the constraints of power flow, node voltage, distributed power generation installation capacity, and distributed power generation output, the address is reasonably selected to build distributed power sources and charging stations, and the corresponding capacity is set to ensure the service level of electric vehicle charging stations.

[0052] In order to facilitate the description of the travel cost, this method abstracts the transportation network of the planning area into Figure 2 The network is composed of points and lines. The lines mainly represent roads, and the points are mainly divided into two types: one is the alternative points of electric vehicle charging stations, which represent the alternative locations where electric vehicle charging stations can be built; the other is the selection points where electric vehicle users have charging needs, which means that at this point electric vehicle users decide whether to charge their electric vehicles. In this method, it is assumed that the alternative points of electric vehicle charging stations and the selection points where electric vehicle users have charging needs are all at traffic nodes. Figure 2 A simple example of a road network.

[0053] Among them, points A, B, C, D and E are traffic network nodes. Assume that electric vehicle charging stations are built at points A, D and E. The distances between the points are as follows: Figure 1As shown. Assuming that an electric car user has a charging demand at point B, the user can choose to charge at three charging stations: A, D and E. From an economic point of view, electric car users will choose to charge at the charging station closest to them, so the user chooses to charge at point E. Since the distance is the shortest, the power consumption on the way to the charging station is also the least, so this option will greatly reduce the distance cost of electric car users.

[0054] This method takes into account the user's travel cost and establishes a site selection and capacity determination model for electric vehicle charging stations with distributed power sources, with the objective function of minimizing the charging station investment and operation and maintenance costs, network loss costs, government subsidies, environmental costs, and user travel costs. The objective function is as follows:

[0055] minC=C1+C2-C3-C4+C5

[0056] In the formula: C1 is the annual investment and operation and maintenance cost of the charging station; C2 is the network loss cost; C3 is the government subsidy; C4 is the environmental cost; C5 is the user's travel cost.

[0057] The cost calculation formulas are as follows:

[0058] (1) Annual investment and operation and maintenance costs of charging stations

[0059] The annual investment and operation and maintenance cost includes the annual investment cost and operation and maintenance cost of the charging station. The investment cost includes the annual investment cost of the distributed power source and the electric vehicle charging station. The expression is as follows:

[0060] C1=C INV +C OM

[0061]

[0062]

[0063] Where: C1 is the annual investment and maintenance cost of the charging station; C INV is the annual investment and construction cost of the charging station; C OM is the annual operation and maintenance cost of the charging station; n DG is the total number of nodes to be installed for distributed generation; P i,WG is the installed capacity of the wind power station at the i-th node; c t,WG P is the investment cost per unit capacity of the wind power station; i,PV is the installed capacity of the solar power station at the i-th node; c t,PV is the investment cost per unit capacity of the solar power station; r is the discount rate; n1 is the economic service life of the distributed power source; n EVCSis the total number of nodes to be installed in the electric vehicle charging station; c g is the fixed construction investment cost of the electric vehicle charging station; P i,EVCS is the installed capacity of the electric vehicle charging station at the i-th node; c t,EVCS is the investment cost per unit capacity of the electric vehicle charging station; n2 is the economic service life of the distributed power source and the electric vehicle charging station; N m is the number of seasons, take 4; d m is the number of days corresponding to the mth season; N s is the number of typical daily scenes after scene reduction, which is 4; P s is the probability of the sth scenario occurring, which is 0.25; c n,WG is the operation and maintenance cost per unit capacity of the wind power station; c n,PV is the operation and maintenance cost per unit capacity of the solar power station; c t,EVCS P is the operation and maintenance cost per unit capacity of the electric vehicle charging station; i,s,t,WG is the actual power generation of the i-th wind power station at the s-th scenario at time t; P i,s,t,PV is the actual power generation of the i-th node solar power station at the s-th scenario at time t.

[0064] (2) Network loss is an important indicator to measure the operating status of the distribution network. The flow of the distribution network will change due to the intervention of distributed power sources, which will in turn change the network loss of the entire distribution network. Therefore, the reasonable connection of distributed power sources to the distribution network can reduce the network loss of the system. In addition, the network loss is also related to the distribution of loads, so the load of the distribution network will change due to the access of electric vehicle charging stations, which will cause the network loss of the system to change. Therefore, this method converts the network loss into an economic indicator, and the expression is as follows:

[0065]

[0066] Where: C2 is the network loss cost; T is the number of days in a year, which is 365; I k(t) is the current of the kth line in period t; R k is the resistance of the kth circuit; C e For electricity price.

[0067] (3) Government subsidies:

[0068]

[0069] Where:

[0070] C3 is government subsidy; c r,WG is the government subsidy per unit capacity of the wind power station; c r,PV It is the government subsidy cost per unit capacity of solar power station.

[0071] (4) Environmental costs:

[0072]

[0073] Where: C4 is environmental cost; M is the type of power generation technology; N is the type of pollution; X n The environmental value generated by the nth type of pollution; Y n The unit pollution fine for the nth type of pollution; Q nm P is the emission of the nth type of pollution when the mth type of power generation technology is used to produce unit electricity; n is the annual power generation of the nth type of power generation technology.

[0074] (5) User’s travel cost:

[0075] From the perspective of electric vehicle charging stations, when electric vehicles do not need to be charged, electric vehicle users will try to choose the shortest path from the departure point to the destination. However, when the battery is insufficient, they will choose to charge at the nearest charging station and then continue to the destination. Therefore, when the destination is the same, different charging behaviors of electric vehicles will cause electric vehicle users to choose different paths in the road network. Moreover, when electric vehicle users choose to charge their electric vehicles, they are not exactly at the charging station, so they will give priority to charging at the nearest electric vehicle charging station. Therefore, the cost of the journey to the charging station can be converted into the product of distance and power consumption to calculate, and the expression is as follows.

[0076]

[0077] a∈A,b∈B

[0078] Where: C5 is the user's travel cost; P is the amount of electricity consumed by the electric vehicle per kilometer; C r is the charging electricity price of electric vehicle charging station; d ab is the actual distance from charging demand point a to charging station b; A is the set of charging demand points, {A|a=1,2,…,l}; B is the set of alternative charging stations, {B|b=1,2,…,m}; N i is the traffic volume; y ab It is the decision variable for whether the vehicle at charging demand point a goes to charging station b to charge. It is 1 if it goes to charge, otherwise it is 0.

[0079] The constraints of the joint site selection and capacity determination of distributed power sources and electric vehicle charging stations considered in the present invention are more complex than those of the two for separate planning, which mainly include inequality constraints and equality constraints. Inequality constraints mainly include node voltage constraints, branch flow constraints, distributed power generation installation capacity constraints, electric vehicle charging station capacity constraints and node installation capacity constraints; equality constraints are mainly system flow constraints.

[0080] (1) Node voltage constraints

[0081] V imin ≤V i ≤V imax

[0082] Where: V imin is the voltage lower limit of node i; V i is the voltage value of node i; V ima x is the upper voltage limit of node i.

[0083] (2) Branch flow constraints

[0084] S ij ≤S ijmax

[0085] Where: S ij is the apparent power of the branch; S ijmax is the maximum power transfer allowed for branch j.

[0086] (3) Total capacity constraints of distributed generation installation

[0087]

[0088] Where: P DG,i is the installed capacity of distributed power generation at node i; η is the ratio of the access capacity of distributed power generation to the total load capacity of the system; P load is the total load capacity of the system.

[0089] (4) Capacity demand constraints for electric vehicle charging stations

[0090]

[0091] Where: P EVCS,i is the installed capacity of the electric vehicle charging station at the i-th node; P demand To plan the charging needs of electric vehicles within the year.

[0092] (5) Installation capacity constraints of distributed generation and electric vehicle charging stations at node i

[0093] 0≤P DG,i ≤P DG,i,max

[0094] 0≤P EVCS,i ≤P EVCS,i,max

[0095] Where: P DG,i,ma x is the upper limit of the distributed generation capacity installed at node i; P EVCS,i,max Install a capacity cap for the electric vehicle charging station at node i.

[0096] (6) Equality constraints

[0097] The equality constraint is mainly the power flow constraint of the system, which is expressed as follows:

[0098]

[0099] Where: P i is the active power of node i; V i is the voltage value of node i; V j is the voltage value of node j; G ij is the conductance between branches i and j; B ij is the susceptance between branches i and j; θ ij is the voltage phase angle difference between nodes i and j; Q i is the reactive power of node i.

[0100] In the study of the location and capacity problem of electric vehicle charging stations, it is popular to use artificial intelligence optimization algorithms to solve it in recent years. Because the complexity of the joint location and capacity problem of distributed power sources and electric vehicle charging stations considered by this method is proportional to the capacity and number of distributed power sources and electric vehicle charging stations, when these coefficients are too large, the possibility of the location and capacity results is relatively large, and the conventional enumeration method cannot calculate the optimal situation at a fast speed. Artificial intelligence algorithms such as the whale optimization algorithm can avoid a lot of repetitive work and can get results at a faster speed. However, when the whale optimization algorithm is applied to the location and capacity problem, it is easy to converge slowly and fall into local optimality, and the results obtained will deviate from the actual situation. Therefore, in order to better apply the distributed power and electric vehicle charging station location and capacity problems of this method, the whale optimization algorithm must be improved to make the location and capacity results more in line with reality.

[0101] By analyzing the basic principle of the whale optimization algorithm, we can conclude that A|C·X *(t)-X(t)| is the encircling step length, where parameter A is the decisive factor in the local development ability and global optimization ability of the whale optimization algorithm. According to the position update formula, the value of parameter A is closely related to the convergence factor a. Therefore, the key to finding the optimal solution is the value of the convergence factor a in the step length. When the convergence factor is relatively large, the global search ability is relatively strong, which can effectively avoid falling into the local optimum; when the convergence factor is relatively small, the local development ability is relatively strong, and the algorithm converges faster. However, in the traditional whale optimization algorithm, the convergence factor decreases linearly with the increase in the number of iterations, which makes the algorithm converge more slowly. Therefore, this method adds a nonlinear adjustment strategy without changing the trend of the convergence factor, which can not only speed up the convergence speed of the algorithm but also ensure the algorithm's search ability. The specific formula is as follows:

[0102]

[0103] Where: t is the current iteration number; t max is the maximum number of iterations.

[0104] The location of the prey in the whale optimization algorithm represents the optimal solution to the optimization problem, but in the traditional whale optimization algorithm, the location of the prey X * (t) is not fully utilized in the position update formula. Therefore, in order to improve the optimization accuracy of the algorithm, this method adds an adaptive weight to the position update formula, which is defined as follows:

[0105]

[0106]

[0107]

[0108] Where: is the adaptive weight of the prey position.

[0109] As the number of iterations increases, the weight coefficient also increases. Therefore, in the encirclement and search phase of the whale optimization algorithm, that is, when p < 0.5, the adaptive weight Add it to the position update formula to fully find the optimal solution in the optimization problem; at the same time, add adaptive weights to the position update formula during the predation process As the number of iterations increases, smaller weights are used to change the position of the prey to improve the local development ability and optimization accuracy of the algorithm.

[0110] Assume that the plane has a point set Q={q1,q2,q3,…,q n}, n≥3, then the conventional Voronoi diagram for any point is defined as follows:

[0111] V(q i )={x∈V(q i )|l(x,q i )≤l(x,q j ),j=1,2,…,n,j≠i}

[0112] Where: V(q i ) is the point q i The inclusion range of l(x,q i )≤l(x,q j ) to q i The European formula of the point is less than q j The Euclidean distance of the points.

[0113] According to the definition of the traditional Freunoi diagram, in the site selection and capacity determination problem, the planned city can be regarded as a two-dimensional plane diagram, and all electric vehicle charging stations can be regarded as a point set in the plane. Therefore, the Freunoi idea can be used to obtain the charging service range of the electric vehicle charging station, and the electric vehicles within the service range can be divided into corresponding electric vehicle charging stations to estimate the charging demand of the charging station.

[0114] When using the Voronoi diagram theory to divide the range of charging stations, the Euclidean distance is used, but in reality the distance from the charging demand point to the charging station is not simply the distance between two points. Therefore, when selecting a site and determining the capacity, the distance between the electric vehicle and the charging station should be calculated in combination with the road conditions in the planning area. When electric vehicle users have charging needs, in most cases they will give priority to charging at the charging station closest to them. Therefore, when calculating the travel cost of electric vehicle users, using the actual distance instead of the Euclidean distance will make the algorithm results more accurate. Therefore, this method uses the typical shortest path algorithm: the Floyd algorithm to calculate the path from the charging demand point to the electric vehicle charging station. The Floyd algorithm calculates the actual distance between two nodes by inputting the weighted distance matrix into the computer. The specific description is as follows:

[0115] (1) The traffic network diagram simulated by IEEE33 nodes is used as the traffic network diagram of the planning area;

[0116] (2) Generate an n×n initial distance matrix D based on the node number n of the traffic network graph (0) , where d ij (0) represents the distance between node i and node j. If there is a road directly connecting node i and node j, then d ij (0) represents the actual distance between node i and node j; if there is no road directly connecting node i and node j, then dij (0) Then it means infinity, which can be expressed by the formula:

[0117]

[0118] Where: w ij is the actual distance between road node i and node j.

[0119] (3) Determine whether there is an intermediate node r between any node i and node j, so that the distance between node i and node j through node r is less than the distance between node i and node j. If node r exists, then the distance matrix D (k) Perform update iterations as shown in the formula:

[0120]

[0121] (4) Update and iterate according to the above rules to obtain the distance matrix D (k+1) , if D (k+1) ≠D (k) Then return to (3); otherwise, terminate the update iteration, D (k) It is the shortest path matrix.

[0122] The distance calculated by the Floyd algorithm in the site selection and capacity determination research will make the algorithm results more practical and improve the accuracy of the algorithm. Combined with the idea of ​​Voronoi diagram, a more rigorous method for calculating user travel cost is formed, which lays a theoretical foundation for site selection and capacity determination research.

[0123] The main solution steps are as follows, and the flow chart is as follows:

[0124] (1) Establish a mathematical model for the joint site selection and sizing of distributed power sources and electric vehicle charging stations, including the objective function and constraints. Set the relevant parameters of the solution algorithm, such as the maximum number of iterations, population size, and dimension;

[0125] (2) Based on the reduced data of wind speed and light intensity scenes, and according to the distributed power output model, the timing curve of distributed power in typical scenes is calculated, and the grid structure information, node load parameters, etc. are obtained to lay the foundation for calculating the distribution network flow;

[0126] (3) Based on the electric vehicle charging load curve, the grid structure information, the timing information of distributed power sources and conventional loads, and the relevant objective functions of the site selection and sizing model, the joint construction and operation and maintenance costs of distributed power sources and electric vehicle charging stations, government subsidies, environmental costs, and user travel costs are calculated;

[0127] (4) The network loss cost generated when the distribution network is connected to distributed generation and electric vehicles is calculated by forward-backward power flow calculation method on IEEE33 nodes;

[0128] (5) Using the improved whale optimization algorithm to optimize the constructed mathematical model of site selection and capacity determination, the fitness value and optimal solution corresponding to the optimal individual are obtained;

[0129] (6) Determine whether the improved whale optimization algorithm has reached the maximum number of iterations. If it converges, the iteration ends; otherwise, return to step (3) and update the algorithm parameters;

[0130] (7) Output the optimal site selection and capacity determination plan and the corresponding objective function value, that is, the optimal site selection and installation capacity and the corresponding cost.

[0131] The present invention constructs distributed power sources and electric vehicle charging stations in the planning area introduced above to minimize the total cost. Under the condition of ensuring generality, for the convenience of calculation, it is assumed that the design service life of the distributed power sources and electric vehicle charging stations determined by the plan is 20 years, and the discount rate is 0.1. The investment cost of the wind power station is 5.274 million yuan / MW, the operation and maintenance cost is 0.01 million yuan / MWh, and the government subsidy is 0.36 yuan / kWh; the investment cost of the solar power station is 4.689 million yuan / MW, the operation and maintenance cost is 0.012 million yuan / MWh, and the government subsidy is 0.1 yuan / kWh; the fixed investment cost of the electric vehicle charging station is 3 million yuan, the investment cost per unit capacity is 0.6 million yuan / kW, and the operation and maintenance cost is 100,000 yuan / MW. The unit electricity price is 0.5 yuan / kWh, and the charging electricity price of the electric vehicle charging station is 1 yuan / kWh. The power consumption of electric vehicles for every kilometer traveled is 0.15kWh / km, and the traffic volume of each node is shown in Table 1.

[0132] Table 1 Traffic node flow statistics

[0133]

[0134] The pollutant emission rates and environmental cost parameters of the thermal power generation industry considered in this method are shown in Table 2.

[0135] Table 2 Environmental pollution penalty cost coefficients for traditional thermal power generation

[0136]

[0137] MATLAB was used for programming. In the improved whale optimization algorithm, the population size of humpback whales was set to 30 and the maximum number of iterations was set to 500.

[0138] Attached Figure 3The traffic network diagram and power grid distribution diagram of the planned area, where the power grid refers to IEEE33 nodes, as shown in the attached Figure 4 As shown. The network contains 32 power lines, 54 traffic roads and 33 power nodes, of which node 1 is used to connect to the upper power grid, so it is set as a balance node, and the remaining nodes are PQ nodes. The voltage level of the power system is 12.66kV, the base power is 10MW, and the maximum conventional load of the network is 5.084+j2.547MW. The solid line in the figure represents the traffic connection line, the dotted line represents the power connection line, and the value between the lines represents the distance between the two nodes, in km.

[0139] With the popularization of distributed power sources and electric vehicles, there will be more and more distributed power sources and electric vehicles in the future distribution network. In order to take into account the impact of the access of distributed power sources and electric vehicles to the distribution network, and to illustrate the effectiveness of the joint planning model studied in this chapter, the feasibility of the improved algorithm and the practicality of adding user travel costs, this chapter will simulate and analyze the following five schemes:

[0140] (1) Separate site selection and sizing of distributed power sources excluding electric vehicle charging stations;

[0141] (2) Individual site selection and sizing of electric vehicle charging stations without distributed power sources;

[0142] (3) Joint site selection and sizing of distributed power sources and electric vehicle charging stations;

[0143] (4) Joint site selection and sizing of distributed power sources and electric vehicle charging stations based on the improved whale optimization algorithm;

[0144] (5) Joint site selection and sizing of distributed power sources and electric vehicle charging stations based on the improved whale optimization algorithm taking into account user travel costs.

[0145] According to the above five schemes, the site selection and capacity determination of the planning area of ​​the reference IEEE33 node were studied, and the calculation results are shown in Table 2.

[0146] Table 2 Site selection and capacity determination results of different schemes

[0147]

[0148]

[0149] Note: “—” means no such value.

[0150] Table 3 gives the annual costs of the five optimal configurations of site selection and capacity planning.

[0151] Table 3 Comparison of annual costs of optimal configuration under different site selection and capacity determination schemes

[0152]

[0153]

[0154] From the perspective of the site builder, the present invention takes into account the mutual constraints and influences between distributed power sources and electric vehicle charging stations, and proposes a joint site selection and capacity determination model and solution method for distributed power sources and electric vehicle charging stations on the basis of fully considering the timing of distributed power sources and conventional loads and the charging load of electric vehicles. The model comprehensively considers the investment economy and environmental protection characteristics of distributed power sources and electric vehicle charging stations, takes the location and capacity of distributed power sources and electric vehicle charging stations as decision variables, and minimizes the investment and operation and maintenance costs of charging stations, network loss costs, government subsidies, environmental costs, and user travel costs as the objective function. On this basis, the site selection and capacity determination constraints, conditional assumptions, and the algorithm flow of site selection and capacity determination corresponding to the model are given.

[0155] The above embodiments of the present invention do not constitute a limitation on the protection scope of the present invention. Any modification, replacement and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for joint site selection and capacity determination of distributed power sources and electric vehicle charging stations, comprising: (S1) A location selection and capacity determination model is established with the objective function of minimizing the investment and operation and maintenance costs of charging stations, network loss costs, government subsidies, environmental costs, and user travel costs. The expression of the objective function is: minC=C1+C2-C3-C4+C5 Among them: C1 is the annual investment and operation and maintenance cost of the charging station; C2 is the network loss cost; C3 is the government subsidy; C4 is the environmental cost; C5 is the user's travel cost; (S2) determining the constraint conditions of the site selection and capacity determination model; (S3) using the improved whale optimization algorithm and constraint conditions to solve the site selection and capacity determination model; The annual investment and operation cost of the charging station includes the annual investment cost of the distributed power supply and the electric vehicle charging station. The mathematical expression is as follows: C1=C INV +C OM Where: C1 is the annual investment and maintenance cost of the charging station; C INV is the annual investment and construction cost of the charging station; C OM is the annual operation and maintenance cost of the charging station; n DG is the total number of nodes to be installed for distributed generation; P i,WG is the installed capacity of the wind power station at the i-th node; c t,WG P is the investment cost per unit capacity of the wind power station; i,PV is the installed capacity of the solar power station at the i-th node; c t,PV is the investment cost per unit capacity of the solar power station; r is the discount rate; n1 is the economic service life of the distributed power source; n EVCS is the total number of nodes to be installed in the electric vehicle charging station; c g is the fixed construction investment cost of electric vehicle charging stations; P i,EVCS is the installed capacity of the electric vehicle charging station at the i-th node; c t,EVCS is the investment cost per unit capacity of the electric vehicle charging station; n2 is the economic service life of the distributed power source and the electric vehicle charging station; N m is the number of seasons, take 4; d m is the number of days corresponding to the mth season; N s is the number of typical daily scenes after scene reduction, which is 4; P s is the probability of the sth scenario occurring, which is 0.25; c n,WG is the operation and maintenance cost per unit capacity of the wind power station; c n,PV is the operation and maintenance cost per unit capacity of the solar power station; c t,EVCS is the operation and maintenance cost per unit capacity of the electric vehicle charging station; P i,s,t,WG is the actual power generation of the i-th wind power station at the s-th scenario at time t; P i,s,t,PV is the actual power generation of the solar power station at the ith node at the sth scenario at time t; In step S1, the network loss is converted into an economic indicator, and its mathematical formula is as follows: Where: C2 is the network loss cost; T is the number of days in a year, which is 365; I k(t) is the current of the kth line in period t; R k is the resistance of the kth circuit; C e For electricity prices; The constraints in step S2 include equality constraints and inequality constraints; wherein: The equality constraints include system power flow constraints; The inequality constraints include node voltage constraints, branch power flow constraints, distributed generation installation capacity constraints, electric vehicle charging station capacity constraints, and node installation capacity constraints.

2. The method for joint site selection and capacity determination of a distributed power source and an electric vehicle charging station according to claim 1, characterized in that: In step S1, the expression of government subsidy is: Where: C3 is the government subsidy; c r,WG is the government subsidy per unit capacity of the wind power station; c r,PV It is the government subsidy cost per unit capacity of solar power station.

3. The method for joint site selection and capacity determination of a distributed power source and an electric vehicle charging station according to claim 1, characterized in that: In step S1, the expression of environmental cost is: Where: C4 is environmental cost; M is the type of power generation technology; N is the type of pollution; X n The environmental value generated by the nth type of pollution; Y n The unit pollution fine for the nth type of pollution; Q nm P is the emission of the nth type of pollution when the mth type of power generation technology is used to produce unit electricity; n is the annual power generation of the nth type of power generation technology.

4. The method for joint site selection and capacity determination of a distributed power source and an electric vehicle charging station according to claim 1, characterized in that: In step S1, the cost of the journey to the charging station is converted into the product of distance and power consumption to calculate the user journey cost. The expression of the obtained user journey cost is as follows: a∈A,b∈B Where: C5 is the user's travel cost; P is the amount of electricity consumed by the electric vehicle per kilometer; C r is the charging electricity price of electric vehicle charging station; d ab is the actual distance from charging demand point a to charging station b; A is the set of charging demand points, {A|a=1,2,…,l}; B is the set of alternative charging stations, {B|b=1,2,…,m}; N i is the traffic volume; y ab It is the decision variable for whether the vehicle at charging demand point a goes to charging station b to charge. It is 1 if it goes to charge, otherwise it is 0.

5. The method for joint site selection and capacity determination of a distributed power source and an electric vehicle charging station according to claim 1, characterized in that: In step S3, the improvements to the whale optimization algorithm include: Adopt nonlinear adjustment strategy; Adaptive weights are added during the position update process.

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