Double-layer collaborative optimization method for extension planning of delivery channel in new energy enriched area

Abstract

The invention discloses a double-layer collaborative optimization method for extension planning of a delivery channel in a new energy-enriched area. The method comprises the following steps: quantifying environmental factors along a line; an upper-layer delivery channel expansion optimization mathematical model is constructed, a lower-layer power system operation optimization mathematical model is constructed, inter-layer information interaction is performed through parameter transmission, and the models are solved by adopting a double-layer iteration hybrid particle swarm algorithm. According to the method, a double-layer collaborative optimization method is adopted, a new energy enriched area line extension scheme is optimized on the upper layer, improvement of the new energy consumption capacity is guaranteed, unit output and unit reserve capacity are optimized on the lower layer, economic operation of a power system is guaranteed, and the power consumption capacity is improved. And reference of the method is provided for solving the problem of new energy consumption in a new energy-enriched region and economic operation of a power system.

Description

Technical Field

[0001] The present invention relates to the field of power system planning, and in particular to a double-layer collaborative optimization method for expansion planning of transmission channels in new energy-rich areas. Background Art

[0002] With the proposal of "dual carbon", the installed capacity of new energy power generation such as wind power and photovoltaic power generation in the power system has gradually increased, forming many new energy-rich areas. In new energy-rich areas, due to the volatility, intermittency and uncertainty of new energy power generation, coupled with the lack of new energy absorption capacity and other problems, wind and solar power abandonment are frequent, which brings certain challenges to the safe and stable operation of the power system. In response to the above problems, it is usually possible to expand the transmission channels of new energy-rich areas, interconnect the power grids in different regions, and use the complementary characteristics of power sources and loads in different regions to improve the absorption capacity of new energy and reduce the frequency of wind and solar power abandonment.

[0003] The planning of transmission channels in traditional power systems generally aims to meet user load demands, but in areas rich in new energy, the planning of transmission lines also needs to consider the absorption capacity of new energy. In order to improve the absorption capacity of new energy, the different spatiotemporal distribution characteristics of power sources and loads between different regions can be used to maximize the absorption potential of new energy in the power system and reduce the phenomenon of wind and solar abandonment. Therefore, the key technology of the expansion planning of transmission channels in areas rich in new energy can be summarized as follows: for the regional power grids in areas rich in new energy that need to improve the absorption capacity, on the basis of considering the distribution characteristics of power sources and loads, appropriate receiving areas are selected, new transmission lines are erected or the existing lines are transformed to increase their capacity, and the expansion plan is optimized on the basis of considering the economic efficiency to maximize the absorption capacity of new energy in areas rich in new energy. Therefore, the expansion planning of transmission channels in areas rich in new energy needs to consider the two contradictory goals of economy and absorption capacity, and a two-layer collaborative optimization model can be used to coordinate the two optimization goals.

[0004] Existing studies on the expansion planning of transmission channels in new energy-rich areas usually focus on improving the absorption capacity or economy, and few studies have simultaneously achieved the improvement of transmission capacity and good investment returns. Therefore, in order to maximize the absorption capacity of new energy at the optimal economic cost, it is necessary to solve two optimization problems at different levels but coupled with each other. One is the optimization of the expansion plan of transmission channels in new energy-rich areas, and the other is the transmission line parameters and generator operation plan of new energy transmission channels. In order to solve this problem, a two-layer collaborative optimization method can be used to optimize the line expansion plan in new energy-rich areas at the upper layer to ensure the improvement of new energy absorption capacity, and optimize the unit output and unit spare capacity in the lower layer to ensure the economic operation of the power system, providing a reference for methods to solve the problem of new energy absorption in new energy-rich areas and the economic operation of the power system. Summary of the invention

[0005] The purpose of the present invention is to provide a double-layer collaborative optimization method for the expansion planning of transmission channels in new energy-rich areas.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] The present invention comprises the following steps:

[0008] S1: Considering the hierarchical division of geographical environmental influencing factors and the spatial quantification of factor weights, the environmental factors along the route are quantified;

[0009] S11: Through expert research on line design and the induction of transmission line design regulations, a geographic information hierarchical structure model is established, including factors such as avoidance areas, geographical environment, engineering foundation, and social impact;

[0010] S12: Use GPS and other technologies to collect geographic information data, rasterize the map of the transmission line planning area, and use the Analytic Hierarchy Process (AHP) to quantitatively compare various geographical environmental factors;

[0011] S13: In order to solve the shortest path problem of a weighted directed graph or an undirected graph by breadth-first search, the Dijkstra algorithm is used to obtain an alternative outbound channel expansion solution.

[0012] S2: Considering the transmission channel capacity and investment cost constraints, a mathematical model for the optimization of the upper transmission channel expansion is constructed, and the objective function is to maximize the level of new energy consumption;

[0013] S21: Taking the maximization of the new energy consumption level as the objective function for optimizing the expansion of the upper transmission channel;

[0014] S22: Construct mathematical models for constraints such as transmission line capacity;

[0015] S23: Construct a mathematical model of power balance constraints, thermal power unit output constraints, and node voltage constraints.

[0016] S3: Considering the line operation status constraints and system operation constraints, a mathematical model for optimizing the operation of the lower power system is constructed, and the objective function is to minimize the regional annual total cost;

[0017] S31: Minimizing the regional annual total cost is used as the objective function for optimizing the operation of the lower-level power system;

[0018] S32: Construct system power flow related constraints, including node power balance, line power upper and lower limits, phase angle upper and lower limits, DC power flow constraints, etc.

[0019] S33: Construct relevant constraints on unit output, mainly including upper and lower limit constraints of thermal power units, spinning reserve constraints, etc.

[0020] S4: The optimization processes of the upper and lower layers are interdependent, and information exchange between layers is carried out through parameter transfer. A two-layer iterative hybrid particle swarm algorithm is used to solve the model.

[0021] S41: Initialize and input the original data of the transmission network to be planned, perform initial power flow calculation, obtain initial branch power flow and node voltage data, and initialize algorithm parameters;

[0022] S42: Initialize the speed, position, individual optimal value and group optimal value of the particle swarm according to the value range of the upper-level planning decision variables, and set the current number of iterations k=0;

[0023] S43: Update the speed and position of the upper layer particles, determine whether the updated speed and position meet the constraint conditions, and update the number of iterations k=k+1;

[0024] S44: passing the outbound channel expansion plan determined in the upper layer optimization as a parameter to the lower layer optimization, outputting the optimization result and passing it to the upper layer optimization;

[0025] S45: Calculate the fitness of each particle, and update the individual optimal position, individual optimal fitness, group optimal position and group optimal fitness of the upper particle group;

[0026] S46: Determine whether the number of iterations has reached the upper limit. If not, return to step S43; otherwise, output the outbound channel expansion optimization plan.

[0027] The beneficial effects of the present invention are:

[0028] The present invention is a double-layer collaborative optimization method for the expansion planning of transmission channels in new energy-rich areas. Compared with the prior art, the present invention adopts a double-layer collaborative optimization method, optimizes the line expansion plan in new energy-rich areas at the upper layer to ensure the improvement of new energy consumption capacity, and optimizes the unit output and unit spare capacity in the lower layer to ensure the economic operation of the power system, providing a reference for methods to solve the new energy consumption problem in new energy-rich areas and the economic operation of the power system.

[0029] The present invention adopts a two-layer iterative hybrid particle swarm algorithm to solve the two-layer collaborative optimization model. It has no special requirements for the model of the optimization objective function, few adjustable parameters, fast algorithm convergence speed and randomness and extensibility. When solving the two-layer planning model, it is not necessary to perform any transformation on the original two-layer planning model, nor does it require the objective function to meet some special conditions. The optimal solution of the two-layer collaborative optimization of the expansion planning of the transmission channel in the new energy-rich area can be quickly and effectively obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of a double-layer collaborative optimization method for expansion planning of transmission channels in new energy-rich areas provided by the present invention;

[0031] Figure 2 is a schematic diagram of a city power grid topology structure of a specific embodiment;

[0032] Figure 3 For specific implementation examples, the planning scheme for expansion of transmission channels in new energy-rich areas under different new energy growth modes is provided;

[0033] Figure 4 It is a comparison chart of the investment cost of sequential expansion of transmission channels under different growth modes of new energy for a specific embodiment. DETAILED DESCRIPTION

[0034] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0035] Aiming at the problem of insufficient new energy consumption capacity in new energy-rich areas, the present invention adopts a two-layer collaborative optimization method to optimize the transmission channel expansion planning scheme, so as to maximize the new energy consumption capacity at the optimal economic cost. The decision variables of the upper and lower optimizations are different. The upper optimization takes the location of the expanded line as the decision variable, and the decision variables of the lower optimization are unit output, rotating reserve, etc. When the objective function is determined, there is a certain constraint relationship between the upper and lower decision variables. The objective function of the upper model represents the maximization of the new energy consumption capacity of the interconnected area. At the same time, the optimal technical and economic indicators of the lower layer under the planning operation mode should meet the requirements. The objective function of the lower model represents the minimum annual total cost of the region. At the location of the expanded line determined by the upper model, the economically optimal unit output, rotating reserve purchase, etc. under this mode are further determined. In view of the characteristics of the proposed two-layer collaborative optimization model containing a large number of variables and non-uniform types, a two-layer iterative hybrid particle swarm algorithm is used to solve it.

[0036] like Figure 1 As shown: The double-layer collaborative optimization method for the expansion planning of the transmission channel in the new energy-rich area of ​​the present invention includes the following steps:

[0037] S1: In order to study the impact of geographical environmental factors on the selection of transmission lines, it is first necessary to quantify the environmental factors along the line, mainly considering the hierarchical division of geographical environmental factors and the spatial quantification of factor weights;

[0038] S11: Through expert research on line design and the induction of transmission line design regulations, a geographic information hierarchical structure model is established, including factors such as avoidance areas, geographical environment, engineering foundation, and social impact. The avoidance areas include nature reserves, military bases, and geological disaster areas. The geographical environment mainly includes mountains, vegetation, and rivers. The engineering foundation mainly includes transportation and transmission lines. Social factors include agricultural land, housing and public facilities.

[0039] S12: Use GPS and other technologies to collect geographic information data, rasterize the map of the transmission line planning area, and use the Analytic Hierarchy Process (AHP) to quantitatively compare various geographical environmental factors. It is necessary to judge the importance of the indicators in the criterion layer and form a judgment matrix, where a scale of 1 means that the two indicators are equally important, a scale of 9 means that the former indicator is much more important than the latter indicator, and a scale of one-ninth means that the latter indicator is much more important than the former indicator. Then calculate the consistency index and perform a consistency test to finally obtain the weight of each indicator;

[0040] S13: In order to solve the shortest path problem of weighted directed or undirected graphs by breadth-first search, Dijkstra algorithm is used to obtain alternative outbound channel expansion solutions. u is the shortest path length from the starting point v to the point u, set S is the set of vertices whose shortest paths have been found, and set U is the set of vertices whose shortest paths have not been determined. The Dijkstra algorithm can be used to find the shortest path from the starting point v to the end point t.

[0041] S2: Construct a mathematical model for the expansion and optimization of the upper transmission channel, with the objective function of maximizing the level of new energy consumption, taking into account constraints such as transmission channel capacity and investment cost;

[0042] S21: The maximum level of new energy consumption is used as the objective function for optimizing the expansion of the upper transmission channel. The mathematical formula is:

[0043]

[0044] Where P i DG (t) and P i DG*(t) represents the maximum available power of renewable energy and the abandoned power at node i in period t, respectively. T and N represent the total number of scheduling periods and the total number of nodes, respectively.

[0045] S22: Construct a mathematical model of constraints such as transmission line capacity. The mathematical formula for transmission line capacity constraint is:

[0046]

[0047] Where fl represents the actual power flow of the lth branch; f l.min With f l.max Respectively represent the maximum and minimum values ​​of the power flow of the lth branch; Indicates the number of original branches between the nodes at both ends of branch l;

[0048] S23: Construct a mathematical model of power balance constraints, thermal power unit output constraints, and node voltage constraints. The mathematical formula is:

[0049]

[0050] Among them, f ij With g ik They represent the flow injection of all branches connected to node i and the active output of the kth unit at node i; d i represents the active load at the i-th node; f l represents the actual power flow of the lth branch; f l.min With f l.max Respectively represent the maximum and minimum values ​​of the power flow of the lth branch; Indicates the number of original branches between the nodes at both ends of branch l; and are the upper and lower limits of the output of unit k respectively; and Represent the upper and lower limits of the voltage of each node respectively; G, Ω, and L represent the set of all generators, the set of channels to be selected, and the set of all lines in the transmission network respectively.

[0051] S3: Construct a mathematical model for optimizing the operation of the lower-level power system, with the objective function being to minimize the regional annual total cost, taking into account line operation status constraints and system operation constraints, etc.;

[0052] S31: Taking the minimum regional annual total cost as the objective function of the lower-level power system operation optimization, the mathematical formula is:

[0053]

[0054] Among them, Ω s Represents the set of renewable energy forecast output scenarios for the planning horizon year; ω op is the weight coefficient; ps is the probability of scenario s occurring; C total is the total annual cost of the region; C con is the line construction cost converted to the planning level year; C swt The cost of switching configuration of the transmission network converted to the planning level year; and They are the system operation cost, penalty cost for abandoning renewable energy and load shedding cost under scenario s respectively. The calculation method of each cost can be expressed as:

[0055]

[0056] Among them, Ω bus is the set of all nodes in the system; Ω reg is the collection of all renewable energy units in the system; Ω per The time period during which the system operates normally; is the construction cost coefficient of line l; are the quadratic coefficient, linear coefficient and constant coefficient of the power generation cost of unit k respectively; c reg With c load are the cost coefficients of abandoning renewable energy and shedding load respectively; l is a 0-1 variable, indicating whether a line is built at the candidate new line l. If so, it is 1; β l is a 0-1 variable, indicating whether line l is configured with a switch, if so, it is 1;

[0057] S32: Construct constraints related to system power flow. The mathematical formula is:

[0058] 0≤ΔD m,t,s ≤D m,t,s ,

[0059] 0≤ΔL i,t,s ≤L i,t,s ,

[0060]

[0061] Among them, F l,t,s is the active power flow of line l; D m,t,s is the predicted value of renewable energy output of renewable energy unit m; L i,t,s is the load demand of node i. If the line is closed, the line is also subject to the capacity upper limit constraint, that is, -η l,s F l max ≤F l,t,s ≤η l,s F l max , where η l,sis a 0-1 variable, which takes 1 when line l is closed, F l max The upper limit of active power transmitted by the line;

[0062] S33: Construct the relevant constraints of unit output:

[0063]

[0064] S4: The optimization processes of the upper and lower layers are interdependent, and information exchange between layers is required through parameter transfer. A two-layer iterative hybrid particle swarm algorithm is used to solve the model;

[0065] S41: Initialize and input the original data of the transmission network to be planned, perform initial power flow calculation, obtain initial branch power flow and node voltage data, and initialize algorithm parameters;

[0066] S42: According to the value range of the upper-level planning decision variables, the speed, position, individual optimal value and group optimal value of the particle swarm are initialized, and the current iteration number k=0 is set. The particle swarm speed and position initialization formula is:

[0067] V i 0 =V min +r0×(V max -V min )

[0068]

[0069] Among them, V i 0 and represents the initial velocity and position of the particle group, V max and V min They represent the upper and lower limits of the particle swarm velocity, respectively, and r0 is a random number between [0,1];

[0070] S43: Update the speed and position of the upper layer particles, determine whether the updated speed and position meet the constraint conditions, and update the number of iterations k=k+1. The particle group speed and position update formula is:

[0071]

[0072] Among them, ω is the inertia factor, c1 and c2 are the group learning factor and individual learning factor respectively, r1-r3 are random numbers uniformly distributed in the interval [0,1], and S(V) represents the sigmoid function;

[0073] S44: The transmission channel expansion plan determined in the upper optimization is passed as a parameter to the lower optimization, and the power system operation plan is obtained by solving the optimization model shown in steps S31-S33, and the calculation result of the new energy consumption capacity is further obtained by using the mathematical formula proposed in step S21, and the optimization result is output and passed to the upper optimization;

[0074] S45: Taking the result of the new energy consumption capacity as the fitness value of each particle, updating the individual optimal position, individual optimal fitness, group optimal position and group optimal fitness of the upper particle group;

[0075] S46: Determine whether the number of iterations has reached the upper limit. If not, return to step S43; otherwise, output the outbound channel expansion optimization plan.

[0076] After the above steps, the urban power grid load management optimization method for coping with extreme high temperature weather can be completed.

[0077] right Figure 2 In the specific embodiment shown in the figure, the method is used to obtain the expansion planning scheme of the transmission channel in the new energy-rich area under different new energy growth modes and the investment cost of the transmission channel timing expansion. Figure 3 and Figure 4 As shown in Tables 1 to 3, the calculation results of the economic performance and new energy consumption capacity of the expansion planning scheme of the transmission channel in the new energy-rich areas under different new energy growth models are shown.

[0078] Table 1 Transmission channel timing expansion scheme when new energy grows in mode 1

[0079]

[0080] Table 2 Transmission channel timing expansion scheme when new energy grows in mode 2

[0081]

[0082]

[0083] Table 3 Transmission channel timing expansion scheme when new energy grows in mode 3

[0084]

[0085] The technical solution of the present invention is not limited to the above-mentioned specific embodiments. All technical variations made according to the technical solution of the present invention fall within the protection scope of the present invention.

Claims

1. A two-layer collaborative optimization method for the expansion planning of transmission channels in new energy-rich areas, characterized in that: The following steps are involved: S1: Considering the hierarchical division of geographical environmental influencing factors and the spatial quantification of factor weights, the environmental factors along the route are quantified; S2: Considering the transmission channel capacity and investment cost constraints, a mathematical model for the optimization of the upper transmission channel expansion is constructed, and the objective function is to maximize the level of new energy consumption; S3: Considering the line operation status constraints and system operation constraints, a mathematical model for optimizing the operation of the lower power system is constructed, and the objective function is to minimize the regional annual total cost; S4: The optimization processes of the upper and lower layers are interdependent, and information exchange between layers is carried out through parameter transfer. A two-layer iterative hybrid particle swarm algorithm is used to solve the model.

2. The double-layer collaborative optimization method for expansion planning of transmission channels in new energy-rich areas according to claim 1 is characterized by: The step S1 specifically includes the following steps: S11: Establish a geographic information hierarchical structure model through expert research on line design and the induction of transmission line design regulations; S12: Collect geographic information data, rasterize the map of the transmission line planning area, and use the analytic hierarchy process to quantitatively compare various geographical environmental factors to obtain the weight of each indicator; S13: Use Dijkstra's algorithm to obtain alternative outbound channel expansion solutions, and use breadth-first search to solve the shortest path problem of weighted directed or undirected graphs.

3. The double-layer collaborative optimization method for expansion planning of transmission channels in new energy-rich areas according to claim 2 is characterized by: The step S2 specifically includes the following steps: S21: The maximum level of new energy consumption is used as the objective function for optimizing the expansion of the upper transmission channel. The mathematical formula is: Where P i DG (t) and P i DG* (t) represents the maximum available power of renewable energy and the abandoned power at node i in period t, respectively. T and N represent the total number of scheduling periods and the total number of nodes, respectively. S22: Construct a mathematical model of constraints such as transmission line capacity. The mathematical formula for transmission line capacity constraint is: where f l represents the actual power flow of the lth branch; f l.min With f l.max They represent the maximum and minimum values ​​of the power flow of the lth branch respectively; Indicates the number of original branches between the nodes at both ends of branch l; S23: Construct a mathematical model of power balance constraints, thermal power unit output constraints, and node voltage constraints. The mathematical formula is: Among them, f ij With g ik They represent the flow injection of all branches connected to node i and the active output of the kth unit at node i; d i represents the active load at the i-th node; f l represents the actual power flow of the lth branch; f l.min With f l.max They represent the maximum and minimum values ​​of the power flow of the lth branch respectively; Indicates the number of original branches between the nodes at both ends of branch l; and are the upper and lower limits of the output of unit k respectively; and Represent the upper and lower limits of the voltage of each node respectively; G, Ω, and L represent the set of all generators, the set of channels to be selected, and the set of all lines in the transmission network respectively.

4. The double-layer collaborative optimization method for expansion planning of transmission channels in new energy-rich areas according to claim 3 is characterized by: The step S3 specifically comprises the following steps: S31: Taking the minimum regional annual total cost as the objective function of the lower-level power system operation optimization, the mathematical formula is: Among them, Ω s Represents the set of renewable energy forecast output scenarios for the planning horizon year; ω op is the weight coefficient; p s is the probability of scenario s occurring; C total is the total annual cost of the region; C con is the line construction cost converted to the planning level year; C swt The cost of switching configuration of the transmission network converted to the planning level year; and They are the system operation cost, penalty cost for abandoning renewable energy and load shedding cost under scenario s respectively. The calculation method of each cost is expressed as: Among them, Ω bus is the set of all nodes in the system; Ω reg is the collection of all renewable energy units in the system; Ω per The time period during which the system operates normally; is the construction cost coefficient of line l; are the quadratic coefficient, linear coefficient and constant coefficient of the power generation cost of unit k respectively; c reg With c load are the cost coefficients of abandoning renewable energy and shedding load respectively; l is a 0-1 variable, indicating whether a line is built at the candidate new line l. If so, it is 1; β l is a 0-1 variable, indicating whether line l is configured with a switch, if so, it is 1; S32: Construct constraints related to system power flow. The mathematical formula is: 0≤ΔD m,t,s ≤D m,t,s , 0≤ΔL i,t,s ≤L i,t,s , Among them, F l,t,s is the active power flow of line l; D m,t,s is the predicted value of renewable energy output of renewable energy unit m; L i,t,s is the load demand of node i; if the line is closed, the line is also subject to the capacity upper limit constraint, that is, where η l,s is a 0-1 variable, which takes 1 when line l is closed, F l max The upper limit of active power transmitted by the line; S33: Construct the relevant constraints of unit output:

5. The double-layer collaborative optimization method for expansion planning of transmission channels in new energy-rich areas according to claim 4 is characterized by: The step S4 specifically comprises the following steps: S41: Initialize and input the original data of the transmission network to be planned, perform initial power flow calculation, obtain initial branch power flow and node voltage data, and initialize algorithm parameters; S42: According to the value range of the upper-level planning decision variables, the speed, position, individual optimal value and group optimal value of the particle swarm are initialized, and the current iteration number k=0 is set. The particle swarm speed and position initialization formula is: V i 0 =V min +r0×(V max -V min ) Among them, V i 0 and represents the initial velocity and position of the particle group, V max and V min They represent the upper and lower limits of the particle swarm velocity, respectively, and r0 is a random number between [0,1]; S43: Update the speed and position of the upper layer particles, determine whether the updated speed and position meet the constraint conditions, and update the number of iterations k=k+1. The particle group speed and position update formula is: Among them, ω is the inertia factor, c1 and c2 are the group learning factor and individual learning factor respectively, r1-r3 are random numbers uniformly distributed in the interval [0,1], and S(V) represents the sigmoid function; S44: The transmission channel expansion plan determined in the upper optimization is passed as a parameter to the lower optimization. By solving the optimization model of S31-S33, the power system operation plan is obtained. The mathematical formula proposed in S21 is further used to obtain the calculation result of the new energy absorption capacity, and the optimization result is output and passed to the upper optimization. S45: Taking the result of the new energy consumption capacity as the fitness value of each particle, updating the individual optimal position, individual optimal fitness, group optimal position and group optimal fitness of the upper particle group; S46: Determine whether the number of iterations has reached the upper limit. If not, return to S43; otherwise, output the outbound channel expansion optimization plan.

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