Method and system for site selection and capacity determination of distributed electric-hydrogen coupled energy station connected to distribution network

Abstract

The invention relates to a method and system for site selection and sizing of a distributed electric-hydrogen coupled energy station connected to a distribution network, and belongs to the field of new energy technology. The invention proposes a planning strategy of partitioning-site selection-sizing. At the partitioning level, a weighted partitioning modularity index of the distribution network with improved edge weights is established, and the edges between nodes of the distribution network are disconnected by introducing an adjacency table, the distribution network is partitioned, and a discrete particle swarm algorithm based on a mutation strategy is used for solving the problem. On this basis, a site selection and sizing configuration model for a distributed electric-hydrogen coupled energy station is established. The site selection model is based on dynamic optimal power flow, with the goal of minimizing the network loss of the distribution network, and the gurobi solver is used to plan the access location of the electric-hydrogen coupled energy station. At the sizing level, based on site selection planning, the constraints such as the climbing constraints of the electrolyzer and the fuel cell are considered, and the optimal capacity of each device is solved with the goal of minimizing the economic cost. The invention provides a method for an electric-hydrogen coupled energy station to be planned in a distribution network.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and more specifically, relates to a method and system for site selection and capacity determination of a distributed electric-hydrogen coupled energy station connected to a distribution network. Background Art

[0002] With the continuous development of hydrogen energy equipment and its applications, such as hydrogen-powered vehicles, the demand for hydrogen energy in cities is increasing. Distributed electric-hydrogen coupling energy stations, as a new type of electric-hydrogen coupling mode, are receiving more and more attention. As an important hub for regulating the two energy sources, electric-hydrogen coupling energy stations can not only actively respond to the operation of the distribution network but also meet the regional hydrogen supply demand. However, the existing site selection and sizing of the electric-hydrogen coupled energy station connected to the distribution network (see "Research on Site Selection and Sizing Methods for Distributed Generation Access to Distribution Network", a 2017 master's thesis of "China University of Mining and Technology"), did not take into account the regional division of the distribution network, and used static power flow in the site selection and sizing process, but static power flow is difficult to meet the needs of multi-time period optimization. In terms of capacity configuration, the existing technology only regards the electric-hydrogen coupled energy station as a load, and does not consider the electric-hydrogen coupled energy station equipped with fuel cells and energy storage batteries; at the same time, the existing electric-hydrogen coupled energy station connected to the distribution network has unreasonable access locations. For the distribution network containing a high proportion of new energy connected to the electric-hydrogen coupled energy station, its different access locations and configuration capacities have a huge impact on the voltage and network loss of the distribution network; therefore, the optimal configuration of the distributed electric-hydrogen coupled energy station connected to the distribution network is an issue that urgently needs to be solved. Summary of the invention

[0003] In view of this, the purpose of the present invention is to solve the problems of unreasonable access location and uneconomical configuration capacity of distributed electric-hydrogen coupled energy stations connected to the existing distribution network, and proposes a method and system for site selection and capacity determination of distributed electric-hydrogen energy coupling stations connected to the distribution network.

[0004] In order to achieve the above object, the present invention adopts the following technical scheme:

[0005] According to a first aspect of the present invention, a method for selecting a site and determining the capacity of a distributed electric-hydrogen coupled energy station connected to a distribution network is proposed, comprising:

[0006] Step S1, inputting distribution network flow data, partitioning the distribution network based on the weighted partition modularity index of the distribution network with improved edge weights and disconnecting the edges between the distribution network nodes by introducing an adjacency table, thereby forming independent areas, and solving the problem using a discrete particle swarm algorithm based on a mutation strategy to obtain a partitioned node set;

[0007] The weighted partition modularity index of the distribution network with improved edge weights is:

[0008]

[0009] in, A ij The improved distribution network edge weights for nodes i and j, max L is the maximum value of all node electrical distances, L ij,t is the electrical distance between node i and node j at time t, is the electrical distance between nodes i and j after dynamic correction, T is the total time, when nodes i and j are not connected, A ij =0, δ(i,j) is the partition coefficient, δ(i,j)=1 if node i and node j are in the same partition, otherwise δ(i,j)=0;

[0010] In the process of solving the problem using discrete particle swarm algorithm based on mutation strategy:

[0011] A decision variable is introduced into the discrete particle swarm algorithm to determine the number of partitions, and the rows of the adjacency table are used as the decision variable, and the decision variable is mutated. The mutation operation is as follows:

[0012] x l+1 =x l +c×rand

[0013] Where: x l represents the lth iteration; c represents the mutation factor, and its value range is the minimum definition domain in this iteration population. Since the decision variable is an integer, it is rounded after the mutation operation is completed. rand is a random function used to generate random numbers;

[0014] The mutation rate calculation formula is as follows:

[0015]

[0016] Where p m is the mutation rate, p m,min is the minimum mutation rate; p m,max is the maximum mutation rate; l represents the current number of iterations; N is the maximum number of iterations;

[0017] Step S2, encode the nodes of the partitions, establish the site selection model and fixed capacity configuration model of the distributed electric-hydrogen coupling energy station, the site selection model is based on the dynamic optimal power flow, and on the basis of the general constraints of the dynamic optimal power flow, introduces the node site selection constraints, and takes the minimum network loss of the distribution network in the whole period as the site selection objective function. The site selection objective function calculates the dynamic optimal power flow by calling the gurobi solver, solves the installation location of the distributed electric-hydrogen coupling energy station and the node interaction power, and obtains the site selection location and node interaction power of each partition; the fixed capacity configuration model, based on the site selection planning, considers the photovoltaic power constraints, electrolyzer start-stop and climbing constraints, fuel cell start-stop and climbing constraints, energy storage battery charging and discharging constraints and charge state constraints, hydrogen storage capacity constraints and inlet and outlet gas constraints and power balance constraints in the electric-hydrogen coupling energy station, takes the lowest economic cost as the capacity configuration objective function, and the capacity configuration objective function solves the optimal capacity of each device in the distributed electric-hydrogen coupling energy station by calling the gurobi solver, thereby performing fixed capacity configuration on the distributed electric-hydrogen coupling energy station;

[0018] At this point, the site selection and capacity determination for the distributed electric-hydrogen coupled energy station to be connected to the distribution network have been completed.

[0019] According to an embodiment of the present invention, in step S1, a larger Q value represents a higher coupling strength of nodes in the region, and the Q value is between 0.3 and 0.7.

[0020] According to an embodiment of the present invention, in step S2, during the site selection process of a distributed electric-hydrogen coupled energy station:

[0021] The site selection of distributed electric-hydrogen coupled energy stations takes the minimum network loss of the distribution network in all periods as the site selection objective function, and the expression is:

[0022]

[0023] Where: T is the total duration, t is the time; n is the total number of nodes in the distribution network; is the square of the branch current between node i and node j at time t; r ij is the resistance between node i and node j; Δt is the time interval between two moments;

[0024] On the basis of the general constraints of dynamic optimal power flow, the node location constraints are introduced, and the formula is:

[0025]

[0026] Where: P in,i,t P is the interaction power between node i and the electric-hydrogen coupling energy station at time t; in,max is the upper limit of the interactive power; C f is the partition node set after partitioning; m indicates the total number of sets, P in,j,tis the interaction power between node j and the electric-hydrogen coupled energy station at time t.

[0027] According to an embodiment of the present invention, in step S2, after the distribution network partition is determined, the node set of each zone is input as a parameter into the site selection model of the distributed electric-hydrogen coupled energy station. Since the node numbers of each area are not uniform, the node numbers of each area are encoded. The encoding method is to convert the originally inconsistent node numbers into node numbers according to the number of nodes in each partition input, and the location is the node number.

[0028] According to an embodiment of the present invention, in step S2, in the fixed-capacity configuration model, photovoltaics provide energy supply for the distributed electric-hydrogen coupled energy station, and the energy provided is used for hydrogen production by the electrolyzer. At the same time, the electrolyzer undertakes the power task issued by the distribution network; the fuel cell serves as a power generation device to supply power to the distribution network, and the power supply task is issued by the distribution network; the energy storage battery serves as a supplement to the insufficient climbing ability of the electrolyzer and the fuel cell; the hydrogen storage device is used to cache the hydrogen produced by the electrolyzer.

[0029] According to an embodiment of the present invention, in step S2, the process of solving the capacity configuration model of the distributed electric-hydrogen coupled energy station is as follows:

[0030] The objective function is established by minimizing the cost of distributed electric-hydrogen coupled energy stations:

[0031]

[0032] Where: C ic,i E is the investment cost of the i-th equipment in the distributed electric-hydrogen coupling energy station; r,i Configure the capacity for the i-th device in the distributed electric-hydrogen coupling energy station; C om,i is the unit capacity operation and maintenance cost of the i-th equipment in the distributed electric-hydrogen coupling energy station; P i,t is the operating power of the i-th device in the distributed electric-hydrogen coupling energy station at time t; i is the equal payment capital recovery coefficient of the i-th equipment in the distributed electric-hydrogen coupling energy station, σ i The calculation formula is:

[0033]

[0034] In the formula: r represents the annual interest rate, y represents the useful life of the equipment;

[0035] The photovoltaic power constraints in the electric-hydrogen coupled energy station are as follows:

[0036] P pv,t =P pvr,t +P pvl,t

[0037] 0≤P pvl,z ≤βPpv,z

[0038] Where: P pv,t is the actual photovoltaic power generation power at time t; P pvr,t P is the photovoltaic power used for hydrogen production in electrolyzers and grid-connected power at selected nodes at time t; pvl,t is the abandoned power at time t; P pvl,z is the total photovoltaic power abandonment; P pv,z is the total photovoltaic power generation; β is the photovoltaic power abandonment coefficient;

[0039] The start-stop and ramp-up constraints of the electrolyzer are:

[0040]

[0041] ΔP opel,min ≤P opel,t -P opel,t-1 ≤ΔP opel,max

[0042] Where P opel,t is the operating power of the electrolytic cell at time t, P opel,t-1 is the operating power of the electrolytic cell at time t-1, P opel,min With P opel,max are the minimum and maximum power of the electrolyzer respectively; ΔP opel,min With ΔP opel,max are the climbing and descending powers of the electrolyzer respectively; γ el is a variable from 0 to 1;

[0043] The fuel cell start-stop and hill climbing constraints are:

[0044]

[0045] ΔP opfc,min ≤P opfc,t -P opfc,t-1 ≤ΔP opfc,max

[0046] γ el +γ fc ≤1

[0047] Where: P opfc,t is the operating power of the fuel cell at time t, P opfc,t-1 is the operating power of the fuel cell at time t-1, P opfc,min With P opfc,max are the minimum and maximum power of the fuel cell respectively; ΔP opfc,min and ΔP opfc,max are the climbing and downhill powers of the fuel cell respectively; γ fc is a variable from 0 to 1;

[0048] Energy storage batteries are used as a supplement to the insufficient power ramp of electrolyzers and fuel cells. The expressions of their state of charge and charge and discharge power are as follows:

[0049]

[0050] Where: SOC t is the state of charge of the energy storage battery at time t, SOC t+1 is the state of charge of the energy storage battery at time t+1; η c is the charging efficiency of the energy storage battery; η f is the discharge efficiency of the energy storage battery; Pess c,t and Pess f,t are the charging power and discharging power of the energy storage battery respectively; Q ess is the rated capacity of the energy storage battery;

[0051] The energy storage battery charging and discharging constraints and charge state constraints are:

[0052]

[0053] SOC min ≤SOC t ≤SOC max

[0054] Where: Pess max SOC is the charging and discharging power limit of the energy storage battery; min With SOC max They are the upper and lower limits of the state of charge of the energy storage battery respectively;

[0055] The hydrogen storage capacity of the hydrogen storage equipment is:

[0056]

[0057] In the formula: LOH t is the hydrogen storage capacity of the hydrogen storage equipment at time t; LOH t+1 is the hydrogen storage capacity of the hydrogen storage equipment at time t+1; and are the hydrogen production of the electrolyzer and the hydrogen consumption of the fuel cell; Q load,t is the amount of hydrogen consumed by the fuel cell vehicle at time t;

[0058] The hydrogen storage capacity constraints and gas inlet and outlet constraints are as follows:

[0059] LOH min ≤LOH t ≤LOH max

[0060] In the formula: LOH min With LOH max are the maximum and minimum values ​​of hydrogen storage capacity of the hydrogen storage equipment respectively;

[0061] The power balance constraint expression within the electric-hydrogen coupled energy station is:

[0062] P in,t +P pv,t +P opfc,t +Pess f,t =P opel,t +Pess c,t

[0063] Where: P in,t is the access node interaction power of the distributed electric-hydrogen coupled energy station at time t;

[0064] After the site selection of the distributed electric-hydrogen coupled energy station is completed, the installation location and interactive power of the electric-hydrogen coupled energy station are input as parameters into the fixed capacity configuration model. The capacity configuration objective function is solved by calling the gurobi solver, and finally the optimal capacity of each equipment configuration in the distributed electric-hydrogen coupled energy station is solved.

[0065] According to a second aspect of the present invention, a site selection and capacity determination system for connecting a distributed electric-hydrogen coupled energy station to a distribution network is provided, comprising: a computer-readable storage medium and a processor;

[0066] The computer-readable storage medium is used to store executable instructions;

[0067] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the steps of the site selection and capacity determination method for connecting the distributed electric-hydrogen coupled energy station to the distribution network.

[0068] Through the above design scheme, the present invention can bring the following beneficial effects: the site selection and capacity determination method and system for the distributed hydrogen-electric coupled energy station connected to the distribution network proposed in the present invention provide a configuration scheme for the hydrogen-electric coupled energy station to be planned in the distribution network, reduce the burden on the distribution network, and reduce the network loss of the distribution network. The method has different degrees of improvement in the stability of the distribution network voltage, the reduction of network loss, and the reduction of the cost of the hydrogen-electric coupled energy station. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings herein are used to provide a further understanding of the present invention and constitute a part of the present application. The exemplary embodiments of the present invention and their descriptions are used to understand the present invention and do not constitute improper limitations of the present invention. In the accompanying drawings:

[0070] Figure 1 It is a system diagram of the distributed electric-hydrogen coupled energy station connected to the distribution network;

[0071] Figure 2 It is the adjacency list update method diagram;

[0072] Figure 3It is the encoding method diagram;

[0073] Figure 4 It is a schematic diagram of distribution network partitioning;

[0074] Figure 5 It is the interactive power diagram of each partition node;

[0075] Figure 6 It is the power diagram of the equipment of the electric-hydrogen coupled energy station in Zone 1;

[0076] Figure 7 It is the power diagram of the equipment of the electric-hydrogen coupled energy station in zone 2;

[0077] Figure 8 It is the power diagram of the equipment of the Zone 3 Electric-Hydrogen Coupled Energy Station;

[0078] Fig. 9 It is the power diagram of the equipment of the Zone 4 Electric-Hydrogen Coupled Energy Station;

[0079] Fig.10 It is the power diagram of the equipment of the electric-hydrogen coupled energy station in Zone 5;

[0080] Fig.11 It is the state of charge and hydrogen storage level diagram of the energy storage battery in zone 1;

[0081] Fig.12 It is the state of charge and hydrogen storage level diagram of the energy storage battery in zone 2;

[0082] Fig.13 It is the state of charge and hydrogen storage level diagram of the energy storage battery in zone 3;

[0083] Fig.14 It is the state of charge and hydrogen storage level diagram of the energy storage battery in zone 4;

[0084] Fig.15 It is the state of charge and hydrogen storage level diagram of the energy storage battery in zone 5;

[0085] Fig.16 It is a flow chart for solving the zoning, site selection and capacity determination of an electric-hydrogen coupled energy station. DETAILED DESCRIPTION

[0086] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention that does not depart from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0087] The present invention provides a method for site selection and capacity determination of a distributed electric-hydrogen coupled energy station connected to a distribution network, including: proposing a planning strategy of zoning-site selection-capacity determination, and constructing a site selection model and a capacity determination configuration model for a distributed electric-hydrogen coupled energy station. The solution is obtained by using a discrete particle swarm algorithm based on a mutation strategy and a commercial solver gurobi.

[0088] As a "power source" and "load", the electric-hydrogen coupling energy station can both transmit power to the distribution network and absorb power from the distribution network. At the same time, the electric-hydrogen coupling energy station must also meet the hydrogen load within the zone. The electric-hydrogen coupling energy station is composed of new energy (photovoltaic), energy storage batteries, electrolyzers, fuel cells and hydrogen storage equipment.

[0089] like Figures 1 to 16 As shown, the site selection and capacity determination method for connecting a distributed electric-hydrogen coupled energy station to a distribution network includes the following steps:

[0090] Step S1: First, a weighted partition modularity index of the distribution network with improved edge weights is established at the partition level; the distribution network flow data is input, and the distribution network is partitioned based on the weighted partition modularity index of the distribution network with improved edge weights and the disconnection of the edges between the distribution network nodes by introducing the adjacency table, thereby forming independent areas, and a discrete particle swarm algorithm based on a mutation strategy is used to solve the problem, and a node set of the partition is obtained;

[0091] Step S2: Encode the nodes of the partitions, establish the site selection model and fixed capacity configuration model of the distributed electric-hydrogen coupling energy station. At the site selection level, the site selection model is based on the dynamic optimal power flow, and the minimum distribution network loss is used as the site selection objective function. The site selection objective function calculates the dynamic optimal power flow by calling the gurobi solver to solve the installation location of the distributed electric-hydrogen coupling energy station and the node interaction power, and obtain the site selection location and node interaction power of each partition; plan the access location of the distributed electric-hydrogen coupling energy station;

[0092] At the capacity level, based on site selection planning, the photovoltaic power constraints, electrolyzer start-stop and ramp constraints, fuel cell start-stop and ramp constraints, energy storage battery charge and discharge constraints and charge state constraints, hydrogen storage capacity constraints and inlet and outlet gas constraints, and power balance constraints in the electric-hydrogen coupled energy station are considered. The capacity configuration objective function is to minimize economic cost. The capacity configuration objective function solves the optimal capacity of each device in the distributed electric-hydrogen coupled energy station by calling the gurobi solver, thereby performing fixed capacity configuration on the distributed electric-hydrogen coupled energy station.

[0093] In step S1, when the weighted network modularity index applicable to the electrical field is adopted, the electrical distance is dynamically corrected due to the time-varying nature of the electrical distance caused by the output of new energy sources. At the same time, the adjacency table is introduced to disconnect the edges between the nodes of the distribution network, such as Figure 2As shown, independent regions are formed.

[0094] The voltage change matrix of the distribution network is:

[0095]

[0096] Where: S P is the active voltage sensitivity matrix, S Q is the reactive voltage sensitivity matrix, ΔP and ΔQ are the active and reactive changes.

[0097] Active and reactive power are injected at node j, and the sensitivity of active and reactive power to the electrical distance of branch ij is:

[0098]

[0099] In the formula, and are respectively the active and reactive power electrical distance sensitivity to branch ij, i=1,2,...,n,j=1,2,…,n,i≠j, or The larger the value, the smaller the influence of node j on node i, that is, the farther the electrical distance between the two nodes is, the greater the value of S P,jj is the active voltage sensitivity of node j to node j, S P,ij is the active voltage sensitivity of node i to node j, S Q,jj is the reactive voltage sensitivity of node j to node j, S Q,ij is the reactive voltage sensitivity of node i to node j.

[0100] From a static perspective, the electrical distance between two nodes is not only related to their own active and reactive power, but also to the active and reactive power of other nodes. and The electrical distance between nodes i and j is not proportional, so it can be expressed as:

[0101]

[0102] Where: τ is the influence coefficient of active power on voltage, is the active electrical distance sensitivity between node i and node k; is the active electrical distance sensitivity between node j and node k, is the reactive electrical distance sensitivity between node i and node k, is the reactive electrical distance sensitivity between node j and node k.

[0103] From a dynamic perspective, the time-varying nature of the distribution network load and renewable energy output will cause the time-varying nature of the electrical distance. Therefore, the present invention dynamically corrects the electrical distance as follows:

[0104]

[0105] Where: L ij,t is the electrical distance between node i and node j at time t, is the electrical distance between nodes i and j after dynamic correction, and T is the total time.

[0106] Therefore, the improved distribution network edge weights of nodes i and j are:

[0107]

[0108] maxL is the maximum value of all node electrical distances.

[0109] The modularity index of the weighted partition of the distribution network with improved edge weights is:

[0110]

[0111] in,

[0112]

[0113] Where: A ij is the improved distribution network edge weight between node i and node j, and node i and node j are not connected. ij =0; δ(i,j) is the partition coefficient, if node i and node j are in the same partition, δ(i,j)=1, otherwise δ(i,j)=0.

[0114] Q reflects the scale of the partition and the coupling strength of the nodes within the partition. The larger the Q value, the higher the structural strength within the partition and the higher the coupling strength of the nodes within the partition. Usually, the Q value is between 0.3 and 0.7.

[0115] The adjacency table is introduced to disconnect the edges between distribution network nodes, such as Figure 2 As shown, independent regions are formed. Discrete particle swarm algorithm (DPSO) is used to solve the problem, and the decision variables are the rows of the adjacency list. Since the discrete particle swarm algorithm cannot determine the number of partitions, a decision variable is introduced in DPSO to determine the number of partitions. The discrete particle swarm algorithm is prone to fall into local optimal solutions. Therefore, in order to improve the global optimization ability, the decision variables with adjacency table rows are mutated. The mutation operation is as follows:

[0116] x l+1 =x l +c×rand (8)

[0117] Where: x lrepresents the lth iteration; c represents the mutation factor, and its value range is the minimum definition domain in this iteration population. Since the decision variable is an integer, it is rounded after the mutation operation is completed. rand is a random function used to generate random numbers;

[0118] The mutation rate calculation formula is as follows:

[0119]

[0120] Where p m is the mutation rate, p m,min is the minimum mutation rate; p m,max is the maximum mutation rate; l represents the current number of iterations; N is the maximum number of iterations.

[0121] In step S2, the traditional static optimal power flow is difficult to meet the needs of multi-period optimization of distributed electric-hydrogen coupled energy stations. Therefore, a dynamic optimal power flow model based on the branch power flow method is used to select sites within the area. At the same time, node site selection constraints are introduced based on the general constraints of the dynamic optimal power flow.

[0122] The site selection process of distributed electric-hydrogen coupled energy station based on dynamic optimal power flow is as follows:

[0123] The site selection of distributed electric-hydrogen coupled energy stations aims to minimize the network loss of the distribution network during the entire period, and its expression is:

[0124]

[0125] Where: T is the total duration, t is the time; n is the total number of nodes in the distribution network; is the square of the branch current between node i and node j at time t; r ij is the resistance between node i and node j; Δt is the time interval between two moments;

[0126] On the basis of the general constraints of dynamic optimal power flow, the node location constraints are introduced, and the formula is:

[0127]

[0128] Where: P in,i,t P is the interaction power between node i and the electric-hydrogen coupling energy station at time t; in,max is the upper limit of the interactive power; C f is the partition node set after partitioning; m indicates the total number of sets, P in,j,t is the interaction power between node j and the electric-hydrogen coupled energy station at time t.

[0129] After the distribution network is divided into zones, the node sets of each zone are input as parameters into the site selection layer of the distributed electric-hydrogen coupled energy station. Since the node numbers of each zone are not uniform, the node numbers of each zone are encoded. The encoding method is as follows: Figure 3 As shown in the figure. The node numbers of the distribution network in the partition are also integers, so the discrete particle swarm algorithm based on the mutation strategy is adopted. The site selection objective function performs dynamic optimal power flow calculation by calling the gurobi solver, and finally solves the installation location of the electric-hydrogen coupling energy station and the node interaction power.

[0130] In the capacity configuration model, photovoltaics provide energy supply for the electric-hydrogen coupling energy station. The energy provided is used to produce hydrogen in the electrolyzer. At the same time, the electrolyzer also has to bear the power task issued by the distribution network. Fuel cells, as power generation equipment, supply power to the grid, and the power supply task is issued by the distribution network. Energy storage batteries supplement the insufficient climbing capacity of both electrolyzers and fuel cells. Hydrogen storage equipment serves as a buffer for the hydrogen produced by the electrolyzer.

[0131] The solution process of the station capacity configuration model of the distributed electric-hydrogen coupled energy station taking into account the hydrogen production and fuel cell ramp constraints is as follows:

[0132] The objective function is established by minimizing the cost of distributed electric-hydrogen coupled energy stations:

[0133]

[0134] Where: C ic,i E is the investment cost of the i-th equipment in the distributed electric-hydrogen coupling energy station; r,i Configure the capacity for the i-th device in the distributed electric-hydrogen coupling energy station; C om,i is the unit capacity operation and maintenance cost of the i-th equipment in the distributed electric-hydrogen coupling energy station; P i,t is the operating power of the i-th device in the distributed electric-hydrogen coupling energy station at time t; i is the equal payment capital recovery coefficient of the i-th equipment in the distributed electric-hydrogen coupling energy station, σ i The calculation formula is:

[0135]

[0136] In the formula: r represents the annual interest rate, y represents the useful life of the equipment;

[0137] The photovoltaic power constraints in the electric-hydrogen coupled energy station are as follows:

[0138] P pv,t =P pvr,t +P pvl,t (14)

[0139] 0≤P pvl,z ≤βP pv,z (15)

[0140] Where: P pv,t is the actual photovoltaic power generation power at time t; P pvr,t P is the photovoltaic power used for hydrogen production in electrolyzers and grid-connected power at selected nodes at time t; pvl,t is the abandoned power at time t; P pvl,z is the total photovoltaic power abandonment; P pv,z is the total photovoltaic power generation; β is the photovoltaic power abandonment coefficient;

[0141] The start-stop and ramp-up constraints of the electrolyzer are:

[0142]

[0143] ΔP opel,min ≤P opel,t -P opel,t-1 ≤ΔP opel,max (17)

[0144] Where P opel,t is the operating power of the electrolytic cell at time t, P opel,t-1 is the operating power of the electrolytic cell at time t-1, P opel,min With P opel,max are the minimum and maximum power of the electrolyzer respectively; ΔP opel,min With ΔP opel,max are the climbing and descending powers of the electrolyzer respectively; γ el is a variable from 0 to 1;

[0145] The fuel cell start-stop and hill climbing constraints are:

[0146]

[0147] ΔP opfc,min ≤P opfc,t -P opfc,t-1 ≤ΔP opfc,max (19)

[0148] γ el +γ fc ≤1 (20)

[0149] Where: P opfc,t is the operating power of the fuel cell at time t, P opfc,t-1 is the operating power of the fuel cell at time t-1, P opfc,min With P opfc,max are the minimum and maximum power of the fuel cell respectively; ΔP opfc,min and ΔP opfc,max are the climbing and downhill powers of the fuel cell respectively; γ fc is a variable from 0 to 1;

[0150] Energy storage batteries are used as a supplement to the insufficient power ramp of electrolyzers and fuel cells. The expressions of their state of charge and charge and discharge power are as follows:

[0151]

[0152] Where: SOC t is the state of charge of the energy storage battery at time t, SOC t+1 is the state of charge of the energy storage battery at time t+1; η c is the charging efficiency of the energy storage battery; η f is the discharge efficiency of the energy storage battery; Pess c,t and Pess f,t are the charging power and discharging power of the energy storage battery respectively; Q ess is the rated capacity of the energy storage battery;

[0153] The energy storage battery charging and discharging constraints and charge state constraints are:

[0154]

[0155] SOC min ≤SOC t ≤SOC max (twenty three)

[0156] Where: Pess max SOC is the charging and discharging power limit of the energy storage battery; min With SOC max They are the upper and lower limits of the state of charge of the energy storage battery respectively;

[0157] The hydrogen storage capacity of the hydrogen storage equipment is:

[0158]

[0159] In the formula: LOH t is the hydrogen storage capacity of the hydrogen storage equipment at time t; LOH t+1 is the hydrogen storage capacity of the hydrogen storage equipment at time t+1; and are the hydrogen production of the electrolyzer and the hydrogen consumption of the fuel cell; Q load,t is the amount of hydrogen consumed by the fuel cell vehicle at time t;

[0160] The hydrogen storage capacity constraints and gas inlet and outlet constraints are as follows:

[0161] LOH min ≤LOH t ≤LOH max (25)

[0162] In the formula: LOH min With LOH maxare the maximum and minimum values ​​of hydrogen storage capacity of the hydrogen storage equipment respectively;

[0163] The power balance constraint expression within the electric-hydrogen coupled energy station is:

[0164] P in,t +P pv,t +P opfc,t +Pess f,t =P opel,t +Pess c,t (26)

[0165] Where: P in,t is the access node interaction power of the distributed electric-hydrogen coupled energy station at time t;

[0166] After the site selection of the distributed electric-hydrogen coupled energy station is completed, the installation location and interactive power of the electric-hydrogen coupled energy station are input as parameters into the fixed capacity configuration model. The capacity configuration objective function is solved by calling the gurobi solver, and finally the optimal capacity of each equipment configuration in the distributed electric-hydrogen coupled energy station is solved.

[0167] A site selection and capacity determination system for connecting a distributed electric-hydrogen coupled energy station to a distribution network, comprising: a computer-readable storage medium and a processor;

[0168] The computer-readable storage medium is used to store executable instructions;

[0169] The processor is used to read the executable instructions stored in the computer-readable storage medium to execute the steps of the above-mentioned method for site selection and capacity determination for connecting the distributed electric-hydrogen coupled energy station to the distribution network.

[0170] In order to verify the effectiveness of the proposed method, the present invention modifies the load and increases the photovoltaic access based on the IEEE33 node system. The load is selected from the real load in Jilin. There are four types of loads, namely residential load, commercial load, industrial load and mixed load. The photovoltaic capacity is 600kW. The time step of the example is set to 5min. Algorithm settings: the number of populations is 100; the maximum number of iterations Iter = 400; p m,min Set to 0.3, p m,max Set to 0.6. The basic parameters of the electric-hydrogen coupling energy station are shown in Table 1-2.

[0171] Table 1 Capacity configuration equipment parameters

[0172]

[0173]

[0174] P in Table 1 elN is the rated capacity of the electrolytic cell; P fcN is the rated capacity of the fuel cell; Cloh is the rated capacity of the hydrogen storage equipment.

[0175] Table 2 Capacity configuration economic parameters

[0176]

[0177] By partitioning the 33-node distribution system, Q is calculated to be 0.4930. The partition results are as follows Figure 4 As shown. Figure 4 It can be seen that the distribution network is divided into five areas by the method of the present invention. Each area has no independent nodes. It can be seen from the divided areas that there is no new energy access in Partition 1. Nodes 1 and 2 on the trunk branch are divided into the same area. The entire branch connected to node 1 is divided into Partition 1, and the entire branch connected to node 2 is divided into Partition 1. Partition 2 has new energy access, and all access nodes are located on the trunk branch. The branch connected to node 5 is separated. Partition 3 has new energy access, and all access nodes are located on the trunk branch. Partition 4 is consistent with Partition 3. Partition 5 access nodes are all branches except node 5.

[0178] The site selection and partition node interaction power are shown in Table 3 and Figure 5 As shown in Table 3 and Figure 5 It can be seen that the site selection of the electric-hydrogen coupling energy station in zone 1 is at node 2. Since there is no new energy access in zone 1, the interactive power of zone 1 is positive. The site selection of the electric-hydrogen coupling energy station in zone 2 is node 4. There is new energy access in zone 2. Therefore, between 7-16 o'clock every day, due to the photovoltaic output, the distribution network flow changes, and the original load node is converted into a power node to transmit electricity to the distribution network, resulting in power reverse transmission. Therefore, the interactive power of zone 2 is negative between 7-16 o'clock every day. The situation of zones 3-5 is similar to that of zone 2. The daily situation of each zone is similar, so it will not be explained.

[0179] Table 3

[0180] Partition number 1 2 3 4 5 Node Number 2 4 10 15 31

[0181] The capacity and operation status of each equipment in the electric-hydrogen coupled energy station are solved, and the results are shown in Table 4 and Figures 6 to 15 As shown. It can be seen from Table 4 that in the capacity configuration of the electric-hydrogen coupled energy station, photovoltaic equipment is responsible for the main energy output in each zone. Since the electric-hydrogen coupled energy station in each zone needs to bear the hydrogenation demand of downstream hydrogen vehicles, that is, the hydrogen load, photovoltaic equipment is installed in large quantities. The electrolyzer equipment not only needs to provide hydrogen for the hydrogen load, but also needs to bear the negative interaction power under the zone node. Fuel cells are used as discharge equipment to bear the positive interaction power under the zone node. The capacity of fuel cells in each zone is related to the negative power of each zone. Energy storage batteries are used to supplement the climbing capacity of electrolyzers and fuel cells when they are insufficient, so the size of energy storage batteries is related to the capacity of these two devices.

[0182] Table 4

[0183] Partition number 1 2 3 4 5 Photovoltaic equipment / Kw 3358.13 2979.06 2799.40 2794.44 2982.65 Electrolyzer / kW 2115.29 2350.70 2198.02 2250.62 2348.58 Fuel cell / kW 115.06 242.86 140.03 147.53 255.09 Electrochemical energy storage / kWh 208.49 72.40 207.95 141.63 211.02 Hydrogen storage equipment / kg 208.02 222.68 207.59 210.27 226.98 Abandoned light rate / % 9.82 4.56 7.17 5.12 4.83 Total cost / 10,000 yuan 136.99 141.57 130.97 132.16 141.83

[0184] Depend on Figures 6 to 10 It can be seen that in zone 1, since the interactive power is positive, the fuel cell outputs. When the fuel cell is running, the battery discharge participates in the supplement when its power climbing is insufficient. From 7 to 16 o'clock every day, the photovoltaic power in the station starts to output. At this time, a small part of the electricity generated by photovoltaic power is used to supplement the power demand of the distribution network, and most of it is used for hydrogen production in the electrolyzer. Therefore, during this period, the power of the fuel cell decreases. During this period, the electrolyzer and the energy storage battery coordinate to absorb electricity, and the energy storage battery absorbs the electricity that the electrolyzer cannot absorb. In zone 2, the interactive power is negative from 7 to 16 o'clock. At the same time, the photovoltaic power in the station generates electricity. The electrolyzer and the energy storage battery coordinate to absorb the negative interactive power and the energy generated by photovoltaic power. Zones 3-5 are similar to it. The daily situation of each zone is similar and will not be explained. The load energy storage battery participates in the regulation when the electrolyzer and the fuel cell start running, as a supplement when the climbing capacity is insufficient. Since the electrolyzer also bears the hydrogen load supply demand, the energy storage battery absorbs higher power when participating in the electrolyzer climbing process. The amount of virtual load that the fuel cell needs to bear, when the energy storage battery participates, it bears more and more frequent low power. This shows that the energy storage battery plays a regulating role.

[0185] Depend on Figures 11 to 15 It can be seen that the power of the energy storage battery is affected by the electrolyzer and the fuel cell, which in turn affects the state of charge of the energy storage battery. The hydrogen storage level is affected by the hydrogen load. The hydrogen storage level of each zone fluctuates significantly every day, reflecting the rationality of the equipment configuration in each zone. It can be seen that the hydrogen system of the electric-hydrogen coupled energy station can not only provide hydrogen for the downstream hydrogen load, but also reduce the burden on the distribution network and reduce the network loss of the distribution network.

[0186] By verifying the optimized configuration of the distributed electric-hydrogen coupled energy station on the improved IEEE33 node system, it can be seen that in the distribution network zoning stage, the proposed strategy of the present invention not only improves the strength of the zoning structure, but also, at the zoning-based site selection layer, the electric-hydrogen coupled energy station can alleviate the impact of new energy on the distribution network and reduce the network loss of the distribution network. At the capacity configuration layer based on zoning and site selection, the station provides support for the hydrogen load in the zone while ensuring the minimum cost and the optimal coordinated operation of each equipment.

Claims

1. A method for site selection and capacity determination of a distributed electric-hydrogen coupled energy station connected to a distribution network, characterized in that: include: Step S1, inputting distribution network flow data, partitioning the distribution network based on the weighted partition modularity index of the distribution network with improved edge weights and disconnecting the edges between the distribution network nodes by introducing an adjacency table, thereby forming independent areas, and solving the problem using a discrete particle swarm algorithm based on a mutation strategy to obtain a partitioned node set; The weighted partition modularity index of the distribution network with improved edge weights is: in, A ij The improved distribution network edge weights for nodes i and j, maxL is the maximum value of all node electrical distances, L ij,t is the electrical distance between node i and node j at time t, is the electrical distance between nodes i and j after dynamic correction, T is the total time, when nodes i and j are not connected, A ij =0, δ(i,j) is the partition coefficient, δ(i,j)=1 if node i and node j are in the same partition, otherwise δ(i,j)=0; In the process of solving the problem using discrete particle swarm algorithm based on mutation strategy: A decision variable is introduced into the discrete particle swarm algorithm to determine the number of partitions, and the rows of the adjacency table are used as the decision variable, and the decision variable is mutated. The mutation operation is as follows: x l+1 =x l +c×rand Where: x l represents the lth iteration; c represents the mutation factor, and its value range is the minimum domain in the population of this iteration. Since the decision variable is an integer, it is rounded after the mutation operation is completed. rand is a random function used to generate random numbers; The mutation rate calculation formula is as follows: Where p m is the mutation rate, p m,min is the minimum mutation rate; p m,max is the maximum mutation rate; l represents the current number of iterations; N is the maximum number of iterations; Step S2, encode the nodes of the partitions, establish the site selection model and fixed capacity configuration model of the distributed electric-hydrogen coupling energy station, the site selection model is based on the dynamic optimal power flow, and on the basis of the general constraints of the dynamic optimal power flow, introduces the node site selection constraints, and takes the minimum network loss of the distribution network in the whole period as the site selection objective function. The site selection objective function calculates the dynamic optimal power flow by calling the gurobi solver, solves the installation location of the distributed electric-hydrogen coupling energy station and the node interaction power, and obtains the site selection location and node interaction power of each partition; the fixed capacity configuration model, based on the site selection planning, considers the photovoltaic power constraints, electrolyzer start-stop and climbing constraints, fuel cell start-stop and climbing constraints, energy storage battery charging and discharging constraints and charge state constraints, hydrogen storage capacity constraints and inlet and outlet gas constraints and power balance constraints in the electric-hydrogen coupling energy station, takes the lowest economic cost as the capacity configuration objective function, and the capacity configuration objective function solves the optimal capacity of each device in the distributed electric-hydrogen coupling energy station by calling the gurobi solver, thereby performing fixed capacity configuration on the distributed electric-hydrogen coupling energy station; At this point, the site selection and capacity determination for the distributed electric-hydrogen coupled energy station to be connected to the distribution network has been completed; In step S2, during the site selection process of the distributed electric-hydrogen coupled energy station: The site selection of distributed electric-hydrogen coupled energy stations takes the minimum network loss of the distribution network in all periods as the site selection objective function, and the expression is: Where: T is the total duration, t is the time; n is the total number of nodes in the distribution network; is the square of the branch current between node i and node j at time t; r ij is the resistance between node i and node j; Δt is the time interval between two moments; On the basis of the general constraints of dynamic optimal power flow, the node location constraints are introduced, and the formula is: Where: P in,i,t P is the interaction power between node i and the electric-hydrogen coupling energy station at time t; in,max is the upper limit of the interactive power; C f is the partition node set after partitioning; m indicates the total number of sets, P in,j,t is the interaction power between node j and the electric-hydrogen coupling energy station at time t; In step S2, the process of solving the fixed capacity configuration model of the distributed electric-hydrogen coupled energy station is as follows: The objective function is established by minimizing the cost of distributed electric-hydrogen coupled energy stations: Where: C ic,i E is the investment cost of the i-th equipment in the distributed electric-hydrogen coupling energy station; r,i Configure the capacity for the i-th device in the distributed electric-hydrogen coupling energy station; C om,i is the unit capacity operation and maintenance cost of the i-th equipment in the distributed electric-hydrogen coupling energy station; P i,t is the operating power of the i-th device in the distributed electric-hydrogen coupling energy station at time t; i is the equal payment capital recovery coefficient of the i-th equipment in the distributed electric-hydrogen coupling energy station, σ i The calculation formula is: In the formula: r represents the annual interest rate, y represents the useful life of the equipment; The photovoltaic power constraints in the electric-hydrogen coupled energy station are as follows: P pv,t =P pvr,t +P pvl,t 0≤P pvl,z ≤βP pv,z Where: P pv,t is the actual photovoltaic power generation power at time t; P pvr,t P is the photovoltaic power used for hydrogen production in electrolyzers and grid-connected power at selected nodes at time t; pvl,t is the abandoned power at time t; P pvl,z is the total photovoltaic power abandonment; P pv,z is the total photovoltaic power generation; β is the photovoltaic power abandonment coefficient; The start-stop and ramp-up constraints of the electrolyzer are: ΔP opel,min ≤P opel,t -P opel,t-1 ≤ΔP opel,max Where P opel,t is the operating power of the electrolytic cell at time t, P opel,t-1 is the operating power of the electrolytic cell at time t-1, P opel,min With P opel,max are the minimum and maximum power of the electrolyzer respectively; ΔP opel,min With ΔP opel,max are the climbing and descending powers of the electrolyzer respectively; γ el is a variable from 0 to 1; The fuel cell start-stop and hill climbing constraints are: ΔP opfc,min ≤P opfc,t -P opfc,t-1 ≤ΔP opfc,max c el +g fc ≤1 Where: P opfc,t is the operating power of the fuel cell at time t, P opfc,t-1 is the operating power of the fuel cell at time t-1, P opfc,min With P cpfc,max are the minimum and maximum power of the fuel cell respectively; ΔP opfc,min and ΔP opfc,max are the climbing and downhill powers of the fuel cell respectively; γ fc is a variable from 0 to 1; Energy storage batteries are used as a supplement to the insufficient power ramp of electrolyzers and fuel cells. The expressions of their state of charge and charge and discharge power are as follows: Where: SOC t is the state of charge of the energy storage battery at time t, SOC t+1 is the state of charge of the energy storage battery at time t+1; η c is the charging efficiency of the energy storage battery; η f is the discharge efficiency of the energy storage battery; Pess c,t and Pess f,t are the charging power and discharging power of the energy storage battery respectively; Q ess is the rated capacity of the energy storage battery; The energy storage battery charging and discharging constraints and charge state constraints are: SOC min ≤SOC t ≤SOC max Where: Pess max SOC is the charging and discharging power limit of the energy storage battery; min With SOC max They are the upper and lower limits of the state of charge of the energy storage battery respectively; The hydrogen storage capacity of the hydrogen storage equipment is: In the formula: LOH t is the hydrogen storage capacity of the hydrogen storage equipment at time t; LOH t+1 is the hydrogen storage capacity of the hydrogen storage equipment at time t+1; and are the hydrogen production of the electrolyzer and the hydrogen consumption of the fuel cell; Q load,t is the amount of hydrogen consumed by the fuel cell vehicle at time t; The hydrogen storage capacity constraints and gas inlet and outlet constraints are as follows: LOH min ≤LOH t ≤LOH max In the formula: LOH min With LOH max are the maximum and minimum values ​​of hydrogen storage capacity of the hydrogen storage equipment respectively; The power balance constraint expression within the electric-hydrogen coupled energy station is: P in,t +P pv,t +P opfc,t +Pess f,t =P opel,t +Pess c,t Where: P in,t is the access node interaction power of the distributed electric-hydrogen coupled energy station at time t; After the site selection of the distributed electric-hydrogen coupled energy station is completed, the installation location and interactive power of the electric-hydrogen coupled energy station are input as parameters into the fixed capacity configuration model. The capacity configuration objective function is solved by calling the gurobi solver, and finally the optimal capacity of each equipment configuration in the distributed electric-hydrogen coupled energy station is solved.

2. The method for site selection and capacity determination of a distributed electric-hydrogen coupled energy station connected to a distribution network according to claim 1, characterized in that: In step S1, a larger Q value indicates a higher coupling strength of nodes in the region, and the Q value is between 0.3 and 0.

7.

3. The method for site selection and capacity determination of a distributed electric-hydrogen coupled energy station connected to a distribution network according to claim 1, characterized in that: In step S2, after the distribution network partition is determined, the node set of each zone is input as a parameter into the site selection model of the distributed electric-hydrogen coupled energy station. Since the node numbers of each area are not uniform, the node numbers of each area are encoded. The encoding method is to convert the originally inconsistent node numbers into node numbers according to the number of nodes in each partition input, and the location is the node number.

4. The method for site selection and capacity determination of a distributed electric-hydrogen coupled energy station connected to a distribution network according to claim 1, characterized in that: In step S2, in the fixed capacity configuration model, photovoltaics provide energy supply for the distributed electric-hydrogen coupled energy station, and the energy provided is used for hydrogen production by the electrolyzer. At the same time, the electrolyzer undertakes the power task issued by the distribution network; the fuel cell serves as a power generation device to supply power to the distribution network, and the power supply task is issued by the distribution network; the energy storage battery supplements the insufficient climbing capacity of the electrolyzer and the fuel cell; the hydrogen storage device is used to cache the hydrogen produced by the electrolyzer.

5. A site selection and capacity determination system for connecting a distributed electric-hydrogen coupled energy station to a distribution network, characterized in that: include: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium to execute the steps of site selection and capacity determination for connecting a distributed electric-hydrogen coupled energy station to a distribution network as described in any one of claims 1 to 4.

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