A power distribution network light storage system coordination planning method based on double-layer particle swarm algorithm

Abstract

The application belongs to the field of distribution network, and particularly relates to a distribution network light storage system coordination planning method based on a double-layer particle swarm algorithm, which comprises the following steps: S1, parameter setting: inputting a distribution network structure, load parameters, constraint conditions, upper and lower layer population size, particle inner loop iteration number, maximum iteration number, initializing power flow to obtain initial branch power flow and node voltage parameters; S2, initializing and updating the upper layer particle swarm: initializing the position and speed of the particle swarm in units of partition, and particles meeting the constraint conditions are put into a target function to obtain a global optimal value, a global optimal fitness, a local optimal value and a local optimal fitness, a photovoltaic energy storage double-layer planning model construction method, the upper layer planning makes the planning capacity of the DPG and the ESS in each partition as evenly distributed as possible; the lower layer minimizes the investment planning cost on the premise of ensuring the minimum network loss, and simultaneously ensures the stability and reliability of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network, and particularly relates to a power distribution network light storage system coordinated planning method based on a double-layer particle swarm algorithm. BACKGROUND

[0002] With the rapid development of renewable energy, especially photovoltaic power generation technology, and the deepening of global consensus on carbon neutralization, photovoltaic power generation has become a key force to promote energy structure transformation and achieve green and low-carbon development. However, the large-scale access of distributed photovoltaic systems, especially combined with energy storage systems, poses new challenges to the operation and management of existing power distribution networks. These challenges mainly include but are not limited to the intermittency and uncertainty of photovoltaic power generation, the scheduling strategy of energy storage systems, and the impact of these new types of loads and power generation methods on the safe and stable operation of the power grid.

[0003] Traditional power distribution network planning and management methods are difficult to effectively cope with these new challenges, especially in ensuring network security, improving energy utilization efficiency, and reducing system operation costs. SUMMARY

[0004] The purpose of the present application is to solve the problem of mismatch between load demand and photovoltaic output time when large-scale distributed photovoltaic energy is connected to the power distribution network in the prior art, especially the difficulty of meeting the requirements of high-penetration photovoltaic grid-connected operation through traditional distributed photovoltaic planning, and to propose a power distribution network light storage system coordinated planning method based on a double-layer particle swarm algorithm.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] A power distribution network light storage system coordinated planning method based on a double-layer particle swarm algorithm, comprising the following steps:

[0007] S1, parameter setting: input power distribution network structure, load parameters, constraint conditions, upper and lower layer population size, particle inner loop iteration number, maximum iteration number, initialize power flow to obtain initial branch power flow and node voltage parameters;

[0008] S2, initialize and update the upper layer particle swarm: initialize the position and speed of the particle swarm in units of partitions, put the particles meeting the constraint conditions into the objective function to obtain the global optimal value, global optimal fitness, local optimal value and local optimal fitness, optimize the value of inertia weight using the difference between the particle and the global optimal particle, and update the speed and position of the particle;

[0009] S3, optimize the lower layer particles and update the fitness of the lower layer particles: the upper layer updated particles are used as the conditions of the lower layer to initialize the particle swarm of each partition to calculate the position and velocity of each partition particle in parallel, to determine the fitness value of the system network loss objective function, the global optimal value, the global optimal fitness, the local optimal value and the local optimal fitness, and the particles meeting the constraint conditions are connected to the power distribution network for power flow calculation to update the DPV output power and

[0010] ESS charge and discharge data connected to the objective function to obtain the fitness value of the lower layer particles;

[0011] S4, verify the convergence condition: according to the optimal power reached by the upper layer particles of the lower layer, update the global optimal value, the global optimal fitness, the local optimal value and the local optimal fitness of the upper layer particles, if the maximum iteration number is reached, take the current global optimal value and global optimal fitness as the optimization result, otherwise return to step S2 to continue iteration optimization;

[0012] In the S2, the upper layer objective function is the lowest annual comprehensive cost:

[0013] minC = C invest +C O +C buy -C s (1)

[0014] In the formula, C invest , C O , C buy , C s are investment and construction cost, operation and maintenance cost, power purchase cost and government subsidy, respectively;

[0015]

[0016] In the formula, N G is the number of partitions, r is the discount rate, which is 0.06, T DPG = 20 years, T ESS = 10 years; are the DPG unit capacity, the ESS investment and construction cost, and the unit power investment cost of the ESS, respectively;

[0017] are the DPG rated capacity of the partition g, the rated capacity and rated power of the ESS, respectively;

[0018]

[0019] In the formula, is the DG power generation of the partition g at time t, and are the charge and discharge power of the ESS of the partition g at time t, These are the unit maintenance costs of DPG and ESS, and the cost of DPG curtailment, respectively.

[0020]

[0021] In the formula, N L It is the number of link branches in the main network. It is the power connected to branch l through the main network at time t;

[0022]

[0023] In the formula, c b The government subsidy fee for each unit of electricity generated by distributed generation, η i For distributed generation efficiency.

[0024] Preferably, in S2, the upper-layer constraints are: DPG capacity and partition power output limit, power balance, main network link branch reverse transmission power constraint, inter-regional interactive branch power supply, ESS power limit, charging and discharging efficiency limit, and ESS charging state constraint.

[0025] DPG capacity and zone power output limits:

[0026]

[0027] In the formula, N G N is the number of partitions. g The number of nodes in partition g. This represents the rated capacity of the DPG in partition g. This represents the maximum capacity of the DPG installed on partition g of node i in the partition. It is the active power of the DPG in partition g at time t.

[0028] Preferred power balance:

[0029]

[0030] In the formula, N L Number of main network link branches, N SL This refers to the number of branches in the distribution network. Let be the load active power of node i in partition g at time t. This refers to the active power loss of branch l at time t, i.e., the active power difference between the sending and receiving ends of branch l.

[0031] Main network link branch reverse transmission power constraints:

[0032]

[0033] In the formula, Maximum reverse transmit power allowed for the main grid link branch l;

[0034] Inter-zone interaction branch power supply:

[0035]

[0036] where N CI is the number of inter-zone interaction branches, is the maximum power allowed for the inter-zone interaction branch l.

[0037] Preferably, the power limit of the ESS:

[0038]

[0039] where is the maximum output power of the ESS in the zone g;

[0040] Charge-discharge efficiency limit:

[0041]

[0042] where η d is the discharge efficiency, η c is the charge efficiency;

[0043] State of charge constraint of the ESS:

[0044]

[0045] where S g,t is the state of charge of the zone t at time t, S max and S min are the upper and lower limits of the state of charge, S0is the initial state of charge.

[0046] Preferably, in the step S3, the lower layer objective function is the annual minimum active power loss:

[0047]

[0048] where P CL is the grid loss.

[0049] Preferably, in the step S3, the lower layer constraint function is:

[0050] (1) Power flow limit

[0051]

[0052] where N is the number of system nodes, U i,t and U j,t are the voltage amplitudes of nodes i and j at time t, G ijand B ij denotes the admittance of the branch ij, θ ij,t is the angle time node power of the branch ij at time t;

[0053] (2) The installation capacity constraint of each node DPG in the partition

[0054]

[0055] In the formula, is the DPG capacity of node i in partition g; is the DPG planning installed capacity of partition g;

[0056] (3) DPG node installation capacity limit

[0057]

[0058] In the formula, is the maximum value of the installed DPG capacity of node i in partition g; is the DPG planning installed capacity of node i in partition g;

[0059] (4) Voltage limit in the partition

[0060]

[0061] In the formula, U g,t,i is the voltage value of node i in partition g at time t, and is the lower limit and upper limit of the voltage of node i in partition g;

[0062] (5) Line transmission power constraint in the partition

[0063]

[0064] In the formula, P g,t,l is the transmission power of branch l in partition g at time t, l is the number of branches in the partition, l∈N SL,g , N SL,g is the number of branches in the group in partition g.

[0065] Preferably, in the S4, the planning and operation evaluation index is:

[0066] (1) Self-balancing degree

[0067] Self-balancing degree S A,g The ratio of the difference between the total load and the power purchase in the planning period in the partition and the total load, the higher the self-balancing degree, the stronger the self-control ability in the partition, and the weaker the connection between the partitions;

[0068]

[0069] (2) Energy Penetration Rate

[0070] The energy penetration rate measures the ratio between the DPG output and the total load power consumption in the planning period, and the higher the ratio, the greater the photovoltaic penetration and absorption in the partition;

[0071]

[0072] (3) Capacity Penetration Rate

[0073] The capacity penetration rate S CP,g is the ratio of the maximum photovoltaic power generation output time to the maximum power consumption time in the planning period, and the higher the ratio, the greater the remaining output of the DPG, the greater the demand for the ESS, and the closer the connection between the partitions;

[0074]

[0075] (4) Power Penetration Rate

[0076] The power penetration rate S PP,g is the maximum value of the ratio of the total DPG output to the total load in the planning period, and the higher the ratio, the greater the demand for the ESS, and the smaller the ratio, the lower the absorption capacity;

[0077]

[0078] In the present application, the beneficial effects of the power distribution network photovoltaic storage system coordinated planning method based on the double-layer particle swarm algorithm are:

[0079] The present application develops a photovoltaic energy storage double-layer planning model for distributed photovoltaic energy storage, aiming at the problem of site selection and scale of distributed photovoltaic and energy storage system to minimize the total cost and loss, the upper layer target is to minimize the annual comprehensive cost of each partition, and the decision variable includes photovoltaic capacity, energy storage capacity and power, and the lower layer target is to minimize the system network loss, and the decision variable is the photovoltaic installation capacity and energy storage installation position of the node in the partition;

[0080] The photovoltaic energy storage double-layer planning model construction method of the present application, the upper layer planning makes the planning capacity of the DPG and the ESS in each partition as evenly distributed as possible; the lower layer minimizes the investment planning cost on the premise of minimizing the network loss, while ensuring the stability and reliability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0081] Figure 1 The model structure diagram of the power distribution network photovoltaic storage system coordinated planning method based on the double-layer particle swarm algorithm proposed in the present application.

[0082] Figure 2This is a flowchart of the algorithm for a coordinated planning method for a power grid photovoltaic-storage system based on a two-layer particle swarm optimization algorithm proposed in this invention.

[0083] Figure 3 This invention presents an IEEE 33 bus allocation network diagram for a coordinated planning method for a power grid photovoltaic-storage system based on a two-layer particle swarm optimization algorithm.

[0084] Figure 4 This is a typical photovoltaic output and load demand curve for a coordinated planning method for photovoltaic-storage systems in a distribution network based on a two-layer particle swarm optimization algorithm proposed in this invention.

[0085] Figure 5 This invention presents the capacity diagrams of each partition of the upper-level planning DPG and ESS for a coordinated planning method of a distribution network photovoltaic-storage system based on a two-layer particle swarm optimization algorithm.

[0086] Figure 6 This invention presents a diagram showing the system loss and power loss ratio of a coordinated planning method for a photovoltaic-storage system in a distribution network based on a two-layer particle swarm optimization algorithm.

[0087] Figure 7 The voltage amplitude diagrams under different conditions are for a coordinated planning method for a distribution network photovoltaic-storage system based on a two-layer particle swarm optimization algorithm proposed in this invention.

[0088] Figure 8 This is a graph showing the evaluation indexes for various cases of a coordinated planning method for a distribution network photovoltaic-storage system based on a two-layer particle swarm optimization algorithm proposed in this invention. Detailed Implementation

[0089] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0090] Reference Figures 1-8 A coordinated planning method for photovoltaic-storage systems in distribution networks based on a two-layer particle swarm optimization algorithm includes the following steps:

[0091] S1. Parameter settings: Input the power distribution network structure, load parameters, constraints, upper and lower layer population sizes, number of iterations in the particle loop, and maximum number of iterations. Initialize the power flow to obtain the initial branch power flow and node voltage parameters.

[0092] S2. Initialize and update the upper-level particle swarm: In units of partitions, initialize the position and velocity of the particle swarm. Particles that meet the constraints are put into the objective function to obtain the global optimum, global optimum fitness, local optimum, and local optimum fitness. Optimize the value of the inertia weight by using the difference between the particles and the global optimum particles, and update the velocity and position of the particles.

[0093] S3, optimizing the lower layer particles and updating the fitness of the lower layer particles: the upper layer updated particles are used as the conditions of the lower layer to initialize the particle swarm of each partition to calculate the position and velocity of each partition particle in parallel, to determine the fitness value of the system network loss objective function, the global optimal value, the global optimal fitness, the local optimal value and the local optimal fitness, and the particles meeting the constraint conditions are connected to the power distribution network for power flow calculation to update the DPV output power and

[0094] ESS charge and discharge data connected to the objective function to obtain the fitness value of the lower layer particles;

[0095] S4, verifying the convergence condition: updating the global optimal value, the global optimal fitness, the local optimal value and the local optimal fitness of the upper layer particles according to the optimal power reached by the lower layer particles, if the maximum number of iterations is reached, taking the current global optimal value and global optimal fitness as the optimization result, otherwise returning to step S2 to continue iterative optimization;

[0096] In the S2, the upper layer objective function is the lowest annual comprehensive cost:

[0097] minC=C invest +C O +C buy -C s (1)

[0098] In the formula, C invest , C O , C buy , C s are the investment and construction cost, the operation and maintenance cost, the electricity purchase cost and the government subsidy, respectively;

[0099]

[0100] In the formula, N G is the number of partitions, r is the discount rate, taken as 0.06, T DPG = 20 years, T ESS = 10 years; are the DPG unit capacity, the ESS investment and construction cost, and the unit power investment cost of the ESS, respectively;

[0101] are the DPG rated capacity of the partition g and the rated capacity and rated power of the ESS, respectively;

[0102]

[0103] In the formula, is the DG power generation of the partition g at time t, and The charging and discharging power of the ESS of the subarea g at time t, The DPG unit maintenance cost and the DPG abandoned power cost, respectively;

[0104]

[0105] In the formula, N L is the number of link branches in the main network, is the power connected to branch l through the main network at time t;

[0106]

[0107] In the formula, c b is the government subsidy fee of the distributed power generation unit power generation, η i is the distributed power generation efficiency.

[0108] In S2, the upper constraint conditions are: DPG capacity and subarea power output limit, power balance, main network link branch reverse transmission power constraint, subarea interaction branch power supply, ESS power limit, charging and discharging efficiency limit, and ESS state of charge constraint;

[0109] DPG capacity and subarea power output limit:

[0110]

[0111] In the formula, N G is the number of subareas, N g is the number of nodes in the subarea g, is the rated capacity of the DPG in the subarea g, is the maximum capacity of the DPG installed on node i in the subarea g, is the active power of the DPG in the subarea g at time t.

[0112] Power balance:

[0113]

[0114] In the formula, N L is the number of main network link branches, N SL is the number of distribution network branches, is the load active power of node i in the subarea g at time t, is the active power loss of branch l at time t, that is, the active power difference between the sending end and the receiving end of branch l;

[0115] Main network link branch reverse transmission power constraint:

[0116]

[0117] In the formula, Maximum reverse transmit power allowed for the main grid link branch l;

[0118] Inter-zone interaction branch power supply:

[0119]

[0120] In the formula, N CI is the number of inter-zone interaction branches, is the maximum power allowed for the inter-zone interaction branch l.

[0121] Power limit of ESS:

[0122]

[0123] In the formula, is the maximum output power of the ESS in the partition g;

[0124] Charge and discharge efficiency limit:

[0125]

[0126] In the formula, η d is the discharge efficiency, η c is the charging efficiency;

[0127] State of charge constraint of ESS:

[0128]

[0129] In the formula, S g,t is the state of charge of partition t at time t, S max and S min are the upper and lower limits of the state of charge, S0 is the initial state of charge.

[0130] In step S3, the objective function of the lower layer is the annual minimum active power loss:

[0131]

[0132] In the formula, P CL is the grid loss.

[0133] In S3, the constraint function of the lower layer is:

[0134] (1) Flow limit

[0135]

[0136] In the formula, N is the number of system nodes, U i,t and U j,t are the voltage amplitudes of nodes i and j at time t, G ij and B ijYij(t) is the admittance of the branch ij, θ ij,t is the angle time node power of the branch ij at time t;

[0137] (2) The DPG installation capacity constraint of each node in the partition

[0138]

[0139] In the formula, is the DPG capacity of node i in partition g; is the DPG planning installed capacity of partition g;

[0140] (3) DPG node installation capacity limit

[0141]

[0142] In the formula, is the maximum value of the installed DPG capacity of node i in partition g; is the DPG planning installed capacity of node i in partition g;

[0143] (4) Voltage limit in the partition

[0144]

[0145] In the formula, U g,t,i is the voltage value of node i in partition g at time t, and is the lower limit and upper limit of the voltage of node i in partition g;

[0146] (5) Line transmission power constraint in the partition

[0147]

[0148] In the formula, P g,t,l is the transmission power of branch l in partition g at time t, l is the number of branches in the partition, l∈N SL,g , N SL,g is the number of branches in the group in partition g.

[0149] In S4, the planning and operation evaluation index is:

[0150] (1) Self-balancing degree

[0151] Self-balancing degree S A,g The ratio of the difference between the total load and the power purchase in the planning period and the total load, the higher the self-balancing degree, the stronger the self-control ability in the partition, and the weaker the connection between the partitions;

[0152]

[0153] (2) Energy Penetration Rate

[0154] The energy penetration rate measures the ratio between the DPG output and the total load power consumption in the planning period, and the higher the ratio, the greater the photovoltaic penetration and absorption in the partition;

[0155]

[0156] (3) Capacity Penetration Rate

[0157] The capacity penetration rate S CP,g is the ratio of the maximum photovoltaic power generation output time to the maximum power consumption time in the planning period, and the higher the ratio, the greater the remaining output of the DPG, the greater the demand for the ESS, and the closer the connection between the partitions;

[0158]

[0159] (4) Power Penetration Rate

[0160] The power penetration rate S PP,g is the maximum value of the ratio between the total DPG output and the total load in the planning period, and the higher the ratio, the greater the demand for the ESS, and the smaller the ratio, the lower the absorption capacity;

[0161]

[0162] The present application simulates the distribution system of IEEE33 nodes. As shown in Figure 3 , node 0 is a slack bus and can be regarded as a power supply point, and the remaining nodes are load points. The main system has 33 nodes and 32 branches; the rated voltage is 12.66kV, and the total load is 3715kW+j2300kvar. The node voltage range is 0.95-1.05. The DG type is DPG, the power factor is 0.85, the annual investment coefficient is 0.06, the single node rated capacity is 50kW, and the node maximum installed active capacity is 200kW. The maximum capacity of the ESS is 1MW, the battery state of charge is 10-90%, and the ESS charging and discharging efficiency is 0.9. The installation cost of DPG and ESS in the system is 12000 yuan / kW and 1270 yuan / kW respectively, the installation cost of ESS per active power is 1650 yuan / kW, the operation and maintenance cost is 0.08 yuan / kWh, the government subsidy for DPG is 0.25 yuan / kWh; the electricity selling and purchasing step price is shown in Table 1. The solar irradiance is 700W / m 2 . The photovoltaic output and load data are shown in Figure 4 . The particle swarm simulation parameters are set as: upperiter=200, loweriter=100, upperpop=upperk=50, lowerpop=lowerk=30, c1=c2=1.5. The number of partitions is 7.

[0163] Table 1 Electricity purchase and sale step price

[0164]

[0165] The advantages of the proposed strategy are highlighted by comparing four different planning cases:

[0166] Case 1: Single-layer non-partition DPG and ESS siting strategy.

[0167] Case 2: Double-layer non-partition DPG and ESS siting and sizing strategy.

[0168] Case 3: Double-layer partition planning strategy considering only DPG.

[0169] Case 4: Double-layer partition DPG and ESS siting and sizing strategy.

[0170] Implementation effect analysis The implementation effect is as follows

[0171] (1) Upper-layer planning results

[0172] Figure 5 The DPG and ESS sizing planning results in each case are shown. Table 2 lists the economic indicators of each planning scheme. From Table 2, it can be seen that when the distribution network is not planned, only the main network supplies power to the load, resulting in a high electricity purchase cost. For the four planned cases, due to the involvement of DPG and ESS, the electricity purchase cost is greatly reduced. Therefore, it is necessary to plan the distribution network.

[0173] Table 2 Capacity and cost of upper-layer DPG and ESS

[0174] Case 1 2 3 4 Distributed generation capacity / kW 360.0 740.180 784.619 837.195 Energy storage system capacity / kW 67.085 593.502 0.0 464.946 Investment cost / million 0.612 1.430 1.360 1.519 Maintenance cost / million 0.154 0.334 0.347 0.364 Electricity purchase / million 8.149 6.616 6.249 5.882 Government subsidy / million 0.169 0.368 0.389 0.401 Total cost / million 8.746 8.013 7.563 7.364

[0175] Comparing Case 1 and Case 4, according to Figure 5 The DPG capacity planning in a, the capacity configuration of DPG in the two cases is quite different. The DPG in Case 4 is 2.33 times the installed capacity in Case 1. In Case 1, the installation nodes of DPG are mainly concentrated in partitions 1, 4, and 7. The DPG capacity in Case 4 is evenly distributed in the seven partitions. From Figure 5The ESS capacity planning in b shows that the capacity configuration of ESS in the two cases is quite different. The ESS capacity in Case 4 is 6.93 times that in Case 1. In Case 1, the ESS installation nodes are mainly concentrated in partitions 1, 4, and 7. In Case 4, the ESS capacity is evenly installed in the seven partitions. As can be seen from Table 2, the installation and operation and maintenance costs of Case 4 are 147.95% and 136.01% higher than those of Case 1 due to the higher DPG and ESS costs. However, the main grid power purchase rate of Case 4 is reduced by 27.82% due to the higher DPG penetration rate, and the government subsidy is increased by 136.30%. From the overall planning results, the distribution network planning with double-layer partitions improves the intervention ability of DPG, makes the DPG capacity of each partition more evenly distributed, and reduces the total investment cost by 15.80%.

[0176] Comparing Case 2 and Case 4, it can be seen that Figure 5 The DPG capacity configuration of the two cases in a is different. The DPG installed capacity in Case 4 is 1.13 times that in Case 2. In Case 2, the DPG installation nodes are mainly concentrated in partitions 2 and 3. In Case 4, the DPG installed capacity is more evenly distributed in each partition. Figure 5 The ESS capacity planning in b shows that the capacity configuration of ESS in the two cases is quite different. The ESS capacity in Case 4 is 6.93 times that in Case 1. In Case 1, the ESS installation nodes are mainly concentrated in partitions 1, 4, and 7. In Case 4, the ESS capacity is evenly installed in the seven partitions. As can be seen from Table 2, the installation and operation and maintenance costs of Case 4 are 147.95% and 136.01% higher than those of Case 1 due to the higher DPG and ESS costs. However, the main grid power purchase rate of Case 4 is reduced by 27.82% due to the higher DPG penetration rate, and the government subsidy is increased by 136.30%. From the overall planning results, the distribution network planning with double-layer partitions improves the intervention ability of DPG, makes the DPG capacity of each partition more evenly distributed, and reduces the total investment cost by 15.80%.

[0177] Comparing Case 3 and Case 4, according to Figure 5 The DPG capacity planning in a shows that the DPG capacity configuration of the two cases is slightly different. The DPG installed capacity in Case 4 is 1.07 times that in Case 1. The DPG installation locations in Case 3 and Case 4 are relatively evenly distributed in each partition. From Figure 5The ESS planning capacity in b can be seen that the capacity configuration of ESS in two cases is quite different. In case 3, ESS is not installed. In case 4, the capacity of ESS is evenly installed in seven partitions. As can be seen from Table 2, due to the high cost of DPG and ESS, the investment and operation cost of case 4 increases by 11.68% and 4.84% respectively compared with case 3. But the DPG popularization rate of case 4 is relatively high, so the main network power purchase rate decreases by 5.81%, and the government subsidy increases by 3.07%. From the overall planning results, the planning of double-layer partitioned distribution network improves the intervention ability of DPG, makes the DPG capacity of each partition more evenly distributed, and reduces the total investment cost by 2.63%.

[0178] (2) Lower layer planning results

[0179] From Figure 6 a, b can be seen that in cases 1-3, due to the intermittent nature of DPG and the lack of ESS or insufficient installed capacity, the power loss of distribution network increases. Therefore, ESS plays a crucial role in the planning of DPG. Compared with case 3, the daily loss of each partition in case 4 is reduced by 13.36%, 5.43%, 18.11%, 41.84%, 12.94%, 5.57%, and 5.82% respectively. The daily loss of branch between partitions is reduced by 12.77%, 15.31%, 27.7%, 8.33%, and 5.03% respectively. The output of DPG during 7:00-18:00 and the output of ESS during 18:00-20:00 can meet the load demand, reduce the branch power flow, so the system network loss and power loss during 7:00-20:00 in case 2 and case 4 are relatively small, especially at 10:00, the system network loss and power loss of case 4 are the lowest, which are reduced by 14.22% and 14.06% respectively compared with case 3.

[0180] According to Figure 7 , the minimum node voltage in cases 1-4 is 1.5%, 1.9%, 1.3% and 3.2% higher than the pre-planning 0.951. Since case 3 does not consider ESS, the voltage value increases slightly. From the voltage variation amplitude, cases 1-4 increase by 52%, 44%, 56% and 20% respectively compared with before planning. The configuration of DPG and ESS in distribution network not only reduces the voltage amplitude variation, but also effectively suppresses the load fluctuation, which makes case 4 the best.

[0181] (4) Planning and operation evaluation index

[0182] According to Figure 8The partition self-balancing index in a is higher in case 4, and the distribution of each partition is more uniform compared with other cases. In addition, the average self-balancing degrees of cases 1-4 are 0.118, 0.181, 0.282, and 0.313 respectively, and case 4 is the highest. According to the above formula, the power transfer between partitions in case 4 is weak, and the coupling degree is high. The partitioning between nodes in the partition is strong, and the partition autonomy is high. From the perspective of the energy penetration rate, the energy penetration rate of each partition in case 4 is the highest, and the energy penetration rate of each partition in case 1 is the lowest. Figure 8 As can be seen from b, the penetration rates of partitions 1, 4, and 7 in case 1 are high, the penetration rate of partition 1 in case 3 is high, and the penetration rates of other partitions are low. The reason is that the installed capacity of ESS in cases 1 and 3 is insufficient or not installed. In case 2, although ESS devices are installed in each partition to achieve uniform penetration rate, the local DPG peak output does not match the peak load time, and the planned DPG capacity is low, resulting in low energy penetration rate. In case 4, ESS devices are planned in units of partitions, which not only relieves the time sequence characteristics of load demand, but also improves the planning and configuration ability of DPG. The energy penetration rates of cases 1-4 are 0.135, 0.195, 0.271, and 0.338 respectively. It shows that case 4 has the strongest absorption capacity for DPG, and the energy penetration capacity of each partition is the best. Figure 8 In c and d, the critical value of capacity and power penetration rate is 1. In cases 1-3, the capacity penetration rate of each partition is greater than the critical value, indicating that the DPG output in each case is greater than the required power passing through the load in the partition, and the remaining power flows to other partitions, which increases the network loss and leads to an increase in power penetration rate. However, in case 4, the capacity and power penetration rates of each partition are less than 1. Therefore, according to the overall planning evaluation index, case 4 is the most reasonable.

[0183] The above is only the preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical solution and inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A power distribution network light storage system coordination planning method based on a double-layer particle swarm algorithm, characterized in that, The method comprises the following steps: S1, parameter setting: inputting power distribution network structure, load parameters, constraint conditions, upper and lower layer population size, particle inner loop iteration number, maximum iteration number, initializing power flow to obtain initial branch power flow and node voltage parameters; S2, initializing and updating upper layer particle swarm: initializing position and speed of particle swarm in units of partition, particles meeting constraint conditions are connected to objective function to obtain global optimal value, global optimal fitness, local optimal value and local optimal fitness, the value of inertia weight is optimized by using difference between particle and global optimal particle, and the speed and position of particle are updated; S3, optimizing lower layer particle and updating fitness of lower layer particle: the updated particle of upper layer is used as condition of lower layer, particle swarm of each partition is initialized, position and speed of each partition particle are calculated in parallel, in order to determine fitness value of system network loss objective function, global optimal value, global optimal fitness, local optimal value and local optimal fitness, particles meeting constraint conditions are connected to power flow calculation of power distribution network to update DPV output power and ESS charging and discharging data, and are connected to objective function to obtain fitness value of lower layer particle; S4, verifying convergence condition: according to optimal power reached by lower layer upper layer particle, global optimal value, global optimal fitness, local optimal value and local optimal fitness of upper layer particle are updated, if maximum iteration number is reached, the current global optimal value and global optimal fitness are taken as optimization result, otherwise step S2 is returned to continue iteration optimization; In the S2, the upper layer objective function is the lowest annual comprehensive cost: minC = C invest +C O +C buy -C s (1) In the formula, C invest , C O , C buy , C s are the investment construction cost, operation and maintenance cost, electricity purchase cost and government subsidies, respectively. In the formula, N G is the number of partitions, r is the discount rate, which is 0.06, T DPG = 20 years, T ESS = 10 years. respectively, DPG unit capacity, ESS investment construction cost, and unit power investment cost of ESS. DPG rated capacity of partition g, rated capacity and rated power of the ESS, respectively; In the formula, DG is the power generation of the DG in the partition g at time t, and ESS is the charge and discharge power of the ESS in the partition g at time t, DPG, ESS, and DPG are the unit maintenance costs of the DPG and the ESS and the DPG abandoned power cost, respectively. where N L is the number of link branches in the main network, is the power connected to branch / through the main network at time t; In the formula, c b is the government subsidy fee for the power generation of the distributed power generation unit, η i is the distributed power generation efficiency.

2. The power distribution network photovoltaic storage system coordinated planning method based on a double-layer particle swarm algorithm according to claim 1, characterized in that, In the S2, the constraint conditions of upper layer are: DPG capacity and partition power output limit, power balance, main network link branch reverse transmission power constraint, inter-partition interaction branch power supply, power limit of ESS, charging and discharging efficiency limit, state of charge constraint of ESS; DPG capacity and partition power output limit: where N G is the number of partitions, N g is the number of nodes in partition g, is the rated capacity of DPG in partition g, is the maximum capacity of DPG installed on node i in partition g, is the active power of DPG in partition g at time t.

3. The method of claim 2, wherein the method is characterized by, Power balance: where N L is the number of main grid link branches, N SL is the number of distribution grid branches, is the active power load of node i in partition g at time t, is the active power loss of branch l at time t, i.e., the active power difference between the sending and receiving ends of branch l. Main network link branch reverse transmission power constraint: where P l p,max Pmax is the maximum reverse transmit power allowed through the main network link leg l; Inter-partition interaction branch power supply: In the formula, N CI is the number of interaction branches between partitions, P l IP,max is the maximum power allowed for interaction branch l between partitions.

4. The power distribution network photovoltaic storage system coordinated planning method based on a double-layer particle swarm algorithm according to claim 3, characterized in that, Power limit of ESS: wherein is the maximum output power of the ESS in partition g; Charging and discharging efficiency limit: wherein η d is the discharge efficiency, η c is the charge efficiency; State of charge constraint of ESS: where S g,t is the state of charge of the partition t at time t, S max and S min are upper and lower limits of the state of charge, and S0is the initial state of charge.

5. The power distribution network coordinated planning method of a light storage system based on a double-layer particle swarm algorithm according to claim 4, characterized in that, In the step S3, the lower layer objective function is annual minimum active power loss: In the formula, P CL is the grid loss.

6. The power distribution network coordinated planning method of a light storage system based on a double-layer particle swarm algorithm according to claim 5, characterized in that, In the S3, the constraint function of lower layer is: (1) flow limit where N is the number of system nodes, U i,t and U j,t are the voltage magnitudes of nodes i and j at time t, G ij and B ij represent the conductance of the branch ij, θ ij,t is the angle of the branch ij at time t, and Pij(t) is the power at the time node t. (2) DPG installation capacity constraint of each node in partition wherein is the DPG capacity of node i in partition g; is the DPG planning installed capacity of partition g; (3) DPG node installation capacity limit wherein is the maximum value of the DPG capacity installed by node i in partition g; is the DPG planned installed capacity of node i in partition g; (4) voltage limit in partition wherein U g,t,i is the voltage value of node i in partition g at time t, and are the lower and upper limits of the voltage of node i in partition g; (5) line transmission power constraint in partition where P g,t,l is the transmission power of branch l in zone g at time t, l is the number of branches in the zone, l ∈ N SL,g , N SL,g is the number of branches in the group in zone g.

7. The method according to claim 6, wherein, In the S4, the planning and operation evaluation index is: (1) self-balancing degree Self-balancing degree S A,g The ratio of the difference between the total load and the electricity purchase amount in the planning period and the total load, the higher the self-balancing degree, the stronger the self-control ability in the subarea, and the weaker the connection between subareas. (2) energy penetration rate The energy penetration rate measures the ratio between DPG output and total load power consumption in partition during planning, the higher the ratio, the greater the penetration and absorption of photovoltaic in the partition; (3) capacity penetration rate Capacity permeability S CP,g is the ratio of the maximum output time of photovoltaic power generation to the maximum electricity consumption time in the planning period. The higher the ratio, the greater the remaining output of DPG, the greater the demand for ESS, and the tighter the connection between partitions. (4) power penetration rate Power penetration rate S PP,g is the maximum value of the ratio of the total output of the DPG in the planning period to the total load in the subarea, the higher the ratio, the greater the demand for the ESS, the smaller the ratio, the lower the absorption capacity;

Images