Power distribution network optical storage system coordination planning method based on double-layer particle swarm optimization

Abstract

The invention belongs to the field of power distribution networks, and particularly relates to a power distribution network optical storage system coordinated planning method based on a double-layer particle swarm algorithm, which comprises the following steps: S1, parameter setting: inputting a power distribution network structure, load parameters, constraint conditions, sizes of upper and lower layers of populations, the number of internal loop iterations of particles and the maximum number of iterations, initializing a power flow to obtain an initial branch power flow and a node voltage parameter; and S2, initializing and updating an upper-layer particle swarm: by taking the partition as a unit, initializing the position and the speed of the particle swarm, and putting particles meeting constraint conditions into a target function to obtain a global optimal value, a global optimal fitness, a local optimal value and a local optimal fitness. The planning capacity of the DPG and the ESS in each partition is uniformly distributed as far as possible through upper-layer planning; and the lower layer minimizes the investment planning cost on the premise of ensuring the minimum network loss, and ensures the stability and reliability of the system at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a coordinated planning method for a photovoltaic storage system of a distribution network based on a double-layer particle swarm algorithm. Background Art

[0002] With the rapid development of renewable energy, especially photovoltaic power generation technology, and the deepening global consensus on carbon neutrality goals, photovoltaic power generation has become a key force in promoting energy structure transformation and achieving green and low-carbon development. However, the large-scale access of distributed photovoltaic systems, especially in combination with energy storage systems, has brought new challenges to the operation and management of existing distribution networks. These challenges mainly include but are not limited to the intermittent and uncertain nature of photovoltaic power generation, the scheduling strategy of energy storage systems, and the impact of these new loads and power generation methods on the safe and stable operation of the power grid.

[0003] Traditional distribution network planning and management methods are unable to effectively respond to these new challenges, especially in terms of ensuring network security, improving energy efficiency, and reducing system operating costs. Summary of the invention

[0004] The purpose of the present invention is to solve the problem in the prior art that when large-scale distributed photovoltaic energy is connected to the distribution network, the load demand does not match the photovoltaic output time, especially the disadvantage that the zoning control of high-penetration photovoltaic grid-connected operation is difficult to meet through traditional distributed photovoltaic planning. A distribution network photovoltaic storage system coordinated planning method based on a double-layer particle swarm algorithm is proposed.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A coordinated planning method for photovoltaic storage system in distribution network based on double-layer particle swarm algorithm, including

[0007] Follow these steps:

[0008] S1, parameter setting: input the distribution network structure, load parameters, constraints, upper and lower population sizes, number of particle inner loop iterations, maximum number of iterations, initialize the power flow to obtain the initial branch power flow and node voltage parameters;

[0009] S2. Initialize and update the upper particle swarm: Initialize the position and speed of the particle swarm in units of partitions. Particles that meet the constraints are put into the objective function to obtain the global optimal value, global optimal fitness, local optimal value and local optimal fitness. The difference between the particle and the global optimal particle is used to optimize the value of the inertia weight, and the speed and position of the particle are updated.

[0010] S3. Optimize the lower layer particles and update the fitness of the lower layer particles: The particles updated in the upper layer 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. In order to determine the fitness value, global optimal value, global optimal fitness, local optimal value and local optimal fitness of the system network loss objective function, the particles that meet the constraints are connected to the distribution network for power flow calculation to update the DPV output power and ESS charging and discharging data, and connected to the objective function to obtain the fitness value of the lower layer particles;

[0011] S4, verify the convergence conditions: according to the optimal power achieved by the particles in the lower layer, update the global optimal value, global optimal fitness, local optimal value and local optimal fitness of the particles in the upper layer. If the maximum number of iterations is reached, take the current global optimal value and global optimal fitness as the optimization result, otherwise return to step S2 to continue iterative optimization.

[0012] Preferably, in S2, the upper objective function is the minimum annual comprehensive cost:

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

[0014] In the formula, C invest , C O , C buy , C s They are investment and construction costs, operation and maintenance costs, electricity purchase costs and government subsidies;

[0015]

[0016] Where N G is the number of partitions, r is the discount rate, which is 0.06, T is the planning period, DPG is 20 years, ESS is 10 years, They are DPG unit capacity, ESS investment and construction cost, and ESS unit power investment cost. and are the rated capacity of DPG, rated capacity and power of ESS in partition g respectively;

[0017]

[0018] In the formula, is the power generation of DG in partition g at time t, and is the charge and discharge power of ESS in partition g at time t, They are the unit maintenance cost of DPG and ESS and the cost of DPG power abandonment;

[0019]

[0020] Where N L is the number of link branches in the main network, is the power connected to branch l through the main grid at time t;

[0021]

[0022] In the formula, c b is the government subsidy for distributed generation per unit of power generation, η i For distributed generation efficiency.

[0023] 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-partition interactive branch power supply, ESS power limit, charge and discharge efficiency limit, ESS charging state constraint;

[0024] DPG capacity and zone power output limits:

[0025]

[0026] Where N G is the number of partitions, N g is the number of nodes in partition g, is the installed capacity of DPG in partition g, is the maximum capacity of the DPG installed on node i partition g in the partition, is the active power of the DPG in partition g at time t.

[0027] Optimally, power balance:

[0028]

[0029] Where N L 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 partition g at time t, The branch l transmitted from the lower layer to the upper layer of the network at time t is lost;

[0030] Reverse transmission power constraint of main network link branch:

[0031]

[0032] Where P l p,max The maximum reverse transmission power allowed to pass through the main network link branch l;

[0033] Interactive branch power supply between partitions:

[0034]

[0035] Where N CI is the number of interactive branches between partitions, P l IP,max is the maximum power allowed for the inter-partition interaction branch l.

[0036] Preferably, the power limit of ESS is:

[0037]

[0038] In the formula, is the maximum output power of the ESS in partition g;

[0039] Charge and discharge efficiency limitations:

[0040]

[0041] Where η d is the discharge efficiency, η c For charging efficiency;

[0042] ESS state of charge constraints:

[0043]

[0044] 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, S 0 is the initial state of charge.

[0045] Preferably, in step S3, the objective function of the lower layer is the annual minimum active power loss:

[0046]

[0047] Where P CL It is the grid loss.

[0048] Preferably, in S3, the constraint function of the lower layer is:

[0049] (1) Current Limitation

[0050]

[0051] Where N is the number of system nodes, U i,t and U j,t is the voltage amplitude of nodes i and j at time t, G ij and B ijrepresents the admittance of the ij branch, θ ij,t is the ij branch angle time node power at time t;

[0052] (2) DPG installation capacity constraints for each node in the partition

[0053]

[0054] In the formula, is the access DPG capacity of node i in partition g;

[0055] (3) DPG node installation capacity limit

[0056]

[0057] In the formula, is the maximum value of the installed DPG capacity of node i in partition g;

[0058] (4) Voltage Limitation in Partitions

[0059]

[0060] Where 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 at node i in partition g;

[0061] (5) Transmission power constraints of lines within a zone

[0062]

[0063] Where 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.

[0064] Preferably, in S4, the planning and operation evaluation indicators are:

[0065] (1) Self-balancing degree

[0066] Self-balancing degree S A,g The ratio of the difference between the total load of the sub-zone and the purchased electricity to the total load during the planning period. The higher the degree of self-balancing, the stronger the self-control ability within the sub-zone and the weaker the connection between sub-zones.

[0067]

[0068] (2) Energy penetration

[0069] The energy penetration ratio measures the ratio between the DPG output and the total load power consumption within the partition during the planning period. The higher the ratio, the greater the PV penetration and absorption in the partition;

[0070]

[0071] (3) Capacity penetration

[0072] Capacity penetration S CP,g It is the ratio of the maximum PV power output moment to the maximum power consumption moment during the planning period. The higher the ratio, the greater the residual output of DPG, the greater the demand for ESS, and the closer the connection between partitions.

[0073]

[0074] (4) Power penetration

[0075] Power penetration S PP,g It is the maximum value of the ratio of the total DPG output to the total load in the zone during the planning period. The higher the ratio, the greater the demand for ESS, and the smaller the ratio, the lower the absorption capacity;

[0076]

[0077] In the present invention, the beneficial effects of the coordinated planning method of the distribution network photovoltaic storage system based on the double-layer particle swarm algorithm are as follows:

[0078] Aiming at the problem of site selection and scale selection of distributed photovoltaic and energy storage systems to minimize total cost and loss, the present invention develops a photovoltaic energy storage two-level planning model for distributed photovoltaic energy storage. The upper level goal is to minimize the annual comprehensive cost of each partition, and the decision variables include photovoltaic capacity, energy storage capacity and power. The lower level goal is to minimize system network losses, and its decision variables are the photovoltaic installation capacity and energy storage installation location of the nodes in the partition.

[0079] The photovoltaic energy storage double-layer planning model construction method of the present invention makes the planning capacity of DPG and ESS in each partition as evenly distributed as possible in the upper layer planning; the lower layer minimizes the investment planning cost while ensuring the stability and reliability of the system under the premise of minimizing the network loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 This is a model structure diagram of a coordinated planning method for a distribution network photovoltaic storage system based on a double-layer particle swarm algorithm proposed in the present invention.

[0081] Figure 2 This is an algorithm flow chart of a coordinated planning method for a distribution network photovoltaic storage system based on a double-layer particle swarm algorithm proposed by the present invention;

[0082] Figure 3This is an IEEE 33 bus allocation network diagram of a distribution network photovoltaic storage system coordination planning method based on a double-layer particle swarm algorithm proposed by the present invention;

[0083] Figure 4 A typical photovoltaic output and load demand curve diagram of a distribution network photovoltaic storage system coordinated planning method based on a double-layer particle swarm algorithm proposed by the present invention;

[0084] Figure 5 The capacity diagram of each partition of DPG and ESS in the upper-level planning of the coordinated planning method of the distribution network photovoltaic storage system based on the double-layer particle swarm algorithm proposed by the present invention;

[0085] Figure 6 A diagram showing the system loss and power loss ratio of a coordinated planning method for a distribution network photovoltaic storage system based on a double-layer particle swarm algorithm proposed by the present invention;

[0086] Figure 7 The voltage amplitude diagram under different conditions of a coordinated planning method for photovoltaic storage system in distribution network based on double-layer particle swarm algorithm proposed by the present invention;

[0087] Figure 8 This is an evaluation index diagram of each case of a coordinated planning method for a distribution network photovoltaic storage system based on a double-layer particle swarm algorithm proposed in the present invention. DETAILED DESCRIPTION

[0088] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0089] Reference Figure 1-Figure 8 , a coordinated planning method for a distribution network photovoltaic storage system based on a double-layer particle swarm algorithm, comprising the following steps:

[0090] S1, parameter setting: input the distribution network structure, load parameters, constraints, upper and lower population sizes, number of particle inner loop iterations, maximum number of iterations, initialize the power flow to obtain the initial branch power flow and node voltage parameters;

[0091] S2. Initialize and update the upper particle swarm: Initialize the position and speed of the particle swarm in units of partitions. Particles that meet the constraints are put into the objective function to obtain the global optimal value, global optimal fitness, local optimal value and local optimal fitness. The difference between the particle and the global optimal particle is used to optimize the value of the inertia weight, and the speed and position of the particle are updated.

[0092] S3. Optimize the lower layer particles and update the fitness of the lower layer particles: The particles updated in the upper layer 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. In order to determine the fitness value, global optimal value, global optimal fitness, local optimal value and local optimal fitness of the system network loss objective function, the particles that meet the constraints are connected to the distribution network for power flow calculation to update the DPV output power and ESS charging and discharging data, and connected to the objective function to obtain the fitness value of the lower layer particles;

[0093] S4, verify the convergence conditions: according to the optimal power achieved by the particles in the lower layer, update the global optimal value, global optimal fitness, local optimal value and local optimal fitness of the particles in the upper layer. If the maximum number of iterations is reached, take the current global optimal value and global optimal fitness as the optimization result, otherwise return to step S2 to continue iterative optimization.

[0094] In this embodiment, in S2, the upper objective function is the minimum annual comprehensive cost:

[0095] minC=C invest +C O +C buy -C s (45)

[0096] In the formula, C invest , C O , C buy , C s They are investment and construction costs, operation and maintenance costs, electricity purchase costs and government subsidies;

[0097]

[0098] Where N G is the number of partitions, r is the discount rate, which is 0.06, T is the planning period, DPG is 20 years, ESS is 10 years, They are DPG unit capacity, ESS investment and construction cost, and ESS unit power investment cost. and are the rated capacity of DPG, rated capacity and power of ESS in partition g respectively;

[0099]

[0100] In the formula, is the power generation of DG in partition g at time t, and is the charge and discharge power of ESS in partition g at time t, They are the unit maintenance cost of DPG and ESS and the cost of DPG power abandonment;

[0101]

[0102] Where N L is the number of link branches in the main network, is the power connected to branch l through the main grid at time t;

[0103]

[0104] In the formula, c b is the government subsidy for distributed generation per unit of power generation, η i For distributed generation efficiency.

[0105] In this embodiment, 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-partition interactive branch power supply, ESS power limit, charge and discharge efficiency limit, and ESS charging state constraint;

[0106] DPG capacity and zone power output limits:

[0107]

[0108] Where N G is the number of partitions, N g is the number of nodes in partition g, is the installed capacity of DPG in partition g, is the maximum capacity of the DPG installed on node i partition g in the partition, is the active power of the DPG in partition g at time t.

[0109] In this embodiment, power balance:

[0110]

[0111] Where N L 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 partition g at time t, The branch l transmitted from the lower layer to the upper layer of the network at time t is lost;

[0112] Reverse transmission power constraint of main network link branch:

[0113]

[0114] Where P l p,max The maximum reverse transmission power allowed to pass through the main network link branch l;

[0115] Interactive branch power supply between partitions:

[0116]

[0117] Where N CI is the number of interactive branches between partitions, P l IP,max is the maximum power allowed for the inter-partition interaction branch l.

[0118] In this embodiment, the power limit of ESS is:

[0119]

[0120] In the formula, is the maximum output power of the ESS in partition g;

[0121] Charge and discharge efficiency limitations:

[0122]

[0123] Where η d is the discharge efficiency, η c For charging efficiency;

[0124] ESS state of charge constraints:

[0125]

[0126] 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, S 0 is the initial state of charge.

[0127] In this embodiment, in step S3, the objective function of the lower layer is the annual minimum active power loss:

[0128]

[0129] Where P CL It is the grid loss.

[0130] In this embodiment, in S3, the constraint function of the lower layer is:

[0131] (1) Current Limitation

[0132]

[0133] Where N is the number of system nodes, U i,t and U j,t is the voltage amplitude of nodes i and j at time t, G ij and B ijrepresents the admittance of the ij branch, θ ij,t is the ij branch angle time node power at time t;

[0134] (2) DPG installation capacity constraints for each node in the partition

[0135]

[0136] In the formula, is the access DPG capacity of node i in partition g;

[0137] (3) DPG node installation capacity limit

[0138]

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

[0140] (4) Voltage Limitation in Partitions

[0141]

[0142] Where 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 at node i in partition g;

[0143] (5) Transmission power constraints of lines within a zone

[0144]

[0145] Where 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.

[0146] Preferably, in S4, the planning and operation evaluation indicators are:

[0147] (1) Self-balancing degree

[0148] Self-balancing degree S A,g The ratio of the difference between the total load of the sub-zone and the purchased electricity to the total load during the planning period. The higher the degree of self-balancing, the stronger the self-control ability within the sub-zone and the weaker the connection between sub-zones.

[0149]

[0150] (2) Energy penetration

[0151] The energy penetration ratio measures the ratio between the DPG output and the total load power consumption within the partition during the planning period. The higher the ratio, the greater the PV penetration and absorption in the partition;

[0152]

[0153] (3) Capacity penetration

[0154] Capacity penetration S CP,g It is the ratio of the maximum PV power output moment to the maximum power consumption moment during the planning period. The higher the ratio, the greater the residual output of DPG, the greater the demand for ESS, and the closer the connection between partitions.

[0155]

[0156] (4) Power penetration

[0157] Power penetration S PP,g It is the maximum value of the ratio of the total DPG output to the total load in the zone during the planning period. The higher the ratio, the greater the demand for ESS, and the smaller the ratio, the lower the absorption capacity;

[0158]

[0159] The present invention simulates the distribution system of IEEE 33 nodes. Figure 3 As shown, node 0 is a slack bus and can be regarded as a power source point, while 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 rated capacity of a single node is 50kW, and the maximum installed active capacity of the node is 200kW. The maximum capacity of ESS is 1MW, the battery state of charge is 10-90%, and the ESS charging and discharging efficiency is 0.9. The installation costs of DPG and ESS in the system are 12,000 yuan / kW and 1,270 yuan / kW respectively, the installation cost of ESS per unit active power is 1,650 yuan / kW, the operation and maintenance cost is 0.08 yuan / kWh, and the government subsidy for DPG is 0.25 yuan / kWh; the ladder electricity prices for electricity sales and purchases are shown in Table 1. The solar irradiance is 700W / m 2 PV output and load data such as Figure 4 The particle swarm simulation parameters are set as follows: upper iter = 200, lower iter = 100, upperpop = upperk = 50, lowerpop = lowerk = 30, c 1 =c 2 =1.5. The number of partitions is 7.

[0160] Table 1 Electricity Sales and Purchase Ladder Price

[0161]

[0162] Four different planning cases are compared to highlight the advantages of the proposed strategy:

[0163] Case 1: Adopting a single-layer non-partitioned DPG and ESS site selection strategy.

[0164] Case 2: Adopting a two-tier non-zoning DPG and ESS siting and sizing strategy.

[0165] Case 3: Considering only the two-layer partition planning strategy of DPG.

[0166] Case 4: DPG and ESS site selection and size strategy using two-tier zoning.

[0167] The results of the implementation effect analysis are as follows

[0168] (1) Top-level planning results

[0169] Figure 5 The results of DPG and ESS size planning in each case are shown. Table 2 lists the economic indicators of each plan. It can be seen from Table 2 that when the distribution network is not planned, only the main grid supplies power to the load, which makes the power purchase cost high. For the four cases after planning, the power purchase cost is greatly reduced due to the intervention of DPG and ESS. Therefore, it is necessary to plan the distribution network.

[0170] Table 2 Capacity and cost of upper DPG and ESS

[0171] Case 1 2 3 4 Distributed power 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 subsidies / million 0.169 0.368 0.389 0.401 Total cost / million 8.746 8.013 7.563 7.364

[0172] Comparing Case 1 and Case 4, according to Figure 5 The capacity planning of DPG in a shows that the capacity configuration of DPG in the two cases is quite different. The installed capacity of DPG in Case 4 is 2.33 times that of Case 1. In Case 1, the installation nodes of DPG are mainly concentrated in partitions 1, 4, and 7. The capacity of DPG in Case 4 is evenly distributed in 7 partitions. Figure 5From the ESS planning capacity in b, it can be seen that there is a big difference in the capacity configuration of ESS in the two cases. The ESS capacity in Case 4 is 6.93 times that of 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 7 partitions. As can be seen from Table 2, due to the high cost of DPG and ESS, the installation and operation and maintenance costs of Case 4 are 147.95% and 136.01% higher than those of Case 1. However, the DPG penetration rate in Case 4 is higher, so the main grid purchase rate is reduced by 27.82% and the government subsidy is increased by 136.30%. From the analysis of the overall planning results, the distribution network planning of double-layer partitions improves the intervention capacity of DPG, makes the DPG capacity distribution of each partition more even, and reduces the total investment cost by 15.80%.

[0173] Comparing Case 2 and Case 4, we can see that Figure 5 There are differences in the DPG capacity configurations of the two cases in a. The installed capacity of DPG in Case 4 is 1.13 times that of Case 2. In Case 2, the installation nodes of DPG are mainly concentrated in Partitions 2 and 3. In Case 4, the installed capacity of DPG 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 very different. The ESS capacity of Case 4 is 0.78 times the installed capacity of Case 1. The installed capacity of ESS in Case 1 is mainly concentrated in zones 2, 5, 6, and 7. The ESS capacity of Case 4 is evenly distributed in seven zones. In Table 2, due to the high cost of DPG and ESS, the investment and operation and maintenance costs of Case 4 increased by 6.24% and 8.94% respectively compared with Case 2. However, the DPG penetration rate in Case 4 is relatively high, so the main grid purchase rate is reduced by 11.10% and the government subsidy is increased by 8.90%. From the analysis of the overall planning results, the double-layer zoning planning of the distribution network improves the uniform distribution of DPG installed capacity and the capacity of each zone, reduces the capacity allocation of ESS in each zone, and reduces the total investment cost by 8.09%.

[0174] Comparing Case 3 and Case 4, according to Figure 5 In the DPG capacity planning in a, the difference in DPG capacity configuration between the two cases is slightly smaller. The DPG installed capacity of Case 4 is 1.07 times that of Case 1. The DPG installation locations of Cases 3 and 4 are relatively evenly distributed in each partition. Figure 5From the ESS planning capacity in b, it can be seen that there is a big difference in the capacity configuration of ESS in the two cases. 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 and maintenance costs of case 4 increased by 11.68% and 4.84% respectively compared with case 3. However, the DPG penetration rate in case 4 is relatively high, so the main grid purchase rate is reduced by 5.81% and the government subsidy is increased by 3.07%. From the analysis of the overall planning results, the double-layer partitioned distribution network planning improves the intervention capacity of DPG, makes the DPG capacity distribution of each partition more even, and reduces the total investment cost by 2.63%.

[0175] (2) Lower-level planning results

[0176] from Figure 6 As can be seen from a and b, in cases 1 to 3, the intermittent characteristics of DPG and the lack of ESS or insufficient installed capacity lead to increased distribution network losses. Therefore, ESS plays a vital role in the planning of DPG. Compared with case 3, the daily losses of each sub-area in case 4 were reduced by 13.36%, 5.43%, 18.11%, 41.84%, 12.94%, 5.57%, and 5.82%, respectively. The daily losses of the branches between sub-areas were 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 and reduce the branch power flow. Therefore, the ratio of system network loss to power in Case 2 and Case 4 is relatively small during 7:00-20:00, especially at 10:00, the system network loss and power loss in Case 4 are the lowest, which are reduced by 14.22% and 14.06% respectively compared with Case 3.

[0177] 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-planned 0.951. Since the planning of case 3 does not take ESS into account, the voltage value increases slightly. From the voltage change amplitude, it can be seen that cases 1-4 increase by 52%, 44%, 56% and 20% respectively compared with before planning. The configuration of DPG and ESS in the distribution network not only reduces the voltage amplitude change, but also effectively suppresses the load fluctuation, which makes case 4 the best.

[0178] (4) Planning and operation evaluation indicators

[0179] according to Figure 8The partition self-balancing index in a has a higher value 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 to 4 are 0.118, 0.181, 0.282, and 0.313, respectively, with case 4 having the highest value. From the above formula, it can be seen that the power transfer between partitions in case 4 is weak and the coupling degree is high. The division between nodes in the partition is strong, and the partition autonomy is high. Figure 8 b It can be seen that 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 equipment is installed in each partition to even out the 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. In case 4, ESS equipment is planned in partitions, which not only alleviates the timing characteristics of load demand, but also improves the planning and configuration capabilities of DPG. The energy penetration rates of cases 1-4 are analyzed as 0.135, 0.195, 0.271, and 0.338 according to the average value. It shows that case 4 has the strongest absorption capacity for DPG and the best energy penetration capacity of each partition. Figure 8 In c and d, the critical value of capacity and power penetration is 1. In cases 1-3, the capacity penetration of each partition is greater than the critical value, indicating that the DPG output in each case is greater than the required power through the load within the partition, and the remaining power flows to other partitions, which increases network losses and leads to an increase in power penetration. However, in case 4, the capacity and power penetration of each partition are less than 1. Therefore, according to the overall planning evaluation indicators, case 4 is the most reasonable.

[0180] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A coordinated planning method for photovoltaic storage system in distribution network based on double-layer particle swarm algorithm, characterized in that: The following steps are involved: S1, parameter setting: input the distribution network structure, load parameters, constraints, upper and lower population sizes, number of particle inner loop iterations, maximum number of iterations, initialize the power flow to obtain the initial branch power flow and node voltage parameters; S2. Initialize and update the upper particle swarm: Initialize the position and speed of the particle swarm in units of partitions. Particles that meet the constraints are put into the objective function to obtain the global optimal value, global optimal fitness, local optimal value and local optimal fitness. The difference between the particle and the global optimal particle is used to optimize the value of the inertia weight, and the speed and position of the particle are updated. S3. Optimize the lower layer particles and update the fitness of the lower layer particles: The particles updated in the upper layer 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. In order to determine the fitness value, global optimal value, global optimal fitness, local optimal value and local optimal fitness of the system network loss objective function, the particles that meet the constraints are connected to the distribution network for power flow calculation to update the DPV output power and ESS charging and discharging data, and connected to the objective function to obtain the fitness value of the lower layer particles; S4, verify the convergence conditions: according to the optimal power achieved by the particles in the lower layer, update the global optimal value, global optimal fitness, local optimal value and local optimal fitness of the particles in the upper layer. If the maximum number of iterations is reached, take the current global optimal value and global optimal fitness as the optimization result, otherwise return to step S2 to continue iterative optimization.

2. According to claim 1, a coordinated planning method for distribution network photovoltaic storage system based on double-layer particle swarm algorithm is characterized in that: In S2, the upper objective function is the minimum annual comprehensive cost: minC=C invest +C O +C buy -C s (1) In the formula, C invest , C O , C buy , C s They are investment and construction costs, operation and maintenance costs, electricity purchase costs and government subsidies; Where N G is the number of partitions, r is the discount rate, which is 0.06, T is the planning period, DPG is 20 years, ESS is 10 years, They are DPG unit capacity, ESS investment and construction cost, and ESS unit power investment cost. and are the rated capacity of DPG, rated capacity and power of ESS in partition g respectively; In the formula, is the power generation of DG in partition g at time t, and is the charging and discharging power of ESS in partition g at time t, They are the unit maintenance cost of DPG and ESS and the cost of DPG power abandonment; Where N L is the number of link branches in the main network, is the power connected to branch l through the main grid at time t; In the formula, c b is the government subsidy for distributed generation per unit of power generation, η i For distributed generation efficiency.

3. According to claim 2, a coordinated planning method for distribution network photovoltaic storage system based on double-layer particle swarm algorithm is characterized in that: In S2, the upper-level constraints are: DPG capacity and partition power output limit, power balance, main network link branch reverse transmission power constraint, inter-partition interactive branch power supply, ESS power limit, charge and discharge efficiency limit, ESS charging state constraint; DPG capacity and zone power output limits: Where N G is the number of partitions, N g is the number of nodes in partition g, is the installed capacity of DPG in partition g, is the maximum capacity of the DPG installed on node i partition g in the partition, is the active power of the DPG in partition g at time t.

4. According to claim 3, a coordinated planning method for distribution network photovoltaic storage system based on double-layer particle swarm algorithm is characterized in that: Power Balance: Where N L 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 partition g at time t, The branch l transmitted from the lower layer to the upper layer of the network at time t is lost; Reverse transmission power constraint of main network link branch: Where P l p,max The maximum reverse transmission power allowed to pass through the main network link branch l; Interactive branch power supply between partitions: Where N CI is the number of interactive branches between partitions, P l IP,max is the maximum power allowed for the inter-partition interaction branch l.

5. According to claim 4, a method for coordinated planning of a distribution network photovoltaic storage system based on a double-layer particle swarm algorithm is characterized in that: Power limitation of ESS: In the formula, is the maximum output power of the ESS in partition g; Charge and discharge efficiency limitations: Where η d is the discharge efficiency, η c For charging efficiency; ESS state of charge constraints: 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, and S0 is the initial state of charge.

6. A coordinated planning method for photovoltaic storage system in distribution network based on double-layer particle swarm algorithm according to claim 5, characterized in that: In step S3, the objective function of the lower layer is the annual minimum active power loss: Where P CL It is the grid loss.

7. A coordinated planning method for distribution network photovoltaic storage system based on double-layer particle swarm algorithm according to claim 6, characterized in that: In S3, the constraint function of the lower layer is: (1) Flow restriction Where N is the number of system nodes, U i,t and U j,t is the voltage amplitude of nodes i and j at time t, G ij and B ij represents the admittance of the ij branch, θ ij,t is the ij branch angle time node power at time t; (2) DPG installation capacity constraints for each node in the partition In the formula, is the access DPG capacity of node i in partition g; (3) DPG node installation capacity limit In the formula, is the maximum value of the installed DPG capacity of node i in partition g; (4) Voltage Limitation in Partitions Where 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 at node i in partition g; (5) Transmission power constraints of lines within a zone Where 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.

8. The coordinated planning method of distribution network photovoltaic storage system based on double-layer particle swarm algorithm according to claim 7 is characterized in that: In S4, the planning and operation evaluation indicators are: (1) Self-balancing degree Self-balancing degree S A,g The ratio of the difference between the total load of the sub-zone and the purchased electricity to the total load during the planning period. The higher the degree of self-balancing, the stronger the self-control ability within the sub-zone and the weaker the connection between sub-zones. (2) Energy penetration The energy penetration ratio measures the ratio between the DPG output and the total load power consumption within the partition during the planning period. The higher the ratio, the greater the PV penetration and absorption in the partition; (3) Capacity penetration Capacity permeability S CP,g It is the ratio of the maximum PV power output moment to the maximum power consumption moment during the planning period. The higher the ratio, the greater the residual output of DPG, the greater the demand for ESS, and the closer the connection between partitions. (4) Power penetration Power penetration S PP,g It is the maximum value of the ratio of the total DPG output to the total load in the zone during the planning period. The higher the ratio, the greater the demand for ESS, and the smaller the ratio, the lower the absorption capacity;

Images