Power distribution network bearing capacity evaluation method considering new energy and energy storage access

Abstract

The invention discloses a power distribution network bearing capacity evaluation method considering new energy and energy storage access, which comprises the following steps of: firstly, performing population initialization on a power distribution network load information sample, inputting the initialized population into a power distribution network bearing capacity model to perform load flow calculation constraint verification, then performing Pareto optimal selection by adopting an improved Pareto optimal genetic algorithm, and finally performing load flow calculation constraint verification on the Pareto optimal genetic algorithm. And finally, carrying out weighted summation on various indexes in the Pareto optimal solution set by adopting a combined weighting method to obtain a power distribution network bearing capacity evaluation result. According to the method, the objective function and the constraint condition of the power distribution network bearing capacity model are constructed, and the improved Pareto optimal genetic algorithm is combined, so that the solving difficulty of the power distribution network bearing capacity is reduced; meanwhile, for energy storage access, a return on investment index CROI is constructed, an optimal energy storage access scheme is confirmed in the bearing capacity solving process, and the power distribution network bearing capacity considering DG and energy storage access is comprehensively considered.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system energy calculation, and specifically relates to the design of a distribution network carrying capacity assessment method considering the access of new energy and energy storage. Background Art

[0002] Distributed renewable energy (DG) uses wind energy, solar energy, etc. to provide energy, effectively alleviating the problem of resource shortage, and can fully improve the flexibility of power supply and voltage support capacity, but the output of renewable energy is unbalanced and uncertain; energy storage can mobilize energy in the time domain to play the role of "peak shaving and valley filling" to alleviate load pressure, but the investment cost is too high. In order to explore the impact of renewable energy and energy storage access to the distribution network on the carrying capacity, realize the reasonable development of regional power grid users and energy construction, and establish a scientific and comprehensive evaluation method to comprehensively analyze the carrying capacity of the distribution network, and solve the planning problems of renewable energy and access to the distribution network. The carrying capacity of the distribution network refers to the limit value of the load that can be accommodated under the various performance requirements and constraints of the distribution network. There are few studies on the evaluation methods of the distribution network limit load. When the load accepted by different nodes in the distribution network increases, the changes in the distribution network node voltage, branch current and other states are more complicated, and it is difficult to confirm the distribution network flow section under the limit load. The relationship between the node voltage and load, and the branch current and load of the entire distribution network are:

[0003] U=U0-J l L+J dg P DG +J ess P ESS

[0004]

[0005] Among them, U represents the distribution network node voltage set, U0 represents the initial node voltage phasor, J1 represents the load factor matrix, L represents the load set, J dg represents the DG coefficient matrix, P DG represents the access DG power set, J ess represents the energy storage coefficient matrix, P ESS Represents the connected energy storage power set, I in represents the branch current between node i-1 and node i, U i represents the voltage at node i, P in and Q in denote the active power and reactive power injected into node i, respectively, P DG,p represents the new energy output at node p, P ESS,k represents the energy storage output at node k, χ i,p represents the new energy coefficient decision factor, χ i,k represents the energy storage coefficient decision factor, θ I,iRepresents the current phase angle.

[0006] For node voltage, when the voltages of all nodes in the distribution network are known, the load set L is unique. When the node is at the allowable lower voltage limit, all nodes are traversed. At this time, there is at least one set of load solutions that makes this value the carrying capacity under the voltage constraint of the distribution network; for branch current, the point with the largest line current is often at the line closest to the upper distribution network. When the current at this point increases to the allowable operating limit of the distribution network, the load reaches the maximum value, that is, the carrying capacity under the branch current constraint.

[0007] There are many improvements on the load-carrying capacity assessment methods in existing research, including: (1) an access capacity planning method based on opportunity constraints, which is solved by artificial intelligence algorithm; (2) analyzing the characteristics of multiple access entities in the distribution network, calculating the similarity index, and establishing a two-stage optimization model with the goals of optimal similarity and maximum access; (3) studying the operating characteristics of distribution networks with high access ratios, selecting appropriate indicators to establish a distribution network DG load-carrying capacity assessment system, and using the comprehensive score as the result to evaluate the limit value of distribution network DG access, and indirectly evaluating the surplus that can accommodate the load; (4) by analyzing and modeling the main loads replaced by electric energy, taking the maximum load access as the goal, a distribution network load-carrying capacity optimization model is established based on the second-order cone relaxation transformation, which reduces the difficulty of solving the problem and verifies the role of flexible resource optimization scheduling and load regulation capabilities in improving the distribution network load-carrying capacity.

[0008] Most of the existing distribution network carrying capacity assessment methods are based on indirect measurement or completely use indicators as assessment means. They do not directly optimize the maximum load that the distribution network can accommodate. The carrying capacity is difficult to solve, and most of them only consider the situation of new energy access, without taking into account the economic benefits of energy storage. Summary of the invention

[0009] The purpose of the present invention is to solve the problem that the existing distribution network carrying capacity assessment method is difficult to solve and does not take into account the economic benefits of energy storage. A distribution network carrying capacity assessment method considering new energy and energy storage access is proposed.

[0010] The technical solution of the present invention is: a method for evaluating the carrying capacity of a distribution network considering the access of new energy and energy storage, comprising the following steps:

[0011] S1. Initialize the population of distribution network load information samples, and input the initialized population into the distribution network carrying capacity model for power flow calculation.

[0012] S2. Determine whether the current population meets the constraints of the distribution network carrying capacity model. If so, proceed to step S3; otherwise, return to step S1.

[0013] S3. Use the improved Pareto optimal genetic algorithm to perform Pareto optimal selection on the current population and obtain the Pareto optimal solution set.

[0014] S4. Use the combined weighting method to perform weighted summation on the various indicators in the Pareto optimal solution set to obtain the distribution network carrying capacity assessment result.

[0015] Furthermore, in step S1, the distribution network carrying capacity model takes the maximum sum of loads as the objective function, and the expression is:

[0016]

[0017] Where f1 represents the objective function of the distribution network carrying capacity model, S i,t It represents the load capacity accepted by node i in the distribution network at time t, and N represents the number of nodes in the distribution network.

[0018] Furthermore, the constraints of the distribution network carrying capacity model in step S2 include line flow constraints, node voltage constraints, branch current constraints, distributed new energy capacity constraints, energy storage model constraints and transformer reverse load rate constraints.

[0019] The line power flow constraint is:

[0020]

[0021] Among them I ij,t represents the current of the branch ij of the distribution network at time t, P ij,t It represents the active power at the head end of the branch ij of the distribution network at time t, Q ij,t It represents the reactive power of the branch ij of the distribution network at time t, U i,t represents the voltage of distribution network node i at time t, U j,t represents the voltage of distribution network node j at time t, P j,t represents the active power injected into node j by the distribution network, Q j,t represents the reactive power injected into node j by the distribution network, P jn,t represents the active power of line jn at time t, Q jn,t represents the reactive power of line jn at time t, R ij represents the resistance of the branch ij of the distribution network, X ij represents the reactance of branch ij of the distribution network, n is the child node of node j, and m represents the number of child nodes of node j.

[0022] The node voltage constraints are:

[0023] U min ≤U i,t ≤U max

[0024] Among them U min Indicates the lower limit of node voltage, U max Indicates the upper limit of the node voltage allowed.

[0025] The branch current constraint is:

[0026] I ij,t ≤I lim

[0027] Among them I lim Indicates the maximum allowable value of the current that can continuously pass through the line.

[0028] Distributed renewable energy capacity constraints are:

[0029]

[0030] Where S DG,i represents the capacity of distributed renewable energy access to node i, S poi_m Indicates the maximum DG capacity allowed to be accessed by the node, S sys_m Indicates the maximum total capacity of DGs allowed to be connected in the system, D DG Represents the node set of the distribution network connected to distributed renewable energy.

[0031] The energy storage model constraints are:

[0032] P DG,t +P ESS,t +P energe,t =P load,t +P loss,t

[0033] -P max,c ≤P ESS,t ≤P max,d

[0034] SOC min ≤SOC i,t ≤SOC max

[0035]

[0036] SOC i,start =SOC i,end

[0037] Where P DG,t represents the DG output at time t, P ESS,t represents the energy storage power at time t, P energe,t represents the transmission power of the upper power grid at time t, P load,t represents the total load power at time t, P loss,t represents the loss at time t, P max,cIndicates the maximum power of energy storage charging, P max,d Indicates the maximum power of energy storage discharge, SOC i,t It represents the energy storage charge state installed at node i at time t, SOC min Indicates the minimum state of charge of energy storage, SOC max Indicates the maximum value of the energy storage charge state, E ESS,t represents the energy storage access capacity at time t, η c,i represents the charging efficiency of node i, η d,i represents the discharge efficiency of node i, represents the charging power of the energy storage at node i at time t, represents the discharge power of the energy storage at node i at time t, SOC i,start Represents the initial value of the energy storage charge at node i, SOC i,end Represents the final value of the energy storage charge at node i.

[0038] The transformer reverse load rate constraint is:

[0039]

[0040] Where S T.max Indicates the maximum transmission capacity allowed by the transformer, P DG,i represents the DG output at node i, P load,i represents the equivalent load output on node i, λ max Indicates the maximum reverse load rate.

[0041] Further, step S3 includes the following sub-steps:

[0042] S31. Calculate the voltage deviation rate C of the nth generation sample in the population VEI,n , Load growth rate C LGR,n and return on investment C ROI,n And the voltage deviation rate C of the n-1 generation sample VEI,n-1 , Load growth rate C LGR,n-1 and return on investment C ROI,n-1 .

[0043] S32, determine whether (C VEI,n <C VEI,n-1 )∩(C LGR,n >C LGR,n-1 )∩(C ROI,n <C ROI,n-1 ), if yes, replace the n-1 generation sample with the n generation sample as the sample screened by the population, otherwise keep the n-1 generation sample as the sample screened by the population.

[0044] S33, calculate the voltage deviation rate C of the sample screened by the population VEI,h, Load growth rate C LGR,h and return on investment C ROI,h And the voltage deviation rate C of the archive sample VEI,g , Load growth rate C LGR,g and return on investment C ROI,g .

[0045] S34, determine whether (C VEI,h <C VEI,g )∩(C LGR,h >C LGR,g )∩(C ROI,h <C ROI,g ), if yes, replace the file set sample with the sample screened by the population as the optimal sample, otherwise keep the file set sample as the optimal sample.

[0046] S35. Determine whether the number of iterations reaches a preset threshold. If so, output the optimal sample as the Pareto optimal solution set. Otherwise, perform replication, crossover and mutation operations on the individuals in the optimal sample, increase the number of iterations by 1, and return to step S2.

[0047] Furthermore, the calculation formula of the voltage deviation rate is:

[0048]

[0049] Among them C VEI Indicates the voltage deviation rate, U i,rated represents the voltage rating of node i, U i,t represents the voltage of distribution network node i at time t, and N represents the total number of distribution network nodes.

[0050] The calculation formula for the load growth rate is:

[0051]

[0052] Among them C LGR represents the load growth rate, S load,t It represents the total load accepted by the distribution network at time t, S l-ave,t Represents the total initial distribution network load.

[0053] The formula for calculating the return on investment is:

[0054]

[0055] Among them C ROI represents the return on investment, C In represents the energy storage investment function, C Re represents the energy storage benefit function, μ cesc represents the unit energy storage capacity investment, E ESS,i represents the access energy storage capacity of node i, NESS Indicates the number of energy storage access nodes, μ espc Represents the unit energy storage power investment, P E-rated,i represents the energy storage rated power of node i, P ESS,t represents the power of energy storage at time t, μ ic represents infrastructure investment, μ toll,t Indicates the electricity fee charged during time period t, μ year represents the time length coefficient, and T represents the scheduling period.

[0056] Further, step S4 includes the following sub-steps:

[0057] S41. Forward and normalize the indicators in the Pareto optimal solution set:

[0058]

[0059] where x ij represents the original index, x ij,I represents the positive index, x ij,II Represents a normalized index.

[0060] S42, according to the positive index and the normalization index, the judgment matrix is ​​constructed using the scaling system to obtain the corresponding maximum eigenvalue ξ max and characteristic phase D.

[0061] S43, for the maximum eigenvalue ξ max To perform a consistency check:

[0062]

[0063] Where k represents the order of the judgment matrix, c CI represents the consistency index, c RI represents the random consistency index, c CR Indicates consistency deviation, when c CR <0.1, the consistency check is passed.

[0064] S44, for the maximum eigenvalue ξ that passes the consistency check max The corresponding characteristic phasor D is normalized to obtain the subjective weight phasor.

[0065] S45. Use the entropy weight method to calculate the objective weights of each indicator in the Pareto optimal solution set:

[0066]

[0067] Where E j represents the information entropy of the j-th indicator, p ijrepresents the information entropy of the i-th indicator of the j-th category, q represents the number of indicators of the j-th category, r represents the number of indicator types, β j Represents the objective weight of the j-th indicator.

[0068] S46. The combined weight of the indicators is obtained based on the subjective weight and objective weight of the indicators:

[0069]

[0070] where α j represents the subjective weight of the j-th indicator, w j Represents the combined weight of the j-th category indicator.

[0071] S47. Perform weighted summation on each indicator in the Pareto optimal solution set according to the combined weight to obtain the distribution network carrying capacity assessment result.

[0072] The beneficial effects of the present invention are:

[0073] (1) The present invention solves the problem that the distribution network state is complex and the power flow section is difficult to determine when the carrying capacity is directly measured in the prior art. By constructing the objective function and constraint conditions of the distribution network carrying capacity model, a distribution network carrying capacity evaluation model is comprehensively constructed from the aspects of safety, economy and flexibility. In combination with the improved Pareto optimal genetic algorithm, the difficulty of solving the distribution network carrying capacity is reduced.

[0074] (2) The present invention solves the problem that the existing distribution network carrying capacity evaluation method does not consider the economic benefits of energy storage. For energy storage access, the present invention constructs an investment return rate indicator C ROI In the process of solving the carrying capacity, the optimal energy storage access plan is confirmed. At the same time, based on the combined weighting method, the calculated carrying capacity indicators are weighted and summed, and the distribution network carrying capacity taking into account DG and energy storage access is comprehensively considered. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 The figure shows a flow chart of a method for evaluating the carrying capacity of a distribution network taking into account the access of new energy and energy storage, provided in an embodiment of the present invention.

[0076] Figure 2 Shown is a Pareto optimal selection strategy diagram provided by an embodiment of the present invention.

[0077] Figure 3 Shown is a load and DG power prediction curve diagram provided by an embodiment of the present invention.

[0078] Figure 4 Shown is a fitness curve diagram of an improved genetic algorithm provided by an embodiment of the present invention.

[0079] Figure 5Shown is a timing curve diagram of various indicators of scenarios 1 to 4 provided in an embodiment of the present invention.

[0080] Figure 6 Shown is a timing curve diagram of various indicators of scenes 5 to 8 provided in an embodiment of the present invention.

[0081] Figure 7 Shown is a graph of comprehensive scoring curves for eight scenarios provided in an embodiment of the present invention.

[0082] Figure 8 Shown is a graph of six indicators of the Pareto solution set provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0083] Now, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are only exemplary and are intended to explain the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0084] The embodiment of the present invention provides a method for evaluating the carrying capacity of a distribution network considering the access of new energy and energy storage. Figure 1 As shown, the following steps S1 to S4 are included:

[0085] S1. Initialize the population of distribution network load information samples, and input the initialized population into the distribution network carrying capacity model for power flow calculation.

[0086] Since the maximum line current is often in the line closest to the upper level of the distribution network, its injected power is approximately the sum of all net loads in the distribution network. Increasing the load accepted by the distribution network will increase the branch current accordingly. When it increases to the current limit allowed for operation, the sum of the accommodated loads reaches the maximum value, which is the carrying capacity under the branch current constraint.

[0087] In the embodiment of the present invention, the distribution network carrying capacity model takes the maximum sum of loads as the objective function, and the expression is:

[0088]

[0089] Where f1 represents the objective function of the distribution network carrying capacity model, S i,t It represents the load capacity accepted by node i in the distribution network at time t, and N represents the number of nodes in the distribution network.

[0090] S2. Determine whether the current population meets the constraints of the distribution network carrying capacity model. If so, proceed to step S3; otherwise, return to step S1.

[0091] In an embodiment of the present invention, the constraints of the distribution network carrying capacity model include line flow constraints, node voltage constraints, branch current constraints, distributed new energy capacity constraints, energy storage model constraints and transformer reverse load rate constraints.

[0092] The line power flow constraint is:

[0093]

[0094] Among them I ij,t represents the current of the branch ij of the distribution network at time t, P ij,t It represents the active power at the head end of the branch ij of the distribution network at time t, Q ij,t It represents the reactive power of the branch ij of the distribution network at time t, U i,t represents the voltage of distribution network node i at time t, U j,t represents the voltage of distribution network node j at time t, P j,t represents the active power injected into node j by the distribution network, Q j,t represents the reactive power injected into node j by the distribution network, P jn,t represents the active power of line jn at time t, Q jn,t represents the reactive power of line jn at time t, R ij represents the resistance of the branch ij of the distribution network, X ij represents the reactance of branch ij of the distribution network, n is the child node of node j, and m represents the number of child nodes of node j.

[0095] The node voltage constraints are:

[0096] U min ≤U i,t ≤U max

[0097] Among them U min Indicates the lower limit of node voltage, U max Indicates the upper limit of the node voltage allowed.

[0098] The branch current constraint is:

[0099] I ij,t ≤I lim

[0100] Among them I lim Indicates the maximum allowable value of the current that can continuously pass through the line.

[0101] Distributed renewable energy capacity constraints are:

[0102]

[0103] Where S DG,i represents the capacity of distributed renewable energy access to node i, S poi_m Indicates the maximum DG capacity allowed to be accessed by the node, S sys_m Indicates the maximum total capacity of DGs allowed to be connected in the system, DDG Represents the node set of the distribution network connected to distributed renewable energy.

[0104] The energy storage model constraints are:

[0105] P DG,t +P ESS,t +P energe,t =P load,t +P loss,t

[0106] -P max,c ≤P ESS,t ≤P max,d

[0107] SOC min ≤SOC i,t ≤SOC max

[0108]

[0109] SOC i,start =SOC i,end

[0110] Where P DG,t represents the DG output at time t, P ESS,t represents the energy storage power at time t, P energe,t represents the transmission power of the upper power grid at time t, P load,t represents the total load power at time t, P loss,t represents the loss at time t, P max,c Indicates the maximum power of energy storage charging, P max,d Indicates the maximum power of energy storage discharge, SOC i,t It represents the energy storage charge state installed at node i at time t, SOC min Indicates the minimum state of charge of energy storage, SOC max Indicates the maximum value of the energy storage charge state, E ESS,t represents the energy storage access capacity at time t, η c,i represents the charging efficiency of node i, η d,i represents the discharge efficiency of node i, represents the charging power of the energy storage at node i at time t, represents the discharge power of the energy storage at node i at time t, SOC i,start Represents the initial value of the energy storage charge at node i, SOC i,end Represents the final value of the energy storage charge at node i.

[0111] The transformer reverse load rate constraint is:

[0112]

[0113] Where S T.max Indicates the maximum transmission capacity allowed by the transformer, P DG,i represents the DG output at node i, P load,i represents the equivalent load output on node i, λ max Indicates the maximum reverse load rate.

[0114] S3. Use the improved Pareto optimal genetic algorithm to perform Pareto optimal selection on the current population and obtain the Pareto optimal solution set.

[0115] Step S3 includes the following sub-steps S31 to S35:

[0116] S31. Calculate the voltage deviation rate C of the nth generation sample in the population VEI,n , Load growth rate C LGR,n and return on investment C ROI,n And the voltage deviation rate C of the n-1 generation sample VEI,n-1 , Load growth rate C LGR,n-1 and return on investment C ROI,n-1 .

[0117] In the embodiment of the present invention, three indicators, namely voltage deviation rate, load growth rate and investment return rate, are introduced to perform Pareto optimal screening. The specific screening process is as follows: Figure 2 shown.

[0118] The voltage deviation rate represents the critical situation of the distribution network system at a certain time point. The smaller the value, the better. The calculation formula is:

[0119]

[0120] Among them C VEI Indicates the voltage deviation rate, U i,rated represents the voltage rating of node i, U i,t represents the voltage of distribution network node i at time t, and N represents the total number of distribution network nodes.

[0121] The load growth rate indicates the growth ratio of the total load in a certain period of time compared with the original load. The larger the value, the better. The calculation formula is:

[0122]

[0123] Among them C LGR represents the load growth rate, S load,t It represents the total load accepted by the distribution network at time t, S l-ave,t Represents the total initial distribution network load.

[0124] The return on investment reflects the resource investment required to maintain the power stability of the distribution network in the time domain. The smaller the value, the better. The calculation formula is:

[0125]

[0126] Among them C ROI represents the return on investment, C In represents the energy storage investment function, C Re represents the energy storage benefit function, μ cesc represents the unit energy storage capacity investment, E ESS,i represents the access energy storage capacity of node i, N ESS Indicates the number of energy storage access nodes, μ espc Represents the unit energy storage power investment, P E-rated,i represents the energy storage rated power of node i, P ESS,t represents the power of energy storage at time t, μ ic represents infrastructure investment, μ toll,t Indicates the electricity fee charged during time period t, μ year represents the time length coefficient, and T represents the scheduling period.

[0127] S32, determine whether (C VEI,n <C VEI,n-1 )∩(C LGR,n >C LGR,n-1 )∩(C ROI,n <C ROI,n-1 ), if yes, it means that the indicators of the n-th generation samples are better than those of the n-1th generation samples. In this case, the n-1th generation samples are replaced by the n-th generation samples as the samples screened by the population. Otherwise, it means that the indicators of the n-1th generation samples are better than those of the n-th generation samples. In this case, the n-1th generation samples are retained as the samples screened by the population.

[0128] S33, calculate the voltage deviation rate C of the sample screened by the population VEI,h , Load growth rate C LGR,h and return on investment C ROI,h And the voltage deviation rate C of the archive sample VEI,g , Load growth rate C LGR,g and return on investment C ROI,g .

[0129] S34, determine whether (C VEI,h <C VEI,g )∩(C LGR,h >C LGR,g )∩(C ROI,h <C ROI,g), if so, it means that the indicators of the sample screened by the population are better than those of the sample in the archive set. In this case, the sample in the archive set is replaced by the sample screened by the population as the optimal sample. Otherwise, it means that the indicators of the sample in the archive set are better than those of the sample screened by the population. In this case, the sample in the archive set is retained as the optimal sample.

[0130] S35. Determine whether the number of iterations reaches a preset threshold. If so, output the optimal sample as the Pareto optimal solution set. Otherwise, perform replication, crossover and mutation operations on the individuals in the optimal sample, increase the number of iterations by 1, and return to step S2.

[0131] S4. Use the combined weighting method to perform weighted summation on the various indicators in the Pareto optimal solution set to obtain the distribution network carrying capacity assessment result.

[0132] Step S4 includes the following sub-steps S41 to S47:

[0133] S41. Forward and normalize the indicators in the Pareto optimal solution set:

[0134]

[0135] where x ij represents the original index, x ij,I represents the positive index, x ij,II Represents a normalized index.

[0136] S42, according to the positive index and the normalization index, the judgment matrix is ​​constructed using the scaling system to obtain the corresponding maximum eigenvalue ξ max and characteristic phase D.

[0137] S43, for the maximum eigenvalue ξ max To perform a consistency check:

[0138]

[0139] Where k represents the order of the judgment matrix, c CI represents the consistency index, c RI represents the random consistency index, c CR Indicates consistency deviation, when c CR <0.1, the consistency check is passed.

[0140] S44, for the maximum eigenvalue ξ that passes the consistency check max The corresponding characteristic phasor D is normalized to obtain the subjective weight phasor.

[0141] S45. Use the entropy weight method to calculate the objective weights of each indicator in the Pareto optimal solution set:

[0142]

[0143] Where E j represents the information entropy of the j-th indicator, p ij represents the information entropy of the i-th indicator of the j-th category, q represents the number of indicators of the j-th category, r represents the number of indicator types, β j Represents the objective weight of the j-th indicator.

[0144] S46. The combined weight of the indicators is obtained based on the subjective weight and objective weight of the indicators:

[0145]

[0146] where α j represents the subjective weight of the j-th indicator, w j Represents the combined weight of the j-th category indicator.

[0147] S47. Perform weighted summation on each indicator in the Pareto optimal solution set according to the combined weight to obtain the distribution network carrying capacity assessment result.

[0148] The application effect of the present invention is described in detail below in conjunction with a simulation example.

[0149] An IEEE33-node distribution network model is established to first verify the effect of new energy and energy storage on the carrying capacity of the distribution network.

[0150] Eight scenarios are set: (1) original IEEE 33 node distribution network; (2) connected to photovoltaics; (3) connected to wind turbines; (4) connected to energy storage; (5) connected to wind turbines and photovoltaics; (6) connected to photovoltaics and energy storage; (7) connected to wind turbines and energy storage; (8) connected to wind, photovoltaics and energy storage. The simulated load and DG power prediction curves are shown in Figure 2. Figure 3 As shown in the figure, with 24 hours as a scheduling cycle, assuming that the access location and total capacity of new energy and energy storage are fixed, the proposed combined weighted improved Pareto optimal genetic algorithm is used to solve the carrying capacity and corresponding indicators of each scenario. The algorithm fitness curve is shown in Figure 4 As shown in the figure, the total number of iterations is 100, the duration is about 1 minute, and the genetic early stage can converge quickly.

[0151] The carrying capacity of each scenario from scenario 1 to 8 is 5.2492MW; 5.2734MW; 5.7850MW; 5.4442MW; 5.7718MW; 5.7563MW; 5.8820MW; 5.7989MW. Scenarios 1 to 4 and 5 to 8 are discussed. Among scenarios 1 to 4, the carrying capacity of scenarios 1 and 2 is very small, and scenarios 3 and 4 are greatly improved compared to scenario 1. The time series curves of various indicators in scenarios 1 to 4 are as follows Figure 5 shown.

[0152] Depend on Figure 5 The analysis leads to the conclusion that:

[0153] (1) The carrying capacity depends on the moment when the system net load is maximum.

[0154] (2) The increase in carrying capacity and the decrease in voltage deviation are affected by the characteristics of the load and DG curve. The more the load and DG characteristic curves fit, the better the effect of increasing carrying capacity.

[0155] (3) Energy storage can improve the carrying capacity of the distribution network and smooth out net load fluctuations, but it has little effect on optimizing the overall voltage quality of the distribution network.

[0156] Scenarios 5 to 8 have all been significantly improved compared to Scenarios 1. Among them, Scenarios 6 has the worst improvement effect. The carrying capacity improvements of Scenarios 5 and 8 are not much different. Scenarios 7 has the largest improvement among all the scenarios. The time series curves of various indicators in Scenarios 5 to 8 are as follows: Figure 6 shown.

[0157] Depend on Figure 6 The analysis leads to the conclusion that:

[0158] (1) The combination of DG and energy storage can further improve the carrying capacity.

[0159] (2) The effect of improving the carrying capacity of energy storage is affected by the net load characteristic curve. The smoother the curve, the smaller the improvement effect.

[0160] Based on the above analysis, the comprehensive scores obtained by weighting the 8 scene combinations are as follows: Figure 7 shown. Figure 7 This shows that scenario 7 has the best comprehensive score in all time periods, which verifies the correctness of the conclusions drawn from the above scenarios.

[0161] Secondly, the rationality of the proposed combined weighted improved Pareto optimal genetic algorithm is verified.

[0162] Taking scenario 8 as an example, the proposed combined weighted improved Pareto optimal genetic algorithm is used to calculate the distribution network carrying capacity. The evaluated Pareto solution set is sorted from high to low according to the score. The output of the optimal solution corresponds to a carrying capacity of 5.622MW, which is a 30.45% increase over the original load and a 7.10% increase over the original distribution network carrying capacity. The six indicator curves corresponding to the first 100 samples are as follows: Figure 8 shown.

[0163] Depend on Figure 8Analysis shows that the curve change trend of the voltage deviation rate is the same as the load growth rate. As the load increases, the system voltage, current, and network loss quality will inevitably deteriorate. The voltage deviation rate plays a constraining role in the assessment; while the trend of the return on investment is similar to the load growth rate, which plays a promoting role in the assessment. The combined weighting integrates the role of various indicators in the assessment, making the carrying capacity assessment more comprehensive.

[0164] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for evaluating the carrying capacity of a distribution network considering the access of new energy and energy storage, characterized in that: The following steps are involved: S1. Initialize the population of distribution network load information samples, and input the initialized population into the distribution network carrying capacity model for power flow calculation; S2, determine whether the current population meets the constraints of the distribution network carrying capacity model, if so, proceed to step S3, otherwise return to step S1; S3, using the improved Pareto optimal genetic algorithm to perform Pareto optimal selection on the current population and obtain the Pareto optimal solution set; S4. Use the combined weighting method to perform weighted summation on the various indicators in the Pareto optimal solution set to obtain the distribution network carrying capacity assessment result.

2. The method for evaluating the carrying capacity of a distribution network considering the access of new energy and energy storage according to claim 1 is characterized in that: In step S1, the distribution network carrying capacity model takes the maximum sum of loads as the objective function, and the expression is: Where f1 represents the objective function of the distribution network carrying capacity model, S i,t It represents the load capacity accepted by node i in the distribution network at time t, and N represents the number of nodes in the distribution network.

3. The method for evaluating the carrying capacity of a distribution network considering the access of new energy and energy storage according to claim 1 is characterized in that: The constraints of the distribution network carrying capacity model in step S2 include line flow constraints, node voltage constraints, branch current constraints, distributed new energy capacity constraints, energy storage model constraints and transformer reverse load rate constraints; The line power flow constraint is: Among them I ij,t represents the current of the branch ij of the distribution network at time t, P ij,t It represents the active power at the head end of the branch ij of the distribution network at time t, Q ij,t It represents the reactive power of the branch ij of the distribution network at time t, U i,t represents the voltage of distribution network node i at time t, U j,t represents the voltage of distribution network node j at time t, P j,t represents the active power injected into node j by the distribution network, Q j,t represents the reactive power injected into node j by the distribution network, P jn,t represents the active power of line jn at time t, Q jn,t represents the reactive power of line jn at time t, R ij represents the resistance of the branch ij of the distribution network, X ij represents the reactance of the branch ij of the distribution network, n is the child node of node j, and m represents the number of child nodes of node j; The node voltage constraint is: IN min ≤U i,t ≤U max Among them U min Indicates the lower limit of node voltage, U max Indicates the upper limit of node voltage allowed; The branch current constraint is: I ij,t ≤I lim Among them I lim Indicates the maximum allowable value of the current that continuously passes through the line; The distributed new energy capacity constraint is: Where S DG,i represents the capacity of distributed renewable energy access to node i, S poi_m Indicates the maximum DG capacity allowed to be accessed by the node, S sys_m Indicates the maximum total capacity of DGs allowed to be connected in the system, D DG Represents the node set of the distribution network connected to distributed renewable energy; The energy storage model constraints are: P DG,t +P ESS,t +P energe,t =P load,t +P loss,t -P max,c ≤P ESS,t ≤P max,d SOC min ≤SOC i,t ≤SOC max SOCIETY i,start =SOC i,end Where P DG,t represents the DG output at time t, P ESS,t represents the energy storage power at time t, P energe,t represents the transmission power of the upper power grid at time t, P load,t represents the total load power at time t, P loss,t represents the loss at time t, P max,c Indicates the maximum power of energy storage charging, P max,d Indicates the maximum power of energy storage discharge, SOC i,t It represents the energy storage charge state installed at node i at time t, SOC min Indicates the minimum state of charge of energy storage, SOC max Indicates the maximum value of the energy storage charge state, E ESS,t represents the energy storage access capacity at time t, η c,i represents the charging efficiency of node i, η d,i represents the discharge efficiency of node i, represents the charging power of the energy storage at node i at time t, represents the discharge power of the energy storage at node i at time t, SOC i,start Represents the initial value of the energy storage charge at node i, SOC i,end represents the final value of the energy storage charge at node i; The transformer reverse load rate constraint is: Where S T.max Indicates the maximum transmission capacity allowed by the transformer, P DG,i represents the DG output at node i, P load,i represents the equivalent load output on node i, λ max Indicates the maximum reverse load rate.

4. The method for evaluating the carrying capacity of a distribution network considering the access of new energy and energy storage according to claim 1, characterized in that: The step S3 comprises the following sub-steps: S31. Calculate the voltage deviation rate C of the nth generation sample in the population VEI,n , Load growth rate C LGR,n and return on investment C ROI,n And the voltage deviation rate C of the n-1 generation sample VEI,n-1 , Load growth rate C LGR,n-1 and return on investment C ROI,n-1 ; S32, determine whether (C VEI,n <C VEI,n-1 )∩(C LGR,n >C LGR,n-1 )∩(C ROI,n <C ROI,n-1 ), if yes, then replace the n-1 generation sample with the n generation sample as the sample screened by the population, otherwise keep the n-1 generation sample as the sample screened by the population; S33, calculate the voltage deviation rate C of the sample screened by the population VEI,h , Load growth rate C LGR,h and return on investment C ROI,h And the voltage deviation rate C of the archive sample VEI,g , Load growth rate C LGR,g and return on investment C ROI,g ; S34, determine whether (C VEI,h <C VEI,g )∩(C LGR,h >C LGR,g )∩(C ROI,h <C ROI,g ), if yes, replace the file set sample with the sample screened by the population as the optimal sample, otherwise keep the file set sample as the optimal sample; S35. Determine whether the number of iterations reaches a preset threshold. If so, output the optimal sample as the Pareto optimal solution set. Otherwise, perform replication, crossover and mutation operations on the individuals in the optimal sample, increase the number of iterations by 1, and return to step S2.

5. The method for evaluating the carrying capacity of a distribution network considering the access of new energy and energy storage according to claim 4 is characterized in that: The calculation formula of the voltage deviation rate is: Among them C VEI Indicates the voltage deviation rate, U i,rated represents the voltage rating of node i, U i,t represents the voltage of distribution network node i at time t, and N represents the total number of distribution network nodes; The calculation formula of the load growth rate is: Among them C LGR represents the load growth rate, S load,t It represents the total load accepted by the distribution network at time t, S l-ave,t represents the total initial distribution network load; The calculation formula for the return on investment is: Among them C ROI represents the return on investment, C In represents the energy storage investment function, C Re represents the energy storage benefit function, μ cesc represents the unit energy storage capacity investment, E ESS,i represents the access energy storage capacity of node i, N ESS Indicates the number of energy storage access nodes, μ espc Represents the unit energy storage power investment, P E-rated,i represents the energy storage rated power of node i, P ESS,t represents the power of energy storage at time t, μ ic represents infrastructure investment, μ toll,t Indicates the electricity fee charged during time period t, μ year represents the time length coefficient, and T represents the scheduling period.

6. The method for evaluating the carrying capacity of a distribution network considering the access of new energy and energy storage according to claim 1, characterized in that: The step S4 comprises the following sub-steps: S41. Forward and normalize the indicators in the Pareto optimal solution set: where x ij represents the original index, x ij,I represents the positive index, x ij,II represents the normalized index; S42, according to the positive index and the normalization index, the judgment matrix is ​​constructed using the scaling system to obtain the corresponding maximum eigenvalue ξ max and characteristic phase D; S43, for the maximum eigenvalue ξ max To perform a consistency check: Where k represents the order of the judgment matrix, c CI represents the consistency index, c RI represents the random consistency index, c CR Indicates consistency deviation, when c CR <0.1, the consistency check is passed; S44, for the maximum eigenvalue ξ that passes the consistency check max The corresponding characteristic phasor D is normalized to obtain the subjective weight phasor; S45. Use the entropy weight method to calculate the objective weights of each indicator in the Pareto optimal solution set: Where E j represents the information entropy of the j-th indicator, p ij represents the information entropy of the i-th indicator of the j-th category, q represents the number of indicators of the j-th category, r represents the number of indicator types, β j represents the objective weight of the j-th indicator; S46. The combined weight of the indicators is obtained based on the subjective weight and objective weight of the indicators: where α j represents the subjective weight of the j-th indicator, w j represents the combined weight of the j-th category indicator; S47. Perform weighted summation on each indicator in the Pareto optimal solution set according to the combined weight to obtain the distribution network carrying capacity assessment result.

Images