Fault reconstruction method for digital micro-grid

Abstract

The invention discloses a fault reconstruction method of a digital micro-grid, and particularly relates to a fault reconstruction method of a digital micro-grid considering uncertain renewable energy power generation and a demand side resource aggregator. According to the method, a load aggregator demand response model based on a price elastic coefficient is established, and the uncertainty of renewable energy power generation accessed into the novel power distribution network is effectively described through chance constraint. In addition, a power flow model suitable for novel power distribution network reconstruction is established, and then a new energy power distribution system reconstruction model is established by considering network loss and switching cost. Aiming at the non-convexity of the reconstruction model, a large M method and second-order cone relaxation are adopted, and an intermediate variable is introduced to convert the reconstruction model into a mixed integer second-order cone programming problem. According to the method, through simulation experiment analysis, detailed verification results are given, and the reliability and effectiveness of the method in the aspect of fault reconstruction of the new energy power distribution system with demand side resources and uncertain renewable energy power generation are explained.

Description

Technical Field

[0001] The present invention relates to a fault reconstruction method, in particular to a digital microgrid fault reconstruction method considering uncertain renewable energy generation and demand-side resource aggregators, and belongs to the field of power system operation control. Background Art

[0002] With the unprecedented increase in electricity demand, people's requirements for the quality and reliability of electricity demand are also increasing, and higher standards are also being put forward for the operation of distribution systems. As a key means of optimizing the operation of distribution networks, distribution system reconstruction can improve the reliability and economy of the entire distribution system by flexibly adjusting the topology of the network, and improve the voltage level to improve the quality of power. However, in recent years, with the energy crisis and environmental deterioration, clean energy has been greatly developed. A large number of distributed generation such as photovoltaics and wind turbines are connected to the distribution network, and the penetration rate of new energy generation in the distribution system has increased year by year. The inherent uncertainty of distributed generation has brought great challenges to the reconstruction of distribution networks. In addition, with the development of power technology, the distribution system has gradually connected to a large number of demand-side resources. Considering demand-side resources in system reconstruction is also of great significance for improving the economy and reliability of system operation. In short, there is an urgent need to study the distribution system reconstruction method suitable for high-proportion clean energy generation and combined with various demand-side resources. And accurately characterizing the uncertainty of distributed generation during the reconstruction process is crucial to ensure the effectiveness and reliability of the reconstruction strategy.

[0003] According to relevant literature, the current methods for solving the distribution system reconstruction model mainly include heuristic algorithms and mathematical optimization methods. Heuristic algorithms (such as genetic algorithms and particle swarm algorithms) find the optimal solution through intelligent optimization. Although these algorithms can solve non-convex problems, they cannot guarantee the global optimal solution. In large-scale systems, their computational efficiency is low and cannot be used efficiently. Mathematical optimization algorithms need to convert non-convex models into convex optimization problems so that the global optimal solution can be solved. Therefore, constructing a reasonable distribution system reconstruction model to fully consider its integrated distributed generation and its related uncertainties, and comprehensively regulating demand-side resources, while converting the distribution network reconstruction model into a convex optimization problem to achieve the optimal reconstruction of the system is of great significance for promoting the efficient and optimal operation of the distribution system. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention establishes a demand response model for load aggregators and an energy storage aggregator model. The method establishes a distributed power generation output model through opportunity constraints to characterize its inherent uncertainty. At the same time, in order to achieve efficient solution, the implicit form of opportunity constraints is equivalently converted into deterministic inequality constraints. In addition, a Distflow power flow model suitable for the reconstruction of the new distribution system is established, and node voltage, branch current, network topology and capacitor bank switching constraints are established to ensure the safe and reliable operation of the distribution system. On this basis, the distribution system reconstruction objective function is established with economy as the goal by comprehensively considering network losses and segmented switch operation costs. In order to solve the non-convexity of the model, the large M method and second-order cone relaxation are used to convert the reconstruction model into a mixed integer second-order cone programming problem, a fault reconstruction method for a digital microgrid.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] 1. A fault reconstruction method for a digital microgrid is performed in the following steps:

[0007] 1) A demand response model for load aggregators is established through the electricity price elasticity coefficient. The demand response measures are used to respond to electricity prices to achieve peak load shaving and valley filling, and smooth the electricity load curve.

[0008] 2) Model the dynamic model of power energy storage and further reduce the operating cost of the distribution network by rationally regulating the charging and discharging behavior of energy storage;

[0009] 3) Although renewable energy generation has low-carbon and renewable characteristics, it exhibits strong uncertainty. Therefore, an opportunity constraint for renewable distributed generation is established to effectively characterize the uncertainty. At the same time, it is assumed that its distribution obeys the Gaussian distribution and is equivalently converted into a deterministic inequality constraint. The relationship between the active power and reactive power of distributed generation is established based on the power factor.

[0010] 4) According to the characteristics of the new distribution system reconstruction, a suitable improved Distflow power flow constraint is established, and the power flow constraint is transformed into a convex cone by introducing intermediate variables and using the large M method and second-order cone relaxation; node voltage, branch current, network topology and capacitor bank switching constraints are established;

[0011] 5) Taking into account the network loss cost and the section switch operation cost, the objective function of the new energy distribution system reconstruction model was established with economy as the goal, and combined with the models and constraints in steps 1) to 4), a new energy distribution system reconstruction model based on mixed integer second-order cone programming was constructed.

[0012] Preferably, the step 1) is specifically as follows:

[0013] The designed load aggregator model based on electricity price elasticity coefficient uses demand response strategy to achieve load smoothing, so as to reduce system network loss and improve the economy of reconstruction; dispatches price incentive load based on electricity price elasticity coefficient to achieve peak load shaving and valley filling; determines the quantification of load demand after adopting time-of-use electricity price based on electricity price elasticity coefficient; the electricity load elasticity coefficient represents the percentage change of electricity demand caused by electricity price change in a given time period, and its calculation method is:

[0014]

[0015] where ξ i,t represents the electricity price elasticity coefficient of node i at time t; ΔP i,t and Δρ i,t They represent the changes in power demand and electricity price after node i executes demand response at time t; and P i,t are the load power of node i before and after executing demand response at time t; and ρ i,t are the electricity prices before and after the time-of-use electricity price is adopted for node i at time t, and It is expressed as:

[0016]

[0017] where ρ peak , ρ shoulder and ρ valley are the electricity prices during peak, flat and valley periods respectively; T peak , T shoulder and T valley They are peak, flat and valley periods respectively;

[0018] In order to ensure the energy demand of the load aggregator, the total power before and after demand response remains unchanged, which can be expressed as:

[0019]

[0020] Where T is the duration of the reconstruction optimization; Δt is the duration of a moment.

[0021] Preferably, the step 2) is specifically as follows:

[0022] The electric energy storage system configured in the system effectively improves its operational flexibility. The dynamic operation model of the electric energy storage is:

[0023]

[0024] Where Q i,t The amount of electricity stored by the energy storage system configured for node i at time t; and They represent the charging power and discharging power of the energy storage system configured at node i at time t respectively; and are the charging and discharging efficiencies of the energy storage system configured at node i;

[0025] The operating power and stored power of the energy storage system must meet the corresponding constraints to ensure its healthy operation, which can be expressed as:

[0026]

[0027] in The upper limit of the charging and discharging power of the energy storage system configured for node i; Q i and The lower and upper limits of the amount of electricity that can be stored in the energy storage system configured for node i respectively.

[0028] Preferably, the step 3) is specifically as follows:

[0029] The output power of distributed power generation will show strong uncertainty. This method designs a chance constraint to capture the uncertainty of distributed generation. The probability that the output power of distributed generation meets the constraint is not less than a certain confidence level, which is expressed as:

[0030]

[0031] Where Pr(i) represents the probability of event (i) occurring; and They represent the actual value and predicted value of the output power of the distributed generation configured at node i at time t; η i The confidence level of the DG output power configured for node i;

[0032] Since the implicit form of opportunity constraints is difficult to solve directly, it is assumed that the prediction error of distributed generation output power satisfies the Gaussian distribution, which can be expressed as:

[0033]

[0034] where σ i,t,fore The standard deviation of the prediction error of the output power of the distributed generation configured for node i at time t is obtained from the following formula:

[0035]

[0036] Where Φ(i) is a random variable The cumulative distribution function of the probability distribution; according to After the predicted value and standard deviation are converted to the standard normal distribution, the formula is obtained:

[0037]

[0038] where Φ a (i) represents the cumulative distribution function of the standard normal distribution. After rearranging it, we get the deterministic equivalent of the chance constraint, expressed as:

[0039]

[0040] According to the operating power factor of the distributed power source, its output reactive power is expressed as:

[0041]

[0042] in represents the actual value of reactive power output by the distributed generation configured at node i at time t; δ i The power factor of the distributed generation configured for node i.

[0043] Preferably, the step 4) is specifically as follows:

[0044] By introducing the branch switch variable α ij A Distflow power flow model suitable for fault reconstruction of new energy distribution system is established, which can be expressed as:

[0045]

[0046]

[0047] where α ij is a binary variable representing the on / off state of branch ij. When it is 1, it means the branch is closed, and when it is 0, it means the branch is open. For the fault branch, α ij Set to 0 to avoid closing the corresponding branch in the reconstruction decision; P ij,t and Q ij,t They represent the active power and reactive power flowing through branch ij at time t respectively; r ij and x ij are the resistance and reactance of branch ij respectively; I ij,t represents the effective value of the current flowing through branch ij at time t; P j,t and Q j,t denote the injected active power and reactive power of node i respectively; is the reactive load of node i at time t; Ui , t represents the voltage amplitude of node i at time t; f(j) and s(j) represent the parent node and child node set of node j, respectively, where the parent node and child node mean the upstream and downstream nodes of the branch flow in the network, respectively; Ω is the set of all branches in the distribution network;

[0048] The above Distflow power flow model is a non-convex constraint and is only applicable to the case where the nodes are closed. For this reason, this method introduces an intermediate variable and The above model is extended to all branch sets Ω by using the uncertainty constraints and relaxing them by the big M method. The introduced intermediate variables and uncertainty constraints are expressed as:

[0049]

[0050] -α ij M1≤P ij,t ≤α ij M1

[0051] -α ij M2≤Q ij,t ≤α ij M2

[0052] -α ij M3≤I ij,t ≤α ij M3

[0053] Where M1, M2 and M3 are sufficiently large positive numbers;

[0054] Combining the intermediate variables with the original Distflow model can obtain the converted constraints, which can be expressed as:

[0055]

[0056]

[0057] The constraint obtained by further relaxation through the big M method is expressed as:

[0058] mij=M4(1-αij)

[0059]

[0060] Where M4 is a sufficiently large positive number; m ij is the intermediate variable required by branch ij;

[0061] In view of the non-convexity of the above power flow model, a second-order cone relaxation is performed on it, which is expressed as:

[0062]

[0063] During the fault reconstruction of the new energy distribution system, the voltage of each node must also meet the upper and lower limit constraints, which can be expressed as:

[0064]

[0065] in and are the lower and upper limits of the voltage amplitude at node i, respectively;

[0066] If node i is a balanced node, its node voltage amplitude is always 1, which can be expressed as:

[0067]

[0068] Due to the limitation of line transmission capacity, the branch current must also be limited to a certain range, expressed as:

[0069]

[0070] in is the upper limit of the current that can be transmitted by branch ij;

[0071] The topology of the power distribution system must satisfy the connectivity and radiation constraints, which can be expressed as:

[0072]

[0073] where f gi,t and f di,t They represent the virtual power supply and virtual load of node i at time t respectively; f ij,t is the virtual power flow of branch ij at time t; N n and N s are the number of nodes and power supplies in the power supply, respectively;

[0074] In order to deal with the undervoltage problem caused by reactive power shortage during the reconstruction process, capacitor banks are put into use for reactive power compensation. The corresponding constraints are expressed as:

[0075]

[0076] in represents the reactive power compensation capacity of node i at time t; represents the number of capacitor banks connected to node i at time t; The capacity of a single capacitor bank configured for node i; is the maximum number of capacitor banks available at node i;

[0077] Preferably, the step 5) is specifically as follows:

[0078] This method takes the operation economy of the distribution system as the goal, takes into account the network loss cost of the distribution system and the operation cost of the sectionalized switch. The objective function of the reconstruction model is expressed as:

[0079]

[0080] Where F represents the objective function; c1 and c2 are the network loss cost coefficient and the section switch operation penalty cost coefficient respectively; g 1,t represents the network loss at time t; g2 represents the number of operations of the section switch; g 1,t The calculation method is expressed as:

[0081]

[0082] The calculation method of g2 is expressed as:

[0083]

[0084] where α ij,0 Indicates the segment switch status before reconstruction.

[0085] The purpose is to solve the reconstruction problem when demand-side resources and renewable energy power generation are widely connected to the new distribution system, so as to achieve the optimal economic operation of the distribution system. A complete analysis method is proposed to address the shortcomings of existing distribution system reconstruction methods, such as the lack of consideration of the uncertainty of renewable energy power generation and demand-side resource aggregation. The designed digital microgrid fault reconstruction method considering uncertain renewable energy power generation and demand-side resource aggregators has guiding significance for the operation and control of the distribution system.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] (1) Because the present invention models the load aggregator as a demand response strategy based on the electricity price elasticity coefficient, it overcomes the problem in the prior art that the original load curve is maintained but the operating cost of the distribution system cannot be reduced, thereby achieving the goal of reducing network loss costs and improving system voltage quality through reasonable load transfer during distribution system reconstruction.

[0088] (2) Because the present invention uses chance constraints to deal with the uncertainty of distributed generation and uses the cumulative distribution function of uncertain variables to perform deterministic conversion of chance constraints, it overcomes the drawbacks of the prior art that uncertainty is not considered in the reconstruction of the distribution system, which reduces the reliability of the reconstruction scheme, and the deficiency that implicit chance constraints are difficult to solve directly, thereby realizing the reconstruction of the distribution system taking into account the uncertainty of distributed generation.

[0089] (3) Because the present invention establishes an improved Distflow power flow model suitable for the reconstruction of the distribution system, and establishes network node voltage, branch current, network topology and capacitor bank switching constraints as well as a reconstruction objective function with economy as the goal, the non-convexity of the model is converted into a mixed integer second-order cone programming problem, thereby overcoming the deficiency of the prior art that the reconstruction model cannot be efficiently solved, thereby achieving the global optimal solution of the system reconstruction model under the operation constraints of the distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 It is a schematic diagram of the new energy distribution system studied in the present invention;

[0091] Figure 2 It is a load curve diagram obtained by the fault reconstruction method of the new energy distribution system designed by the present invention;

[0092] Figure 3 This is a system voltage curve at 4:00 obtained by the fault reconstruction method of the new energy distribution system designed by the present invention;

[0093] Figure 4 It is a system voltage curve diagram at 21:00 obtained by the fault reconstruction method of the new energy distribution system designed by the present invention;

[0094] Figure 5 The fault reconstruction method of the new energy distribution system designed by the present invention obtains the system voltage curve at 4:00 under different chance constraint confidence levels;

[0095] Figure 6 The new energy distribution system fault reconstruction method designed by the present invention obtains the system voltage curve at 4:00 under different distributed generation penetration rates. DETAILED DESCRIPTION

[0096] The technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with 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.

[0097] Embodiment 1: Figure 1 This is a schematic diagram of the research object of the present invention. Figure 1 The schematic diagram of the new energy distribution system of the study shown is a fault reconstruction method of a digital microgrid, which includes the following steps:

[0098] 1) The demand response model of the load aggregator is established through the electricity price elasticity coefficient. The electricity price is responded to through demand response measures to achieve peak shaving and valley filling of the power load and smooth the power load curve. The designed load aggregator model based on the electricity price elasticity coefficient can use the demand response strategy to achieve load smoothing, so as to reduce system network losses and improve the economy of reconstruction. The price-incentive load is dispatched based on the electricity price elasticity coefficient to achieve peak shaving and valley filling of the power load. Based on the electricity price elasticity coefficient, the quantification of load demand after adopting time-of-use electricity prices can be determined. The electricity load elasticity coefficient represents the percentage change in electricity demand caused by changes in electricity prices within a given time period, and is calculated as follows:

[0099]

[0100] where ξi,t represents the electricity price elasticity coefficient of node i at time t; ΔP i,t and Δρ i,t They represent the changes in power demand and electricity price after node i executes demand response at time t; and P i,t are the load power of node i before and after executing demand response at time t; and ρ i,t are the electricity prices before and after the time-of-use electricity price is adopted for node i at time t, and It can be expressed as:

[0101]

[0102] where ρ peak , ρ shoulder and ρ valley are the electricity prices during peak, flat and valley periods respectively; T peak , T shoulder and T valley They are peak, flat and valley periods respectively.

[0103] In order to ensure the energy demand of the load aggregator, the total power before and after demand response remains unchanged, which can be expressed as:

[0104]

[0105] Where T is the duration of the reconstruction optimization; Δt is the duration of a moment.

[0106] Reference Figure 2 The load curves obtained by the simulation experiment of the present invention after reconstruction with and without considering the demand response strategy designed by the present invention are compared. It can be clearly seen from the figure that part of the load is transferred from peak hours (11:00-15:00 and 19:00-23:00) to valley hours (1:00-6:00 and 24:00) through demand response. In addition, the peak-to-valley ratios of the load under the implementation of demand response and no response are 0.0416pu and 0.8pu respectively, and the operating costs of the distribution system before and after demand response are 989.2 yuan and 1025.0 yuan respectively. The above results show that considering demand response in the reconstruction of the distribution system can effectively smooth the load level, thereby effectively reducing the peak-to-valley difference of the network load and thus reducing the operating cost. In addition, the distribution system only needs to operate the section switch 6 times in the fault reconstruction considering demand response, while the section switch needs to be operated 8 times in the case of no response.

[0107] Reference Figure 3 and Figure 4The voltage curves of each node at 4:00 (valley time) and 21:00 (peak time) after reconstruction obtained by the simulation experiment of the present invention with and without considering the demand response strategy designed by the present invention are compared. The impact of demand response on voltage quality can be clearly seen from the comparison of the results of the two figures. More specifically, during the valley period (4:00), due to demand response, some peak loads are transferred to this period, which increases the total load and reduces the total voltage level of the system. During the peak period (21:00), some loads are transferred to other periods, which reduces the total load and increases the total voltage level of the system. The above results show that considering demand response in the reconstruction of the distribution system can effectively reduce the system operating cost by reducing the peak-to-valley difference of the load, while improving the voltage quality and improving the economy and safety of system operation during peak periods.

[0108] 2) The electric energy storage system configured in the distribution network can effectively improve its operation flexibility. The dynamic model of electric energy storage is modeled, and the operation cost of the distribution network is further reduced by reasonably regulating the charging and discharging behavior of energy storage. The dynamic operation model of electric energy storage is:

[0109]

[0110] Where Q i,t The amount of electricity stored by the energy storage system configured for node i at time t; and They represent the charging power and discharging power of the energy storage system configured at node i at time t respectively; and The charging and discharging efficiencies of the energy storage system configured for node i, respectively.

[0111] The operating power and stored power of the energy storage system must meet the corresponding constraints to ensure its healthy operation, which can be expressed as:

[0112]

[0113] in The upper limit of the charging and discharging power of the energy storage system configured for node i; Q i and The lower and upper limits of the amount of electricity that can be stored in the energy storage system configured for node i respectively.

[0114] 3) Although renewable energy generation has low-carbon and renewable characteristics, it exhibits strong uncertainty. Therefore, this method designs a chance constraint to capture the uncertainty of distributed generation, which can limit the probability that the distributed generation output power meets the constraint to not less than a certain confidence level, expressed as:

[0115]

[0116] Where Pr(i) represents the probability of event (i) occurring; and They represent the actual value and predicted value of the output power of the distributed generation configured at node i at time t; η i Confidence level of the DG output power configured for node i.

[0117] Since the implicit form of opportunity constraints is difficult to solve directly, it can be assumed that the prediction error of distributed generation output power satisfies the Gaussian distribution, which can be expressed as:

[0118]

[0119] where σ i,t,fore The standard deviation of the prediction error of the output power of the distributed generation configured for node i at time t, based on which the following formula can be obtained:

[0120]

[0121] Where Φ(i) is a random variable The cumulative distribution function of the probability distribution. After the predicted value and standard deviation are converted to the standard normal distribution, we can get the formula:

[0122]

[0123] where Φ a (i) represents the cumulative distribution function of the standard normal distribution. After rearranging it, we can get the deterministic equivalent form of the chance constraint, which is expressed as:

[0124]

[0125] According to the operating power factor of the distributed power source, its output reactive power can be expressed as:

[0126]

[0127] in represents the actual value of reactive power output by the distributed generation configured at node i at time t; δ i The power factor of the distributed generation configured for node i.

[0128] Reference Figure 5The voltage curve of the entire system at 4:00 obtained by the simulation experiment of the present invention considering different confidence levels of chance constraints can show the impact of the confidence level of chance constraints on the reconstruction of the distribution system, when the distributed generation penetration rate is set to 60%. It can be seen that the overall voltage level of the system decreases with the increase of the confidence level. This is because at a higher confidence level, the distribution system operator's expectations for the output power of distributed generation become conservative, which leads to an increase in the net load of the entire system, which in turn reduces the voltage level of the system. This shows that the distribution system operator should formulate a reasonable confidence level for the chance constraint based on the actual prediction accuracy of the output power of distributed generation.

[0129] Reference Figure 6 The voltage curve of the entire system at 4:00 obtained by the simulation experiment of the present invention considering different distributed generation penetration rates can show the impact of penetration rate on the reconstruction of the distribution system. At this time, the distributed generator will be constrained to be set to 0.95. It can be seen that the voltage level of the system increases with the increase of penetration rate. This is because the increase of distributed generation penetration rate reduces the load level of the system by 3. The above results show that the appropriate clean energy penetration rate can reduce the network loss of the system during the network reconstruction process and improve the voltage quality. The costs when the penetration rate is 20%, 40%, 60% and 80% are 4461.5, 1763.9, 1194.7 and 803.8 yuan respectively. The results show that with the increase of distributed generation penetration rate, the operating cost decreases, so the development of clean energy generation is of great significance to improving the quality and efficiency of the power system.

[0130] 4) According to the characteristics of distribution system reconstruction, a suitable improved Distflow power flow constraint is established, and the intermediate variables are introduced. The large M method and second-order cone relaxation are used to transform the power flow constraint into a convex cone; node voltage, branch current, network topology and capacitor bank switching constraints are established.

[0131] By introducing the branch switch variable α ij A Distflow power flow model suitable for fault reconstruction of new energy distribution system is established, which can be expressed as:

[0132]

[0133] where α ij is a binary variable representing the on / off state of branch ij. When it is 1, it means the branch is closed, and when it is 0, it means the branch is open. For the fault branch, α ij Set to 0 to avoid closing the corresponding branch in the reconstruction decision; P ij,t and Q ij,t They represent the active power and reactive power flowing through branch ij at time t respectively; r ij and x ijare the resistance and reactance of branch ij respectively; I ij,t represents the effective value of the current flowing through branch ij at time t; P j,t and Q j,t denote the injected active power and reactive power of node i respectively; is the reactive load of node i at time t; Ui , t represents the voltage amplitude of node i at time t; f(j) and s(j) represent the parent node and child node set of node j, respectively, where the parent node and child node mean the upstream and downstream nodes of the branch flow in the network, respectively; Ω is the set of all branches in the distribution network.

[0134] The above Distflow power flow model is a non-convex constraint and is only applicable to the case where the nodes are closed. For this reason, this method introduces an intermediate variable and The above model is extended to all branch sets Ω by using the uncertainty constraints and relaxing them using the big M method. The introduced intermediate variables and uncertainty constraints are expressed as:

[0135]

[0136] -α ij M1≤P ij,t ≤α ij M1

[0137] -α ij M2≤Q ij,t ≤α ij M2

[0138] -α ij M3≤I ij,t ≤α ij M3

[0139] Wherein M1, M2 and M3 are sufficiently large positive numbers.

[0140] Combining the intermediate variables with the original Distflow model can obtain the converted constraints, which can be expressed as:

[0141]

[0142] The constraint obtained by further relaxation through the big M method is expressed as:

[0143] mij=M4(1-αij)

[0144]

[0145] Where M4 is a sufficiently large positive number; m ij It is the intermediate variable required by branch ij.

[0146] In view of the non-convexity of the above power flow model, a second-order cone relaxation is performed on it, which is expressed as:

[0147]

[0148] During the fault reconstruction of the new energy distribution system, the voltage of each node must also meet the upper and lower limit constraints, which can be expressed as:

[0149]

[0150] in and are the lower and upper limits of the voltage amplitude at node i respectively.

[0151] If node i is a balanced node, its node voltage amplitude is always 1, which can be expressed as:

[0152]

[0153] Due to the limitation of line transmission capacity, the branch current must also be limited to a certain range, expressed as:

[0154]

[0155] in is the upper limit of the current that can be transmitted by branch ij.

[0156] The topology of the power distribution system must satisfy the connectivity and radiation constraints, which can be expressed as:

[0157]

[0158] where f gi,t and f di,t They represent the virtual power supply and virtual load of node i at time t respectively; f ij,t is the virtual power flow of branch ij at time t; N n and N s are the number of nodes and the number of power supplies in the power supply, respectively.

[0159] In order to deal with the undervoltage problem caused by reactive power shortage during the reconstruction process, a capacitor bank can be put into use for reactive power compensation. The corresponding constraints are expressed as:

[0160]

[0161] in represents the reactive power compensation capacity of node i at time t; represents the number of capacitor banks connected to node i at time t; The capacity of a single capacitor bank configured for node i; is the maximum number of capacitor banks available at node i.

[0162] 5) Taking into account the network loss cost and the section switch operation cost, the objective function of the distribution system reconstruction model is established with economy as the goal, and a mixed integer second-order cone programming model of the distribution system reconstruction is established in combination with the models and constraints in steps 1) to 4).

[0163] This method takes the operation economy of the distribution system as the goal, takes into account the network loss cost of the distribution system and the operation cost of the sectionalized switch. The objective function of the reconstruction model is expressed as:

[0164]

[0165] Where F represents the objective function; c1 and c2 are the network loss cost coefficient and the section switch operation penalty cost coefficient respectively; g 1,t represents the network loss at time t; g2 represents the number of times the sectionalizer is operated. 1,t The calculation method is expressed as:

[0166]

[0167] The calculation method of g2 is expressed as:

[0168]

[0169] where α ij,0 Indicates the segment switch status before reconstruction.

Claims

1. A fault reconstruction method for a digital microgrid, characterized in that: Proceed as follows: 1) A demand response model for load aggregators is established through the electricity price elasticity coefficient. The demand response measures are used to respond to electricity prices to achieve peak load shaving and valley filling, and smooth the electricity load curve. 2) Model the dynamic model of power energy storage and further reduce the operating cost of the distribution network by rationally regulating the charging and discharging behavior of energy storage; 3) Although renewable energy generation has low-carbon and renewable characteristics, it exhibits strong uncertainty. Therefore, an opportunity constraint for renewable distributed generation is established to effectively characterize the uncertainty. At the same time, it is assumed that its distribution obeys the Gaussian distribution and is equivalently converted into a deterministic inequality constraint. The relationship between the active power and reactive power of distributed generation is established based on the power factor. 4) According to the characteristics of the new distribution system reconstruction, a suitable improved Distflow power flow constraint is established, and the power flow constraint is transformed into a convex cone by introducing intermediate variables and using the large M method and second-order cone relaxation; node voltage, branch current, network topology and capacitor bank switching constraints are established; 5) Taking into account the network loss cost and the section switch operation cost, the objective function of the new energy distribution system reconstruction model was established with economy as the goal, and combined with the models and constraints in steps 1) to 4), a new energy distribution system reconstruction model based on mixed integer second-order cone programming was constructed.

2. A fault reconstruction method for a digital microgrid according to claim 1, characterized in that: The step 1) is specifically as follows: The designed load aggregator model based on electricity price elasticity coefficient uses demand response strategy to achieve load smoothing, so as to reduce system network loss and improve the economy of reconstruction; dispatches price incentive load based on electricity price elasticity coefficient to achieve peak load shaving and valley filling; determines the quantification of load demand after adopting time-of-use electricity price based on electricity price elasticity coefficient; the electricity load elasticity coefficient represents the percentage change of electricity demand caused by electricity price change in a given time period, and its calculation method is: where ξ i,t represents the electricity price elasticity coefficient of node i at time t; ΔP i,t and Δρ i,t They represent the changes in power demand and electricity price after node i executes demand response at time t; and P i,t are the load power of node i before and after executing demand response at time t; and ρ i,t are the electricity prices before and after the time-of-use electricity price is adopted for node i at time t, and It is expressed as: where ρ peak , ρ shoulder and ρ valley are the electricity prices during peak, flat and valley periods respectively; T peak , T shoulder and T valley They are peak, flat and valley periods respectively; In order to ensure the energy demand of the load aggregator, the total power before and after demand response remains unchanged, which can be expressed as: Where T is the duration of the reconstruction optimization; Δt is the duration of a moment.

3. A fault reconstruction method for a digital microgrid according to claim 1, characterized in that: The step 2) is specifically as follows: The electric energy storage system configured in the system effectively improves its operational flexibility. The dynamic operation model of the electric energy storage is: Where Q i,t The amount of electricity stored by the energy storage system configured for node i at time t; and They represent the charging power and discharging power of the energy storage system configured at node i at time t respectively; and are the charging and discharging efficiencies of the energy storage system configured at node i; The operating power and stored power of the energy storage system must meet the corresponding constraints to ensure its healthy operation, which can be expressed as: in The upper limit of the charging and discharging power of the energy storage system configured for node i; Q i and The lower and upper limits of the amount of electricity that can be stored in the energy storage system configured for node i respectively.

4. A fault reconstruction method for a digital microgrid according to claim 1, characterized in that: The step 3) is specifically as follows: The output power of distributed power generation will show strong uncertainty. This method designs a chance constraint to capture the uncertainty of distributed generation. The probability that the output power of distributed generation meets the constraint is not less than a certain confidence level, which is expressed as: Where Pr(i) represents the probability of event (i) occurring; and They represent the actual value and predicted value of the output power of the distributed generation configured at node i at time t respectively; η i The confidence level of the DG output power configured for node i; Since the implicit form of opportunity constraints is difficult to solve directly, it is assumed that the prediction error of distributed generation output power satisfies the Gaussian distribution, which can be expressed as: where σ i,t,fore The standard deviation of the prediction error of the output power of the distributed generation configured for node i at time t is obtained from the following formula: Where Φ(i) is a random variable The cumulative distribution function of the probability distribution; according to After the predicted value and standard deviation are converted to the standard normal distribution, the formula is obtained: where Φ a (i) represents the cumulative distribution function of the standard normal distribution. After rearranging it, we get the deterministic equivalent of the chance constraint, expressed as: According to the operating power factor of the distributed power source, its output reactive power is expressed as: in represents the actual value of reactive power output by the distributed generation configured at node i at time t; δ i The power factor of the distributed generation configured for node i.

5. A fault reconstruction method for a digital microgrid according to claim 1, characterized in that: The step 4) is specifically as follows: By introducing the branch switch variable α ij A Distflow power flow model suitable for fault reconstruction of new energy distribution system is established, which can be expressed as: where α ij is a binary variable representing the on / off state of branch ij. When it is 1, it means the branch is closed, and when it is 0, it means the branch is open. For the fault branch, α ij Set to 0 to avoid closing the corresponding branch in the reconstruction decision; P ij,t and Q ij,t They represent the active power and reactive power flowing through branch ij at time t respectively; r ij and x ij are the resistance and reactance of branch ij respectively; I ij,t represents the effective value of the current flowing through branch ij at time t; P j,t and Q j,t denote the injected active power and reactive power of node i respectively; is the reactive load of node i at time t; U i,t represents the voltage amplitude of node i at time t; f(j) and s(j) represent the parent node and child node set of node j, respectively, where the parent node and child node mean the upstream and downstream nodes of the branch flow in the network, respectively; Ω is the set of all branches in the distribution network; The above Distflow power flow model is a non-convex constraint and is only applicable to the case where the nodes are closed. For this reason, this method introduces an intermediate variable and The above model is extended to all branch sets Ω by using the uncertainty constraints and relaxing them by the big M method. The introduced intermediate variables and uncertainty constraints are expressed as: -a ij M1≤P ij,t ≤α ij M1 -α ij M2≤Q ij,t ≤α ij M2 -α ij M3≤I ij,t ≤α ij M3 Where M1, M2 and M3 are sufficiently large positive numbers; Combining the intermediate variables with the original Distflow model can obtain the converted constraints, which can be expressed as: The constraint obtained by further relaxation through the big M method is expressed as: mij=M4(1-αij) Where M4 is a sufficiently large positive number; m ij is the intermediate variable required by branch ij; In view of the non-convexity of the above power flow model, a second-order cone relaxation is performed on it, which is expressed as: During the fault reconstruction of the new energy distribution system, the voltage of each node must also meet the upper and lower limit constraints, which can be expressed as: in and are the lower and upper limits of the voltage amplitude at node i, respectively; If node i is a balanced node, its node voltage amplitude is always 1, which can be expressed as: Due to the limitation of line transmission capacity, the branch current must also be limited to a certain range, expressed as: in is the upper limit of the current that can be transmitted by branch ij; The topology of the power distribution system must satisfy the connectivity and radiation constraints, which can be expressed as: where f gi,t and f di,t They represent the virtual power supply and virtual load of node i at time t respectively; f ij,t is the virtual power flow of branch ij at time t; N n and N s are the number of nodes and power supplies in the power supply, respectively; In order to deal with the undervoltage problem caused by reactive power shortage during the reconstruction process, capacitor banks are put into use for reactive power compensation. The corresponding constraints are expressed as: in represents the reactive power compensation capacity of node i at time t; represents the number of capacitor banks connected to node i at time t; The capacity of a single capacitor bank configured for node i; is the maximum number of capacitor banks available at node i.

6. A fault reconstruction method for a digital microgrid according to claim 1, characterized in that The step 5) is specifically as follows: This method takes the operation economy of the distribution system as the goal, takes into account the network loss cost of the distribution system and the operation cost of the sectionalized switch. The objective function of the reconstruction model is expressed as: Where F represents the objective function; c1 and c2 are the network loss cost coefficient and the section switch operation penalty cost coefficient respectively; g 1,t represents the network loss at time t; g2 represents the number of operations of the section switch; g 1,t The calculation method is expressed as: The calculation method of g2 is expressed as: where α ij,0 Indicates the segment switch status before reconstruction.

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