CN108964037A - Based on the reconstitution model of high voltage distribution network - Google Patents

Abstract

The invention discloses a reconfigurable model based on a high-voltage distribution network. The method for building the model includes the following steps: step S1: building a dynamic load model; step S2: according to the intermittent energy output, electric vehicle charging/discharging, Calculate the network congestion risk index NCRI based on random factors such as system load removal; step S3: calculate the HVDN topological network of the high-voltage distribution network according to the dynamic load model; step S4, evaluate the HVDN topological network of the high-voltage distribution network according to the calculated NCRI ; Solve the problem of unbalanced system load, resulting in poor practicability and stability of existing models, and it is difficult to adapt to the development of current technology.

Description

Based on the reconfigurability model of high voltage distribution network

technical field

The invention relates to the field of high-voltage distribution network reconfiguration, in particular based on a high-voltage distribution network reconfiguration model.

Background technique

In the process of rapid development of new energy loads in cities, the high-voltage distribution network topology reconfiguration technology plays an important role in the consumption of large-scale photovoltaic power stations connected to the grid and the random charging and discharging of high-permeability electric vehicles, which leads to the aggravation of local congestion in urban power grids. There are obvious deficiencies in the processing. The current technology only focuses on the single-section static model constructed for the uneven distribution of regional loads, and fails to consider the disordered charging and discharging behavior of electric vehicles and the random change characteristics of photovoltaic power generation systems, which make the system power flow in time series. Increased randomness, increased difficulty in urban power grid topology risk assessment, and poor convergence of non-convex model intelligent algorithms lead to frequent switching operations; if the extreme static deterministic evaluation method is still used to investigate system security at this time, it will make the solution Too conservative, unable to quantitatively estimate and analyze the uncertain fluctuations caused by the new distributed energy grid connection, the system economy is poor, and it is difficult to adapt to the grid security analysis of new energy; and at this stage based on high-voltage distribution network topology reconstruction technology The research aims to solve the problem of local network congestion caused by the uneven distribution of conventional loads, and does not strictly study the strong fluctuations of photovoltaic power sources and electric vehicles after large-scale grid connection, which will increase the unbalanced load of the system, resulting in the practicability of the existing models. The stability is poor, and it is difficult to adapt to the development of current technology.

Contents of the invention

In order to solve the problems existing in the prior art, the present invention provides a reconfigurable model based on the high-voltage distribution network, which solves the problem of system load imbalance, which leads to poor practicability and stability of the existing model and is difficult to adapt to the development of current technology question.

The technical scheme adopted in the present invention is: based on the high-voltage distribution network reconfigurability model, it is characterized in that the method for model construction includes the following steps:

The method of model construction includes the following steps:

Step S1: building a dynamic load model;

Step S2: Calculate the network congestion risk index NCRI according to random factors such as intermittent energy output in the current urban power grid, charging/discharging of electric vehicles, and system load shedding;

Step S3: Calculate the HVDN topology network of the high-voltage distribution network according to the dynamic load model;

Step S4, evaluating the HVDN topology network of the high voltage distribution network according to the calculated NCRI;

Step S5: When the NCRI does not exceed the confidence range, it is considered that the photovoltaic consumption degree of the network and the system congestion at the moment are within the allowable range, and there is no operation action; when the NCRI exceeds the confidence range, HVDN reconfiguration is triggered, based on the two-layer optimization model, Under the comprehensive goal of maximizing photovoltaic consumption, maximizing system congestion relief and minimizing load shedding, the optimal topology state of the network at this moment is obtained.

The beneficial effects of the present invention based on the high-voltage distribution network reconfigurability model are as follows:

1. The dynamic risk assessment strategy of network topology based on probabilistic power flow is coordinated with the multi-objective optimization strategy of new energy consumption, environmental benefits and user satisfaction based on HVDN reconstruction, that is, to make full use of the NCRI index of network topology risk assessment for Evaluate the degree of congestion and consumption, realize real-time monitoring of network status, make full use of HVDN's multi-ring and non-deep network characteristics, and carry out reasonable transfer of comprehensive loads including electric vehicles and photovoltaic power loads, so as to The problem of aggravated local congestion of the system caused by the unbalanced load distribution of the urban power grid.

2. The urban grid consumption and congestion control strategy proposed by the present invention aims at maximizing photovoltaic consumption while eliminating local congestion, and takes into account the local load caused by the difference in time and space between clean energy photovoltaics and electric vehicle loads. For the problem of intensified congestion and insufficient absorption, the NCRI index including absorption and blocking factors is fully utilized for prediction, which has high practicability.

Description of drawings

Fig. 1 is a flow chart of the present invention based on a high-voltage distribution network reconfigurability model.

Detailed ways

The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

As shown in Figure 1, based on the high-voltage distribution network reconfigurability model, it is characterized in that the method of model construction includes the following steps:

Step S1: building a dynamic load model;

Step S2: Calculate the network congestion risk index NCRI according to random factors such as intermittent energy output in the current urban power grid, charging/discharging of electric vehicles, and system load shedding;

Step S3: Calculate the HVDN topology network of the high-voltage distribution network according to the dynamic load model;

Step S4, evaluating the HVDN topology network of the high voltage distribution network according to the calculated NCRI;

Step S5: When the NCRI does not exceed the confidence range, it is considered that the photovoltaic consumption degree of the network and the system congestion at the moment are within the allowable range, and there is no operation action; when the NCRI exceeds the confidence range, HVDN reconfiguration is triggered, based on the two-layer optimization model, Under the comprehensive goal of maximizing photovoltaic consumption, maximizing system congestion relief and minimizing load shedding, the optimal topology state of the network at this moment is obtained.

The dynamic load model in step S1 of this scheme includes the electric vehicle charging/discharging load simulation model, the comprehensive load model of electric vehicle charging/discharging load, the distribution model of the number of electric vehicles connected to the electric vehicle in a certain period of time, and the electric vehicle connected to the power grid in a certain period of time The model of the number of units, the forecast output model of photovoltaic generators, the probability density function of photovoltaic output active power and the expected value of output power of photovoltaic power generation system.

The electric vehicle charging/discharging load simulation model is

Comprehensive load model of electric vehicle charging/discharging load:

In the formula, _{λEV is the expected value of connecting electric vehicles in this time period, and n EV is} the number of electric vehicles that may be connected to the grid. If the probability density function of the number of electric vehicles connected to the system in each period is known, the time period can be obtained The mean value, standard deviation and high-order central moment of electric vehicle charging/discharging power;

The distribution model of the number of connected electric vehicles in a period of time is:

The model of the number of electric vehicles connected to the grid for a period of time is:

In the formula, n is the total number of electric vehicles in the system, μ _t0 = 1 is the expected event when electric vehicles are connected to the power system, σ _t is the standard deviation, which refers to the charging/discharging time range, so that the charging/discharging time distribution range is σ _t =2; if the expected charging time is μ _t0 =1, it refers to period 1, namely 00:00-01:00, and the discharge time is μ _t0 =13, which refers to period 13, namely 12:00-13:00; The number of electric vehicles connected to the grid in a certain period of time λ _EV is the expectation of n _EV ;

Forecast output model of photovoltaic generator:

In the formula, the basic function is the output of photovoltaic in the case of expected value, and the random variable θ(t) represents the hindering effect of the atmosphere on the sunlight;

The probability density function of the photovoltaic output active power is:

In the formula: Γ is the Gamma function, α and β are the shape parameters of the beta distribution, and P _PV is the output power of the photovoltaic generator; is the maximum output power of the photovoltaic array.

The expected value of the output power of the photovoltaic power generation system is:

The network risk assessment index NCRI in step S2 of this program is represented by G _ZN :

In the formula, α, β, γ are the weight coefficients of overall blockage, local blockage and photovoltaic consumption degree, respectively, and Respectively represent the network overload risk vector and the photovoltaic penetration degree vector;

Risk Overload Vector

In the formula, M is the total branch number of the system, is a column vector, g _i represents the overload risk of the i-th branch;

Branch overload risk g _m :

In the formula, w _{xi, k} is the probability weight, a is an integer, is the overload of branch m;

branch overload

In the formula, L _m is the ratio of the actual transmission power of the branch m to the power limit, L ₀ is the safety threshold of the set branch, m=1, 2, ..., M;

Photovoltaic Penetration Degree Vector

In the formula, r is the number of substations containing photovoltaic power sources, and yt represents the degree of photovoltaic consumption of the _tth substation containing photovoltaic power sources;

In the formula, P _act is the actual output power of the photovoltaic power supply, and P _plan is the planned output power.

In the double-layer optimization process of step S5 of this scheme, the specific steps of information transmission between the upper and lower optimization layer decision variables and state variables are as follows:

Step A1: Initialize PSO algorithm parameters and initial particle positions, input network parameters, initialize samples and random variable dimension m;

Step A2: Transform the sample matrix Z in the standard normal space into the sample matrix X in the input variable through the Nataf inverse transformation, and use the kth of the matrix X in the state of the upper optimal topology TP _i , aiming at photovoltaic consumption The deterministic power flow calculation is carried out in the column, and the weight of the load is determined to find the optimal load combination ζ _i for photovoltaic consumption, and the optimization result is passed to the upper topology optimization model in the form of fitness function;

Step A3: Under the optimal goal of photovoltaic consumption in the lower layer, take the maximization of economic and environmental benefits as the model optimization goal to find the optimal topology state TP _i of the network;

Step A4: Update the optimal position of the particle history and the optimal position of the population;

Step A5: Judging whether it converges or reaches the maximum number of iterations, if it converges or reaches the maximum number of iterations, go to step A6; if it does not converge, go back to step A2;

Step A6: Calculate whether NCRI is within the confidence range, if it exceeds the confidence range, then return to step A1, otherwise, go to step A7;

Step A7: output the optimal topology state of the network.

When this implementation plan is implemented, it is based on the reconfigurable model of the high-voltage distribution network. Step 1: Construction of the dynamic load model: for the centralized charging/discharging of electric vehicle charging/discharging loads in charging piles in different functional areas, based on Nataf transformation Deal with the principle of random correlation, de-correlate the load of electric vehicles and the inherent load of the region, and use the semi-invariant method to superimpose the two random variables that have been mutually interposed to form a comprehensive load model that considers new energy sources; based on seasonal climate The state models the photovoltaic power output per unit time, uses the real meteorological daily value data to simulate the sunshine hours in different seasons, and simulates the ratio of radiation to the total radiation in each time period from sunrise to sunset to characterize photovoltaic power in different time periods Strong volatility and randomness of output.

Step 2: Network topology dynamic risk assessment strategy based on probabilistic power flow: In view of random factors such as intermittent energy output, electric vehicle charging/discharging, and system load shedding in the current urban power grid, a Network Congestion Risk Index (NCRI) is proposed for assessment The operational risk of the urban power transmission system is quantitatively considered from local to overall system risk. NCRI, as a quantitative criterion for triggering HVDN reconfiguration, provides a theoretical basis for the action strategy of transmission network topology reconfiguration technology.

Step 3. Urban power grid consumption and congestion control strategy:

When NCRI exceeds the confidence range, HVDN reconstruction will be triggered; the upper model takes the maximization of economic and environmental benefits as the model optimization goal, and through the optimization of the HVDN topology, the system’s consumption of photovoltaics and the load bearing under the high penetration of electric vehicles are improved. force;

The lower-level optimization model is based on the initial optimal topological state of the upper-level optimization target, and maximizes the degree of photovoltaic consumption by the system to find the optimal load combination for photovoltaic consumption. Control costs generated by network constraints such as power flow constraints are fed back to the upper optimization model in the form of fitness values.

The network topology dynamic risk assessment strategy in the second step is based on the requirements of fast and stable network status assessment for the high penetration rate of new energy systems and strong volatility, and constructs a network topology risk assessment method guided by the Network Congestion Risk Index (NCRI); Auxiliary tools characterize the volatility of PVs and EVs, and by directly reflecting the risk probability distribution information of each state quantity of the system, quickly make an evaluation strategy for the system topology state from local to overall; network overload risk vector The infinite norm and the two norm represent the overall risk situation and the maximum local risk situation of the network topology respectively, and the linear combination of their norms constitutes the network risk assessment index NCRI;

The network risk assessment index NCRI is characterized by G _ZN :

In the formula, α, β, γ are the weight coefficients of overall blockage, local blockage and photovoltaic consumption degree, respectively, and Respectively represent the network overload risk vector and the photovoltaic penetration degree vector;

Risk Overload Vector

In the formula, M is the total branch number of the system, is a column vector, g _i represents the overload risk of the i-th branch;

Branch overload risk g _m :

In the formula, w _{xi, k} is the probability weight, a is an integer, is the overload of branch m;

branch overload :

In the formula, L _m is the ratio of the actual transmission power of the branch m to the power limit, L ₀ is the safety threshold of the set branch, m=1, 2, ..., M;

Photovoltaic Penetration Degree Vector

In the formula, r is the number of substations containing photovoltaic power sources, and yt represents the degree of photovoltaic consumption of the tth substation containing photovoltaic power sources;

In the formula, P _act is the actual output power of the photovoltaic power supply, and P _plan is the planned output power.

The network dynamic risk assessment based on the network overload risk index provides a theoretical basis for the action strategy of the transmission network topology reconfiguration technology, and can quantitatively estimate and analyze the uncertain fluctuations caused by the new distributed energy grid connection.

The urban power grid consumption and congestion control strategy in Step 3 aims to improve the system’s consumption of photovoltaics and the carrying capacity under the high penetration of electric vehicles. Considering the topological constraints of HVDN substation unit groups, power flow equations and inequality constraints, Node voltage constraints, branch power constraints and second-order cone relaxation conversion condition constraints, formulate a double-layer optimization strategy; the upper-level optimization model of the two-layer optimization strategy takes the maximum economic and environmental benefits as the model optimization goal to find the optimal network Topological state; the lower-level optimization model of the two-layer optimization strategy, in the upper-level optimal topological state, takes photovoltaic consumption as the goal to find the optimal load combination for photovoltaic consumption, so as to achieve the purpose of extinction and slow resistance;

The objective function of the optimal economic benefits of the upper layer:

In the formula, optimization target for the lower layer, is the switching action cost, M represents the number of random variables, and δ _i represents the probability weight corresponding to the i-th load combination;

Switching action cost:

In the formula, τ represents the economic cost of each switching action, χ represents the number of substation unit groups, Indicates the number of switching actions of the substation unit group during the t period;

The constraints of the optimization model for the maximum economic benefit of the upper layer are mainly the topological constraints of the HVDN substation unit group:

a) The topology structure in the unit group needs to satisfy the radial constraint, that is, any substation unit in the unit group has one and only one path connected to the power point;

b) The change of the switch state in the unit group changes the connection relationship between the power point and the substation unit, and then changes the load rate of the power point, and the change of the switch state of different unit groups and the influence of the load rate of the power point are independent of each other.

The objective function of the maximum photovoltaic consumption in the lower layer is:

In the formula, P _wg,i ^t , P _g ^t respectively represent the expected value of photovoltaic output and photovoltaic consumption power of node i in period t, n refers to the total number of nodes in the 110kV substation, and ω represents the environmental benefit impact caused by the abandonment of light;

The power flow constraints of the lower optimization model after cone transformation:

where, are the equational constraints of the power flow after cone conversion, the equational constraints of the power flow of the high-voltage transmission line DC method, the voltage constraints of the nodes and the power constraints of the branches, respectively, is the set of 110kV substation nodes connected to 110kV substation node i;

When the feasible region is relaxed into a second-order cone, forming a convex feasible region, the transformed constraints are relaxed:

The construction of dynamic load model, including the comprehensive load model of electric vehicle charging/discharging and the fluctuation model of photovoltaic power supply, the network topology dynamic risk assessment strategy based on probability flow, uses NCRI as the index of risk assessment, and the urban power grid consumption With the congestion control strategy, a two-level optimization strategy is formulated based on the second-order cone relaxation conversion non-convex power flow condition constraints.

In the double-layer optimization process, the information transmission between the upper and lower optimization layer decision variables and state variables:

Step 1: Initialize the parameters of the PSO algorithm and the initial position of the particles, input the network parameters, initialize the sample and the dimension m of the random variable;

Step 2: Transform the sample matrix Z in the standard normal space into the sample matrix X in the input variable through the Nataf inverse transformation. Under the state of the upper optimal topology TP _i , aiming at photovoltaic consumption, use the kth of the matrix X The deterministic power flow calculation is carried out in the column, and the weight of the load is determined to find the optimal load combination ζ _i for photovoltaic consumption, and the optimization result is passed to the upper topology optimization model in the form of fitness function;

Step 3: Under the optimal goal of photovoltaic consumption in the lower layer, take the maximization of economic and environmental benefits as the model optimization goal to find the optimal topology state TP _i of the network;

Step 4: Update the optimal position of the particle history and the optimal position of the population;

Step 5: Determine whether it converges or reaches the maximum number of iterations. If it converges or reaches the maximum number of iterations, proceed to step 6. If not, proceed to step 2;

Step 6: Calculate whether NCRI is within the confidence range. If it exceeds the confidence range, enter step 1. Otherwise, output the optimal topology state of the output network.

Claims (5)

1. Based on the high-voltage distribution network reconfigurability model, it is characterized in that the method for model construction includes the following steps: Step S1: building a dynamic load model; Step S2: Calculate the network congestion risk index NCRI according to random factors such as intermittent energy output in the current urban power grid, charging/discharging of electric vehicles, and system load shedding; Step S3: Calculate the HVDN topology network of the high-voltage distribution network according to the dynamic load model; Step S4, evaluating the HVDN topology network of the high voltage distribution network according to the calculated NCRI; Step S5: When the NCRI does not exceed the confidence range, it is considered that the photovoltaic consumption degree of the network and the system congestion at the moment are within the allowable range, and there is no operation action; when the NCRI exceeds the confidence range, HVDN reconfiguration is triggered, based on the two-layer optimization model, Under the comprehensive goal of maximizing photovoltaic consumption, maximizing system congestion relief and minimizing load shedding, the optimal topology state of the network at this moment is obtained. 2. The reconfigurable model based on the high-voltage distribution network according to claim 1, wherein the dynamic load model of the step S1 includes an electric vehicle charging/discharging load simulation model, a comprehensive load of the electric vehicle charging/discharging load Model, the distribution model of the number of electric vehicles connected to the grid for a period of time, the model of the number of electric vehicles connected to the grid for a period of time, the forecast output model of photovoltaic generators, the probability density function of photovoltaic output active power and the expected value of output power of photovoltaic power generation system . 3. The reconfigurable model based on the high-voltage distribution network according to claim 2, wherein the electric vehicle charging/discharging load simulation model is The comprehensive load model of the charging/discharging load of the electric vehicle: In the formula, _{λEV is the expected value of connecting electric vehicles in this time period, and n EV is} the number of electric vehicles that may be connected to the grid. If the probability density function of the number of electric vehicles connected to the system in each period is known, the time period can be obtained The mean value, standard deviation and high-order central moment of electric vehicle charging/discharging power; The distribution model of the number of connected electric vehicles in the period of time is: The model of the number of electric vehicles connected to the power grid in a certain period of time is: In the formula, n is the total number of electric vehicles in the system, μ _t0 = 1 is the expected event when electric vehicles are connected to the power system, σ _t is the standard deviation, which refers to the charging/discharging time range, so that the charging/discharging time distribution range is σ _t =2; if the expected charging time is μ _t0 =1, it refers to period 1, namely 00:00-01:00, and the discharge time is μ _t0 =13, which refers to period 13, namely 12:00-13:00; The number of electric vehicles connected to the grid in a certain period of time λ _EV is the expectation of n _EV ; The forecast output model of the photovoltaic generator: In the formula, the basic function is the output of photovoltaic in the case of expected value, and the random variable θ(t) represents the hindering effect of the atmosphere on the sunlight; The probability density function of the photovoltaic output active power is: In the formula: Γ is the Gamma function, α and β are the shape parameters of the beta distribution, and P _PV is the output power of the photovoltaic generator; is the maximum output power of the photovoltaic array. The expected value of the output power of the photovoltaic power generation system is: 4. The reconfigurable model based on high-voltage distribution network according to claim 1, wherein the network risk assessment index NCRI of the step S2 is characterized by _GzN : In the formula, α, β, γ are the weight coefficients of overall blockage, local blockage and photovoltaic consumption degree, respectively, and Respectively represent the network overload risk vector and the photovoltaic penetration degree vector; Risk Overload Vector In the formula, M is the total branch number of the system, is a column vector, g _i represents the overload risk of the i-th branch; Branch overload risk g _m : In the formula, w _{xi, k} is the probability weight, a is an integer, is the overload of branch m; branch overload In the formula, L _m is the ratio of the actual transmission power of the branch m to the power limit, L ₀ is the safety threshold of the set branch, m=1, 2, ..., M; Photovoltaic Penetration Degree Vector In the formula, r is the number of substations containing photovoltaic power sources, and yt represents the degree of photovoltaic consumption of the _tth substation containing photovoltaic power sources; In the formula, P _act is the actual output power of the photovoltaic power supply, and P _plan is the planned output power. 5. The reconfigurable model based on high-voltage distribution network according to claim 1, characterized in that, in the double-layer optimization process of the step S5, the specific steps of the transfer of information between the upper and lower optimization layer decision variables and the state variables are as follows: Step A1: Initialize PSO algorithm parameters and initial particle positions, input network parameters, initialize samples and random variable dimension m; Step A2: Transform the sample matrix Z in the standard normal space into the sample matrix X in the input variable through the Nataf inverse transformation, and use the kth of the matrix X in the state of the upper optimal topology TP _i , aiming at photovoltaic consumption The deterministic power flow calculation is carried out in the column, and the weight of the load is determined to find the optimal load combination ζ _i for photovoltaic consumption, and the optimization result is passed to the upper topology optimization model in the form of fitness function; Step A3: Under the optimal goal of photovoltaic consumption in the lower layer, take the maximization of economic and environmental benefits as the model optimization goal to find the optimal topology state TP _i of the network; Step A4: Update the optimal position of the particle history and the optimal position of the population; Step A5: Judging whether it converges or reaches the maximum number of iterations, if it converges or reaches the maximum number of iterations, go to step A6; if it does not converge, go back to step A2; Step A6: Calculate whether NCRI is within the confidence range, if it exceeds the confidence range, then return to step A1, otherwise, go to step A7; Step A7: output the optimal topology state of the network.