Electric power system transient stability constraint optimal power flow method considering new energy access

Abstract

A transient stability constraint optimal power flow method of a power system considering new energy access comprises the following steps: step 1, modeling uncertain wind power output into a few robust test scenes by adopting a TOAT method; 2, using the ANN to approximately estimate the CCT under the anticipated accident set according to the input variable so as to construct the transient stability constraint; 3, adding the obtained transient stability constraint to the optimal power flow model to obtain a TSCOPF model; and step 4, solving the TSCOPF model containing the new energy based on an improved CHGS optimization algorithm. In order to solve the technical problem that the influence of new energy grid connection on the transient stability of the power system is not considered in the prior art, the invention provides the power system transient stability constraint optimal power flow method considering new energy access.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to power system transient stability analysis and optimal power flow calculation technology, and specifically to a power system transient stability constrained optimal power flow method taking into account the access of new energy sources. Background Art

[0002] Due to environmental benefits, the penetration rate of wind turbines in my country's power system has been increasing in recent years. Since wind turbines are mainly connected to the power system by power electronic converters and their output power is uncertain, this has led to a significant trend of complexity in the operation and dynamic behavior of the power system. Therefore, how to construct a high-precision transient stability constrained optimal power flow model considering wind power uncertainty is of great significance to ensure the safe operation of the power system.

[0003] The patent document with application publication number CN116436101A discloses a transmission and distribution coordinated random scheduling method based on scenario reduction. The scenario reduction is mainly based on the target optimality distance between the original scenario and the reduced scenario, and each original scenario is divided into the category of the reduced scenario for iterative clustering, thereby obtaining multiple classic scenarios after reduction. The patent document with application publication number CN116720436A discloses a method for fast solution of optimal power flow combining neural networks and physical models. It mainly constructs the optimal power flow model through neural networks and physical models, and then uses the training set to continuously solve the model until no new learning samples are identified. Although the above patent method improves the speed of solving the model, it does not take into account the impact of the grid connection of new energy on the transient stability of the power system, which will make the above method difficult to apply to solving the transient stability constrained optimal power flow model containing new energy.

[0004] In summary, the current research on power systems with a high proportion of wind power connected to the grid is not in-depth. In this regard, a transient stability constrained optimal power flow method for power systems taking into account the access of renewable energy is proposed, which can make the current power system better adapt to the access of renewable energy to the grid, thereby ensuring the safe operation of the new power system. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problem that the existing technology does not consider the impact of renewable energy grid connection on the transient stability of the power system, and proposes a transient stability constrained optimal power flow method for the power system taking renewable energy access into account.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] The optimal power flow method for transient stability constraints of power system considering the access of new energy sources includes the following steps:

[0008] Step 1: Using the TOAT method, the uncertain wind power output is modeled as a small number of robust test scenarios;

[0009] Step 2: Use ANN to approximate the CCT under the expected accident set based on the input variables to construct transient stability constraints;

[0010] Step 3: Add the obtained transient stability constraints to the optimal power flow model to obtain the TSCOPF model;

[0011] Step 4: Solve the TSCOPF model with new energy based on the improved CHGS optimization algorithm.

[0012] In step 1, the TOAT method is used to model the uncertain wind power output as a small number of robust test scenarios, using the following sub-steps:

[0013] Step 1-1: Assume that the power system contains M wind turbines, each turbine has A levels of output power, and the wind power output power follows a normal distribution;

[0014] Step 1-2: Use an orthogonal array L Y (A M ) represents all combined Y wind power test scenarios, and the entire orthogonal array uses Y hierarchical combinations to represent the uncertainty space of wind power output;

[0015] Steps 1-3: Use two test levels and To evaluate the accuracy of the test scenario, and β represent the orthogonal array L Y (A M ), and the average output of wind power forecast is recorded as the benchmark value ±10% of the reference value is recorded as the standard deviation ±β, so two inspection levels are used for a wind turbine. and To evaluate the accuracy of the test scenario.

[0016] In step 2, ANN is used to approximate the CCT under the expected accident set based on the input variables to construct the transient stability constraint. The ANN training process is divided into the following two steps:

[0017] Step 2-1: Generate the active power and reactive power at the load bus before the fault. The following model represents the active and reactive power on the load bus:

[0018]

[0019] Where: and are respectively the active power and reactive power at the load bus i before the fault; PL,i and QL,i are independent random variables; △P L,i and △Q L,i are the changes in active power and reactive power allowed at load bus i respectively; P L,i and Q L,i are the reference active power and reactive power at load bus i respectively;

[0020] Step 2-2: Solve the power flow to obtain the power and voltage under the corresponding conditions; the following model represents the active power and voltage injected at the generator bus:

[0021]

[0022] Where: and are the active power and voltage injected at the generator bus i respectively; PG,i and UG,i are independent random variables; △P G,i and △U G,i is the allowable change in active power and voltage at generator bus i; P G,i and U G,i are the reference active power and voltage at the generator bus i respectively;

[0023] Through the above steps, after the ANN training is completed, the function f can be obtained c The first and second order analytical expressions for the input variables and the function f in each iteration c are all linearized, and then the new transient stability constraints are constructed as follows:

[0024]

[0025] Where: u=[P G ,Q G ] T is the function f c The input vector of the function f c is the estimated value of CCT under the expected accident set; u * is the current active and reactive power of the generator; CCT min is the minimum CCT required for the power system.

[0026] The step 3 adopts the following steps:

[0027] Step 3-1: Considering the uncertainty of wind power, the objective function of the model is expressed in the form of expected value:

[0028] min F(du ,d l ,x i )(4)

[0029] Where: Function F(du,dl,xi) is the target function of power generation dispatch cost; d u and d l are the decision variables of the upper and lower layers respectively; x i is the output power of the wind turbine in the i-th scenario;

[0030] Step 3-2: Considering the power balance of the entire system, construct the static equality constraint as follows:

[0031]

[0032] Where: N G is the total number of generators; P i and Q i is the active power and reactive power injected into bus i; δ ij is the phase angle difference; G ij and B ij are the real and imaginary parts of the busbar admittance matrix respectively; V i and V j are the voltages of bus i and bus j respectively;

[0033] Step 3-3: Considering the stable operation of the entire system, construct the static safety inequality constraint as follows:

[0034]

[0035] Where: P Gi,max and P Gi,min They are the upper and lower limits of the active output of the generator respectively; Q Gi,min , Q Gi,max are the upper and lower limits of the reactive power output of the generator respectively; V i,max and V i,min are the upper and lower limits of the node voltage respectively; S li,max is the maximum value of the apparent power of the ith bus; V i is the voltage of node i; S li is the apparent power of the ith bus; N G 、N b and N L They are the power system generator set, node set, and line set respectively;

[0036] Step 3-4: The transient stability constraints added are as follows:

[0037]

[0038] Where: u=[P G,Q G ] T is the function f c The input vector of the function f c is the estimated value of CCT under the expected accident set; u * is the current active and reactive power of the generator; CCT min is the minimum CCT required for the power system.

[0039] In step 4, the HGS algorithm is combined with the chaotic map to form the CHGS optimization algorithm. The specific steps of using this algorithm to solve the above TSCOPF model are:

[0040] Step 4-1: Use the chaos mapping algorithm to improve the selection of the initial population, initialize the population size, hunger weight and search limit; the mathematical model of the initial population is expressed as:

[0041]

[0042] Where: rand is a vector randomly set from 0 to 1; X is the initial population size; t is the current iteration number; T is the maximum iteration number;

[0043] Step 4-2: Calculate the fitness value of each individual, i.e., the power generation cost, and divide the population into producers, foragers, and wanderers according to the fitness value of each individual;

[0044] Step 4-3: Update the position of each human body according to the fitness value and hunger weight of each individual, and then evaluate each new position to determine whether they meet the constraints set by the TSCOPF model and calculate the objective function at this moment;

[0045] Step 4-4: If a better fitness value appears, update the global optimal position and the best fitness value, and repeat the above steps until the maximum number of iterations is met, and output the optimal solution of the TSCOPF model.

[0046] Compared with the prior art, the present invention has the following technical effects:

[0047] 1) The TOAT method is used to model the uncertain wind power output as a small number of robust test scenarios, which can make the power system better adapt to the grid connection of renewable energy without significantly losing the characteristics of wind power uncertainty;

[0048] 2) Using ANN to estimate CCT under different accidents can greatly reduce the time spent on solving CCT and improve the model convergence speed more quickly;

[0049] 3) An improved CHGS optimization algorithm is proposed for solving the TSCOPF model, which can make the calculation speed faster and the accuracy higher when solving the TSCOPF model. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0051] Figure 1 is a flow chart of the method of the present invention;

[0052] Figure 2 is a schematic diagram of an IEEE 68-node system in an example of the present invention;

[0053] Figure 3 It is a power angle diagram of the IEEE 68-node system under a preset fault set in the example of the present invention;

[0054] Figure 4 is a power angle diagram of the system after the IEEE 68-node system in the example of the present invention solves the model according to the optimization algorithm of the present invention;

[0055] Figure 5 It is a comparison chart of generator dispatching costs based on IEEE 68-node system, CHGS optimization algorithm, genetic algorithm and particle swarm optimization algorithm. DETAILED DESCRIPTION

[0056] The optimal power flow method for power system transient stability constraints considering the integration of renewable energy, such as Figure 1 As shown, the method comprises the following steps:

[0057] Step 1: Use the Taguchi Orthogonal Array Test (TOAT) method to model the uncertain wind power output as a small number of robust test scenarios. When using the TOAT method to model the uncertain wind power output as a small number of robust test scenarios, the following sub-steps are used:

[0058] Step 1-1: Assume that the power system contains M wind turbines, each turbine has A levels of output power, and the wind power output power follows a normal distribution;

[0059] Step 1-2: Use the orthogonal array L Y (A M ) represents all combined Y wind power test scenarios, and the entire orthogonal array uses Y hierarchical combinations to represent the uncertainty space of wind power output.

[0060] Steps 1-3: Use two test levels and To evaluate the accuracy of the test scenario, and β represent the orthogonal array L Y (A M ), and the average output of wind power forecast is recorded as the benchmark value ±10% of the reference value is recorded as the standard deviation ±β, so two inspection levels are used for a wind turbine. and To evaluate the accuracy of the test scenario.

[0061] Step 2: Use artificial neural network (ANN) to estimate the critical clearing time (CCT) under the expected accident set based on the input variables, and use CCT to construct transient stability constraints. Since each accidental event considered in the TSCOPF model must be approximated by a separate ANN, in order to accurately approximate CCT, it is most important to select parameters that have a high correlation with CCT as the input of the ANN. For power systems with new energy grid connection, using P G and Q G As the input of ANN, better estimation accuracy can be achieved.

[0062] Furthermore, the process of ANN training is divided into the following two steps:

[0063] Step 2-1: Generate the active power and reactive power at the load bus before the fault. The following model represents the active and reactive power on the load bus:

[0064]

[0065] Where: and are respectively the active power and reactive power at the load bus i before the fault; PL,i and QL,i are independent random variables; △P L,i and △Q L,i are the changes in active power and reactive power allowed at load bus i respectively; P L,i and Q L,i are the reference active power and reactive power at load bus i respectively.

[0066] Step 2-2: Solve the power flow to obtain the power and voltage under the corresponding conditions. The following model represents the active power and voltage injected at the generator bus:

[0067]

[0068] Where: and are the active power and voltage injected at the generator bus i respectively; PG,i and UG,i are independent random variables; △P G,iand △U G,i is the allowable change in active power and voltage at generator bus i; P G,i and U G,i are the reference active power and voltage at generator bus i respectively.

[0069] Through the above steps, after the ANN training is completed, the function f can be obtained c The first and second order analytical expressions for the input variables and the function f in each iteration c All are linearized, and the transient stability constraints are as follows:

[0070]

[0071] Where: u=[P G ,Q G ] T is the function f c The input vector of the function f c is the estimated value of CCT under the expected accident set; u * For the current solution; CCT min is the minimum CCT required for the power system.

[0072] Step 3: Add the obtained transient stability constraints to the optimal power flow model to obtain the Transient Stability Constrained Optimal Power Flow (TSCOPF) model. The specific steps are as follows:

[0073] Step 3-1: Considering the uncertainty of wind power, the objective function of the model is expressed in the form of expected value:

[0074] min F(d u ,d l ,x i )(4)

[0075] Where: Function F(du,dl,xi) is the target function of power generation dispatch cost; d u and d l are the decision variables of the upper and lower layers respectively; x i is the output power of the wind turbine in the i-th scenario.

[0076] Step 3-2: Considering the power balance of the entire system, construct the static equality constraint as follows:

[0077]

[0078] Where: N G is the total number of generators; P i and Q iis the active power and reactive power injected into bus i; δ ij is the phase angle difference; G ij and B ij are the real and imaginary parts of the busbar admittance matrix respectively; V i and V j are the voltages of bus i and bus j respectively.

[0079] Step 3-3: Considering the stable operation of the entire system, construct the static safety inequality constraint as follows:

[0080]

[0081] Where: P Gi,max and P Gi,min They are the upper and lower limits of the active output of the generator respectively; Q Gi,min , Q Gi,max are the upper and lower limits of the reactive power output of the generator respectively; V i,max and V i,min are the upper and lower limits of the node voltage respectively; S li,max is the maximum value of the apparent power of the ith bus; V i is the voltage of node i; S li is the apparent power of the ith bus; N G 、N b and N L They are respectively the generator set, node set and line set of the power system.

[0082] Step 3-4: The transient stability constraints added are as follows:

[0083]

[0084] Where: u=[P G ,Q G ] T is the function f c The input vector of the function f c is the estimated value of CCT under the expected accident set; u * For the current solution; CCT min is the minimum CCT required for the power system.

[0085] Step 4: Solve the TSCOPF model with new energy based on the improved Chaos Hunger Games Search (CHGS) optimization algorithm. The HGS algorithm is combined with the chaos map to form the CHGS optimization algorithm. The specific steps of using this algorithm to solve the above TSCOPF model are:

[0086] Step 4-1: Use the chaos mapping algorithm to improve the selection of the initial population, initialize the population size, hunger weight and search limit. The mathematical model of the initial population is expressed as:

[0087]

[0088] Where rand is a vector randomly set from 0 to 1; X is the initial population size; t is the current iteration number; T is the maximum iteration number.

[0089] Step 4-2: Solve the fitness value of each individual, that is, the power generation cost, and divide the population into producers, foragers, and wanderers according to the fitness value of each individual.

[0090] Step 4-3: Update the position of each human body according to the fitness value and hunger weight of each individual, and then evaluate each new position to determine whether they meet the constraints set by the TSCOPF model and calculate the objective function at this moment.

[0091] Step 4-4: If a better fitness value appears, update the global optimal position and the best fitness value, and repeat the above steps until the maximum number of iterations is met, and output the optimal solution of the TSCOPF model.

[0092] Example:

[0093] In order to verify the effectiveness of the present invention, it is applied to the IEEE 68 node system for testing, and the G10, G11 and G12 synchronous generators in the node system are replaced by wind turbines with the same power generation as the original generators, and the capacity of the remaining synchronous generators is increased by 1.5 times to fully compensate for the change in wind power. 3 ) represent four test scenarios of wind power output.

[0094] First, in the first scenario, assume that a three-phase short circuit fault occurs on branch 26-28 and the fault duration is 0.3s. At this time, the power angle diagram of the current system is as follows: Figure 4 As shown in the figure, it can be seen that under the set fault set, the system is transiently unstable, and then the improved CHGS optimization algorithm is used to solve it. After completing one iteration, the system becomes stable in this wind power scenario. The improved CHGS optimization algorithm is used to repeat the calculation until the set fault conditions become stable in all generated scenarios. The power angle diagram after the final system is stable is shown in the figure below: Figure 5 As shown, it can be seen that under the set fault set, the system is in a stable state after continuous calculation.

[0095] In addition, under the IEEE 68-node system, the proposed CHGS optimization algorithm requires fewer iterations than the genetic algorithm (GA) to stabilize the system. It can be seen that the proposed CHGS optimization algorithm has the characteristics of fast and smooth convergence, and CHGS can provide better results in the case of larger systems.

[0096] Compared with the prior art, the transient stability constrained optimal power flow method for the power system taking into account the access of new energy sources proposed in the present invention can significantly improve the calculation time by using the TOAT method to model the uncertain wind power output as four robust test scenarios. The ANN is used to approximate the CCT under the preset accident, which can greatly reduce the time spent on solving the CCT. Finally, the improved CHGS optimization algorithm is used to solve the TSCOPF model. The present invention can make the TSCOPF model better adapt to the grid connection of wind power, thereby ensuring the safe and stable operation of the new power system.

Claims

1. A transient stability constrained optimal power flow method for a power system taking into account the access of new energy sources, characterized in that: The following steps are involved: Step 1: Using the TOAT method, the uncertain wind power output is modeled as a small number of robust test scenarios; Step 2: Use ANN to approximate the CCT under the expected accident set based on the input variables to construct transient stability constraints; Step 3: Add the obtained transient stability constraints to the optimal power flow model to obtain the TSCOPF model; Step 4: Solve the TSCOPF model with new energy based on the improved CHGS optimization algorithm.

2. The method according to claim 1, characterized in that In step 1, the TOAT method is used to model the uncertain wind power output as a small number of robust test scenarios, using the following sub-steps: Step 1-1: Assume that the power system contains M wind turbines, each turbine has A levels of output power, and the wind power output power follows a normal distribution; Step 1-2: Use an orthogonal array L Y (A M ) represents all combined Y wind power test scenarios, and the entire orthogonal array uses Y hierarchical combinations to represent the uncertainty space of wind power output; Steps 1-3: Use two test levels and To evaluate the accuracy of the test scenario, and β represent the orthogonal array L Y (A M ), and the average output of wind power forecast is recorded as the benchmark value ±10% of the reference value is recorded as the standard deviation ±β, so two inspection levels are used for a wind turbine. and To evaluate the accuracy of the test scenario.

3. The method according to claim 2, characterized in that In step 2, ANN is used to approximate the CCT under the expected accident set based on the input variables to construct the transient stability constraint. The ANN training process is divided into the following two steps: Step 2-1: Generate the active power and reactive power at the load bus before the fault. The following model represents the active and reactive power on the load bus: Where: and are respectively the active power and reactive power at the load bus i before the fault; PL,i and QL,i are independent random variables; △P L,i and △Q L,i are the changes in active power and reactive power allowed at load bus i respectively; P L,i and Q L,i are the reference active power and reactive power at load bus i respectively; Step 2-2: Solve the power flow to obtain the power and voltage under the corresponding conditions; the following model represents the active power and voltage injected at the generator bus: Where: and are the active power and voltage injected at the generator bus i respectively; PG,i and UG,i are independent random variables; △P G,i and △U G,i is the allowable change in active power and voltage at generator bus i; P G,i and U G,i are the reference active power and voltage at the generator bus i respectively; Through the above steps, after the ANN training is completed, the function f can be obtained c The first and second order analytical expressions for the input variables and the function f in each iteration c are all linearized, and then the new transient stability constraints are constructed as follows: Where: u=[P G ,Q G ] T is the function f c The input vector of the function f c is the estimated value of CCT under the expected accident set; u * is the current active and reactive power of the generator; CCT min is the minimum CCT required for the power system.

4. The method according to claim 2, characterized in that: The step 3 adopts the following steps: Step 3-1: Considering the uncertainty of wind power, the objective function of the model is expressed in the form of expected value: min F(d u ,d l ,x i )(4) Where: Function F(du,dl,xi) is the target function of power generation dispatch cost; d u and d l are the decision variables of the upper and lower layers respectively; x i is the output power of the wind turbine in the i-th scenario; Step 3-2: Considering the power balance of the entire system, construct the static equality constraint as follows: Where: N G is the total number of generators; P i and Q i is the active power and reactive power injected at bus i; δ ij is the phase angle difference; G ij and B ij are the real and imaginary parts of the busbar admittance matrix respectively; V i and V j are the voltages of bus i and bus j respectively; Step 3-3: Considering the stable operation of the entire system, construct the static safety inequality constraint as follows: Where: P Gi,max and P Gi,min They are the upper and lower limits of the active output of the generator respectively; Q Gi,min , Q Gi,max are the upper and lower limits of the reactive power output of the generator respectively; V i,max and V i,min are the upper and lower limits of the node voltage respectively; S li,max is the maximum value of the apparent power of the ith bus; V i is the voltage of node i; S li is the apparent power of the ith bus; N G 、N b and N L They are the power system generator set, node set, and line set respectively; Step 3-4: The transient stability constraints added are as follows: Where: u=[P G ,Q G ] T is the function f c The input vector of the function f c is the estimated value of CCT under the expected accident set; u * is the current active and reactive power of the generator; CCT min is the minimum CCT required for the power system.

5. The method according to claim 1, characterized in that In step 4, the HGS algorithm is combined with the chaotic map to form the CHGS optimization algorithm. The specific steps of using this algorithm to solve the above TSCOPF model are: Step 4-1: Use the chaos mapping algorithm to improve the selection of the initial population, initialize the population size, hunger weight and search limit; the mathematical model of the initial population is expressed as: Where: rand is a vector randomly set from 0 to 1; X is the initial population size; t is the current iteration number; T is the maximum iteration number; Step 4-2: Solve the fitness value of each individual, that is, the power generation cost, and divide the population into producers, foragers, and wanderers according to the fitness value of each individual; Step 4-3: Update the position of each human body according to the fitness value and hunger weight of each individual, and then evaluate each new position to determine whether they meet the constraints set by the TSCOPF model and calculate the objective function at this moment; Step 4-4: If a better fitness value appears, update the global optimal position and the best fitness value, and repeat the above steps until the maximum number of iterations is met, and output the optimal solution of the TSCOPF model.

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