Power grid flexibility improvement method based on wind-hydrogen collaborative optimization control technology

Abstract

The invention discloses a power grid flexibility improvement method based on a wind-hydrogen collaborative optimization control technology, solves the problem of how to improve the flexibility of a power grid, and belongs to the field of electric hydrogen production collaborative control. The method comprises the steps that S1, the stability of a field network oscillation mode and the stability of an in-field oscillation mode are considered, and an objective function and constraint conditions are constructed with the maximum field network dissipation strength and the minimum in-field circulation energy; and S2, screening a flexibility index, solving a relative optimal solution of a target function under a constraint condition by using a particle swarm algorithm, and completing power grid flexibility improvement according to the optimal solution. Optimization is carried out from the two sides of power generation and load, the flexibility of the power grid is improved, and the power grid can better cope with fluctuation of new energy power generation output.

Description

Technical Field

[0001] The present invention relates to a method for improving the flexibility of a power grid based on wind-hydrogen collaborative optimization control technology, and belongs to the field of electric hydrogen production collaborative control. Background Art

[0002] Flexibility resource planning is of great significance to the realization of a high proportion of renewable energy power systems in the future. In traditional power systems, flexibility resources are mainly provided by conventional power sources. However, as the proportion of renewable energy power sources increases significantly, the capacity of conventional power sources further decreases. It is not economical and technically infeasible to rely solely on conventional power sources to provide flexibility. The contradiction between the gradually decreasing flexibility resources and the increasing flexibility demand becomes prominent. Therefore, it is necessary to fully tap the flexibility resource potential of each link of the system source, grid, load and storage and make overall planning for them. As a sustainable, high-energy-density clean energy carrier, the development of the entire hydrogen energy industry chain is a strong support for the green transformation of the energy system. The wind-hydrogen coupling system uses hydrogen energy as an energy carrier to promote the consumption of renewable energy and has significant low-carbon benefits. At the same time, the electric hydrogen production system can also be used as a source of flexibility in the power grid. The wind-hydrogen coupling system contains multiple energies and has a complex structure. In order to ensure reliable energy supply and maximize benefits, it needs to be optimized. Research on its framework and system optimization configuration has become a current research hotspot. Summary of the invention

[0003] In order to solve the problem of how to improve the flexibility of the power grid, the present invention provides a method for improving the flexibility of the power grid based on wind-hydrogen collaborative optimization control technology.

[0004] A method for improving power grid flexibility based on wind-hydrogen collaborative optimization control technology of the present invention comprises:

[0005] S1. Taking into account the stability of the field network oscillation mode and the field internal oscillation mode, the objective function and constraint conditions are constructed with the maximum field network dissipation intensity and the minimum field internal circulation energy;

[0006] S2. Select flexibility indicators, use particle swarm algorithm to solve the relatively optimal solution of the objective function under the constraints, and improve the flexibility of the power grid based on the optimal solution.

[0007] Preferably, the objective function of the present invention is:

[0008]

[0009] Among them, W s is the energy dissipated by the wind farm network interaction, W ii is the energy dissipated by wind turbine i in terms of the network interaction, W ij is the inter-machine circulation energy between fans i and j, K p3 is the dissipation coefficient of the fan control link, KPLL is the phase-locked loop coefficient, W x is the total circulation energy between wind turbines in the wind farm, W H is the comprehensive index of flexibility, d k is the kth flexibility index, n is the number of wind turbines, and m is the number of flexibility indicators.

[0010] Preferably, the constraints include low voltage ride-through resistance constraints, parameter adjustment constraints, load balance constraints, hydrogen production constraints and grid DC power flow constraints.

[0011] Preferably, the low voltage through resistance constraint is:

[0012] R eq (ω LVRT )>R c

[0013] In the formula, R eq (ω LVRT ) is the resistance at the equivalent low voltage ride-through frequency, R c is the critical resistance;

[0014] The parameter adjustment constraints are:

[0015]

[0016] In the formula, F , They are the inner loop current control parameters F(K p3 ,K P_PLL )’s lower and upper limits;

[0017] The load balancing constraints are:

[0018]

[0019] Where P load,t , P t ET,abs are the load power and electrolytic cell power at time t respectively; P g,t , P w,t They are the hydrogen-fired unit n at time t. HF , the output of conventional thermal power unit g and wind farm w; w and W are the number and set of wind farms respectively; g and G are the number and set of conventional thermal power respectively, n HF 、N HF are the numbers and collections of hydrogen-fired units respectively;

[0020] The hydrogen production constraints are:

[0021]

[0022] In the formula, Electrolytic cell n ET Hydrogen production and input power, n ET 、N ET are the number and set of electrolytic cells, μ ET ,φ ET are the hydrogen production conversion rate and electrolyzer efficiency of the electrolyzer, respectively;

[0023] The DC power flow constraint of the power grid is:

[0024]

[0025] Where P i,t is the sum of active power of node i at time t; P ij,t is the active power flow on line ij at time t; S i is the set of nodes connected to node i; θ i,t ,θ j,t are the voltage phase angles of node i and node j respectively; ij is the reactance value of line ij.

[0026] Preferably, the screened flexibility indicators include the probability density function of the system flexibility margin, the new energy consumption rate, the line margin and the stability margin.

[0027] Preferably, the probability density function of the system flexibility margin is:

[0028]

[0029] in, represents the conditional probability distribution of the total flexibility supply of the system under system condition C, Θ is the conditional roll difference operation, represents the conditional distribution of the total flexibility demand of the system under system condition C;

[0030] The new energy consumption rate is:

[0031]

[0032] In the formula, R NE represents the new energy consumption index, P NENi is the theoretical active power output of the renewable energy connected to node i, P NEi is the active power of the renewable energy connected to node i that can be absorbed by the distribution network, n NE Indicates the total number of nodes connected to new energy sources;

[0033] The line margin is:

[0034]

[0035] In the formula, Rload Indicates the line margin index, I k is the actual current on the kth feeder, I sk is the maximum operating current of the kth feeder, N F Indicates the total number of feeders;

[0036] The stability margin is:

[0037]

[0038] L V =max{L ij}

[0039] Where, L ij is the static voltage stability of the feeder between node i and node j, P j and Q j denote the active and reactive net loads of node j, R ij and X ij They represent the line impedance between node i and node j; L V is the static voltage stability index of the distribution network, which is the maximum value of all feeders, U i Indicates the static voltage of the distribution network.

[0040] Preferably, the method of using the particle swarm algorithm to solve the relatively optimal solution of the objective function under the constraint conditions includes:

[0041] Step 1: Initialize all particles:

[0042] Assume that the particle population size is N, randomly set the initial position and velocity of the particle within the allowed range, and set the initial position of each particle to the initial solution P_besti and the solution of the entire population G_best;

[0043] Step 2, calculate the fitness value of each particle and compare it with the current P_besti and G_best. If it is better than the current P_besti and G_best, replace the current P_besti and G_best with the calculated fitness value, and the fitness value is the value of the objective function;

[0044] Step 3, particle optimal update: select particles from the current particle and the individual optimal particle respectively. When neither of the two selected particles is the dominant particle, randomly select one of the two selected particles as the individual optimal particle. The group optimal particle is a particle randomly selected from the non-inferior solution set.

[0045] Step 4: Screen the non-inferior solution set, including initial screening of non-inferior solutions and updating of non-inferior solutions: initial screening of non-inferior solutions is to put a particle into the non-inferior solution set when it is not dominated by other particles after particle initialization; updating of non-inferior solutions is to put a new particle into the non-inferior solution set when it is not dominated by other particles and particles in the current non-inferior solution set;

[0046] Step 5, particle speed and position update: update the particle speed and position according to the individual optimal particle position and the global particle position;

[0047] Step 6: If the maximum number of iterations is reached or the Pareto optimal solution condition is reached, the iteration is terminated; otherwise, step 2 is executed.

[0048] The beneficial effects of the present invention are as follows: the present invention considers the oscillation stability between the machine room and the machine network, and uses the operation constraint function of hydrogen production to construct a wind farm optimization control strategy that takes into account the oscillation modes between the machine room and the machine network. Then, in view of the current situation where the distribution network is greatly affected by new energy and load fluctuations, several important indicators are selected to evaluate the flexibility of the power grid. Finally, based on the particle swarm algorithm, a solution method for the optimization model is given. The present invention optimizes both the power generation and load sides, improves the flexibility of the power grid, and enables it to better cope with the fluctuations in the power generation output of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the process of the present invention;

[0050] Figure 2 Flowchart of parameter optimization strategy. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0053] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0054] The method for improving the flexibility of the power grid based on the wind-hydrogen coordinated optimization control technology of this embodiment includes "

[0055] Step 1: Considering the oscillation stability between the generator and the grid, construct the objective function of the wind farm optimization control strategy that takes into account the oscillation modes between the generator and the grid, consider the constraints related to the wind farm and hydrogen production, and achieve wind farm level / supersynchronous oscillation suppression and safe operation of the grid. The objective function of this implementation method aims to maximize the dissipation intensity of the grid and minimize the circulating energy within the grid.

[0056] Step 2: In view of the fact that the current distribution network is greatly affected by new energy and load fluctuations, several important flexibility indicators are selected to evaluate flexibility to reflect the basic operating performance of the distribution network. The particle swarm algorithm is used to solve the relative optimal solution of the model, and the flexibility of the power grid is improved according to the optimal solution.

[0057] In this implementation, wind farm oscillation suppression needs to take into account the stability of the field network oscillation mode and the field intra-field oscillation mode. Therefore, the objective function is constructed with the maximum field network dissipation intensity and the minimum field intra-field circulation energy:

[0058]

[0059] Among them, W s is the energy dissipated by the wind farm network interaction, W ii is the energy dissipated by wind turbine i in terms of the network interaction, W ij is the inter-machine circulation energy between fans i and j, K p3 is the dissipation coefficient of the fan control link, K PLL is the phase-locked loop coefficient, W x is the total circulation energy between wind turbines in the wind farm, W H is the comprehensive index of flexibility, d k is the kth flexibility index, n is the number of wind turbines, and m is the number of flexibility indicators.

[0060] The constraints in this implementation include:

[0061] (1) Low voltage ride-through resistance constraints

[0062] In order to ensure that the wind turbine still has low voltage ride-through capability after parameter adjustment, it is necessary to ensure that the resistance is still positive at the low voltage ride-through frequency, that is:

[0063] R eq (ω LVRT )>R c (4)

[0064] In the formula, R eq (ω LVRT ) is the resistance at the equivalent low voltage ride-through frequency, and Rc is the critical resistance.

[0065] (2) Parameter adjustment constraints

[0066] The fan current inner loop control parameter adjustment needs to meet the range:

[0067]

[0068] In the formula, F , They are the inner loop current control parameters F(K p3 ,K P_PLL )’s lower and upper limits;

[0069] (3) Load balancing constraints:

[0070]

[0071] Where P load,t , P t ET,abs are the load power and electrolytic cell power at time t respectively; P g,t , P w,t They are the hydrogen-fired unit n at time t. HF , the output of conventional thermal power unit g and wind farm w; w and W are the number and set of wind farms respectively; g and G are the number and set of conventional thermal power respectively, n HF 、N HF are the numbers and collections of hydrogen-fired units respectively;

[0072] (4) Hydrogen production constraints

[0073] There is energy loss in the hydrogen production process, which can be expressed as the following constraints:

[0074]

[0075] In the formula, Electrolytic cell n ET Hydrogen production and input power, n ET 、N ET are the number and set of electrolytic cells, μ ET ,φ ET are the hydrogen production conversion rate and electrolyzer efficiency of the electrolyzer, respectively;

[0076] (5) Grid DC power flow constraints:

[0077]

[0078] Where P i,t is the sum of active power of node i at time t; P ij,t is the active power flow on line ij at time t; S i is the set of nodes connected to node i; θ i,t ,θ j,tare the voltage phase angles of node i and node j respectively; ij is the reactance value of line ij.

[0079] System flexibility demand is defined as the volatility and uncertainty of load and renewable energy timing. It can be characterized by multiple indicators. In view of the fact that the current distribution network is greatly affected by renewable energy and load fluctuations, this implementation method selects the following indicators to evaluate flexibility to reflect the basic operating performance of the distribution network.

[0080] (1) In the planning stage, the probability distribution of load and renewable energy fluctuations and uncertainties under different conditions C can be analyzed through historical data and extended to the planning stage. The probability distribution of system flexibility demand source i is denoted as Then, the convolution operation of formula (2) can be used to obtain the conditional distribution of the total flexibility requirement of the system.

[0081]

[0082] Where: D is the set of flexibility demand sources.

[0083]

[0084] Using the conditional roll difference operation Θ, the probability density function of the system flexibility margin can be calculated:

[0085]

[0086] Where: C max and C min are the upper and lower limits of the conditional parameters respectively; f(·) is the probability density function represents the conditional probability distribution of the total flexibility supply of the system under system condition C, Θ is the conditional roll difference operation, represents the conditional distribution of the total flexibility demand of the system under system condition C;

[0087] (2) New energy consumption rate. Whether the network can adapt to the penetration of new energy and reduce the phenomenon of wind and solar power abandonment in most scenarios is one of the important indicators for measuring the flexibility of the distribution network.

[0088]

[0089] In the formula, R NE represents the new energy consumption index, P NENi is the theoretical active power output of the renewable energy connected to node i, P NEi is the active power of the renewable energy connected to node i that can be absorbed by the distribution network, n NE Indicates the total number of nodes connected to new energy sources;

[0090] (3) Line margin. Sufficient line margin is a necessary condition for the distribution network to adjust its own operation mode. It is also the basic function of the distribution network to meet the future load growth needs of emerging industrial parks, newly built residential buildings, etc.

[0091]

[0092] In the formula, R load Indicates the line margin index, I k is the actual current on the kth feeder, I sk is the maximum operating current of the kth feeder, N F Indicates the total number of feeders;

[0093] (4) Stability margin. New energy sources such as wind power and photovoltaic power have natural volatility, and the distribution network needs to maintain stable operation of the system. The static voltage stability index of the distribution network is introduced to evaluate the ability of the distribution network to maintain system stability when the node power fluctuates greatly.

[0094]

[0095] L V =max{L ij}(15)

[0096] Where, L ij is the static voltage stability of the feeder between node i and node j, P j and Q j denote the active and reactive net loads of node j, R ij and X ij They represent the line impedance between node i and node j; L V is the static voltage stability index of the distribution network, which is the maximum value of all feeders, U i Indicates the static voltage of the distribution network.

[0097] This implementation method optimizes the optimal grid flexibility improvement solution based on the objective function:

[0098] In view of the above flexibility index, this implementation method uses a particle swarm algorithm to determine its optimization parameters. The specific scheme is as follows:

[0099] Step 1: Initialize all particles. Assume that the particle population size is N, randomly set the initial position and velocity of the particles within the allowed range, and set the original position of each particle to its initial solution P_besti and the solution of the entire population G_best.

[0100] Step 2: Fitness calculation. Calculate the fitness value (the value of the objective function) of each particle and compare it with the current solution P_besti and G_best. If it is better than the current solution P_besti and G_best, use the fitness value to replace the current solution.

[0101] Step 3: Particle optimal update. Particle optimality includes individual optimality and group optimality. The individual optimal particle update is to select the dominant particle from the current particle and the individual optimal particle. When both particles are not the dominant particle, a particle is randomly selected as the individual optimal particle. The group optimal particle is a particle randomly selected from the non-inferior solution set.

[0102] Step 4: Screen the non-inferior solution set. Considering that this control strategy needs to meet multiple flexibility index requirements and belongs to a multi-objective optimization problem, it is necessary to determine the non-inferior solution set when updating particles. This step is mainly divided into initial screening of non-inferior solutions and updating of non-inferior solutions. Initial screening of non-inferior solutions means that after the particle is initialized, when a particle is not dominated by other particles (that is, there is no other solution so that all its indicators are better than the energy generated by the corresponding parameters of the particle), the particle is placed in the non-inferior solution set, and a particle is randomly selected from the non-inferior solution set as the optimal particle of the group before the particle is updated. Updating the non-inferior solution set means that when the new particle is not dominated by other particles and particles in the current non-inferior solution set, the new particle is placed in the non-inferior solution set, and a particle is randomly selected from the non-inferior solution set as the optimal particle of the group before each particle update.

[0103] Step 5: Particle velocity and position update: Update the particle velocity and position according to the individual optimal particle position and the global particle position.

[0104] Step 6: If the maximum number of iterations is reached or the Pareto optimal solution condition is reached, the iteration is terminated, otherwise return to step 2 to continue the iteration. The parameter optimization flow chart is as follows Figure 2 shown.

[0105] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. A method for improving power grid flexibility based on wind-hydrogen collaborative optimization control technology, characterized in that: The method comprises: S1. Taking into account the stability of the field network oscillation mode and the field internal oscillation mode, the objective function and constraint conditions are constructed with the maximum field network dissipation intensity and the minimum field internal circulation energy; S2. Select flexibility indicators, use particle swarm algorithm to solve the relatively optimal solution of the objective function under the constraints, and improve the flexibility of the power grid based on the optimal solution.

2. The method for improving power grid flexibility based on wind-hydrogen collaborative optimization control technology according to claim 1 is characterized in that: The objective function is: Among them, W s is the energy dissipated by the wind farm network interaction, W ii is the energy dissipated by wind turbine i in terms of the network interaction, W ij is the inter-machine circulation energy between fans i and j, K p3 is the dissipation coefficient of the fan control link, K PLL is the phase-locked loop coefficient, W x is the total circulation energy between wind turbines in the wind farm, W H is the comprehensive index of flexibility, d k is the kth flexibility index, n is the number of wind turbines, and m is the number of flexibility indicators.

3. The method for improving power grid flexibility based on wind-hydrogen collaborative optimization control technology according to claim 1 is characterized in that: The constraints include low voltage ride-through resistance constraints, parameter adjustment constraints, load balance constraints, hydrogen production constraints and grid DC power flow constraints.

4. The method for improving power grid flexibility based on wind-hydrogen collaborative optimization control technology according to claim 3 is characterized in that: The low voltage through resistance constraint is: R eq (oh LVRT )>R c In the formula, R eq (ω LVRT ) is the resistance at the equivalent low voltage ride-through frequency, R c is the critical resistance; The parameter adjustment constraints are: In the formula, F, They are the inner loop current control parameters F(K p3 ,K P_PLL )’s lower and upper limits; The load balancing constraints are: Where P load,t , P t ET,abs are the load power and electrolytic cell power at time t respectively; P g,t , P w,t They are hydrogen-fired unit n at time t. HF , the output of conventional thermal power unit g and wind farm w; w and W are the number and set of wind farms respectively; g and G are the number and set of conventional thermal power respectively, n HF 、N HF are the numbers and collections of hydrogen-fired units respectively; The hydrogen production constraints are: In the formula, Electrolytic cell n ET Hydrogen production and input power, n ET 、N ET are the number and set of electrolytic cells, μ ET ,φ ET are the hydrogen production conversion rate and electrolyzer efficiency of the electrolyzer, respectively; The DC power flow constraint of the power grid is: Where P i,t is the sum of active power of node i at time t; P ij,t is the active power flow on line ij at time t; S i is the set of nodes connected to node i; θ i,t ,θ j,t are the voltage phase angles of node i and node j respectively; ij is the reactance value of line ij.

5. The method for improving power grid flexibility based on wind-hydrogen collaborative optimization control technology according to claim 1 is characterized in that: The selected flexibility indicators include the probability density function of system flexibility margin, new energy consumption rate, line margin and stability margin.

6. The method for improving power grid flexibility based on wind-hydrogen collaborative optimization control technology according to claim 5 is characterized in that: The probability density function of the system flexibility margin is: in, represents the conditional probability distribution of the total flexibility supply of the system under system condition C, Θ is the conditional roll difference operation, represents the conditional distribution of the total flexibility demand of the system under system condition C; The new energy consumption rate is: In the formula, R NE represents the new energy consumption index, P NENi is the theoretical active power output of the renewable energy connected to node i, P NEi is the active power of the renewable energy connected to node i that can be absorbed by the distribution network, n NE Indicates the total number of nodes connected to new energy sources; The line margin is: In the formula, R load Indicates the line margin index, I k is the actual current on the kth feeder, I sk is the maximum operating current of the kth feeder, N F Indicates the total number of feeders; The stability margin is: L V =max{L ij } Where, L ij is the static voltage stability of the feeder between node i and node j, P j and Q j denote the active and reactive net loads of node j, R ij and X ij They represent the line impedance between node i and node j; L V is the static voltage stability index of the distribution network, which is the maximum value of all feeders, U i Indicates the static voltage of the distribution network.

7. The method for improving power grid flexibility based on wind-hydrogen collaborative optimization control technology according to claim 1 is characterized in that: Using the particle swarm algorithm, the methods for solving the relatively optimal solution of the objective function under constraints include: Step 1: Initialize all particles: Assume that the particle population size is N, randomly set the initial position and velocity of the particle within the allowed range, and set the initial position of each particle to the initial solution P_besti and the solution of the entire population G_best; Step 2, calculate the fitness value of each particle and compare it with the current P_besti and G_best. If it is better than the current P_besti and G_best, replace the current P_besti and G_best with the calculated fitness value, and the fitness value is the value of the objective function; Step 3, particle optimal update: select particles from the current particle and the individual optimal particle respectively. When neither of the two selected particles is the dominant particle, randomly select one of the two selected particles as the individual optimal particle. The group optimal particle is a particle randomly selected from the non-inferior solution set. Step 4: Screen the non-inferior solution set, including initial screening of non-inferior solutions and updating of non-inferior solutions: initial screening of non-inferior solutions is to put a particle into the non-inferior solution set when it is not dominated by other particles after particle initialization; updating of non-inferior solutions is to put a new particle into the non-inferior solution set when it is not dominated by other particles and particles in the current non-inferior solution set; Step 5, particle speed and position update: update the particle speed and position according to the individual optimal particle position and the global particle position; Step 6: If the maximum number of iterations is reached or the Pareto optimal solution condition is reached, the iteration is terminated; otherwise, step 2 is executed.

8. A computer-readable storage device storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for improving the flexibility of a power grid based on wind-hydrogen collaborative optimization control technology as described in any one of claims 1 to 7 are implemented.

9. A device for improving the flexibility of a power grid based on wind-hydrogen collaborative optimization control technology, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor, characterized in that: The processor executes the computer program to implement the steps of the method for improving grid flexibility based on wind-hydrogen collaborative optimization control technology as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for improving the flexibility of a power grid based on wind-hydrogen collaborative optimization control technology as described in any one of claims 1 to 7 are implemented.

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