Peak regulation optimization method, device and equipment for accessing distributed power supply power grid, and medium

Abstract

The invention relates to the technical field of power grid peak regulation optimization, in particular to a distributed power supply access power grid peak regulation optimization method and device, equipment and a medium, and the method comprises the steps: firstly, obtaining a target function with stable system load and minimum operation resource consumption as targets; then, constructing a plurality of constraint conditions by taking the safe operation of the system as a constraint; a plurality of particles are constructed according to the number of controllable devices in the power grid, the position of each particle is updated through the positions of the individual optimal particle and the overall optimal particle, and the fitness of each particle is calculated after the positions are updated; and finally, if the fitness of the plurality of particles does not meet the termination condition, adjusting the inertia weight by adopting a linear programming method, re-updating the positions of the plurality of particles until the particles meeting the fitness are generated, and adjusting the controllable equipment in the power grid according to the particles meeting the fitness. According to the invention, the system network loss is reduced, the consumption of new energy is increased, and the operation resource consumption of the power grid is obviously reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid peak load optimization, and in particular to a method, device, equipment and medium for peak load optimization of a power grid connected to a distributed power source. Background Art

[0002] With the large-scale access of distributed generation (DG) to the power grid, traditional power grid peak-shaving methods face challenges. The time-varying nature of DG output increases the complexity and difficulty of power grid dispatching, posing a threat to the stability and economy of the system. Traditional power grid peak-shaving strategies mainly rely on the regulation capabilities of large thermal power units, which are difficult to adapt to the rapidly changing output characteristics of distributed generation.

[0003] Some current technologies use vertical and horizontal optimization to dynamically reconstruct the two-stage distribution network containing distributed power sources. This scheme takes into account the periodicity of loads and distributed power sources, and can perform static reconstruction in each time period through the chaotic gravitational mouse swarm algorithm. It has certain technical significance. However, this method cannot perform multi-objective peak-shaving optimization of power supply grids.

[0004] Some technologies use an improved vulture search algorithm to locate faults in distribution network partitions containing distributed power sources. This method can find the fault location in a timely manner, but cannot achieve multi-objective peak-shaving optimization of distributed power grids.

[0005] Some technologies have proposed a prediction model based on neural networks to predict the output of distributed power sources, thereby improving the predictability and accuracy of power grid dispatch. However, this system mainly focuses on improving the prediction accuracy and is still insufficient for the overall solution of peak load optimization problem.

[0006] Some technologies have proposed a peak-shaving method based on particle swarm optimization. Although it achieves the optimal scheduling of distributed power output, this method does not fully consider the multi-objective optimization requirements such as the economy and environmental protection of the power grid.

[0007] Based on this, it is necessary to develop and design a peak-shaving optimization method for accessing a distributed power grid. Summary of the invention

[0008] The embodiments of the present invention provide a peak-shaving optimization method, device, equipment and medium for accessing a distributed power grid, which are used to solve the problem that multi-objective optimization cannot be performed on the accessed distributed power grid in the prior art.

[0009] In a first aspect, an embodiment of the present invention provides a peak load optimization method for a power grid connected to a distributed power source, comprising:

[0010] Obtaining an objective function with the goal of stabilizing system load and minimizing operating resource consumption;

[0011] Taking the safe operation of the system as a constraint, multiple constraints are constructed, where the constraints are used to constrain the total load, energy storage charging and discharging, energy storage balance or new energy output;

[0012] Construct multiple particles according to the number of controllable devices in the power grid, update the position of each particle through the position of the individual optimal particle and the overall optimal particle, and calculate the fitness of each particle after the position is updated;

[0013] If the fitness of the multiple particles does not meet the termination condition, the inertia weight is adjusted by a linear programming method, and the positions of the multiple particles are updated again until particles meeting the fitness are generated, and the controllable devices in the power grid are adjusted according to the particles meeting the fitness.

[0014] In a possible implementation, the objective function includes: a load variance objective function and a system operation resource consumption objective function, wherein the load variance objective function is:

[0015]

[0016] In the formula, is the system load variance, L t is the system load at time period t, is the average value of the system load, T is the total number of time periods considered;

[0017] The objective function of the system operation resource consumption is:

[0018]

[0019] In the formula, C total is the total system operating cost, C i (P i,t ) is the power generation cost of the i-th generator set in the t-th period, P i,t is the output of the i-th generator set in the t-th period, N is the total number of generator sets, C j (D j,t ) is the cost of the jth interruptible load in period t, D j,t is the adjustment amount of the jth interruptible load in the tth period, M is the total number of interruptible loads, C storage is the operating cost of the energy storage unit, C interruption is the interruption cost of the interruptible load.

[0020] In a possible implementation, the multiple constraints include:

[0021] Generator output constraints:

[0022] P min,i ≤P i,t ≤P max,i

[0023] Where P i,t is the output of the i-th generator set in the t-th period, P min,i is the minimum output limit of the ith generator set, P max,i is the maximum output limit of the i-th generator set;

[0024] System total load balance constraints:

[0025]

[0026] Where, L t is the total system load in period t, P storage,t is the discharge amount of the energy storage unit in the tth period;

[0027] Interruptible load constraints:

[0028] D min,j ≤D j,t ≤D max,j

[0029] Where D j,t is the adjustment amount of the jth interruptible load in the tth period, D min,j is the minimum adjustment limit of the jth interruptible load, D max,j is the maximum adjustment limit of the jth interruptible load;

[0030] New energy output constraints:

[0031] P min,renewable ≤P renewable,t ≤P max,renewable

[0032] Where P renewable,t is the predicted output of renewable energy in the tth period, P min,renewable is the minimum output limit of new energy, P max,renewable It is the maximum output limit of new energy;

[0033] Dispatchable DG unit operation constraints:

[0034]

[0035] In the formula, is the predicted output of the dispatchable DG unit in the tth period, is the minimum output limit of the dispatchable DG unit, is the maximum output limit of the dispatchable DG unit.

[0036] In a possible implementation, the plurality of constraints further include:

[0037] Energy storage unit charging and discharging constraints:

[0038] E min ≤E t ≤E max

[0039] In the formula, E t is the energy storage capacity of the energy storage unit in the tth period, E min is the minimum storage capacity limit of the energy storage unit, E max is the maximum storage capacity limit of the energy storage unit;

[0040] Energy balance constraints of energy storage units:

[0041]

[0042] In the formula, E t+1 is the energy storage capacity of the energy storage unit in the tth period, η charge is the charging efficiency of the energy storage unit, P charge,t is the charging power of the energy storage unit in the tth period, η discharge is the discharge efficiency of the energy storage unit, P discharge,t is the discharge power of the energy storage unit in the tth period.

[0043] In a possible implementation, updating the position of each particle according to the positions of the individual optimal particle and the overall optimal particle includes:

[0044] The particle velocity is updated according to the first formula, wherein the first formula is:

[0045]

[0046] In the formula, is the velocity of the i-th particle in the j-th dimension at the next iteration, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers in the range [0,1], P best,i,j is the historical optimal position of the ith particle in the jth dimension, G best,j is the value of the global optimal position in the jth dimension, is the position of the i-th particle in the j-th dimension at the current iteration;

[0047] Update the particle position according to the second formula, where the second formula is:

[0048]

[0049] In the formula, is the position of the i-th particle in the j-th dimension at the next iteration.

[0050] In a possible implementation manner, re-updating the positions of the plurality of particles includes:

[0051] The adjustment amount of the particle position is determined according to the third formula, wherein the third formula is:

[0052]

[0053] In the formula, is the solution vector at the k+1th generation, J is the Jacobian matrix, λ is the tuning parameter of the linear programming algorithm, and I is x k The identity matrix, J i and They are respectively for particle i at its current position The Jacobian matrix and function vector at .

[0054] In a possible implementation, the method of adjusting the inertia weight by using a linear programming method includes:

[0055] The inertia weight is adjusted according to the fourth formula, wherein the fourth formula is:

[0056]

[0057] In the formula, w max and w min are the maximum and minimum values ​​of the inertia weight, K max is the maximum number of iterations, k is the current number of iterations, and w k is the adjusted inertia weight.

[0058] In a second aspect, an embodiment of the present invention provides a peak-shaving optimization device for accessing a distributed power grid, which is used to implement the peak-shaving optimization method for accessing a distributed power grid as described in the first aspect or any possible implementation of the first aspect, wherein the peak-shaving optimization device for accessing a distributed power grid comprises:

[0059] A target acquisition module is used to obtain an objective function with the goal of stabilizing the system load and minimizing the consumption of operating resources;

[0060] The constraint condition construction module is used to construct multiple constraint conditions based on the safe operation of the system, where the constraint conditions are used to constrain the total load, energy storage charging and discharging, energy storage balance or new energy output;

[0061] The particle initialization module is used to construct multiple particles according to the number of controllable devices in the power grid, update the position of each particle through the position of the individual optimal particle and the overall optimal particle, and calculate the fitness of each particle after the position is updated;

[0062] as well as,

[0063] The peak load optimization module is used to adjust the inertia weight by a linear programming method if the fitness of the multiple particles does not meet the termination condition, re-update the positions of the multiple particles until particles that meet the fitness are generated, and adjust the controllable devices in the power grid according to the particles that meet the fitness.

[0064] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0065] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0066] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0067] The embodiment of the present invention discloses a peak-shaving optimization method for accessing a distributed power grid. The embodiment of the peak-shaving optimization method for accessing a distributed power grid first obtains an objective function with the goal of stabilizing the system load and minimizing the consumption of operating resources; then, with the safe operation of the system as a constraint, multiple constraint conditions are constructed, wherein the constraint conditions are used to constrain the total load, energy storage charging and discharging, energy storage balance or new energy output; then, multiple particles are constructed according to the number of controllable devices in the power grid, the position of each particle is updated by the position of the individual optimal particle and the overall optimal particle, and the fitness of each particle is calculated after the position is updated; finally, if the fitness of the multiple particles does not meet the termination condition, the inertia weight is adjusted by a linear programming method, and the positions of the multiple particles are re-updated until particles that meet the fitness are generated, and the controllable devices in the power grid are adjusted according to the particles that meet the fitness. The present invention considers the time-varying DG output access distributed power grid multi-objective peak-shaving optimization technology system, and solves the multi-objective peak-shaving optimization by combining the multi-objective optimization model constructed with the minimum load variance and the minimum operating cost as the objectives and the PSO-LF optimization algorithm, thereby successfully improving the system's peak-shaving capability and effectively coping with the uncertainty of DG output. The model guides DER to produce reasonably through the time-of-use electricity price mechanism, guides energy storage units to charge and store energy during off-peak periods, and appropriately reduces load demand through interruptible loads during peak periods, thereby reducing system network losses, increasing the absorption of new energy, and significantly reducing the operating resource consumption of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0069] Figure 1 It is a flow chart of a peak-shaving optimization method for accessing a distributed power grid provided by an embodiment of the present invention;

[0070] Figure 2 It is a principle diagram of a multi-objective peak-shaving model provided in an embodiment of the present invention;

[0071] Figure 3 is a flow chart of a target model optimization method provided by an embodiment of the present invention;

[0072] Figure 4 It is a schematic diagram of an IEEE33 node power distribution system provided by an embodiment of the present invention;

[0073] Figure 5 is a daily load prediction curve diagram provided by an embodiment of the present invention;

[0074] Figure 6 It is a graph of wind turbine and photovoltaic power station output power provided by an embodiment of the present invention;

[0075] Figure 7 is a comparison diagram of power grid peak load optimization capabilities provided by an embodiment of the present invention;

[0076] Figure 8 It is a functional block diagram of a peak-shaving optimization device for accessing a distributed power grid provided in an embodiment of the present invention;

[0077] Fig. 9 It is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0078] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0079] In order to make the purpose, technical solutions and advantages of the present invention more clear, a specific implementation method will be described below in conjunction with the accompanying drawings.

[0080] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and a specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0081] Figure 1 A flow chart of a peak-shaving optimization method for accessing a distributed power grid provided in an embodiment of the present invention.

[0082] like Figure 1 As shown, it shows a flowchart of the implementation of the peak load optimization method for accessing a distributed power grid provided by an embodiment of the present invention, which is described in detail as follows:

[0083] In step 101, an objective function with the goals of stabilizing the system load and minimizing the consumption of operating resources is obtained.

[0084] In some implementations, the objective function includes: a load variance objective function and a system operation resource consumption objective function, wherein the load variance objective function is:

[0085]

[0086] In the formula, is the system load variance, L t is the system load at time period t, is the average value of the system load, T is the total number of time periods considered;

[0087] The objective function of the system operation resource consumption is:

[0088]

[0089] In the formula, C total is the total system operating cost, C i (P i,t ) is the power generation cost of the i-th generator set in the t-th period, P i,t is the output of the i-th generator set in the t-th period, N is the total number of generator sets, C j (D j,t ) is the cost of the jth interruptible load in period t, D j,t is the adjustment amount of the jth interruptible load in the tth period, M is the total number of interruptible loads, C storage is the operating cost of the energy storage unit, C interruption is the interruption cost of the interruptible load.

[0090] For example, DG output refers to the power output provided to the grid by distributed power sources (such as solar photovoltaic power generation, wind power generation, small hydropower, etc.) within a specific time period. The main purpose of peak load regulation is to maintain the balance between supply and demand of the grid and prevent the fluctuation of grid frequency and voltage from affecting the safe and stable operation of the grid.

[0091] Studying the impact of DG output time variability on power grid peak regulation has important practical significance.

[0092] The present invention expands on the traditional power system peak-shaving model, not only taking into account the output optimization on the power generation side, but also comprehensively incorporating interruptible loads and energy storage units as important peak-shaving means.

[0093] The present invention constructs a multi-objective peak-shaving model, such as Figure 2 As shown in the figure, the overall objective function consists of two parts: one is the minimization of load variance, which is used to measure the stability of system load and reduce the impact of load fluctuations on the power grid; the other is the minimization of operating costs, including power generation costs, compensation costs for interruptible loads, and operation and maintenance costs of energy storage units, etc., aiming to improve the economic benefits of the system.

[0094] The load variance objective function is:

[0095]

[0096] In the formula, is the system load variance, L t is the system load at time period t, is the average value of the system load, T is the total number of time periods considered;

[0097] The objective function of system operation resource consumption is:

[0098]

[0099] In the formula, C total is the total system operating cost, C i (P i,t ) is the power generation cost of the i-th generator set in the t-th period, P i,t is the output of the i-th generator set in the t-th period, N is the total number of generator sets, C j (D j,t ) is the cost of the jth interruptible load in period t, D j,t is the adjustment amount of the jth interruptible load in the tth period, M is the total number of interruptible loads, C storage is the operating cost of the energy storage unit, C interruption is the interruption cost of the interruptible load.

[0100] In step 102, multiple constraint conditions are constructed with the safe operation of the system as a constraint, wherein the constraint conditions are used to constrain the total load, energy storage charging and discharging, energy storage balance or new energy output.

[0101] In some embodiments, the plurality of constraints include:

[0102] Generator output constraints:

[0103] P min,i ≤P i,t ≤P max,i

[0104] Where P i,t is the output of the i-th generator set in the t-th period, P min,i is the minimum output limit of the ith generator set, P max,i is the maximum output limit of the i-th generator set;

[0105] System total load balance constraints:

[0106]

[0107] Where, L t is the total system load in period t, P storage,t is the discharge amount of the energy storage unit in the tth period;

[0108] Interruptible load constraints:

[0109] D min,j ≤D j,t ≤D max,j

[0110] Where D j,t is the adjustment amount of the jth interruptible load in the tth period, D min,j is the minimum adjustment limit of the jth interruptible load, D max,j is the maximum adjustment limit of the jth interruptible load;

[0111] New energy output constraints:

[0112] P min,renewable ≤P renewable,t ≤P max,renewable

[0113] Where P renewable,t is the predicted output of renewable energy in the tth period, P min,renewable is the minimum output limit of new energy, P max,renewable It is the maximum output limit of new energy;

[0114] Dispatchable DG unit operation constraints:

[0115]

[0116] In the formula, is the predicted output of the dispatchable DG unit in the tth period, is the minimum output limit of the dispatchable DG unit, is the maximum output limit of the dispatchable DG unit.

[0117] In some implementations, the plurality of constraints further include:

[0118] Energy storage unit charging and discharging constraints:

[0119] E min ≤E t ≤E max

[0120] In the formula, E t is the energy storage capacity of the energy storage unit in the tth period, E min is the minimum storage capacity limit of the energy storage unit, E max is the maximum storage capacity limit of the energy storage unit;

[0121] Energy balance constraints of energy storage units:

[0122]

[0123] In the formula, E t+1 is the energy storage capacity of the energy storage unit in the tth period, η charge is the charging efficiency of the energy storage unit, P charge,t is the charging power of the energy storage unit in the tth period, η discharge is the discharge efficiency of the energy storage unit, P discharge,t is the discharge power of the energy storage unit in the tth period.

[0124] For example, in the optimization problem of the peak load regulation strategy of the power system, in addition to the objective function, a series of constraints need to be defined to ensure the safe and stable operation of the system.

[0125] In some application scenarios, multiple constraints include:

[0126] Generator output constraints:

[0127] P min,i ≤P i,t ≤P max,i

[0128] Where P i,t is the output of the i-th generator set in the t-th period, P min,i is the minimum output limit of the ith generator set, P max,i is the maximum output limit of the i-th generator set;

[0129] System total load balance constraints:

[0130]

[0131] Where, L t is the total system load in period t, P storage,t is the discharge amount of the energy storage unit in the tth period;

[0132] Interruptible load constraints:

[0133] D min,j ≤D j,t ≤D max,j

[0134] Where D j,t is the adjustment amount of the jth interruptible load in the tth period, D min,j is the minimum adjustment limit of the jth interruptible load, D max,j is the maximum adjustment limit of the jth interruptible load;

[0135] New energy output constraints:

[0136] P min,renewable ≤P renewable,t ≤P max,renewable

[0137] Where P renewable,t is the predicted output of renewable energy in the tth period, P min,renewable is the minimum output limit of new energy, P max,renewable It is the maximum output limit of new energy;

[0138] Dispatchable DG unit operation constraints:

[0139]

[0140] In the formula, is the predicted output of the dispatchable DG unit in the tth period, is the minimum output limit of the dispatchable DG unit, is the maximum output limit of the dispatchable DG unit.

[0141] In other application scenarios, multiple constraints also include:

[0142] Energy storage unit charging and discharging constraints:

[0143] E min ≤E t ≤E max

[0144] In the formula, E tis the energy storage capacity of the energy storage unit in the tth period, E min is the minimum storage capacity limit of the energy storage unit, E max is the maximum storage capacity limit of the energy storage unit;

[0145] Energy balance constraints of energy storage units:

[0146]

[0147] In the formula, E t+1 is the energy storage capacity of the energy storage unit in the tth period, η charge is the charging efficiency of the energy storage unit, P charge,t is the charging power of the energy storage unit in the tth period, η discharge is the discharge efficiency of the energy storage unit, P discharge,t is the discharge power of the energy storage unit in the tth period.

[0148] In step 103, a plurality of particles are constructed according to the number of controllable devices in the power grid, the position of each particle is updated by the position of the individual optimal particle and the overall optimal particle, and the fitness of each particle is calculated after the position is updated.

[0149] In some embodiments, updating the position of each particle by using the positions of the individual optimal particle and the overall optimal particle comprises:

[0150] The particle velocity is updated according to the first formula, wherein the first formula is:

[0151]

[0152] In the formula, is the velocity of the i-th particle in the j-th dimension at the next iteration, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers in the range [0,1], P best,i,j is the historical optimal position of the ith particle in the jth dimension, G best,j is the value of the global optimal position in the jth dimension, is the position of the i-th particle in the j-th dimension at the current iteration;

[0153] Update the particle position according to the second formula, where the second formula is:

[0154]

[0155] In the formula, is the position of the i-th particle in the j-th dimension at the next iteration.

[0156] In step 104, if the fitness of the plurality of particles does not meet the termination condition, a linear programming method is used to adjust the inertia weight, and the positions of the plurality of particles are re-updated until particles meeting the fitness are generated, and the controllable devices in the power grid are adjusted according to the particles meeting the fitness.

[0157] In some embodiments, re-updating the positions of the plurality of particles comprises:

[0158] The adjustment amount of the particle position is determined according to the third formula, wherein the third formula is:

[0159]

[0160] In the formula, is the solution vector at the k+1th generation, J is the Jacobian matrix, λ is the tuning parameter of the linear programming algorithm, and I is x k The identity matrix, J i and They are respectively for particle i at its current position The Jacobian matrix and function vector at .

[0161] In some embodiments, the adjusting the inertia weight using a linear programming method comprises:

[0162] The inertia weight is adjusted according to the fourth formula, wherein the fourth formula is:

[0163]

[0164] In the formula, w max and w min are the maximum and minimum values ​​of the inertia weight, K max is the maximum number of iterations, k is the current number of iterations, and w k is the adjusted inertia weight.

[0165] For example, in order to solve the above multi-objective peak load optimization model, the present invention implements a hybrid optimization algorithm based on particle swarm optimization and linear programming. The flow chart of the optimization method is as follows: Figure 3 shown.

[0166] The speed update mechanism is particularly important when considering the time-varying nature of DG output. We use a dynamic penalty function method so that the algorithm can respond to output changes in a timely manner and adjust the particle search strategy. When the particle position does not meet the constraints, the non-compliant solutions are effectively eliminated by reducing its fitness and introducing a penalty coefficient. In addition, combined with the projection method, the solutions that do not meet the constraints are adjusted to the feasible domain, thereby ensuring the effectiveness of the search process. Particle speed update formula:

[0167]

[0168] In the formula, is the velocity of the i-th particle in the j-th dimension at the next iteration, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers in the range [0,1], P best,i,j is the historical optimal position of the ith particle in the jth dimension, G best,j is the value of the global optimal position in the jth dimension, is the position of the i-th particle in the j-th dimension at the current iteration.

[0169] To ensure the stability and economy of the power system, we adopt a comprehensive constraint processing strategy. By introducing a dynamic penalty function, we impose penalties on solutions that do not meet the load balance and stability requirements, thereby guiding the search towards feasible solutions. At the same time, we use repair and projection techniques to check the feasibility of each solution to ensure that all solutions are within the feasible domain. These methods not only minimize the cost, but also effectively maintain the stability of the system. Particle position update formula:

[0170]

[0171] In the formula, is the position of the i-th particle in the j-th dimension at the next iteration.

[0172] In the optimization of power grid peak load regulation, we introduce an effective local search mechanism to accelerate convergence and improve the accuracy of the solution by dynamically adjusting the particle positions. When considering the variability of DG output, LF adjustment becomes a key strategy. We make small random perturbations through neighborhood exploration to quickly find a better peak load regulation solution. At the same time, the penalty function method is used to handle the constraints, penalties are imposed on solutions that do not meet the constraints, and a repair mechanism is introduced to automatically adjust solutions that exceed the limits. Experimental results show that this method significantly improves the convergence speed and the feasibility of the solution, showing its application potential in actual power grid peak load regulation. LF adjustment formula:

[0173]

[0174] Where ΔX is the adjustment of the particle position, H is the Hessian matrix (H) of the objective function, which represents the curvature of the objective function, λ is the linear programming factor used to adjust the convergence speed and stability of the algorithm, I is the unit matrix, is the gradient of the objective function.

[0175] In the multi-objective peak-shaving optimization of power grids, the introduction of recursive formulas provides an effective tool for dealing with the time-varying nature of output. By dynamically adjusting the output, the recursive formula can reflect load changes and resource scheduling needs in real time. On this basis, we proposed a series of constraint processing methods to ensure the feasibility of the solution. In particular, using the penalty function method, we impose corresponding penalties on solutions that do not meet the constraints, and automatically adjust solutions that exceed the limits in combination with the repair mechanism, thereby achieving a balance in the optimization objectives. Experimental results show that these methods significantly improve the feasibility of the solution and the optimization effect, showing broad application prospects in actual power grid peak-shaving. Recursive formula of the LF algorithm:

[0176] x k+1 =x k -(J T J+λI) -1 J T f(x k )

[0177] In the formula, x k+1 is the solution vector at the k+1th generation, J is the Jacobian matrix, that is, the derivative matrix of function f with respect to x, λ is the adjustment parameter of the LM algorithm, which controls the trade-off between the Newton method and the gradient descent method, and I is x k The identity matrix, f(x k ) is the function vector at .

[0178] In peak load optimization considering the time-varying DG output, it is very important to ensure the feasibility of the solution. We use a penalty function method to reduce the fitness of solutions that do not meet the constraints, thereby giving priority to solutions that meet the constraints. In addition, when the generated solution exceeds the limit, the system automatically adjusts to ensure that the result is within the feasible range. By updating the constraints and the neighborhood search strategy in real time, we are able to quickly find the optimal solution while maintaining economy and stability. Combining PSO and LF position update:

[0179]

[0180] In the formula, is the solution vector at the k+1th generation, J is the Jacobian matrix, λ is the tuning parameter of the linear programming algorithm, and I is x k The identity matrix, J i and They are respectively for particle i at its current position The Jacobian matrix and function vector at .

[0181] Inertia weight update strategy:

[0182]

[0183] In the formula, w max and wmin are the maximum and minimum values ​​of the inertia weight, K max is the maximum number of iterations, k is the current number of iterations, and w k is the adjusted inertia weight.

[0184] Experimental results and analysis:

[0185] This study uses matlab language to implement the multi-objective optimization model and PSO-LF algorithm. We compared several classic optimization methods: genetic algorithm, ant colony algorithm and simulated annealing. Genetic algorithm has strong global search ability, but it converges slowly and may fall into local optimum in advance. Relatively speaking, particle swarm optimization converges quickly, but is also susceptible to local optimum. Ant colony algorithm performs well on complex scheduling problems, but is less efficient in large-scale problems. Compared with traditional methods, our research has greatly improved the multi-objective peak-shaving optimization capability of power grids.

[0186] Considering the random characteristics of the particle swarm algorithm, the results of a single run may fluctuate greatly. Therefore, this study ran the PSO-LF algorithm 10 times to obtain stable results. By statistically analyzing the results, we calculated the mean, standard deviation, and frequency distribution of the optimal solution. The results show that the PSO-LF algorithm exhibits a relatively stable convergence trend in multiple runs, and the average objective function value is significantly lower than other traditional optimization methods. In addition, the small value of the standard deviation indicates that the algorithm has good consistency in the solutions of different runs.

[0187] For the distributed energy devices installed in the system, such as fuel cells (nodes 8 and 15), dispatchable DG, this paper adopts micro gas turbines (nodes 7, 24 and 25), interruptible loads (nodes 26 and 32), energy storage units (node ​​23), wind turbines and photovoltaic panels (nodes 14 and 31), and their operation strategies and output scheduling are designed based on the overall optimization goal of the system. These devices balance the supply and demand of the system, improve energy utilization efficiency, and ensure the stable operation of the power grid through the optimization scheduling model and PSO-LF optimization algorithm. IEEE33 node distribution system such as Figure 4 shown.

[0188] The daily load forecast curve is the key basis for system scheduling and optimization. By analyzing historical load data, combined with the current economic situation, weather conditions, holiday arrangements and other factors, a suitable forecasting method is used to obtain the load curve for the next day, providing strong support for the real-time scheduling and long-term planning of the system. The daily load power statistics are shown in Table 1.

[0189] Table 1 Daily load power table

[0190]

[0191]

[0192] Daily load forecast curve is as follows Figure 5 shown.

[0193] From Table 1 and Figure 6 It can be seen from the daily load power table that the load fluctuates greatly during the day, especially in the morning and evening (such as 8 to 9, and 18 to 21), reaching a peak, and at night (such as 1 to 6 in the morning) is at a low point. This significant peak-to-valley difference places high demands on the peak-shaving capacity of the power grid.

[0194] Figure 6 The prediction results of wind turbine and photovoltaic output are shown.

[0195] from Figure 6 It can be observed that the power generation output of wind power and photovoltaic power shows a significant anti-peaking characteristic. When the power demand is at peak time, the power generation of wind power and photovoltaic power tends to be low, while in the period of low demand, their power generation is relatively high.

[0196] In order to verify the practicality and benefits of the constructed peak load regulation model, this paper designs three dispatching methods based on different distributed energy resources (DER) participating in the grid peak load regulation strategy, and conducts comparative analysis from three dimensions: load stability, grid operation cost and network loss. The simulation results are summarized in Table 2.

[0197] Table 2 Simulation results of different peak load regulation methods

[0198]

[0199] As can be seen from Table 2, the comprehensive peak-shaving strategy innovatively proposed in this paper incorporates energy storage units and interruptible loads into the peak-shaving system, achieving a more efficient and economical peak-shaving effect. In order to verify the improvement of the peak-shaving optimization capability of the power grid in this study, the genetic algorithm and ant colony algorithm systems were used as comparative experiments.

[0200] Under the same conditions, all DERs participate in the dispatching mode of the power grid peak load regulation strategy, and are compared and analyzed from three dimensions: load stability, power grid operation cost, and network loss. The data shown in Table 3 are obtained.

[0201] Table 3 Comparison of power grid peak load optimization capabilities using different methods

[0202]

[0203] From Table 3, it can be seen that the grid peak regulation of the method used in this study can reduce the load variance to a greater extent, while reducing the cost of grid operation and network loss, which has certain practicality. However, the grid peak regulation capabilities of the genetic algorithm and ant colony algorithm systems are not as high as those of the method in this study.

[0204] The comparison of power grid peak load optimization capabilities of different methods is shown in the figure below. Figure 7 As shown, from Figure 7 It can be seen that when the research algorithm is used, the power grid peak load optimization capability is the best, the load stability and network loss are the lowest, and the power grid operation cost is the lowest, while the system winding of the genetic algorithm and the ant colony algorithm are higher than this method. According to the above analysis, the peak load model and its comprehensive peak load strategy proposed in this paper, while ensuring the stable operation of the power grid, significantly improve the economy of the peak load process, and provide strong theoretical support and practical reference for future power grid peak load optimization.

[0205] In order to more comprehensively evaluate the efficiency of the PSO-LF optimization algorithm, we designed a series of experiments to measure the CPU running time under different parameter settings. In the experiment, we selected different numbers of particles and different dimensions and recorded the average time of each run. The results show that when the number of particles increases from 50 to 100, the average running time increases from 2.5 seconds to 4.8 seconds; in the high-dimensional case, even if the number of particles remains unchanged, the running time will increase significantly, reaching an average of 5.1 seconds. These results show that the running time of the algorithm is positively correlated with the number of particles and the dimension of the problem, emphasizing the importance of parameter selection for optimization performance.

[0206] The present invention provides an implementation method for peak load optimization of a distributed power grid, which first obtains an objective function with the goal of stabilizing the system load and minimizing the consumption of operating resources; then, with the safe operation of the system as a constraint, multiple constraint conditions are constructed, wherein the constraint conditions are used to constrain the total load, energy storage charging and discharging, energy storage balance or new energy output; then, multiple particles are constructed according to the number of controllable devices in the grid, the position of each particle is updated by the position of the individual optimal particle and the overall optimal particle, and the fitness of each particle is calculated after the position is updated; finally, if the fitness of the multiple particles does not meet the termination condition, the inertia weight is adjusted by a linear programming method, and the positions of the multiple particles are re-updated until particles that meet the fitness are generated, and the controllable devices in the grid are adjusted according to the particles that meet the fitness. The present invention considers the time-varying DG output access distributed power grid multi-objective peak-shaving optimization technology system, and solves the multi-objective peak-shaving optimization by combining the multi-objective optimization model constructed with the minimum load variance and the minimum operating cost as the objectives and the PSO-LF optimization algorithm, thereby successfully improving the system's peak-shaving capability and effectively coping with the uncertainty of DG output. The model guides DER to produce reasonably through the time-of-use electricity price mechanism, guides energy storage units to charge and store energy during off-peak periods, and appropriately reduces load demand through interruptible loads during peak periods, thereby reducing system network losses, increasing the absorption of new energy, and significantly reducing the operating resource consumption of the power grid.

[0207] It should be understood that the size of the serial numbers of the steps in the above implementation does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation method of the present invention.

[0208] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0209] Figure 8 This is a functional block diagram of a peak load optimization device for accessing a distributed power grid provided by an embodiment of the present invention, referring to Figure 8 , the peak-shaving optimization device connected to the distributed power grid includes: a target acquisition module 801, a constraint condition construction module 802, a particle initialization module 803 and a peak-shaving optimization module 804, wherein:

[0210] The target acquisition module 801 is used to acquire the target function with the goal of stabilizing the system load and minimizing the consumption of operating resources;

[0211] The constraint condition building module 802 is used to build multiple constraint conditions based on the safe operation of the system, wherein the constraint conditions are used to constrain the total load, energy storage charging and discharging, energy storage balance or new energy output;

[0212] A particle initialization module 803 is used to construct a plurality of particles according to the number of controllable devices in the power grid, update the position of each particle by the position of the individual optimal particle and the overall optimal particle, and calculate the fitness of each particle after the position is updated;

[0213] The peak load optimization module 804 is used to adjust the inertia weight by a linear programming method if the fitness of the multiple particles does not meet the termination condition, re-update the positions of the multiple particles until particles that meet the fitness are generated, and adjust the controllable devices in the power grid according to the particles that meet the fitness.

[0214] Fig. 9 is a functional block diagram of an electronic device provided by an embodiment of the present invention. Fig. 9 As shown, the electronic device 9 of this embodiment includes: a processor 900 and a memory 901, wherein the memory 901 stores a computer program 902 that can be run on the processor 900. When the processor 900 executes the computer program 902, the steps in the above-mentioned methods and embodiments for optimizing peak load regulation of a distributed power grid are implemented, for example Figure 1 Steps 101 to 104 are shown.

[0215] Exemplarily, the computer program 902 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 901 and executed by the processor 900 to implement the present invention.

[0216] The electronic device 9 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 9 may include, but is not limited to, a processor 900 and a memory 901. Those skilled in the art will appreciate that Fig. 9 It is only an example of the electronic device 9 and does not constitute a limitation of the electronic device 9. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 9 may also include input and output devices, network access devices, buses, etc.

[0217] The processor 900 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0218] The memory 901 may be an internal storage unit of the electronic device 9, such as a hard disk or memory of the electronic device 9. The memory 901 may also be an external storage device of the electronic device 9, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 9. Further, the memory 901 may also include both an internal storage unit of the electronic device 9 and an external storage device. The memory 901 is used to store the computer program 902 and other programs and data required by the electronic device 9. The memory 901 may also be used to temporarily store data that has been output or is to be output.

[0219] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0220] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0221] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0222] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0223] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0224] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0225] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various methods and device implementation methods. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0226] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A peak load optimization method for a distributed power grid, characterized in that: include: Obtaining an objective function with the goal of stabilizing system load and minimizing operating resource consumption; Taking the safe operation of the system as a constraint, multiple constraints are constructed, where the constraints are used to constrain the total load, energy storage charging and discharging, energy storage balance or new energy output; Construct multiple particles according to the number of controllable devices in the power grid, update the position of each particle through the position of the individual optimal particle and the overall optimal particle, and calculate the fitness of each particle after the position is updated; If the fitness of the multiple particles does not meet the termination condition, the inertia weight is adjusted by a linear programming method, and the positions of the multiple particles are updated again until particles that meet the fitness are generated, and the controllable devices in the power grid are adjusted according to the particles that meet the fitness.

2. The peak load optimization method for accessing a distributed power grid according to claim 1 is characterized in that: The objective function includes: a load variance objective function and a system operation resource consumption objective function, wherein the load variance objective function is: In the formula, is the system load variance, L t is the system load at time period t, is the average value of the system load, T is the total number of time periods considered; The objective function of the system operation resource consumption is: In the formula, C total is the total system operating cost, C i (P i,t ) is the power generation cost of the i-th generator set in the t-th period, P i,t is the output of the i-th generator set in the t-th period, N is the total number of generator sets, C j (D j,t ) is the cost of the jth interruptible load in period t, D j,t is the adjustment amount of the jth interruptible load in the tth period, M is the total number of interruptible loads, C storage is the operating cost of the energy storage unit, C interruption is the interruption cost of the interruptible load.

3. The peak load optimization method for accessing a distributed power grid according to claim 1, characterized in that: The multiple constraints include: Generator output constraints: P min,i ≤P i,t ≤P max,i Where P i,t is the output of the i-th generator set in the t-th period, P min,i is the minimum output limit of the ith generator set, P max,i is the maximum output limit of the i-th generator set; System total load balance constraints: Where, L t is the total system load in period t, P storage,t is the discharge amount of the energy storage unit in the tth period; Interruptible load constraints: D min,j ≤D j,t ≤D max,j Where D j,t is the adjustment amount of the jth interruptible load in the tth period, D min,j is the minimum adjustment limit of the jth interruptible load, D max,j is the maximum adjustment limit of the jth interruptible load; New energy output constraints: P min,renewable ≤P renewable,t ≤P max,renewable Where P renewable,t is the predicted output of renewable energy in the tth period, P min,renewable is the minimum output limit of new energy, P max,renewable It is the maximum output limit of new energy; Dispatchable DG unit operation constraints: In the formula, is the predicted output of the dispatchable DG unit in the tth period, is the minimum output limit of the dispatchable DG unit, is the maximum output limit of the dispatchable DG unit.

4. The peak load optimization method for accessing a distributed power grid according to claim 3 is characterized in that: The plurality of constraints also include: Energy storage unit charging and discharging constraints: AND min ≤E t ≤E max In the formula, E t is the energy storage capacity of the energy storage unit in the tth period, E min is the minimum storage capacity limit of the energy storage unit, E max is the maximum storage capacity limit of the energy storage unit; Energy balance constraints of energy storage units: In the formula, E t+1 is the energy storage capacity of the energy storage unit in the tth period, η charge is the charging efficiency of the energy storage unit, P charge,t is the charging power of the energy storage unit in the tth period, η discharge is the discharge efficiency of the energy storage unit, P discharge,t is the discharge power of the energy storage unit in the tth period.

5. The peak load optimization method for accessing a distributed power grid according to claim 1, characterized in that: The updating of the position of each particle by using the positions of the individual optimal particle and the overall optimal particle includes: The particle velocity is updated according to the first formula, wherein the first formula is: In the formula, is the velocity of the i-th particle in the j-th dimension at the next iteration, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers in the range [0,1], P best,i,j is the historical optimal position of the ith particle in the jth dimension, G best,j is the value of the global optimal position in the jth dimension, is the position of the i-th particle in the j-th dimension at the current iteration; Update the particle position according to the second formula, where the second formula is: In the formula, is the position of the i-th particle in the j-th dimension at the next iteration.

6. The peak load optimization method for accessing a distributed power grid according to any one of claims 1 to 5, characterized in that: The re-updating the positions of the plurality of particles comprises: The adjustment amount of the particle position is determined according to the third formula, wherein the third formula is: In the formula, is the solution vector at the k+1th generation, J is the Jacobian matrix, λ is the tuning parameter of the linear programming algorithm, and I is x k The identity matrix, J i and They are respectively for particle i at its current position The Jacobian matrix and function vector at .

7. The peak load optimization method for accessing a distributed power grid according to claim 6, characterized in that: The method of adjusting the inertia weight by using a linear programming method comprises: The inertia weight is adjusted according to the fourth formula, wherein the fourth formula is: In the formula, w max and w nin are the maximum and minimum values ​​of the inertia weight, K max is the maximum number of iterations, k is the current number of iterations, and w k is the adjusted inertia weight.

8. A peak load optimization device connected to a distributed power grid, characterized in that: Used to implement the peak-shaving optimization method for accessing a distributed power grid according to any one of claims 1 to 7, the peak-shaving optimization device for accessing a distributed power grid comprises: A target acquisition module is used to obtain an objective function with the goal of stabilizing the system load and minimizing the consumption of operating resources; The constraint condition construction module is used to construct multiple constraint conditions based on the safe operation of the system, where the constraint conditions are used to constrain the total load, energy storage charging and discharging, energy storage balance or new energy output; The particle initialization module is used to construct multiple particles according to the number of controllable devices in the power grid, update the position of each particle through the position of the individual optimal particle and the overall optimal particle, and calculate the fitness of each particle after the position is updated; as well as, The peak load optimization module is used to adjust the inertia weight by a linear programming method if the fitness of the multiple particles does not meet the termination condition, re-update the positions of the multiple particles until particles that meet the fitness are generated, and adjust the controllable devices in the power grid according to the particles that meet the fitness.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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