A large air conditioner "electricity-storage-supply" integrated comprehensive energy regulation method

Abstract

The present application relates to a kind of large air conditioner " electricity-storing-supply " integrated comprehensive energy regulation method, belong to microgrid economic dispatch field, including the following steps: step 1: with the energy storage material cost of energy storage water tank material in energy storage air conditioning system, conventional unit generation cost, unconventional unit generation cost, operating environment cost as objective function, model is established;Step 2: establish the operating constraint condition in scheduling period;Step 3: the model and constraint condition are brought into a kind of large air conditioner " electricity-storing-supply " integrated comprehensive energy regulation method;Step 4: the optimal allocation scheme of system is obtained after continuous iteration, so that microgrid is in stable operation while making economy further optimized.

Description

Technical Field

[0001] This invention belongs to the field of microgrid technology, specifically referring to an integrated energy regulation method for large-scale air conditioning systems that combines electricity, storage, and supply. Background Technology

[0002] The power industry is characterized by the simultaneous completion of power generation, transmission, distribution, and consumption, requiring continuous production and a constant supply of electricity. Therefore, to ensure a continuous and high-quality power supply, frequency regulation and peak-shaving capacity are essential, along with various reserve capacities such as load reserves, emergency reserves, and maintenance reserves. With the further improvement of living standards, power load fluctuations will become increasingly drastic, necessitating even greater regulation and reserve capacity. Against this backdrop, grid peak shaving is both necessary and urgent. How to achieve rational and optimal dispatching under increasingly complex power generation conditions has become a key focus of the current energy and power industry development.

[0003] With the development of modern power grid technology, energy storage technology has been gradually introduced into power systems. Energy storage systems, with their excellent charging and discharging characteristics, can effectively achieve demand-side management. Energy storage air conditioning systems absorb and store excess electricity during off-peak hours and release it during peak hours, effectively eliminating the peak-valley difference between day and night, smoothing the load, and thus solving the problem of needing to invest in transmission and distribution lines during peak loads. Traditional dispatching methods have limitations in addressing these issues and cannot meet the requirements of stable power supply, economic security, and economic efficiency in power systems. Therefore, proposing new methods and establishing new economic control models has significant practical implications for the economic dispatching of microgrids. Summary of the Invention

[0004] To address the shortcomings of traditional microgrid economic dispatch methods, this invention proposes an integrated energy regulation method for large-scale air conditioning systems, encompassing electricity, storage, and supply. The model establishes an objective function based on the cost of energy storage materials in the energy storage tank, the power generation cost of conventional units, the power generation cost of unconventional units, and the operating environment cost. It comprehensively considers the power generation costs, operating characteristics, and environmental costs of various energy sources to meet power quality and dynamic power balance requirements. Furthermore, this integrated energy regulation method for large-scale air conditioning systems, building upon the particle swarm optimization algorithm, incorporates black hole theory and the elite individual perturbation mechanism of Cauchy mutation, enhancing information transfer between particles and achieving superior performance compared to traditional optimization methods while possessing global search capabilities.

[0005] This invention provides an integrated energy regulation method for large-scale air conditioning systems that combines electricity, storage, and supply, comprising the following steps:

[0006] Step 1: Establish a model with the energy storage material cost of the energy storage water tank in the energy storage air conditioning system, the power generation cost of conventional units, the power generation cost of unconventional units, and the operating environment cost as the objective function;

[0007] Step 2: Establish constraints for the objective function;

[0008] Step 3: Solve the objective function using an improved particle swarm optimization algorithm;

[0009] Step 3.1: Initialize various parameters, including the water tank filling volume. , number of particles This includes initializing particle velocity and position, black hole radius, probability threshold for particle entry into the black hole, fitness of all individuals in the population, and learning factor. and Inertia weight Maximum number of iterations r max ;

[0010] Step 3.2: Calculate the fitness function value of each particle based on the objective function value, the nonlinear adaptive coefficient C adjustment strategy, and the elite individual perturbation mechanism; reduce the attraction domain, comfort domain, and repulsion domain among individuals based on the number of iterations; and determine the particle with the best current fitness, X. best It plays a leading role in population position updates;

[0011] Step 3.3: Based on the particle individual X with optimal fitness best It plays a guiding role in updating the population position and updating the probability threshold of each particle entering the black hole. black hole radius and particle velocity;

[0012] Step 3.4: Determine if the current iteration count is the maximum iteration count. If yes, stop the iteration and output the optimal solution; otherwise, continue the iterative calculation.

[0013] Furthermore, in step 3, the improved particle swarm optimization algorithm is as follows:

[0014] when Sometimes,

[0015]

[0016] when Sometimes,

[0017]

[0018] In the formula, The first Second and third Particles in the next iteration The The search speed for each variable; Inertial weights are used to balance local and global search capabilities. The learning factor is 1.0 in this paper. and is a random number that follows a uniform distribution on [0,1]. The first Sub-iteration particles The The current value, individual extreme value, and global extreme value of each variable position. In order to be with the first Sub-iteration particles The The probability values ​​corresponding to each variable are random numbers that follow a uniform distribution on [0,1]. The probability threshold for a particle to enter a black hole is a constant on [0,1]. is a random number that follows a uniform distribution on [0,1]. The constant represents the radius of the black hole; The above are random numbers that follow a uniform distribution; and The selection of the appropriate function depends on its own characteristics and can be obtained through multiple simulation calculations. and .

[0019] The velocity formula is improved to:

[0020]

[0021] In the formula, X best This refers to the particle with the best current fitness.

[0022] Furthermore, in step 1, the energy storage material cost of the energy storage water tank in the energy storage air conditioning system includes material cost and operating cost, wherein the expression for material cost is:

[0023]

[0024]

[0025] In the formula, The average capacity coefficient, Provides heating power for energy storage air conditioners; Rated power for heating; The time period contained within the scheduling cycle; This represents the total number of energy storage air conditioning units; Indicates the first Are the individual units currently in operation? For the first Average annual interest rate percentage for each energy storage air conditioning unit; The average operating life of each energy storage air conditioning unit; This refers to the water injection volume, used to calculate the amount of water needed to replenish the energy storage tank.

[0026] The expression for operating cost is:

[0027]

[0028] In the formula, This refers to the operating cost of energy storage air conditioning units.

[0029] Furthermore, in step 1, the power generation cost of a conventional unit is:

[0030]

[0031] In the formula, S c A collection of conventional generating units; Indicates the first The first hour Are the conventional generating units currently in operation? This is the operating cost coefficient for the nth unit; for Within the time period The power of each unit;

[0032] Considering the impact of random factors on system operation, including the cost caused by wind and solar power generation costs and load forecast deviations, the cost expression is as follows:

[0033]

[0034] In the formula, and The formulas represent the prices of wind power generation and photovoltaic power generation, respectively. ; The price for load forecast deviation, .

[0035] The expression for the cost of the operating environment is:

[0036]

[0037] In the formula, α is a function of wastewater discharge during the scheduling period; i ,β i ,γ i For the unit The wastewater discharge coefficient.

[0038] Furthermore, in step 2, the constraints on the objective function include:

[0039] The maximum output constraint of the energy storage air conditioning unit is:

[0040]

[0041] The power generation constraints of conventional generating units are:

[0042]

[0043]

[0044] In the formula, and Output limits for conventional generating units; for The load demand at any given time.

[0045] Wastewater discharge constraints are:

[0046] F f (P ma )≤F f max (P ma ).

[0047] Furthermore, in step 3, the velocity of the particle is expressed by the following formula:

[0048]

[0049] The formula for expressing the position of a particle is:

[0050] .

[0051] The advantages of this invention compared to the prior art are:

[0052] 1. This invention establishes a model with the energy storage material cost of the energy storage tank in the energy storage air conditioning system, the power generation cost of conventional units, the power generation cost of unconventional units, and the operating environment cost as the objective function. It not only considers the power generation cost, operating characteristics, and environmental cost requirements of various energy sources, but also the peak-valley difference smoothing effect of the energy storage air conditioning system.

[0053] 2. Furthermore, this invention ensures high-quality initial solutions by integrating chaotic Tent mapping with a reverse learning mechanism for population initialization. A nonlinear adaptive coefficient update mechanism using a cosine function balances the coordination between search and development. A Cauchy mutation perturbation mechanism effectively avoids local optima. Applying the algorithm to feature selection problems demonstrates that it reduces feature dimensionality and improves data classification accuracy.

[0054] 3. This invention combines a large-scale air conditioning "electricity-storage-supply" integrated energy regulation method with a constructed objective function to obtain the optimal solution of the objective function, that is, to obtain the configuration of various generator sets in each time period of the microgrid during the scheduling period, thereby improving the economic efficiency of microgrid operation. Attached Figure Description

[0055] Figure 1 An optimized flowchart for a comprehensive energy regulation method integrating "electricity-storage-supply" in a large-scale air conditioning system;

[0056] Figure 2 This is a typical daytime forecast data chart of wind, solar and load for a microgrid.

[0057] Figure 3 The simulation optimization comparison chart shows the fitness convergence curves of the three algorithms. Detailed Implementation

[0058] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0059] This invention provides an integrated energy regulation method for large-scale air conditioning systems that combines electricity, storage, and supply. Figure 1 As shown, it includes the following steps:

[0060] Step 1: Establish a model with the energy storage material cost of the energy storage water tank in the energy storage air conditioning system, the power generation cost of conventional units, the power generation cost of unconventional units, and the operating environment cost as the objective function;

[0061] The energy storage material cost of the energy storage tank in an energy storage air conditioning system includes material cost and operating cost. The expression for material cost is as follows:

[0062]

[0063]

[0064] The expression for operating cost is:

[0065]

[0066] In the formula, This refers to the operating cost of energy storage air conditioning units.

[0067] Conventional generating unit power generation cost:

[0068]

[0069] In the formula, A collection of conventional generating units; Indicates the first The first hour Are the conventional generating units currently in operation? This is the operating cost coefficient for the nth unit; for Within the time period The power of each unit;

[0070] Considering the impact of random factors on system operation, including the cost caused by wind and solar power generation costs and load forecast deviations, the cost expression is as follows:

[0071]

[0072] In the formula, and The formulas represent the prices of wind power generation and photovoltaic power generation, respectively. ; The price for load forecast deviation, .

[0073] The expression for the cost of the operating environment is:

[0074]

[0075] In the formula, This is a function of wastewater discharge during the scheduling period; For the unit The wastewater discharge coefficient.

[0076] Step 2: Establish constraints for the objective function;

[0077] The maximum output constraint of the energy storage air conditioning unit is:

[0078]

[0079] The power generation constraints of conventional generating units are:

[0080]

[0081]

[0082] In the formula, and Output limits for conventional generating units; for The load demand at any given time.

[0083] Wastewater discharge constraints are:

[0084] F f (P ma )≤F f max (P ma )

[0085] Step 3: Solve the objective function using an improved particle swarm optimization algorithm. The improved particle swarm optimization algorithm is as follows:

[0086] when Sometimes,

[0087]

[0088] when Sometimes,

[0089]

[0090] In the formula, The first Second and third In the next iteration, the particle The The search speed for each variable; Inertial weights are used to balance local and global search capabilities. The learning factor is 1.0 in this paper. and is a random number that follows a uniform distribution on the interval [0,1]. The first Sub-iteration particles The The current value, individual extreme value, and global extreme value of each variable position. In order to be with the first Sub-iteration particles The The probability values ​​corresponding to each variable are random numbers that follow a uniform distribution on [0,1]. The probability threshold for a particle to enter a black hole is a constant on [0,1]. is a random number that follows a uniform distribution on [0,1]. The constant represents the radius of the black hole; The above are random numbers that follow a uniform distribution; and The selection of the appropriate function depends on its own characteristics and can be obtained through multiple simulation calculations. and ;

[0091] The velocity formula is improved to:

[0092]

[0093] In the formula, X best This refers to the particle with the best current fitness.

[0094] Step 4: Initialize various parameters, including water tank volume and particle count. This includes initializing particle velocity and position, black hole radius, probability threshold for particle entry into the black hole, fitness of all individuals in the population, and learning factor. and Inertia weight ω′, maximum number of iterations ;

[0095] Step 5: Calculate the fitness function value for each particle based on the objective function value, the nonlinear adaptive coefficient adjustment strategy, and the elite individual perturbation mechanism; reduce the attraction domain, comfort domain, and repulsion domain among individuals based on the number of iterations; and determine the particle with the best current fitness, X. best It plays a leading role in population position updates;

[0096] Step 6: Based on the particle individual X with the best fitness best It plays a guiding role in updating the population position and updating the probability threshold of each particle entering the black hole. black hole radius and particle velocity;

[0097] The formula for expressing particle velocity is:

[0098]

[0099] The formula for expressing the position of a particle is:

[0100]

[0101] Step 7: Determine if the current iteration count is the maximum iteration count. If yes, stop the iteration and output the optimal solution; otherwise, continue the iterative calculation.

[0102] This paper extends a comprehensive energy regulation method for large-scale air conditioning systems integrating electricity, storage, and supply to the multi-objective dispatching domain, forming a multi-objective stochastic black hole particle swarm optimization algorithm. This algorithm is applied to single-objective power generation regulation and multi-objective environmental and economic power generation regulation. This method belongs to the field of economic dispatching. Through this algorithm, the optimal solution to the objective function can be obtained, i.e., the configuration of various generator units and interruptible loads in the microgrid during different time periods of dispatch, thereby improving the economic efficiency of microgrid operation. A nonlinear adaptive coefficient update mechanism using a cosine function balances the coordination between search and exploitation. A Cauchy mutation perturbation mechanism effectively avoids local optima. The algorithm is applied to solving the feature selection problem, demonstrating that it reduces feature dimensionality and improves data classification accuracy. Simulation results also show that the improved stochastic black hole particle swarm optimization algorithm converges faster.

[0103] This invention utilizes the mining and global search capabilities of the Chaos-Locust Optimization Algorithm. This method combines black hole theory and the elite individual perturbation mechanism of Cauchy mutation to enhance information transmission between particles. Based on global search capabilities, it achieves better results than traditional optimization methods, increasing the economic efficiency while ensuring the stable operation of microgrids.

[0104] Example Analysis:

[0105] The microgrid system used in this paper includes various distributed power sources, including PV, WT, DE, MT and energy storage. The operating parameters and costs of each DG in the microgrid are shown in Table 1, the pollutant emission coefficients and costs of each DG are shown in Table 2, and the energy storage parameters are shown in Table 3.

[0106] Table 1 Unit Parameters

[0107]

[0108] Table 2 Pollutant Emission Coefficients and Costs

[0109]

[0110] Table 3 Energy Storage Parameters

[0111]

[0112] Typical daytime wind, solar and load forecasts for microgrids are as follows: Figure 2 As shown.

[0113] Results Analysis

[0114] The solutions are obtained using the PSO algorithm, the BH·PSO algorithm, and the improved algorithm presented in this paper, respectively. In the PSO algorithm... , In the BH·PSO algorithm, In the improved algorithm presented in this paper, ωσ =0.6,C 1e =2.6,C 2e =0.4. The three algorithms share the same parameters: particle swarm size of 100 and maximum number of iterations of 500. All three methods are run 100 times. Table 4 provides a comparison of the results for the three algorithms.

[0115] Table 4 Comparison of the three algorithms

[0116]

[0117] As shown in Table 4, the improved algorithm outperforms the first two algorithms in terms of running time, optimal value, and average value. Therefore, the improved algorithm is superior. A comparison of the fitness convergence curves of the three algorithms is provided below. Figure 3 As shown.

[0118] from Figure 3 It can be seen that the improved algorithm converges faster than the other two algorithms, converging around the 250th iteration, while the other two algorithms show significantly poorer convergence. This means that the improved algorithm is more likely to get trapped in the optimum and has better global search capabilities. Therefore, the improved algorithm proposed in this paper is superior to the traditional PSO algorithm.

[0119] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made to the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this description is for the purpose of describing particular embodiments only and is not intended to limit the present invention.

Claims

1. A comprehensive energy regulation method for large-scale air conditioning systems integrating electricity, storage, and supply, characterized in that: Includes the following steps: Step 1: Establish a model with the energy storage material cost of the energy storage water tank in the energy storage air conditioning system, the power generation cost of conventional units, the power generation cost of unconventional units, and the operating environment cost as the objective function; The energy storage material cost of the energy storage tank includes material cost and operating cost, wherein the expression for material cost is: In the formula, The average capacity coefficient, Provides heating power for energy storage air conditioners; Rated power for heating; The time period contained within the scheduling cycle; This represents the total number of energy storage air conditioning units; Indicates whether the nth unit is currently running; The annual average interest rate percentage for the nth energy storage air conditioning unit; The average operating life of each energy storage air conditioning unit; This refers to the water injection volume, used to calculate the amount of water needed to replenish the energy storage tank. The expression for operating cost is: In the formula, The operating cost of the energy storage air conditioning unit; The power generation cost of conventional generating units: In the formula, A collection of conventional generating units; Indicates the first The first hour Are the conventional generating units currently in operation? This is the operating cost coefficient for the nth unit; for Within the time period The power of each unit; Considering the impact of random factors on system operation, including the cost caused by wind and solar power generation costs and load forecast deviations, the cost expression is as follows: In the formula, and The formulas represent the prices of wind power generation and photovoltaic power generation, respectively. ; the price for load forecasting deviation, ; The expression for the cost of the operating environment is: In the formula, This is a function of wastewater discharge during the scheduling period; For the unit Wastewater discharge coefficient; Step 2: Establish constraints for the objective function; Step 3: Solve the objective function using an improved particle swarm optimization algorithm; Step 3.1: Initialize various parameters, including the water tank filling volume. , particle number This includes initializing particle velocity and position, black hole radius, probability threshold for particle entry into the black hole, fitness of all individuals in the population, and learning factor. and Inertia weight Maximum number of iterations ; Step 3.2: Based on the objective function value and the nonlinear adaptive coefficients Adjust the strategy and elite individual perturbation mechanism to calculate the fitness function value of each particle; reduce the attraction domain, comfort domain, and repulsion domain between individuals based on the number of iterations; and select the particle with the best current fitness. It plays a leading role in population position updates; Step 3.3: Based on the particle with optimal fitness It plays a guiding role in updating the population position and updating the probability threshold of each particle entering the black hole. black hole radius and particle velocity; The formula for expressing particle velocity is: The formula for expressing particle position is: ； In the formula: For the first In the next iteration, the particle The The search speed for each variable; Step 3.4: Determine if the current iteration count is the maximum iteration count. If yes, stop the iteration and output the optimal solution; otherwise, continue the iterative calculation.

2. The integrated energy regulation method for a large-scale air conditioner "electricity-storage-supply" system according to claim 1, characterized in that, In step 2, the maximum output constraint of the energy storage air conditioning unit is: The power generation constraints of conventional generating units are: In the formula, and Output limits for conventional generating units; For the load demand at any given time; Pollutant emission constraints are as follows: 。

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