A method for operating a combined cooling and power microgrid system

Abstract

The application provides a combined cooling heating and power micro-grid system operation optimization method, first, an optimization objective function and constraint condition of the combined cooling heating and power micro-grid system operation are constructed, thereby a combined cooling heating and power micro-grid system multi-objective optimization model is established; then a target function of the combined cooling heating and power micro-grid system is determined, thereby the cost is reduced and the economic benefit is improved; secondly, an improved chameleon swarm algorithm is introduced in the traditional method in the combined cooling heating and power micro-grid system operation strategy to realize energy optimization management of the combined cooling heating and power micro-grid system, thereby problems such as population diversity attenuation and falling into a local optimal solution of the swarm intelligence optimization algorithm in the chameleon swarm algorithm are avoided; finally, a hybrid following heat load strategy operation strategy is provided, and the operation cost of the combined cooling heating and power micro-grid system is more effectively reduced. The application has the beneficial effects that the renewable energy is utilized, the stable operation of the power grid is maintained, and the operation cost of the combined cooling heating and power micro-grid system is effectively reduced.

Description

A method for optimizing the operation of a combined cooling, heating and power microgrid system Technical Field

[0001] The technical solution of this invention belongs to the field of system operation optimization technology, specifically a method for optimizing the operation of a combined cooling, heating and power microgrid system. Background Technology

[0002] In recent years, due to users' higher demands for power quality and the severe impact of excessive fossil fuel use on the natural environment, the development of power supply systems and the use of electricity have become a focus of attention in the global power industry. Compared to traditional single microgrid systems, combined cooling, heating, and power (CCHP) microgrid systems are comprehensive energy management systems that integrate load demand, distributed power sources, and energy storage devices while simultaneously introducing renewable energy. Therefore, the operation of CCHP microgrids is particularly affected by power output equipment factors, and the randomness and volatility of renewable energy output lead to the instability of CCHP microgrid systems. With the continuous advancement of industrialization and rapid socio-economic development, the electricity load of industrial production and residential life is increasing, and the energy consumed for cooling, heating, and power supply is increasing. The uncertainty of renewable energy factors is increasingly affecting CCHP microgrids. The correlation of the power system requires the combined cooling, heating and power (CCHP) microgrid system to maintain dynamic balance. Therefore, the uncontrollability of renewable energy and the unreasonable configuration of distributed power generation capacity prevent the entire system from reaching the optimal operating state. This not only fails to improve the economic efficiency of CCHP microgrids but also leads to energy waste or shortages, resulting in huge economic losses and environmental damage, and greatly hindering the further development of CCHP microgrid systems.

[0003] Renewable energy sources in distributed generation systems are highly random and intermittent, severely impacting the normal operation of combined cooling, heating, and power (CCHP) microgrids and the reliability of the power grid. Furthermore, the integration of large-scale CCHP microgrids into the main grid will impact the entire power system, causing voltage and frequency fluctuations and posing significant safety risks. Optimizing the energy supply system and energy structure not only helps adjust the scheduling of CCHP microgrids but also allows for optimal allocation of equipment capacity, reducing operating costs and significantly improving the system's environmental friendliness and reliability.

[0004] In the field of system operation optimization, intelligent optimization algorithms have made rapid progress. Compared with linear programming models, intelligent optimization algorithms have shown outstanding advantages in solving various complex problems. Facing highly coupled and complex problems such as capacity optimization of combined cooling, heating, and power (CCHP) microgrid systems, emerging optimization algorithms can achieve a faster optimization process, shortening the time interval between scheduling arrangements. Therefore, intelligent optimization algorithms are an ideal method for solving energy management and operation optimization problems in CCHP microgrid systems. Rational optimization of CCHP microgrid systems is of great significance for realizing the utilization of renewable energy and maintaining the stable operation of the power grid. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for optimizing the operation of a combined cooling, heating and power microgrid system.

[0006] The objective of this invention is achieved through the following technical solution: A method for optimizing the operation of a combined cooling, heating, and power (CCHP) microgrid system, comprising the following steps:

[0007] Step 1: Establish a multi-objective model for a combined cooling, heating and power microgrid system;

[0008] Step 2: Determine the objective function and constraints of the combined cooling, heating and power (CCHP) microgrid system. The objective function includes an economic objective function and an environmental objective function. The constraints should satisfy load balance under various load conditions, thereby establishing a multi-objective optimization model for the CCHP microgrid system.

[0009] Step 3: In the operation strategy of the combined cooling, heating and power microgrid system, the optimization parameters of the improved Chameleon Group Algorithm (ICSA) are obtained based on the economic objective function and the environmental objective function. Through the improved Chameleon Group Algorithm (ICSA), the energy optimization management of the combined cooling, heating and power microgrid system is realized.

[0010] Step four: Adopt the hybrid heat load following strategy of HFHL operation strategy, which distributes the cooling load demand between AC power supply and current transformer, with AC power supply as the main cooling load.

[0011] As a further preferred technical solution, in step one, the combined cooling, heating and power microgrid system includes a biogas power generation model, a photovoltaic power generation model, a micro gas turbine, a waste heat boiler, an adsorption chiller, a gas boiler, an electric chiller, and an energy storage system model. The modeling of each device is as follows:

[0012] (1) Biogas power generation model

[0013] The biogas power generation model is represented as follows:

[0014]

[0015] In the formula, P BIO (t) represents the output power of the biogas power generation system; Q BIO (t) represents the output heat of the biogas power generation system; C BIO (t) represents the biogas intake per unit time; u p u q These represent the power generation and calorific value per unit cubic meter of biogas, respectively; η p η q The power generation efficiency and thermal efficiency of the biogas power generation system;

[0016] (2) Photovoltaic power generation model

[0017] The photovoltaic power generation model is represented as follows:

[0018]

[0019] In the formula, P PV T represents the output power of the photovoltaic power generation system. STC G STC These represent ambient temperature and light intensity under standard conditions, respectively; P STC T represents the rated output power of a photovoltaic power generation system under standard conditions. C G C These are the photovoltaic panel temperature and light intensity under the current environment; T t The ambient temperature is the current temperature; k is the power temperature coefficient.

[0020] (3) Miniature gas turbine model

[0021] The micro gas turbine model is represented as follows:

[0022]

[0023] In the formula, P MT (t), Q MT (t) represent the power generation and heat generation of the micro gas turbine at time t, respectively; G MT (t) represents the total amount of fuel consumed by the micro gas turbine at time t; Q G η is the calorific value of natural gas. MT The power generation efficiency of a micro gas turbine; η MTloss The gas loss rate of a micro gas turbine;

[0024] (4) Waste heat boiler and adsorption chiller model

[0025] The micro gas turbine transfers the generated heat to a waste heat boiler for thermal energy supply. Its equivalent model is as follows:

[0026] QHE (t)=Q MT,HE (t)COP he (4)

[0027] In the formula, Q HE (t) represents the heat output of the waste heat boiler at time t; Q MT,HE (t) represents the heat supplied by the micro gas turbine at time t; COP he The heating efficiency of the waste heat boiler is considered; meanwhile, the adsorption chiller uses an adsorbent to convert thermal energy into cooling power to supply the cooling load, and the micro gas turbine transfers the generated heat to the adsorption chiller. Its equivalent model is:

[0028] Q CO (t)=Q MT,CO (t)COP co (5)

[0029] In the formula, Q CO (t) represents the cooling power output of the adsorption chiller at time t; Q MT,CO (t) represents the heat supplied by the micro gas turbine at time t; COP co The heat conversion efficiency of the waste heat boiler;

[0030] (5) Gas boiler and electric chiller model

[0031] The energy efficiency model for gas-fired boilers is as follows:

[0032] Q GB (t)=G GB (t)Q G η GB (6)

[0033] In the formula, Q GB (t) represents the heat output of the gas-fired boiler at time t; G GB (t) represents the total amount of fuel consumed by the micro gas boiler at time t; η GB The heating efficiency of the gas-fired boiler;

[0034] The energy efficiency model for electric chillers is as follows:

[0035] Q ER (t)=P ER (t)η ER (7)

[0036] In the formula, Q ER (t) The cooling power output of the gas-fired boiler at time t; P ER (t) represents the electrical power consumed by the electric chiller at time t; η ER The refrigeration efficiency of an electric chiller;

[0037] (6) Energy storage system model

[0038] The working process of the battery in the energy storage system model is represented as follows:

[0039]

[0040] In the formula, E BAT,C (t), E BAT,D (t) represents the remaining battery charge at time t under charging and discharging states, respectively; E BAT,C (t-1), E BAT,D (t-1) represents the remaining battery charge at time t-1 under charging and discharging states, respectively; E c (t) represents the actual capacity of the battery at time t; P c (t), P d (t) represents the charging and discharging power of the battery at time t, respectively; η c η d These are the charging and discharging efficiencies of the battery, respectively; E STC T STC These represent the battery capacity and ambient temperature under standard conditions, respectively; T(t) is the ambient temperature at time t; δ b Δt represents the capacity temperature coefficient of the battery; Δt represents the time interval.

[0041] As a further preferred technical solution, in the energy storage system model, the heat storage tank stores excess heat power in the system and releases it for use when needed. Its working process is represented as follows:

[0042]

[0043] In the formula, Q HSK,C (t), Q HSK,D (t) represents the thermal energy stored in the heat storage tank during the heat storage and heat release states at time t; Q HSK,C (t-1), Q HSK,D (t-1) represents the thermal energy stored in the heat storage tank during the heat storage and heat release states at time t-1, respectively; Q c (t), Q d (t) represents the heat storage and heat release power of the heat storage tank at time t; α is the self-loss coefficient of the heat storage tank; η chr η dis These represent the heat storage and heat release efficiencies of the heat storage tank, respectively.

[0044] As a further preferred technical solution, the specific steps in step two are as follows:

[0045] (1) Determine the economic objective function and the environmental objective function

[0046] The economic objective function F1 is expressed as equation (10), which includes the installation cost F. DG Operating costs F OM Fuel cost F Fuel Electricity transaction costs F Grid and penalty cost F PU ;

[0047] minF1=F DG +F OM +F Fuel +F Grid +F PU (10)

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] In the formula, c g The price per unit of natural gas; P is the investment cost of the i-th device; i This is the rated capacity of the i-th device; It is the operating and maintenance cost of the i-th facility; k buy (t) and k sell (t) is the unit price for buying and selling electricity; P buy (t) and P sell (t) represents the power purchased and sold; P vacancy (t) and P waste (t) represents the insufficient and wasted portion; Q vacancy (t) and Q waste (t) represents the missing and wasted thermal energy; λ is the penalty coefficient;

[0054] The environmental objective function F2 is shown in equation (16):

[0055]

[0056] In the formula, K represents the type of pollutant; C k χ represents the cost of the kth pollutant; ik and χ gridkP is the emission coefficient; i (t) represents the amount of electricity generated;

[0057] The objective function F is optimized as shown in equation (17):

[0058]

[0059] In the formula, ω1 and ω2 are adaptive weighting coefficients; when F1 decreases, ω1 decreases; if F1 increases, ω1 also increases; n1 and n2 are natural coefficients; G1(t) is the total amount of fuel consumed at time t.

[0060] (2) System constraints

[0061] The constraints of a combined cooling, heating and power (CCHP) microgrid system include base load constraints and equipment operation constraints. The constraints should meet the load balance under various loads, and the output power of the microgrid system should be consistent with the demand of all loads.

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] In the formula, P BIO (t), P PV (t), P MT (t) represents the output power of the biogas power generation system, photovoltaic power generation system, and wind power generation system at time t, respectively; P c (t), P d (t) represents the charging and discharging power of the battery at time t, respectively; P Load (t) represents the electrical load of the microgrid system at time t; P ER (t) represents the electrical energy consumed by the electric chiller at time t; Q c (t), Q d (t) represents the heat storage and heat release power of the heat storage tank at time t, respectively; Q HE (t), Q GB (t) represents the thermal energy input to the absorption chiller and the heat exchanger at time t, respectively; Q CO (t), Q ER(t) represents the cooling energy output of the absorption chiller and the electric chiller at time t, respectively; Q Load,h (t) represents the heat load demand of the microgrid system at time t, Q Load,c (t) represents the cooling load demand of the microgrid system at time t; and These are the minimum and maximum energy storage capacities of the battery, respectively; E BAT (t) represents the remaining electrical energy of the battery at time t; and These are the minimum and maximum charging power of the battery, respectively. and These are the minimum and maximum discharge power of the battery, respectively. and These represent the minimum and maximum energy storage capacities of the thermal storage tank, respectively; Q HST (t) represents the thermal energy stored in the heat storage tank at time t; and These are the minimum and maximum thermal storage capacities of the thermal storage tank, respectively. and These are the minimum and maximum heat release power of the heat storage tank, respectively. These are the minimum outputs of wind power generation systems, photovoltaic power generation systems, biogas power generation systems, gas turbines, and gas boilers, respectively. and These represent the maximum output of the wind power generation system, photovoltaic power generation system, biogas power generation system, gas turbine, and gas boiler, respectively.

[0068] As a further preferred technical solution, the specific steps in step three are as follows: In the operation cost optimization problem, the improved Chameleon Group Algorithm (ICSA) is selected as the solution method for the total operation cost optimization problem, and equation (18) is taken as the fitness function of ICSA.

[0069] (1) Chameleon Swarm Algorithm Model (CSA)

[0070] The model simulates the process of a chameleon searching for food, dividing it into three stages: searching for prey, turning its eyes, and capturing prey; CSA needs to determine a population matrix with population size Pop and dimension Dim, and initialize the population according to equation (24);

[0071] y i,j =LB + rand·(UB - LB) (24)

[0072] In the formula, LB and UB are the ranges of the search space; rand is a random number between 0 and 1; i represents an individual; j represents the dimension; y i,j This represents the position of the i-th individual in the j-th dimension;

[0073] (1.1) Search for prey

[0074] At this stage, the chameleon starts to search for prey according to Equation (25).

[0075]

[0076] In the formula, s is the number of iterations; S max is the maximum number of iterations; the subscript p is the current individual optimal position; b is the global optimal position; r, R1, R2, R3 are random numbers between 0 and 1; P1 and P2 are development parameters; sgn(rand - 0.5) controls the development direction, with values of 1 or -1; α, β, γ are set values; P represents the probability of perceiving prey. If r ≥ P, the individual will update its position according to the prey observed in the search space; if r < P, this individual will search randomly in different directions.

[0077] (1.2) Eye rotation

[0078] The eyes of the chameleon rotate and focus in two different directions simultaneously. At this stage, according to Equation (26), the rotation of the eyeballs is used to simulate the position update when locating prey.

[0079]

[0080] In the formula, the subscript "-" represents the average position of the chameleon before the rotation of the position in each dimension; M is the rotation matrix; θ controls the eye rotation angle in the range of [-180°, 180°].[[]]

[0081] (1.3) Catch prey

[0082] The chameleon catches prey with its tongue. Before the position update, it is necessary to determine the speed model of the tongue. The specific process of this stage is as follows;

[0083]

[0084] [[ID=三十九]]In the formula, ω is the inertia weight; C1 and C2 are the control coefficients of the current individual optimal position and the global optimal position respectively; ρ is the control coefficient of ω; a is the acceleration of the chameleon's tongue. represents the speed of the chameleon's tongue when the i-th individual is in the j-th dimension at the s-th iteration;

[0085] (2) Improvement strategies and processes

[0086] First, select the Tent chaotic mapping to initialize the population, as follows:

[0087]

[0088] In the formula, k is a random number between 0 and 1; x s It is a random value generated by s iterations; x s+1 It is a random value generated by the (s+1)th iteration;

[0089] Secondly, according to equation (29), the improved dynamic weighting factor b is used instead of ω;

[0090]

[0091] In the formula, b is the global optimal position; C1 and C2 are the control coefficients for the current individual's optimal position and the global optimal position, respectively; R1 and R2 are random numbers between 0 and 1; S max This represents the maximum number of iterations.

[0092] Finally, a multi-strategy prey search is added, as shown in Equation (25). When the fitness value of the sine-cosine transform is better than that of the original strategy, the sine-cosine transform is selected in ICSA. The specific process of the sine-cosine transform is as follows:

[0093]

[0094] In the formula, r1 is the step size search coefficient; r2 and r3 are used to control the individual search distance.

[0095] The beneficial effects of this invention are as follows:

[0096] (1) This invention achieves better utilization of energy. The proposed energy optimization management model maintains the stable and reasonable distribution of the power grid by involving biogas power generation, photovoltaic power generation and micro gas turbines. The combined cooling, heating and power microgrid system is becoming an important measure to solve the current regional energy supply problem due to its advantages such as high utilization rate of renewable energy, flexible operation and control and low environmental pollution.

[0097] (2) In order to achieve the economical operation of the combined cooling, heating and power microgrid system, this invention uses standard functions to test the performance of the improved algorithm, which shows that ICSA is superior to the original algorithm in search capability and avoids the problems of population diversity decay and getting trapped in local optima in the swarm intelligence optimization algorithm in CSA.

[0098] (3) This invention solves the optimal operating cost of the system by integrating different improvement strategies. In a combined cooling, heating and power microgrid system, each device operates in coordination. The commonly used operating strategies are the power-first-load (FEL) strategy and the heat-first-load (FHL) strategy. However, these two traditional operating strategies lack the support of energy storage systems, which will cause energy waste. The proposed FHL strategy can effectively realize the economic operation of the combined cooling, heating and power microgrid system. This operating strategy can more effectively reduce the economic and environmental costs of the combined cooling, heating and power microgrid system. Attached Figure Description

[0099] Figure 1 is a model structure diagram of the combined cooling, heating and power microgrid system of the present invention;

[0100] Figure 2 is a flowchart of the energy optimization management algorithm for a combined cold, heat and electricity microgrid based on the improved chameleon swarm algorithm in this invention.

[0101] Figure 3 shows the load demand curves for three typical seasons in this embodiment;

[0102] Figure 4 is a schematic diagram of temperature and light intensity curves for three typical seasons in this embodiment;

[0103] Figure 5 is a schematic diagram of the daily power dispatch results in summer according to this embodiment;

[0104] Figure 6 is a schematic diagram of the daily power dispatch results in winter according to this embodiment;

[0105] Figure 7 is a schematic diagram of the daily power dispatch results during the transition season in this embodiment;

[0106] Figure 8 is a schematic diagram of the average convergence curve of this embodiment. Detailed Implementation

[0107] The present invention will now be described in detail with reference to the accompanying drawings and embodiments:

[0108] This invention provides an optimization method for the operation of a combined cooling, heating, and power (CCHP) microgrid system. First, it constructs the objective function and constraints for the operation of the CCHP microgrid system, thus establishing a multi-objective optimization model. Then, it determines the objective function of the CCHP microgrid system, thereby reducing costs and improving economic efficiency. Second, it introduces an improved Chameleon Swarm Algorithm (ICSA) into the traditional methods of CCHP system operation strategies to achieve energy optimization management of the CCHP microgrid system, thus avoiding the problems of population diversity decay and getting trapped in local optima that exist in swarm intelligence optimization algorithms in the Chameleon Swarm Algorithm (CSA). Finally, it proposes an operation strategy using a hybrid following heating load (HFHL) strategy to more effectively reduce the operating costs of the CCHP microgrid system.

[0109] Figure 1 shows a model of a combined cooling, heating and power (CCHP) microgrid system. Its main distributed power sources include wind power generation system, photovoltaic power generation system, biogas power generation system and gas turbine; energy storage system includes batteries and thermal storage tanks; auxiliary energy supply equipment includes waste heat boilers, gas boilers, heat exchangers, absorption chillers and electric chillers, etc., and the user loads within the microgrid system include electrical load, heat load and cooling load.

[0110] The embodiment shown in Figure 2 illustrates the following procedure flow for a multi-objective optimization method for a combined cooling, heating, and power (CCHP) microgrid system: Start → Set basic parameters of the CCIIP microgrid and determine the output and load demand of the BIO-PV system → Calculate the output value based on the cooling and heating load balance → Calculate the output value bascd (electric load balance) on the function → Construct the objective function and solve it using ICSA → Determine if the optimal result can be achieved → No, readjust the upper and lower limits of the "decision variables" → Yes, save the optimal value of the objective function for each stage of the equipment's output → Determine if the stopping condition is met → Output the result → End.

[0111] The present invention provides a method for optimizing the operation of a combined cooling, heating and power microgrid system, the specific steps of which are as follows:

[0112] Step 1: Establish a multi-objective model for a combined cooling, heating and power microgrid system;

[0113] The combined cooling, heating, and power (CCHP) microgrid system includes models of biogas power generation, photovoltaic power generation, micro gas turbines, waste heat boilers, adsorption chillers, gas boilers, refrigerators, and energy storage systems. The modeling of each device is as follows:

[0114] (1.1) Biogas power generation model

[0115] Methane is the main component of biogas, accounting for approximately 60% to 75%. In a biogas power generation system, each cubic meter of standard biogas can generate 1.4 to 2.6 kWh of electricity, with a heat output greater than 21,520 kJ, equivalent to the heat provided by 0.7 cubic meters of natural gas or 0.7 kg of anthracite. Its simplified output model can be expressed as:

[0116]

[0117] In the formula, P BIO (t) represents the output power of the biogas power generation system; Q BIO (t) represents the output heat of the biogas power generation system; C BIO (t) represents the biogas intake per unit time; u p u q These represent the power generation and calorific value per unit cubic meter of biogas, respectively; η p η q The power generation efficiency and thermal efficiency of the biogas power generation system.

[0118] (1.2) Photovoltaic power generation model

[0119] Ambient temperature and light intensity are usually important factors affecting the output of photovoltaic power generation systems. A simplified model of its output power can be expressed as:

[0120]

[0121] In the formula, P PV T represents the output power of the photovoltaic power generation system. STC G STC The values ​​are ambient temperature and light intensity under standard conditions, respectively: 25℃ and 1000 W / m²; P STC T represents the rated output power of a photovoltaic power generation system under standard conditions. C G C These are the photovoltaic panel temperature and light intensity under the current environment; T t is the ambient temperature under the current environment; k is the power temperature coefficient.

[0122] (1.3) Micro gas turbine model

[0123] A micro gas turbine is a power output device that simultaneously provides electrical energy and meets the internal heat energy needs of a system. Chenet et al. (2019) presented an equivalent model of a natural gas-based micro gas turbine, which can be expressed as:

[0124]

[0125] In the formula, P MT (t), Q MT (t) represent the power generation and heat generation of the micro gas turbine at time t, respectively; G MT (t) represents the total amount of fuel consumed by the micro gas turbine at time t; Q G The calorific value of natural gas is typically taken as 9.7 (kW·h) / m³; η MT The power generation efficiency of a micro gas turbine; η MTloss This represents the gas loss rate of a micro gas turbine.

[0126] (1.4) Waste heat boiler and adsorption chiller model

[0127] The micro gas turbine transfers the generated heat to a waste heat boiler for thermal energy supply. Its equivalent model is as follows:

[0128] Q HE (t)=Q MT,HE (t)COP he (4)

[0129] In the formula, Q HE (t) represents the heat output of the waste heat boiler at time t; Q MT,HE (t) represents the heat supplied by the micro gas turbine at time t; COP he This refers to the heating efficiency of the waste heat boiler. Meanwhile, the adsorption chiller utilizes an adsorbent to convert heat energy into cooling power to supply the cooling load. The micro gas turbine transfers the generated heat to the adsorption chiller; its equivalent model is:

[0130] Q CO (t)=Q MT,CO (t)COP co (5)

[0131] In the formula, Q CO (t) represents the cooling power output of the adsorption chiller at time t; Q MT,CO (t) represents the heat supplied by the micro gas turbine at time t; COP co This refers to the heat conversion efficiency of the waste heat boiler.

[0132] (1.5) Gas boiler and electric chiller model

[0133] A gas-fired boiler is a device that obtains heat by burning natural gas. Its working process includes two stages: flue gas combustion and steam-water combustion. Its energy efficiency model is as follows:

[0134] Q GB (t)=G GB (t)Q G η GB (6)

[0135] In the formula, Q GB (t) represents the heat output of the gas-fired boiler at time t; G GB (t) represents the total amount of fuel consumed by the micro gas boiler at time t; η GB This refers to the heating efficiency of a gas-fired boiler.

[0136] An electric chiller is a device that converts electrical energy into cooling power. Its energy efficiency model is as follows:

[0137] Q ER (t)=P ER (t)η ER (7)

[0138] In the formula, Q ER (t) The cooling power output of the gas-fired boiler at time t; P ER (t) represents the electrical power consumed by the electric chiller at time t; η ER The cooling efficiency of an electric refrigeration unit.

[0139] (1.6) Energy Storage System Model

[0140] When charging, the battery stores electrical energy using charging power; when discharging, the battery releases electrical energy using discharging power. Therefore, the working process of a battery can be represented as follows:

[0141]

[0142] In the formula, E BAT,C (t), E BAT,D (t) represents the remaining battery charge at time t under charging and discharging states, respectively; E BAT,C (t-1), E BAT,D (t-1) represents the remaining battery charge at time t-1 under charging and discharging states, respectively; E c (t) represents the actual capacity of the battery at time t; P c (t), P d (t) represents the charging and discharging power of the battery at time t, respectively; η c η d These are the charging and discharging efficiencies of the battery, respectively; E STC T STCThese represent the battery capacity and ambient temperature under standard conditions, respectively; T(t) is the ambient temperature at time t; δ b Δt represents the capacity temperature coefficient of the battery; Δt is the time interval. Furthermore, a heat storage tank can store excess heat power in the system and release it when needed; its working process can be represented as:

[0143]

[0144] In the formula, Q HSK,C (t), Q HSK,D (t) represents the thermal energy stored in the heat storage tank during the heat storage and heat release states at time t; Q HSK,C (t-1), Q HSK,D (t-1) represents the thermal energy stored in the heat storage tank during the heat storage and heat release states at time t-1, respectively; Q c (t), Q d (t) represents the heat storage and heat release power of the heat storage tank at time t; α is the self-loss coefficient of the heat storage tank; η chr η dis These represent the heat storage and heat release efficiencies of the heat storage tank, respectively.

[0145] Step 2: Determine the objective function and constraints of the combined cooling, heating and power (CCHP) microgrid system. The objective function includes an economic objective function and an environmental objective function. The constraints should satisfy load balance under various load conditions, thereby establishing a multi-objective optimization model for the CCHP microgrid system.

[0146] (2.1) Determine the economic objective function and the environmental objective function

[0147] The economic objective function F1 is expressed as equation (10), which includes the installation cost F. DG Operating costs F OM Fuel cost F Fuel Electricity transaction costs F Grid and penalty cost F PU ;

[0148] minF1=F DG +F OM +F Fuel +F Grid +F PU (10)

[0149]

[0150]

[0151]

[0152]

[0153]

[0154] In the formula, c g The cost is 2.05 yuan / m³, calculated based on the unit price of natural gas. 3 Note the exchange rate conversion between RMB and USD; P is the investment cost of the i-th device; i It is the rated capacity of the i-th device. It is the operating and maintenance cost of the i-th facility; k buy (t) and k sell (t) is the unit price for buying and selling electricity; P buy (t) and P sell (t) represents the power purchased and sold; P vacancy (t) and P waste (t) represents the insufficient and wasted portion; Q vacancy (t) and Q waste (t) represents the missing and wasted thermal energy; λ is the penalty coefficient.

[0155] The environmental objective function F2 is shown in equation (16):

[0156]

[0157] In the formula, K represents the type of pollutant, and C... k Let χ be the cost of the k-th pollutant. ik and χ gridk P is the emission coefficient. i (t) represents the amount of electricity generated.

[0158] The objective function F is optimized as shown in equation (17):

[0159]

[0160] In the formula, ω1 and ω2 are adaptive weighting coefficients; when F1 decreases, ω1 decreases; if F1 increases, ω1 also increases; n1 and n2 are natural coefficients; G1(t) is the total amount of fuel consumed at time t.

[0161] (2.2) System Constraints

[0162] The constraints of a combined cooling, heating and power (CCHP) microgrid system include base load constraints and equipment operation constraints. The constraints should meet the load balance under various load conditions. In addition, the output power of the micro-sources should be consistent with the demand of all loads.

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] In the formula, P BIO (t), P PV (t), P MT (t) represents the output power of the biogas power generation system, photovoltaic power generation system, and wind power generation system at time t, respectively; P c (t), P d (t) represents the charging and discharging power of the battery at time t, respectively; P Load (t) represents the electrical load of the microgrid system at time t; P ER (t) represents the electrical energy consumed by the electric chiller at time t; Q c (t), Q d (t) represents the heat storage and heat release power of the heat storage tank at time t, respectively; Q HE (t), Q GB (t) represents the thermal energy input to the absorption chiller and the heat exchanger at time t, respectively; Q CO (t), Q ER (t) represents the cooling energy output of the absorption chiller and the electric chiller at time t, respectively; Q Load,h (t) represents the heat load demand of the microgrid system at time t, Q Load,c (t) represents the cooling load demand of the microgrid system at time t; and These are the minimum and maximum energy storage capacities of the battery, respectively; E BAT (t) represents the remaining electrical energy of the battery at time t; and These are the minimum and maximum charging power of the battery, respectively. and These are the minimum and maximum discharge power of the battery, respectively. and These represent the minimum and maximum energy storage capacities of the thermal storage tank, respectively; Q HST (t) represents the thermal energy stored in the heat storage tank at time t; and These are the minimum and maximum thermal storage capacities of the thermal storage tank, respectively. and These are the minimum and maximum heat release power of the heat storage tank, respectively. These are the minimum outputs of wind power generation systems, photovoltaic power generation systems, biogas power generation systems, gas turbines, and gas boilers, respectively. and These represent the maximum output of the wind power generation system, photovoltaic power generation system, biogas power generation system, gas turbine, and gas boiler, respectively.

[0169] Step 3: In the operation strategy of the combined cooling, heating and power microgrid system, the optimization parameters of the improved Chameleon Group Algorithm (ICSA) are obtained based on the economic objective function and the environmental objective function. Through the improved Chameleon Group Algorithm (ICSA), the energy optimization management of the combined cooling, heating and power microgrid system is realized.

[0170] The total operating cost of a CCHP microgrid system includes an economic objective function (F1) and an environmental objective function (F2). In the operating cost optimization problem, ICSA is chosen as the solution method, with equation (18) as the fitness function of ICSA. Furthermore, to avoid problems such as population diversity decay and getting trapped in local optima in CSA, ICSA is proposed.

[0171] (3.1) Chameleon Swarm Algorithm Model

[0172] The model simulates the process of a chameleon searching for food, dividing it into three stages: searching for prey, turning its eyes, and capturing prey. Similar to the group search algorithm, CSA needs to determine a population matrix with population size (Pop) and dimension (Dim), and initialize the population according to equation (24).

[0173] y i,j =LB + rand·(UB - LB) (24)

[0174] In the formula, LB and UB are the ranges of the search space; rand is a random number between 0 and 1; i represents an individual; j represents the dimension; y i,j This represents the position of the i-th individual in the j-th dimension;

[0175] (3.1.1) Searching for prey

[0176] At this stage, the chameleon begins to search for prey according to formula (25).

[0177]

[0178] In the formula, s is the number of iterations; S maxis the maximum number of iterations; the subscript p is the current individual optimal position; b is the global optimal position; r, R1, R2, and R3 are random numbers between 0 and 1; P1 and P2 are development parameters; sgn(rand - 0.5) controls the development direction and takes values of 1 or -1; α, β, and γ are set values; P represents the probability of sensing prey. If r ≥ P, the individual will update its position according to the prey observed in the search space; if r < P, the individual will search randomly in different directions.

[0179] (3.1.2) Eye rotation

[0180] The eyes of a chameleon can rotate and focus in two different directions simultaneously. At this stage, according to Equation (26), the position update during prey localization is simulated using eye rotation, which greatly improves the probability of the algorithm discovering prey.

[0181]

[0182] In the formula, the subscript "-" represents the average position of the chameleon before rotation in each dimension; M is the rotation matrix; θ controls the eye rotation angle within [-180°, 180°].

[0183] (3.1.3) Catching prey

[0184] A chameleon catches prey with its tongue, so the speed model of the tongue needs to be determined before position update. The specific process of this stage is as follows.

[0185]

[0186] In the formula, ω is the inertia weight; C1 and C2 are the control coefficients of the current individual optimal position and the global optimal position respectively; ρ is the control coefficient of ω; a is the acceleration of the chameleon's tongue, represents the speed of the chameleon's tongue when the i-th individual is in the j-th dimension at the s-th iteration.

[0187] (3.2) Improvement strategies and processes

[0188] First, the initialization is improved using Tent chaotic mapping; second, in order to further enhance the algorithm's ability in later iterations, an improved dynamic weight coefficient is used to replace ω; finally, in order to improve the development efficiency of the chameleon during the prey search stage, multi-strategy prey search is added in this stage.

[0189] Tent chaotic mapping In this study, we choose Tent chaotic mapping to initialize the population, as follows:

[0190]

[0191] where k is a random number between 0 and 1; x s is the random numerical value generated by the s-th iteration; x s+1 is the random value generated by the (s + 1)-th iteration;

[0192] The dynamic weight factor is used to further balance the search methods in the early and late stages of iteration. According to Equation (29), the improved dynamic weight factor is used to replace ω.

[0193]

[0194] where b is the global optimal position; C1 and C2 are the control coefficients of the current individual optimal position and the global optimal position respectively; R1, R2 are random numbers between 0 and 1; S max is the maximum number of iterations;

[0195] Multi-strategy prey search is shown in Equation (25). When r ≥ P, the update of the individual position is more affected by the optimal position; when r < P, the update of the individual position will have great randomness. Therefore, on the basis of the current search strategy, the sine-cosine transform is introduced to expand the ability of the chameleon to search for prey. In addition, the individual selects a suitable search strategy according to the fitness value under different search strategies; when the fitness value of the sine-cosine transform is better than the original strategy, the sine-cosine transform is selected in ICSA. The specific process of the sine-cosine transform is as follows:

[0196]

[0197] where r1 is the step size search coefficient; r2 and r3 are used to control the single search distance.

[0198] Step 4. In order to more effectively reduce the operating cost of the CCHP microgrid system, an operating strategy of adopting the hybrid following heat load strategy (HFHL) is proposed, including the specific operating strategies for power load and heat load demands.

[0199] In the combined cooling, heating and power (CCHP) microgrid system, each device operates in coordination. The commonly used operating strategies are the first electricity then load strategy (FEL) and the first heat then load strategy (FHL). However, these two traditional operating strategies lack the support of the energy storage system and will cause energy waste. Therefore, in order to more effectively reduce the operating cost of the CCHP microgrid system, an operating strategy of adopting the hybrid following heat load strategy (HFHL) is proposed. It includes the specific operating strategies for power load and heat load demands.

[0200] Waste heat generated by MT and BIO is used to supply cooling and heating loads. If the generated waste heat exceeds the cooling and heating load requirements, HSK will discharge the excess energy. Meanwhile, GB can serve as a backup power source. Electrical energy is generated by MT, PV, and BIO. When the supplied electrical energy exceeds the demand of the power load and current change rate, the excess electrical energy will be absorbed by BAT and the main grid. On the other hand, if the power generated by the generation system is insufficient, the required additional power will be provided by BAT and the main grid. Based on a hybrid control strategy, cooling load demand is distributed between AC power sources and current transformers, with AC power serving as the primary cooling load.

[0201] Based on the load demand curves of the cogeneration microgrid system in three typical seasons (summer, winter, and transitional season) shown in Figure 3, the temperature and light intensity curves in the three typical seasons shown in Figure 4, the daily power dispatch results in summer shown in Figure 5, the daily power dispatch results in this case shown in Figure 6, and the daily power dispatch results in the transitional season shown in Figure 7, it can be seen that in the three typical seasons, as the purchased electricity increases, the economic cost weight ω1, used to limit the power interaction cost, increases. When the output of refrigerators and micro gas turbines is large, the environmental cost weight ω2 is increased to limit the cost of pollutant treatment.

[0202] The optimization results using ICSA under the FEL, FHL, and HFHL operating strategies are shown in Table 1:

[0203] Table 1. Optimization and Comparison Results of Different Operating Strategies

[0204]

[0205] As shown in Table 1, the economic objective function (F1), environmental objective function (F2), and total economic and environmental operating cost (C) of FEL and FHL are all higher than those of HFHL. Compared with FEL, C is reduced by 1.12% in summer, 21.35% in winter, and 13.62% in the transition season. Compared with FHL, C is reduced by 1.67% in summer, 6.89% in winter, and 3.42% in the transition season. Therefore, HFHL can more effectively reduce the total operating cost of combined cooling, heating, and power (CCHP) microgrid systems.

[0206] To verify the specific effectiveness of ICSA in solving system operating costs, Equation (17) was used as the fitness function for each algorithm. The comparative analysis results of different algorithms, Min, Avg, and Std, are shown in Table 2:

[0207] Table 2. Comparative Analysis of Different Algorithms

[0208]

[0209] Table 2 shows that ICSA has smaller Min and Std results, indicating high solution efficiency and no obvious outliers. Compared with other algorithms, ALO has a larger Min result, indicating that it is prone to getting trapped in local optima during the solution process, making it unsuitable for the CCHP microgrid system designed in this study. ICSA has significantly improved convergence accuracy, and the Avg result can be optimal. WOA's convergence performance is close to that of ICSA, while ALO and CSA's convergence performance is not as good as ICSA. Overall, ICSA performs better than CSA, WOA, and ALO. Meanwhile, the final convergence results of each algorithm are not significantly different, and none of the four algorithms converged to a local optimum. Furthermore, the early development capability of the algorithm is closely related to the later convergence performance. Figure 8 shows the overall average convergence curve, indicating that ICSA's convergence speed is significantly better than other algorithms in the summer and transitional seasons before generation 200. In winter, ICSA's convergence speed is worse than WOA and ALO, but faster than CSA, with the convergence performance changing between generations 400-600. In summary, ICSA has certain advantages in energy optimization management of combined cooling, heating and power microgrid systems.

[0210] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing the operation of a combined cooling, heating, and power (CCHP) microgrid system, characterized in that: The method includes the following steps: Step 1, establishing a multi-objective model of a combined cooling, heating, and power (CCHP) microgrid system; Step 2, determining the objective function and constraints of the CCHP microgrid system, including an economic objective function and an environmental objective function, and ensuring load balance under various load conditions, thereby establishing a multi-objective optimization model of the CCHP microgrid system; Step 3, in the operation strategy of the CCHP microgrid system, obtaining the optimization parameters of the improved Chameleon Group Algorithm (ICSA) based on the economic and environmental objective functions, and applying the improved ICSA algorithm. CSA, to realize energy optimization management of the combined cooling, heating and power microgrid system; Step 4, adopt the operation strategy of the hybrid heat load following strategy HFHL, and distribute the cooling load demand in the AC power supply and current transformer, of which the AC power supply is the main cooling load; In the first step, the combined cooling, heating and power microgrid system includes biogas power generation model, photovoltaic power generation model, micro gas turbine, waste heat boiler, adsorption chiller, gas boiler, electric chiller and energy storage system model related equipment, the modeling of each equipment is as follows: (1) Biogas power generation model The biogas power generation model is represented as: (1) In the formula, P BIO (t) represents the output power of the biogas power generation system; Q BIO (t) represents the output heat of the biogas power generation system; C BIO (t) represents the biogas intake per unit time; u p u q These represent the power generation and calorific value per unit cubic meter of biogas, respectively; η p η q (2) Photovoltaic power generation model: The photovoltaic power generation model is expressed as follows: (2) In the formula, P PV T represents the output power of the photovoltaic power generation system. STC G STC These represent ambient temperature and light intensity under standard conditions, respectively; P STC T represents the rated output power of a photovoltaic power generation system under standard conditions. C G C These are the photovoltaic panel temperature and light intensity under the current environment; T t The ambient temperature is given by the current environment; k is the power temperature coefficient; (3) Micro gas turbine model The micro gas turbine model is represented as: (3) In the formula, P MT (t), Q MT (t) represent the power generation and heat generation of the micro gas turbine at time t, respectively; G MT (t) represents the total amount of fuel consumed by the micro gas turbine at time t; Q G η is the calorific value of natural gas. MT The power generation efficiency of a micro gas turbine; η MTloss The gas loss rate of the micro gas turbine; (4) Waste heat boiler and adsorption chiller model The micro gas turbine transfers the heat it produces to the waste heat boiler for heat supply. Its equivalent model is: (4) In the formula, Q HE (t) represents the heat output of the waste heat boiler at time t; Q MT, HE (t) represents the heat supplied by the micro gas turbine at time t; COP he The heating efficiency of the waste heat boiler is considered; meanwhile, the adsorption chiller uses an adsorbent to convert thermal energy into cooling power to supply the cooling load, and the micro gas turbine transfers the generated heat to the adsorption chiller. Its equivalent model is: (5) In the formula, Q CO (t) represents the cooling power output of the adsorption chiller at time t; Q MT , CO (t) represents the heat supplied by the micro gas turbine at time t; COP co The heat conversion efficiency of the waste heat boiler; (5) Models of gas boilers and electric chillers The energy efficiency model of the gas boiler is: (6) In the formula, Q GB (t) represents the heat output of the gas-fired boiler at time t; G GB (t) represents the total amount of fuel consumed by the micro gas boiler at time t; η GB The heating efficiency of the gas-fired boiler is given; the energy efficiency model for the electric chiller is as follows: (7) In the formula, Q ER (t) The cooling power output of the gas-fired boiler at time t; P ER (t) represents the electrical power consumed by the electric chiller at time t; η ER The cooling efficiency of the electric chiller; (6) Energy storage system model The working process of the battery in the energy storage system model is represented as: (8) In the formula, E BAT,C (t), E BAT,D (t) represents the remaining battery charge at time t under charging and discharging states, respectively; E BAT,C (t-1), E BAT,D (t-1) represents the remaining battery charge at time t-1 under charging and discharging states, respectively; E c (t) represents the actual capacity of the battery at time t; P c (t), P d (t) represents the charging and discharging power of the battery at time t; η c η d These are the charging and discharging efficiencies of the battery, respectively; E STC T STC These represent the battery capacity and ambient temperature under standard conditions, respectively; T(t) is the ambient temperature at time t; δ b Δt represents the capacity temperature coefficient of the battery; Δt represents the time interval.

2. The method for optimizing the operation of a combined cooling, heating, and power microgrid system according to claim 1, characterized in that: In the energy storage system model, the thermal storage tank stores excess heat power in the system and releases it for use when needed. Its working process is represented as follows: (9) In the formula, Q HSK,C (t), Q HSK,D (t) represents the thermal energy stored in the heat storage tank during the heat storage and heat release states at time t; Q HSK,C (t-1), Q HSK,D (t-1) represents the thermal energy stored in the heat storage tank during the heat storage and heat release states at time t-1, respectively; Q c (t), Q d (t) represents the heat storage and heat release power of the heat storage tank at time t; α is the self-loss coefficient of the heat storage tank; η chr η dis These represent the heat storage and heat release efficiencies of the heat storage tank, respectively.

3. The method for optimizing the operation of a combined cooling, heating, and power microgrid system according to claim 2, characterized in that: In step two, the specific steps are as follows: (1) Determine the economic objective function and the environmental objective function. The economic objective function F1 is expressed as equation (10), including the installation cost F DG Operating costs F OM Fuel cost F Fuel Electricity transaction costs F Grid and penalty cost F PU ; (10) (11) (12) (13) (14) In equation (15), The price per unit of natural gas; It is the investment cost of the i-th device; This is the rated capacity of the i-th device; It is the cost of operation and maintenance of the i-th facility; and It is the unit price for buying and selling electricity; and It refers to the power used for buying and selling; and It is the part that is insufficient and wasteful; and This refers to the loss and waste of thermal energy; It is the penalty coefficient; the environmental objective function F2 is shown in equation (16): (16) In the formula, K is the type of pollutant; C k The cost of the kth pollutant; and The emission coefficient; It is the power generation; the optimization objective function F is shown in equation (17): In equation (17), ， These are adaptive weighting coefficients; when F1 decreases, Decrease; if F1 increases, It will also increase; n1 and n2 are natural coefficients; G1(t) is the total amount of fuel consumed at time t; (2) Constraints of the system The constraints of the combined cooling, heating and power microgrid system include basic load constraints and equipment operation constraints. The constraints should meet the load balance under various loads, and the output power of the microgrid system should be consistent with all load requirements. (18) (20) (21) (22) (23) In the formula, P BIO (t), P PV (t), P MT (t) represents the output power of the biogas power generation system, photovoltaic power generation system, and wind power generation system at time t, respectively; P c (t), P d (t) represents the charging and discharging power of the battery at time t, respectively; P Load (t) represents the electrical load of the microgrid system at time t; P ER (t) represents the electrical energy consumed by the electric chiller at time t; Q c (t), Q d (t) represents the heat storage and heat release power of the heat storage tank at time t, respectively; Q HE (t), Q GB (t) represents the thermal energy input to the absorption chiller and the heat exchanger at time t, respectively; Q CO (t), Q ER (t) represents the cooling energy output of the absorption chiller and the electric chiller at time t, respectively; Q Load,h (t) represents the heat load demand of the microgrid system at time t, Q Load,c (t) represents the cooling load demand of the microgrid system at time t; and These are the minimum and maximum energy storage capacities of the battery, respectively; E BAT (t) represents the remaining electrical energy of the battery at time t; and These are the minimum and maximum charging power of the battery, respectively. and These are the minimum and maximum discharge power of the battery, respectively. and These represent the minimum and maximum energy storage capacities of the thermal storage tank, respectively; Q HST (t) represents the thermal energy stored in the heat storage tank at time t; and These are the minimum and maximum thermal storage capacities of the thermal storage tank, respectively. and These are the minimum and maximum heat release power of the heat storage tank, respectively. 、 、 、 These are the minimum outputs of wind power generation systems, photovoltaic power generation systems, biogas power generation systems, gas turbines, and gas boilers, respectively. 、 、 、 and These represent the maximum output of the wind power generation system, photovoltaic power generation system, biogas power generation system, gas turbine, and gas boiler, respectively.

4. The method for optimizing the operation of a combined cooling, heating, and power microgrid system according to claim 3, characterized in that: In step three, the specific steps are as follows: In the operation cost optimization problem, the improved chameleon swarm algorithm ICSA is selected as the solution method for the total operation cost optimization problem, and equation (18) is taken as the fitness function of ICSA; (1) Chameleon swarm algorithm model CSA This model simulates the process of chameleons looking for food and divides it into three stages: looking for prey, turning eyes and capturing prey. CSA needs to determine a population matrix with population size Pop and dimension Dim, and initialize the population according to equation (24); (24) In the formula, LB and UB are the range of the search space; rand is a random number between 0 and 1; i represents an individual; j represents the dimension; y i,j Represents the position of the i-th individual in the j-th dimension; (1.1) Searching for prey In this stage, the chameleon begins to search for prey according to equation (25). (25) In the formula, s is the number of iterations; S max is the maximum number of iterations; the subscript p is the current individual optimal position; b is the global optimal position; r, R1, R2, R3 are random numbers between 0 and 1; P1 and P2 are development parameters; sgn (rand - 0.5) controls the development direction, with values of 1 or -1; α, β, γ are set values; P represents the probability of perceiving prey. If r ≥ P, the individual will update its position according to the prey observed in the search space; if r < P, this individual will search randomly in different directions; (1.2) Eye rotation. The eyes of the chameleon rotate and focus in two different directions simultaneously. At this stage, according to formula (26), the rotation of the eyeballs is used to simulate the position update when locating prey; (26) In the formula, the subscript "-" indicates the average position of the chameleon before rotating in each dimension; M is the rotation matrix; θ controls the eye rotation angle in [-180°, 180°]; (1.3) Catching prey Chameleons use their tongues to catch prey. Before updating the position, the speed model of the tongue needs to be determined. The specific process of this stage is as follows; (27) In the formula, ω is the inertial weight; C1 and C2 are the control coefficients of the current individual optimal position and the global optimal position, respectively; ρ is the control coefficient of ω; a is the acceleration of the chameleon tongue; represents the speed of the chameleon tongue of the i-th individual in the j-th dimension during the s-th iteration; (2) Improvement strategy and process First, the Tent chaotic map is selected to initialize the population, as follows: In equation (28), k is a random number between 0 and 1; x s It is a random value generated by s iterations; x s+1 It is a random value generated by s+1 iterations; secondly, according to equation (29), the improved dynamic weighting factor b is used instead of ω; In equation (29), b is the global optimal position; C1 and C2 are the control coefficients for the current individual's optimal position and the global optimal position, respectively; R1 and R2 are random numbers between 0 and 1; S max The maximum number of iterations is set; finally, a multi-strategy prey search is added, as shown in equation (25). When the fitness value of the sine-cosine transform is better than the original strategy, the sine-cosine transform is selected in ICSA. The specific process of the sine-cosine transform is as follows: In equation (30), r1 is the step size search coefficient; r2 and r3 are used to control the individual search distance.

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