Optimal load allocation method for generator sets based on epidemic virus propagation algorithm

Abstract

The present invention discloses a method for optimizing the load distribution of generator sets based on an epidemic virus propagation algorithm, specifically: establishing a scheduling model with the goal of minimizing the operating cost of the generator set, and forming system operating constraints; according to the epidemic virus propagation algorithm, solving the optimal solution and the worst solution of each operating cost under the constraints of the equality and inequality operating constraints of the power system; and then using a comprehensive ranking index to sort a large number of Pareto optimal solutions, and the highest composite ranking index is used as the best solution after weighing multiple costs. The method for optimizing the load distribution of generator sets of the present invention realizes intelligent optimization of power load distribution by simulating the propagation mechanism of the virus, can effectively improve the utilization rate of various heterogeneous power sources, and through the optimized distribution of loads, the power system can better accept clean energy such as wind power, photovoltaic power, and hydropower, which is of great significance to helping achieve the dual carbon goals.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy-saving power generation optimization scheduling of power systems, and in particular relates to a method for optimizing load distribution of generator sets based on an epidemic virus propagation algorithm. Background Art

[0002] Against the backdrop of the dual carbon goals and the transition to clean, low-carbon energy, power system energy-saving power generation optimization and dispatching technologies face new challenges and opportunities. Energy conservation and emission reduction are practical approaches. Considering the significant differences in energy conservation and emission reduction across various power generation technologies, power systems must maximize energy efficiency during dispatch. Furthermore, the rational allocation of loads across units can significantly improve system operational flexibility, ensure load demand, reduce energy consumption, and improve power generation efficiency, thereby achieving energy conservation, emission reduction, and maximizing economic benefits. With the promotion of clean, low-carbon energy reforms and the integration of large-scale renewable energy, the highly volatile and uncontrollable nature of wind and photovoltaic power generation complicates the optimal allocation of loads and the scheduling of energy-efficient power generation. Therefore, power systems need to utilize advanced dispatching algorithms and real-time response mechanisms to optimize the coordinated operation of traditional power generation and renewable energy, maximizing the absorption of clean energy sources such as wind and solar power.

[0003] With the integration of large-scale, uncertain renewable energy into power systems, the number and types of units are increasing, and the corresponding variables and constraints are increasing. Energy-saving power generation optimization scheduling models are becoming increasingly complex, facing a severe curse of dimensionality. Traditional power system optimization methods, such as dynamic programming, mixed-integer linear programming, and Lagrangian relaxation, are widely used in energy-saving power generation scheduling. However, when dealing with large-scale, multi-objective, and complex nonlinear problems, they often suffer from slow convergence, strong parameter sensitivity, and a tendency to fall into local optimal solutions. Furthermore, they exhibit significant deficiencies in flexibility and adaptability when faced with the numerous uncertainties in modern power systems, resulting in limited effectiveness. In recent years, intelligent optimization algorithms (such as particle swarm optimization, genetic algorithms, ant colony algorithms, bee colony optimization algorithms, and sparrow search algorithms) have been widely used in power system load optimization. By simulating biological behaviors in nature (such as animal foraging and genetic inheritance), they achieve global optimization and are capable of handling complex nonlinear, multi-objective optimization problems, potentially overcoming the shortcomings of traditional optimization methods. However, these algorithms still suffer from shortcomings in solution efficiency and global search capabilities. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for optimizing load distribution of generator sets based on an epidemic virus propagation algorithm, which can effectively balance local and global search capabilities and improve the accuracy and efficiency of load distribution.

[0005] The technical solution adopted by the present invention is a method for optimizing the load distribution of generator sets based on an epidemic virus propagation algorithm, which is specifically implemented according to the following steps:

[0006] Step 1: Establish a dispatch model with the goal of minimizing unit operating costs. The costs include the coal cost, startup and shutdown costs, wind and solar curtailment penalty costs, and environmental emission penalty costs of each generator unit. The dispatch model is refined from the perspective of system operation constraints.

[0007] Step 2: After step 1, according to the epidemic virus propagation algorithm, under the constraints of the power system equations and inequalities, solve the optimal and worst solutions for each operating cost; then use the comprehensive ranking index to sort the large number of Pareto optimal solutions obtained, and the highest composite ranking index is used as the best solution after weighing multiple costs.

[0008] The present invention is also characterized in that:

[0009] In step 1, the objective function is to minimize the total operating cost of the unit, as shown in formula (1):

[0010]

[0011] Where: F represents the total operating cost of each unit in the power generation system; is the coal burning cost of thermal power units; is the start-up and shutdown cost of thermal power units; Penalty costs for curtailing wind and solar power; Penalty costs for environmental emissions;

[0012] The coal burning cost of thermal power units is shown in formula (2):

[0013]

[0014] Where: P Gi,t is the active power output of thermal power unit i at time t; a i 、b i 、c i is the coal cost coefficient corresponding to thermal power unit i; N G is the total number of thermal power units; e i and h i is the valve point effect cost coefficient of thermal power unit i; P Gi,min is the minimum output power of thermal power unit i; T represents the 24-hour power generation scheduling time series;

[0015] The start-up and shutdown cost of the unit is shown in formula (3):

[0016]

[0017] Among them, S i,tis the operating state variable of thermal power unit i at time t; F i,t is the cost incurred during the start-up and shutdown of thermal power unit i during period t;

[0018] The penalty cost for curtailing wind and solar power is shown in formula (4):

[0019]

[0020] in: N is the penalty cost coefficient for curtailing wind and solar power; W 、N Pv is the total number of wind farms and photovoltaic power stations; is the actual output power of wind power and photovoltaic generator set i at time t; is the predicted output of wind power and photovoltaic generator set i at time t;

[0021] The environmental emission penalty cost function is shown in Equation (5) and Equation (6);

[0022]

[0023] The environmental emission penalty function is shown in formula (7):

[0024]

[0025] Where: γ represents the environmental penalty coefficient; is the penalty variable of the system at time t; is the carbon emission function of thermal power units at time t; E max is the maximum carbon emission allowed for thermal power units; i 、m i 、n i Represents the carbon emission coefficient of thermal power unit i.

[0026] Power system operation constraints include system power balance constraints, spinning reserve constraints, unit output power constraints, thermal power unit ramping and start-up and shutdown time constraints, hydropower flow and storage capacity constraints, and pumped storage operation and energy balance constraints.

[0027] The system power balance constraint is shown in formula (8):

[0028]

[0029] Where: N H is the total number of hydropower units; N PH is the total number of pumped storage units; P Hi,t is the output power of the i-th hydropower unit at time t; is the output power of the i-th pumped storage unit at time t; u i,tis the operating state variable of the i-th pumped storage unit at time t; P D,t is the load demand of the system at time t;

[0030] The positive and negative spinning reserve constraints of the system are shown in formula (9):

[0031]

[0032] Where: P Gi,max 、P Gi,min is the maximum and minimum output power of thermal power unit i; P Hi,max 、P Hi,min is the maximum and minimum output power of hydropower unit i; is the maximum and minimum power generation capacity of pumped storage unit i; is the spinning reserve required by the system at time t;

[0033] The output power constraints of each unit are shown in formula (10);

[0034]

[0035] in: is the maximum and minimum pumping power of pumped storage unit i; is the maximum and minimum output power of wind turbine i; is the maximum and minimum output power of photovoltaic generator set i;

[0036] The climbing constraint of thermal power unit is shown in formula (11):

[0037] -R Gi,down Δt≤P Gi,t+1 -P Gi,t ≤R Gi,up Δt (11);

[0038] Where: R Gi,down 、R Gi,up Indicates the rate of increase and decrease of the output power of the thermal power unit; Δt is the interval time;

[0039] The minimum start-up and shutdown time constraint of thermal power units is shown in formula (12):

[0040]

[0041] in: is the number of consecutive on and off hours of thermal power unit i in period t-1; is the minimum start-up and shutdown time of thermal power unit i;

[0042] The nonlinear relationship between hydropower generation power, turbine flow, and reservoir capacity is expressed by a binary quadratic function, as shown in Equations (13) and (14):

[0043] P Hi,t =f H (V t ,Q Hi,t ) (13);

[0044]

[0045] Where: α1, α2, α3, α4, α5, α6 are hydropower generation coefficients; V t is the storage capacity of the upstream reservoir of the hydropower station during period t; Q Hi,t is the reference flow of hydropower generation in period t;

[0046] The water flow constraint used when the hydropower unit generates electricity is shown in formula (15):

[0047] Q Hi，min ≤Q Hi,t ≤Q Hi，max (15);

[0048] Where: Q Hi,min , Q Hi,max are the maximum and minimum flow rates allowed for hydropower generation;

[0049] The reservoir capacity constraint of a hydropower station is shown in formula (16):

[0050] V t+1 =V t +V in -Q Hi,t -SP t (16);

[0051] Where: V in Indicates the amount of water flowing into the upstream reservoir of the hydropower station in each scheduling period; SP t It represents the amount of water evaporated or overflowed from the reservoir during period t;

[0052] Pumped storage unit operating condition constraints: Pumped storage units cannot operate in both power generation and pumping conditions at the same time, as shown in formula (17):

[0053] P gi,t ×P pi,t =0(17);

[0054] Where: P gi,t represents the power generation of pumped storage unit i in period t; P pi,t represents the pumping power of pumped storage unit i in period t;

[0055] The reservoir energy balance constraint is shown in Equations (18) and (19):

[0056]

[0057] 0≤E t ≤E t,max (19);

[0058] Where: E t Represents the reservoir capacity; E t，max represents the maximum storage capacity of the reservoir; η g ,η p They represent the power generation efficiency and pumping efficiency of the pumped storage unit respectively.

[0059] In step 2, specifically:

[0060] Step 2.1: By simulating the process of the epidemic virus spreading population gradually transitioning from susceptible to herd immunity, and mapping these states to the solution space of the optimization problem, we can iteratively optimize the complex optimization problem. The specific steps are:

[0061] Step 2.1.1, parameter initialization stage, set the dimension of the search space to D, which is the number of independent variables; the size of the population N Pop ; Initial number of iterations k = 0, maximum number of iterations I ter,max ; Mutation rate M R ; V min 、V max is the minimum and maximum value range of the variable in the objective function;

[0062] Step 2.1.2: Randomly initialize the solution population. Each solution represents the optimal output plan for each unit within the scheduling period, that is, the optimal load distribution. Evaluate each solution and sort all solutions in ascending order according to the objective function. The first solution is the optimal solution, and the last solution is the worst solution.

[0063] Step 2.1.3, during the viral replication phase based on frameshifting technology, new proteins in the population are generated through frameshifting technology to form a new optimal solution set;

[0064] Step 2.1.4: Apply the distance crossover operator to simulate immune behavior and randomly select gene segments from the optimal immune individual for crossover mutation to generate a new mutation solution;

[0065] Step 2.1.5, evaluate the objective function value of the new solution after mutation and update the population;

[0066] Step 2.1.6: Repeat steps 2.1.2-2.1.5 for the obtained new population until the termination condition of the maximum number of iterations is reached;

[0067] Step 2.1.7: Output the optimal solution and the worst solution to obtain the output plan of each generator set under the conditions of the lowest and highest unit operating costs;

[0068] Step 2.2: Design comprehensive ranking indicators to further optimize each generator set under the condition of lowest unit operating cost obtained in step 2.1 to form the best load distribution plan; specifically:

[0069] Step 2.2.1, for the problem of minimizing the unit operating cost, the membership function is shown in formula (22):

[0070]

[0071] Then the average fuzzy membership value can be expressed as formula (23):

[0072]

[0073] Where: μ m,n is the membership value of the mth allocation scheme under the nth cost function; F m,n is the mth solution of the nth cost function; F n,max 、F n,min is the optimal solution and the worst solution of the nth cost function; N is the total number of cost functions; w n is the weight factor of the objective function, and ∑w n =1;

[0074] In step 2.2.2, based on the ranking index of the TOPSIS method, each objective function value is first normalized, and then the normalized value is multiplied by the corresponding weight to obtain the normalized value, as shown in formula (24):

[0075]

[0076] Among them: m,n Normalized value of the mth allocation scheme under the nth cost function;

[0077] According to the optimal solution and the worst solution obtained by the popular virus propagation algorithm, the optimal solution is defined as The worst solution is defined as Calculate the Euclidean distance between each allocation scheme and the optimal solution and the worst solution, as shown in formula (25):

[0078]

[0079] in: is the Euclidean distance between the mth allocation scheme and the optimal solution and the worst solution;

[0080] Calculate the closeness of each allocation scheme to the optimal solution. The higher the closeness, the more obvious the superiority of the scheme. The optimal load distribution scheme can be determined by sorting, as shown in formula (26):

[0081]

[0082] In step 2.2.3, since the optimization performance of each indicator is different, the optimal load distribution scheme for the mth load is finally determined by the composite indicator, as shown in formula (27):

[0083]

[0084] According to the ranking results of the composite indicators, the highest composite ranking indicator is used as the optimal load distribution solution that weighs multiple costs; if the result after the ranking indicators is the same as the optimal solution obtained by the algorithm in step 2.1, it is retained; otherwise, it is updated to the optimal solution after sorting.

[0085] In step 2.1.3, the specific process is:

[0086] Using the +1 shifting technique, the value of the initialized optimal load distribution solution is shifted to the right by 1, and the value of the first position is set to [V min ,V max ] range, the specific process is shown in formula (20):

[0087]

[0088] Where: Z k represents the current optimal solution generated by the kth generation, X is the initial optimal solution, and D is the dimension of the optimization problem.

[0089] In step 2.1.4, the specific process is:

[0090] Apply the distance crossover operator to generate a new optimal solution sequence to simulate immune behavior, select gene fragments from the optimal immune individual for crossover mutation, and evaluate the fitness of the new mutation solution;

[0091] The distance crossover operator is divided according to the social distance between individuals, and is divided into infected individuals, susceptible individuals and optimal immune individuals, which correspond to the following formula (21):

[0092]

[0093] Where: X i (t) is the solution before crossover mutation; X i (t+1) is the solution after crossover mutation; r is [V min ,V max ]A random value in the range; X i,bestis the optimal position found for the current i-th individual; X i,a1 、X i,a2 、X i,a3 is the position of the i-th individual in the search space; R is the distance parameter.

[0094] In step 2.1.5, the specific process is:

[0095] Compare the solution obtained after the mutation in step 2.1.4 with the optimal solution sequence obtained in step 2.1.3. If the value is smaller, it means that the optimization ability is stronger, that is, the operating cost of each unit in the power generation system is lower, then the original optimal solution sequence in step 2.1.3 is replaced; otherwise, the individual optimization ability after mutation is weaker, that is, the operating cost of each unit in the power generation system is increased, then the original optimal solution sequence in step 2.1.3 is retained.

[0096] The beneficial effects of the present invention are as follows: the load optimization distribution method for generator sets of the present invention, firstly, realizes the intelligent optimization of power load distribution by simulating the propagation mechanism of the virus, and can effectively improve the utilization rate of various types of heterogeneous power sources. Secondly, it performs outstandingly in reducing the operating costs of the units. By optimizing the output power of each generator set, the output power is guaranteed to be stable and safe when the proportion of new energy is increasing. At the same time, combined with energy storage technology, through flexible power generation and flexible power use, instability is turned into controllable flexibility, and renewable energy is used more efficiently, thereby improving overall economic efficiency. In addition, reasonable load distribution can reduce the risk of power outages and ensure the stability and reliability of the power system under different operating conditions. Especially in the face of emergencies or load fluctuations, the dynamic optimization capability based on the virus algorithm enables the system to adjust quickly and effectively respond to various challenges. Finally, the method also helps to promote the integration of renewable energy. Through the optimized distribution of loads, the power system can better accept clean energy such as wind power, photovoltaics, and hydropower, which is of great significance in helping to achieve the dual carbon goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 is a flow chart of a generator set load optimization allocation model of an epidemic virus propagation algorithm of the present invention;

[0098] Figure 2 This is a data graph of load, wind power, and photovoltaic output power on a typical day;

[0099] Figure 3 It is the optimal distribution scheme for the load of energy-saving power generation system on a typical day. DETAILED DESCRIPTION

[0100] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0101] Example 1

[0102] The method for optimizing the load distribution of generator sets based on the epidemic virus propagation algorithm of the present invention is specifically implemented according to the following steps:

[0103] Step 1: Establish a dispatch model that aims to minimize the total operating cost of all types of generator units. The costs include the coal cost, start-up and shutdown costs, wind and solar power curtailment penalty costs, and environmental emission penalty costs of each generator unit. The dispatch model is refined from the perspective of system operation constraints.

[0104] Specifically, the objective function is to minimize the total operating cost of the unit, which includes the coal cost, start-up and shutdown costs, environmental emission penalty costs, and wind and solar power curtailment penalty costs of thermal power units;

[0105] The overall objective function for minimizing the unit operating cost is composed of multiple interrelated and mutually constrained single cost objectives, as shown in formula (1):

[0106]

[0107] Where: F represents the total operating cost of each unit in the power generation system; is the coal burning cost of thermal power units; is the start-up and shutdown cost of thermal power units; Penalty costs for curtailing wind and solar power; Penalty costs for environmental emissions;

[0108] There is a nonlinear quadratic function relationship between the coal burning cost and the unit output of a thermal power unit, which is used to describe the fuel consumption of the thermal power unit under different load conditions. When the valve point effect of the thermal power unit is considered, the total coal burning cost is shown in formula (2):

[0109]

[0110] Where: P Gi,t is the active power output of thermal power unit i at time t; a i 、b i 、c i is the coal cost coefficient corresponding to thermal power unit i; N G is the total number of thermal power units; e i and h i is the valve point effect cost coefficient of thermal power unit i; P Gi,min is the minimum output power of thermal power unit i; T represents the 24-hour power generation scheduling time series;

[0111] Relying on the flexibility of each generator set to adjust to the volatility of renewable energy inevitably leads to frequent adjustments of the units. Hydropower units and pumped storage units have faster adjustment speeds, taking only a few minutes to go from no-load to full load. The scheduling interval of the present invention is 1 hour, so their start-up and shutdown costs are negligible. Therefore, the start-up and shutdown costs of the units are mainly caused by the frequent start-up and shutdown adjustments of thermal power units, as shown in formula (3):

[0112]

[0113] Among them, S i,t is the operating state variable of thermal power unit i at time t (S i,t =1, the thermal power unit is turned on or off; S i,t =0, the thermal power unit has not started or stopped); F i,t is the cost incurred during the start and stop of thermal power unit i during period t.

[0114] In order to maximize the use of wind power and photovoltaic power to improve the low-carbon economy of energy-saving power generation systems, the penalty cost for abandoning wind and solar power is introduced to restrict the abandonment of wind and solar power. The penalty cost for abandoning wind and solar power is shown in formula (4):

[0115]

[0116] in: N is the penalty cost coefficient for curtailing wind and solar power; W 、N Pv is the total number of wind farms and photovoltaic power stations; is the actual output power of wind power and photovoltaic generator set i at time t; is the predicted output of wind power and photovoltaic generator set i at time t.

[0117] It should be noted that this paper only considers the case where wind and photovoltaic power generation are underestimated. When wind power is overestimated, the primary task of energy-saving generation scheduling is to compensate for the power shortfall by increasing the output of conventional thermal and hydropower units. This incurs additional operating costs for the units, which conflicts with our objective function of minimizing unit operating costs. Therefore, this paper does not consider the penalty costs associated with overestimation of wind and photovoltaic power generation.

[0118] Renewable energy, conventional hydropower, and pumped storage are all clean energy sources, and their power generation does not pollute the environment. Pollutant emissions from the power system mainly come from the combustion of fossil fuels in thermal power units. In order to mitigate the greenhouse effect and achieve the "dual carbon" goals, it is crucial to reduce pollutant emissions from thermal power units. When pollutant emissions exceed the limit value, the penalty function will be activated. The environmental emission penalty cost function is shown in Equation (5);

[0119]

[0120] The environmental emission penalty function can also be approximately fitted into a quadratic function curve, as shown in the following formula (7):

[0121]

[0122] Where: γ represents the environmental penalty coefficient; is the penalty variable of the system at time t; is the carbon emission function of thermal power units at time t; E max is the maximum carbon emission allowed for thermal power units; i 、m i 、n i Represents the carbon emission coefficient of thermal power unit i.

[0123] The operating constraints of the power system include system power balance constraints, spinning reserve constraints, output power constraints of each unit, ramping and start-stop time constraints of thermal power units, flow and storage capacity constraints of hydropower, and pumped storage operation and energy balance constraints.

[0124] The system power balance constraint is shown in formula (8):

[0125]

[0126] Where: N H is the total number of hydropower units; N PH is the total number of pumped storage units; P Hi,t is the output power of the i-th hydropower unit at time t; is the output power of the i-th pumped storage unit at time t; u i,t is the operating state variable of the i-th pumped storage unit at time t (1 means power generation, -1 means pumping, and 0 means the unit is idle); P D,t is the load demand of the system at time t.

[0127] The positive and negative spinning reserve constraints of the system are shown in formula (9):

[0128]

[0129] Where: P Gi,max 、P Gi,min is the maximum and minimum output power of thermal power unit i; P Hi,max 、P Hi,min is the maximum and minimum output power of hydropower unit i; is the maximum and minimum power generation capacity of pumped storage unit i; is the spinning reserve required by the system at time t.

[0130] The output power constraints of each unit are shown in formula (10);

[0131]

[0132] in: is the maximum and minimum pumping power of pumped storage unit i; is the maximum and minimum output power of wind turbine i; is the maximum and minimum output power of photovoltaic generator set i;

[0133] The climbing constraint of thermal power unit is shown in formula (11):

[0134] -R Gi,down Δt≤P Gi,t+1 -P Gi,t ≤R Gi,up Δt (11);

[0135] Where: R Gi,down 、R Gi,up It indicates the rate of increase and decrease of the output power of the thermal power unit; Δt is the interval time.

[0136] The minimum start-up and shutdown time constraint of thermal power units is shown in formula (12):

[0137]

[0138] in: is the number of consecutive on and off hours of thermal power unit i in period t-1; is the minimum startup and shutdown time of thermal power unit i.

[0139] The nonlinear relationship between conventional hydropower generation power, turbine flow, and reservoir capacity is expressed by a binary quadratic function, as shown in Equations (13) and (14):

[0140] P Hi,t =f H (V t ,Q Hi,t ) (13);

[0141]

[0142] Where: α1, α2, α3, α4, α5, α6 are hydropower generation coefficients; V t is the storage capacity of the upstream reservoir of the hydropower station during period t; Q Hi,t is the reference flow of hydropower generation in period t.

[0143] The water flow constraint used when the hydropower unit generates electricity is shown in formula (15):

[0144] Q Hi，min ≤QHi,t ≤Q Hi，max (15);

[0145] Where: Q Hi,min , Q Hi,max They are the maximum and minimum flow rates allowed for hydropower generation.

[0146] The reservoir capacity constraint of a hydropower station is shown in formula (16):

[0147] V t+1 =V t +V in -Q Hi,t -SP t (16);

[0148] Where: V in Indicates the amount of water flowing into the upstream reservoir of the hydropower station in each scheduling period; SP t It represents the amount of water evaporated or overflowed from the reservoir during period t;

[0149] Pumped storage unit operating condition constraints: Pumped storage units cannot operate in both power generation and pumping conditions at the same time, as shown in formula (17):

[0150] P gi,t ×P pi,t =0(17);

[0151] Where: P gi,t represents the power generation of pumped storage unit i in period t; P pi,t It represents the pumping power of pumped storage unit i in period t.

[0152] The reservoir energy balance constraint is shown in Equations (18) and (19):

[0153]

[0154] 0≤E t ≤E t,max (19);

[0155] Where: E t Represents the reservoir capacity; E t，max represents the maximum storage capacity of the reservoir; η g ,η p They represent the power generation efficiency and pumping efficiency of the pumped storage unit respectively.

[0156] Step 2: After step 1, according to the epidemic virus propagation algorithm, under the constraints of the power system equations and inequalities, solve the optimal and worst solutions for each operating cost; then use the comprehensive ranking index to sort the large number of Pareto optimal solutions obtained, and the highest composite ranking index is used as the best solution after weighing multiple costs, such as Figure 1 As shown, the details are as follows:

[0157] Step 2.1: The epidemic virus propagation algorithm is an optimization algorithm that simulates the process of virus propagation and infection. This optimization algorithm draws on three different states of virus propagation, infection, and immunity in a population. By simulating the process of the virus propagation population gradually transitioning from susceptible to herd immunity, and mapping these states to the solution space of the optimization problem, it iteratively optimizes the complex optimization problem. The specific steps are:

[0158] Step 2.1.1, parameter initialization stage, set the dimension of the search space to D, which is the number of independent variables; the size of the population N Pop ; Initial number of iterations k = 0, maximum number of iterations I ter,max , as the criterion for the end of algorithm iteration; mutation rate M R ; V min 、V max is the minimum and maximum value range of the variable in the objective function.

[0159] In step 2.1.2, randomly initialize the solution population. Each solution represents the optimal output plan for each unit within the scheduling period, that is, the optimal load distribution. Evaluate each solution and then sort all solutions in ascending order according to the objective function. The first solution is the optimal solution, and the last solution is the worst solution.

[0160] Step 2.1.3, based on the viral replication stage of frameshifting technology, new proteins in the population are generated by frameshifting technology to form a new optimal solution set; the specific process is as follows:

[0161] By shifting the code of each solution, the code shifting technique optimizes its position in the solution space, allowing the solution to more effectively move toward high-quality areas. This process helps improve the fitness of the solution, thereby accelerating the algorithm's convergence and optimization process.

[0162] Specifically, each encoded bit (or feature) in the solution is shifted in a certain direction, that is, the value is increased or decreased. During the shift process, it is ensured that the encoding still meets the constraints of the variable.

[0163] Using the +1 shifting technique, the value of the initialized optimal load distribution solution is shifted to the right by 1, and the value of the first position is set to [V min ,V max ] range, the specific process is shown in formula (20):

[0164]

[0165] Where: Z k represents the kth generated current optimal solution, X is the initial optimal solution, and D is the dimension of the optimization problem (the number of variables in the solution). The result of the code shift represents a new current optimal solution sequence.

[0166] In step 2.1.4, the distance crossover operator is applied to simulate immune behavior, and gene fragments are randomly selected from the optimal immune individual for crossover mutation to generate new mutation solutions. The specific process is as follows:

[0167] The algorithm applies a distance crossover operator to generate a new optimal solution sequence to simulate immune behavior, selects gene fragments from the optimal immune individuals for crossover mutation, and evaluates the fitness of the new mutated solution. This effectively improves the algorithm's global search capabilities and enhances the diversity of solutions, thereby better tackling complex optimization problems.

[0168] When crossing over, the distance between individuals is taken into account. Crossover is performed only when the distance between two individuals is less than a certain threshold. This mechanism can prevent the generation of overly similar solutions and increase the diversity of solutions. The distance crossover operator can be divided according to the social distance between individuals, and divided into infected individuals, susceptible individuals, and optimal immune individuals, which correspond to the following equations (21):

[0169]

[0170] Where: X i (t) is the solution before crossover mutation; X i (t+1) is the solution after crossover mutation; r is [V min ,V max ]A random value in the range; X i,best The optimal position (scheduling optimal solution) found for the current i-th individual; X i,a1 、X i,a2 、X i,a3 is the position of the i-th individual in the search space; R is the distance parameter, which is related to the mutation rate M R Jointly control the process of virus spread and herd immunity behavior.

[0171] Step 2.1.5: Evaluate the objective function value of the new solution after mutation and update the population. The specific process is as follows:

[0172] Compare the solution obtained after the mutation in step 2.1.4 with the optimal solution sequence obtained in step 2.1.3. If the value is smaller, it means that the optimization ability is stronger, that is, the operating cost of each unit in the power generation system is lower, then the original optimal solution sequence in step 2.1.3 is replaced; otherwise, the individual optimization ability after mutation is weaker, that is, the operating cost of each unit in the power generation system is increased, then the original optimal solution sequence in step 2.1.3 is retained.

[0173] Step 2.1.6: Repeat steps 2.1.2-2.1.5 for the obtained new population until the termination condition of the maximum number of iterations is reached.

[0174] Step 2.1.7: Output the optimal solution and the worst solution to obtain the output plan of each generator set under the conditions of the lowest and highest unit operating costs;

[0175] Step 2.2: Design comprehensive ranking indicators to further optimize each generator set under the condition of lowest unit operating cost obtained in step 2.1 to form the best load distribution plan;

[0176] Considering the complexity of multi-cost coordination in the system, it is difficult to ensure that the minimum value of each optimization cost is the best load distribution solution. Because different indicators show different results, this paper proposes a comprehensive ranking index. By following different optimization mechanisms, a correct overall evaluation of the solution to the optimal load distribution problem of the generator sets is obtained. The solution corresponding to the highest comprehensive ranking index value is regarded as the best load distribution solution.

[0177] Step 2.2.1, the ranking index based on the average fuzzy membership reflects the average satisfaction of multiple cost solutions. For the problem of minimizing the unit operating cost, the membership function is shown in Equation (22):

[0178]

[0179] Then the average fuzzy membership value can be expressed as formula (23):

[0180]

[0181] Where: μ m,n is the membership value of the mth allocation scheme under the nth cost function; F m,n is the mth solution of the nth cost function; F n,max 、F n,min is the optimal solution and the worst solution of the nth cost function; N is the total number of cost functions; w n is the weight factor of the objective function, and ∑w n =1.

[0182] Step 2.2.2, ranking index based on the TOPSIS method, is a multi-attribute decision-making method that aims to select the solution that is closest to the optimal solution and farthest from the worst solution.

[0183] In TOPSIS, each objective function value is first normalized, and then the normalized value is multiplied by the corresponding weight to obtain the normalized value, as shown in formula (24):

[0184]

[0185] Among them: m,n Normalize the value of the mth allocation solution under the nth cost function.

[0186] According to the optimal solution and the worst solution obtained by the popular virus propagation algorithm, the optimal solution is defined as The worst solution is defined as Calculate the Euclidean distance between each allocation scheme and the optimal solution and the worst solution, as shown in formula (25):

[0187]

[0188] in: is the Euclidean distance between the mth allocation solution and the optimal solution and the worst solution.

[0189] Calculate the closeness of each allocation scheme to the optimal solution. The higher the closeness, the more obvious the superiority of the scheme. The optimal load distribution scheme can be determined by sorting, as shown in formula (26):

[0190]

[0191] In step 2.2.3, since the optimization performance of each indicator is different, the optimal load distribution scheme for the mth load is finally determined by the composite indicator, as shown in formula (27):

[0192]

[0193] Based on the ranking results of the composite index, the highest composite ranking index is used as the optimal load distribution solution that weighs multiple costs. If the result after the ranking index is the same as the optimal solution obtained by the algorithm in step 2.1, it is retained. Otherwise, it is updated to the optimal solution after sorting.

[0194] Example 2

[0195] To verify the effectiveness of the proposed method for optimizing generator load allocation based on the viral epidemic propagation algorithm, a simulation analysis was conducted using an improved four-unit test system consisting of wind power, photovoltaic power, thermal power, hydropower, and pumped storage. The system consists of four thermal power units with a total installed capacity of 1730 MW, one wind farm with a total installed capacity of 680 MW, one photovoltaic power station with a total installed capacity of 500 MW, two hydropower units with a single unit capacity of 180 MW, and a total pumped storage capacity of 150 MW. The parameters of each thermal power unit are shown in Tables 1, 2, and 3.

[0196] Table 1 Parameters of each thermal power unit

[0197]

[0198] Table 2 Main parameters of conventional hydropower stations

[0199] <![CDATA[P Hi,max / P Hi,min (MW)]]> 300 / 0 <![CDATA[α3]]> 0.0300 <![CDATA[Q Hi,max / Q Hi,min (×10 4 m 3 )]]> 15 / 5 <![CDATA[α4]]> 0.9000 <![CDATA[α1]]> -0.0042 <![CDATA[α5]]> 10 <![CDATA[α2]]> -0.4200 <![CDATA[α6]]> -50

[0200] Table 3 Main parameters of pumped storage power station

[0201] <![CDATA[P g,max ]]> <![CDATA[P g,min ]]> <![CDATA[P p,max ]]> <![CDATA[P p,min ]]> <![CDATA[E max ]]> <![CDATA[E ini ]]> <![CDATA[η d ]]> <![CDATA[η c ]]> 150MW 0 150MW 0 1200MWh 600MWh 85％ 87％

[0202] A typical day was selected as the scheduling scenario to analyze the coordinated operation optimization of the energy-saving power generation system. The load, wind power output, and photovoltaic output curves of the typical day are as follows: Figure 2 As shown in Table 4, the optimal output of each generator set in the dispatch cycle on a typical day in January obtained by the epidemic virus propagation algorithm is shown in Table 4. The optimal dispatch output bar chart is shown in Figure 3 As shown;

[0203] Table 4 Optimal dispatch output values ​​of energy-saving power generation system in each period on a typical day

[0204]

[0205]

[0206] As shown in the table above, thanks to the regulatory effects of flexible power sources—hydropower, thermal power, and pumped storage—wind and photovoltaic power generation were fully connected to the grid throughout the entire dispatch cycle, with no curtailment of wind and solar power, and thus no penalty costs. Hydropower and pumped storage collaborated to smooth out fluctuations in renewable energy. Pumped storage not only maintained rated power during off-peak periods but also generated power at rated power during peak load periods, demonstrating its ultimate regulatory capacity. Hydropower units not only shut down during off-peak periods to minimize thermal power unit output adjustments and reduce startup and shutdown costs, but also collaborated with pumped storage units during peak load periods to meet peak power demand, avoiding load shedding or power shortages in the power system. Overall, the load distribution model for energy-saving power generation systems developed in this invention fully leverages the natural characteristics of different energy sources to achieve mutual complementarity, thereby improving the operating characteristics of each power source and significantly reducing the negative impact of wind and photovoltaic power generation randomness on system operation. This achieves optimal load distribution for the system, achieving the optimal economic benefits and the most stable operation.

[0207] Example 3

[0208] To verify the effectiveness and feasibility of the viral propagation algorithm in solving the optimal load allocation problem, other meta-heuristic algorithms, including particle swarm optimization, genetic algorithm, and ant colony algorithm, were used under the same model and computing platform to solve the problem. The results were compared with those of the viral propagation algorithm. In addition to meta-heuristics, mixed integer quadratic programming was also used for comparison. The specific comparison results are shown in Table 5 below.

[0209] Table 5 Comparison of statistical results of different algorithms

[0210]

[0211] Comparing the results in Table 5, we find that the minimum, maximum, and average values ​​obtained by the viral propagation algorithm are significantly lower than those obtained by other metaheuristic algorithms and mixed-integer quadratic programming. Even its average value is significantly lower than the optimal solution found by other algorithms. This fully demonstrates that the optimization algorithm of the present invention outperforms other methods in terms of solution quality and optimization performance. This verifies the effectiveness and feasibility of applying the viral propagation algorithm to the field of energy-saving power generation optimization scheduling technology.

Claims

1. A method for optimizing load distribution of generator sets based on an epidemic virus propagation algorithm, characterized in that: Please follow the steps below to implement: Step 1: Establish a dispatch model with the goal of minimizing unit operating costs. The costs include the coal cost, startup and shutdown costs, wind and solar curtailment penalty costs, and environmental emission penalty costs of each generator unit. The dispatch model is refined from the perspective of system operation constraints. Step 2: After step 1, the optimal and worst solutions for each operating cost are solved based on the epidemic virus propagation algorithm, while satisfying the power system equation and inequality operation constraints. A comprehensive ranking index is then used to sort the large number of Pareto optimal solutions obtained, and the highest composite ranking index is used as the best solution after weighing multiple costs. Specifically: Step 2.1: By simulating the process of the epidemic virus spreading population gradually transitioning from susceptible to herd immunity, and mapping these states to the solution space of the optimization problem, we can iteratively optimize the complex optimization problem. The specific steps are: Step 2.1.1, parameter initialization stage, set the dimension of the search space to D, which is the number of independent variables; the size of the population N Pop ; Initial number of iterations k = 0, maximum number of iterations I ter,max ; Mutation rate M R ; V min 、V max is the minimum and maximum value range of the variable in the objective function; Step 2.1.2: Randomly initialize the solution population. Each solution represents the optimal output plan for each unit within the scheduling period, that is, the optimal load distribution. Evaluate each solution and sort all solutions in ascending order according to the objective function. The first solution is the optimal solution, and the last solution is the worst solution. Step 2.1.3, during the viral replication phase based on frameshifting technology, new proteins in the population are generated through frameshifting technology to form a new optimal solution set; Step 2.1.4: Apply the distance crossover operator to simulate immune behavior and randomly select gene segments from the optimal immune individual for crossover mutation to generate a new mutation solution; Step 2.1.5, evaluate the objective function value of the new solution after mutation and update the population; Step 2.1.6: Repeat steps 2.1.2-2.1.5 for the obtained new population until the termination condition of the maximum number of iterations is reached; Step 2.1.7: Output the optimal solution and the worst solution to obtain the output plan of each generator set under the conditions of the lowest and highest unit operating costs; Step 2.2: Design comprehensive ranking indicators to further optimize each generator set under the condition of lowest unit operating cost obtained in step 2.1 to form the best load distribution plan; specifically: Step 2.2.1, for the problem of minimizing the unit operating cost, the membership function is shown in formula (22): Then the average fuzzy membership value can be expressed as formula (23): Where: μ m,n is the membership value of the mth allocation scheme under the nth cost function; F m,n is the mth solution of the nth cost function; F n,max 、F n,min is the optimal solution and the worst solution of the nth cost function; N is the total number of cost functions; w n is the weight factor of the objective function, and ∑w n =1; In step 2.2.2, based on the ranking index of the TOPSIS method, each objective function value is first normalized, and then the normalized value is multiplied by the corresponding weight to obtain the normalized value, as shown in formula (24): Among them: m,n Normalized value of the mth allocation scheme under the nth cost function; According to the optimal solution and the worst solution obtained by the popular virus propagation algorithm, the optimal solution is defined as The worst solution is defined as Calculate the Euclidean distance between each allocation scheme and the optimal solution and the worst solution, as shown in formula (25): in: is the Euclidean distance between the mth allocation solution and the optimal solution and the worst solution; Calculate the closeness of each allocation scheme to the optimal solution. The higher the closeness, the more obvious the superiority of the scheme. The optimal load distribution scheme can be determined by sorting, as shown in formula (26): In step 2.2.3, since the optimization performance of each indicator is different, the optimal load distribution scheme for the mth load is finally determined by the composite indicator, as shown in formula (27): According to the ranking results of the composite indicators, the highest composite ranking indicator is used as the optimal load distribution solution that weighs multiple costs; if the result after the ranking indicators is the same as the optimal solution obtained by the algorithm in step 2.1, it is retained; otherwise, it is updated to the optimal solution after sorting.

2. The method for optimizing load distribution of generator sets based on epidemic virus propagation algorithm according to claim 1, characterized in that: In step 1, the objective function is to minimize the total operating cost of the unit, as shown in formula (1): Where: F represents the total operating cost of each unit in the power generation system; is the coal burning cost of thermal power units; is the start-up and shutdown cost of thermal power units; Penalty costs for curtailing wind and solar power; Penalty costs for environmental emissions; The coal burning cost of thermal power units is shown in formula (2): Where: P Gi,t is the active power output of thermal power unit i at time t; a i 、b i 、c i is the coal cost coefficient corresponding to thermal power unit i; N G is the total number of thermal power units; e i and h i is the valve point effect cost coefficient of thermal power unit i; P Gi,min is the minimum output power of thermal power unit i; T represents the 24-hour power generation scheduling time series; The start-up and shutdown cost of the unit is shown in formula (3): Among them, S i,t is the operating state variable of thermal power unit i at time t; F i,t is the cost incurred during the start-up and shutdown of thermal power unit i during period t; The penalty cost for curtailing wind and solar power is shown in formula (4): in: N is the penalty cost coefficient for curtailing wind and solar power; W 、N Pv is the total number of wind farms and photovoltaic power stations; is the actual output power of wind power and photovoltaic generator set i at time t; is the predicted output of wind power and photovoltaic generator set i at time t; The environmental emission penalty cost function is shown in Equation (5) and Equation (6); The environmental emission penalty function is shown in formula (7): Where: γ represents the environmental penalty coefficient; is the penalty variable of the system at time t; is the carbon emission function of thermal power units at time t; E max is the maximum carbon emission allowed for thermal power units; i 、m i 、n i Represents the carbon emission coefficient of thermal power unit i.

3. The method for optimizing load distribution of generator sets based on epidemic virus propagation algorithm according to claim 2, characterized in that: Power system operation constraints include system power balance constraints, spinning reserve constraints, unit output power constraints, thermal power unit ramping and start-up and shutdown time constraints, hydropower flow and storage capacity constraints, and pumped storage operation and energy balance constraints. The system power balance constraint is shown in formula (8): Where: N H is the total number of hydropower units; N PH is the total number of pumped storage units; P Hi,t is the output power of the i-th hydropower unit at time t; is the output power of the i-th pumped storage unit at time t; u i,t is the operating state variable of the i-th pumped storage unit at time t; P D,t is the load demand of the system at time t; The positive and negative spinning reserve constraints of the system are shown in formula (9): Where: P Gi,max 、P Gi,min is the maximum and minimum output power of thermal power unit i; P Hi,max 、P Hi,min is the maximum and minimum output power of hydropower unit i; is the maximum and minimum power generation capacity of pumped storage unit i; is the spinning reserve required by the system at time t; The output power constraints of each unit are shown in formula (10); in: is the maximum and minimum pumping power of pumped storage unit i; is the maximum and minimum output power of wind turbine i; is the maximum and minimum output power of photovoltaic generator set i; The climbing constraint of thermal power unit is shown in formula (11): -R Gi,down Δt≤P Gi,t+1 -P Gi,t ≤R Gi,up Δt (11); Where: R Gi,down 、R Gi,up Indicates the rate of increase and decrease of the output power of the thermal power unit; Δt is the interval time; The minimum start-up and shutdown time constraint of thermal power units is shown in formula (12): in: is the number of consecutive on and off hours of thermal power unit i in period t-1; is the minimum start-up and shutdown time of thermal power unit i; The nonlinear relationship between hydropower generation power, turbine flow, and reservoir capacity is expressed by a binary quadratic function, as shown in Equations (13) and (14): P Hi,t =f H (V t ,Q Hi,t ) (13); Where: α1, α2, α3, α4, α5, α6 are hydropower generation coefficients; V t is the storage capacity of the upstream reservoir of the hydropower station during period t; Q Hi,t is the reference flow of hydropower generation in period t; The water flow constraint used when the hydropower unit generates electricity is shown in formula (15): Q Hi，min ≤Q Hi,t ≤Q Hi，max (15); Where: Q Hi,min , Q Hi,max are the maximum and minimum flow rates allowed for hydropower generation; The reservoir capacity constraint of a hydropower station is as shown in formula (16): V t+1 =V t +V in -Q Hi,t -SP t (16); Where: V in Indicates the amount of water flowing into the upstream reservoir of the hydropower station in each scheduling period; SP t It represents the amount of water evaporated or overflowed from the reservoir during period t; Pumped storage unit operating condition constraints: Pumped storage units cannot operate in both power generation and pumping conditions at the same time, as shown in formula (17): P gi,t ×P pi,t =0(17); Where: P gi,t represents the power generation of pumped storage unit i in period t; P pi,t represents the pumping power of pumped storage unit i in period t; The reservoir energy balance constraint is shown in Equations (18) and (19): 0≤E t ≤E t,max (19); Where: E t Represents the reservoir capacity; E t，max represents the maximum storage capacity of the reservoir; η g ,η p They represent the power generation efficiency and pumping efficiency of the pumped storage unit respectively.

4. The method for optimizing load distribution of generator sets based on epidemic virus propagation algorithm according to claim 1, characterized in that: In step 2.1.3, the specific process is: Using the +1 shifting technique, the value of the initialized optimal load distribution solution is shifted to the right by 1, and the value of the first position is set to [V min ,V max ] range, the specific process is shown in formula (20): Where: Z k represents the current optimal solution generated by the kth generation, X is the initial optimal solution, and D is the dimension of the optimization problem.

5. The method for optimizing load distribution of generator sets based on epidemic virus propagation algorithm according to claim 4, characterized in that: In step 2.1.4, the specific process is: Apply the distance crossover operator to generate a new optimal solution sequence to simulate immune behavior, select gene fragments from the optimal immune individual for crossover mutation, and evaluate the fitness of the new mutation solution; The distance crossover operator is divided according to the social distance between individuals, and is divided into infected individuals, susceptible individuals and optimal immune individuals, which correspond to the following formula (21): Where: X i (t) is the solution before crossover mutation; X i (t+1) is the solution after crossover mutation; r is [V min ,V max ]A random value in the range; X i,best is the optimal position found for the current i-th individual; X i,a1 、X i,a2 、X i,a3 is the position of the i-th individual in the search space; R is the distance parameter.

6. The method for optimizing load distribution of generator sets based on epidemic virus propagation algorithm according to claim 5, characterized in that: In step 2.1.5, the specific process is: Compare the solution obtained after the mutation in step 2.1.4 with the optimal solution sequence obtained in step 2.1.

3. If the value is smaller, it means that the optimization ability is stronger, that is, the operating cost of each unit in the power generation system is lower, then the original optimal solution sequence in step 2.1.3 is replaced; otherwise, the individual optimization ability after mutation is weaker, that is, the operating cost of each unit in the power generation system is increased, then the original optimal solution sequence in step 2.1.3 is retained.

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