A multi-objective unit maintenance scheduling optimization method and device

Abstract

The present invention discloses a multi-objective unit maintenance scheduling optimization method and device, belonging to the technical field of power system optimization. The method takes the minimum standard deviation of system reliability indicators, the minimum regional carbon emission cost and the minimum total system cost as multiple objectives, and takes the safe operation of the unit as a constraint condition, to establish a multi-objective unit maintenance optimization scheduling model; then a multi-objective particle swarm algorithm is used to solve the unit maintenance optimization scheduling model, and the Pareto optimal solution set is optimized; finally, a method of sorting close to the ideal solution is used to select the final scheduling solution. The method of the present invention introduces a carbon trading mechanism into unit maintenance scheduling, which can not only reduce the carbon emissions of the power system to a certain extent, but also ensure the safe and economical operation of the power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization, and more specifically, relates to a multi-objective unit maintenance scheduling optimization method and device. Background Art

[0002] With the rapid development of social economy, the electricity demand of enterprises and residents is increasing day by day, which increases the power generation burden of the power grid. In order to rationally utilize the power generation resources in the power grid, scholars at home and abroad have proposed the concept of unit maintenance plan, which ensures the safe, economical and stable operation of the power grid by optimizing the allocation of power resources. For large-scale and complex power systems, maintenance plans are generally prepared manually to achieve overall optimal scheduling. However, the use of manual methods to prepare maintenance plans has problems such as large workload and low efficiency, and it is difficult to take into account the reliability and economy of power system operation. Therefore, how to establish a reasonable maintenance plan has become a difficulty and hot spot in related fields.

[0003] The "China Energy and Power Development Outlook 2020" released by the State Grid Energy Research Institute shows that in 2020, the industrial and power sectors account for 70% of carbon dioxide emissions generated by all energy. With the acceleration of electricity substitution, part of the carbon emissions will be transferred from other industries to the power industry. The power sector will become the main source of carbon emissions, but it can also be seen that the power industry has great potential for emission reduction compared with other industries. Based on the above considerations, as an important part of the power system, unit maintenance scheduling should not only take into account the stability of the power system and the operating cost of the system, but also its impact on carbon emissions. Therefore, reducing carbon emissions by reasonably formulating maintenance plans is an issue worthy of in-depth study. How to achieve optimal scheduling of unit maintenance under the carbon trading mechanism is an urgent problem that the current power system needs to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-objective unit maintenance scheduling optimization method and device. In the unit maintenance scheduling under the power system market environment, the minimization of carbon emissions under the carbon trading background is taken as a separate optimization goal, and the pairwise relationship between the minimum system reliability index goal and the minimum system total cost goal is analyzed to obtain an optimized unit maintenance scheduling plan.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] The present invention provides a multi-objective unit maintenance scheduling optimization method, comprising:

[0007] Construct a unit maintenance scheduling model with the goals of optimizing power system reliability, minimizing total system cost, and minimizing regional carbon emission cost;

[0008] Solve the constructed unit maintenance scheduling model and obtain the Pareto optimal solution set;

[0009] The optimal dispatching solution is selected from the Pareto solution set, including the output of the unit in each time period and the state; the state includes the maintenance state, the startup state and the working state.

[0010] Furthermore, the unit maintenance scheduling model is:

[0011]

[0012] Minimize: F2=std(I(t,s)) t=1, 2, 3,..., N t , s = 1, 2, 3, ..., N s ;

[0013]

[0014]

[0015] Among them, F1 represents the total cost of the power system, F2 is the standard deviation of the power system reliability index, F3 is the carbon trading cost of the power system, I(t,s) is the power system reliability index, N t Indicates the number of time periods included in a maintenance scheduling cycle, N s represents the number of sub-periods contained in a period, T(t,s) represents the number of hours in a sub-period, G i Indicates the number of units assigned to the i-th generator, N i represents the number of power generators, P Gij (t,s) is the output of the jth unit of the i-th generator in the s-th sub-period of the t-th period, C 0ij , C 1ij , C 2ij represents the power generation cost coefficient of the j-th unit of the i-th power producer, represents the startup cost of the jth unit of the i-th generator, y ij (t,s) represents the startup status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the maintenance cost of the jth unit of the i-th generator, represents the maximum output of the jth unit of the i-th generator, M ij (t) represents the maintenance status of the jth unit of the i-th generator in the t-th period, P D (t,s) represents the power demand of the sth sub-period in the tth period, Ct 0ij , Ct 1ij , Ct 2ijrepresents the carbon emission coefficient of the j-th unit of the ith power producer, λ represents the carbon trading price, and η represents the emission factor per unit of electricity.

[0016] Furthermore, the unit maintenance scheduling model needs to meet the following constraints:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] Among them, R Min (t,s) represents the minimum net reserve in the sth sub-period of the tth period, K i (t) represents the maximum number of units that the i-th generator can overhaul simultaneously in the t-th period, v ij (t,s) represents the working status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, Represents the minimum output of the j-th unit of the i-th generator.

[0023] Furthermore, the status of the unit is encoded as follows:

[0024]

[0025]

[0026]

[0027] Among them, x ij represents the period when the j-th unit of the ith power producer starts maintenance, and ∨ represents or.

[0028] Furthermore, the unit electricity emission factor is obtained by weighted average of the marginal electricity emission factor and the marginal capacity emission factor of the system area.

[0029] Furthermore, a multi-objective particle swarm algorithm is used to solve the unit maintenance scheduling model;

[0030] In the solution process, the unit output and maintenance status are used as particle positions, the objective function of the unit maintenance scheduling model is used as fitness, and the Pareto solution set is obtained according to the constraint-Pareto dominance principle. The Pareto optimal solution set is selected from the Pareto solution set using the adaptive grid method.

[0031] Furthermore, an improved approach to ideal solution sorting method is used to select the optimal dispatching solution from the Pareto solution set, including the output and status of the unit in each time period.

[0032] The present invention also provides a multi-objective unit maintenance scheduling optimization device, comprising:

[0033] Model module, used to build a unit maintenance scheduling model with the goals of optimizing power system reliability, minimizing total system cost and minimizing regional carbon emission cost;

[0034] A solution module is used to solve the constructed unit maintenance scheduling model and obtain the Pareto optimal solution set;

[0035] as well as,

[0036] The output module is used to select the optimal scheduling solution from the Pareto solution set, including the output of the unit in each period and the state; the state includes the maintenance state, the startup state and the working state.

[0037] Furthermore, the model module is specifically used to:

[0038] The unit maintenance scheduling model is constructed as follows:

[0039]

[0040] Minimize: F2=std(I(t,s)) t=1, 2, 3,..., N t , s = 1, 2, 3, ..., N s ;

[0041]

[0042]

[0043] Among them, F1 represents the total cost of the power system, F2 is the standard deviation of the power system reliability index, F3 is the carbon trading cost of the power system, I(t,s) is the power system reliability index, N t Indicates the number of time periods included in a maintenance scheduling cycle, N s represents the number of sub-periods contained in a period, T(t,s) represents the number of hours in a sub-period, G i represents the number of units assigned to the i-th generator, N i represents the number of generators, is the output of the jth unit of the i-th generator in the s-th sub-period of the t-th period, C 0ij , C 1ij , C 2ij represents the power generation cost coefficient of the j-th unit of the i-th power producer, represents the startup cost of the jth unit of the i-th generator, y ij (t,s) represents the startup status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the maintenance cost of the jth unit of the i-th generator, represents the maximum output of the jth unit of the i-th generator, M ij (t) represents the maintenance status of the jth unit of the i-th generator in the t-th period, P D (t,s) represents the power demand of the sth sub-period in the tth period, Ct 0ij , Ct 1ij , Ct 2ij represents the carbon emission coefficient of the j-th unit of the i-th power producer, λ represents the carbon trading price, and η represents the emission factor per unit of electricity;

[0044] The unit maintenance scheduling model needs to meet the following constraints:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] Among them, R Min (t,s) represents the minimum net reserve in the sth sub-period of the tth period, K i (t) represents the maximum number of units that the i-th generator can overhaul simultaneously in the t-th period, v ij (t,s) represents the working status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the minimum output of the jth unit of the i-th generator;

[0051] The status codes of the unit are as follows:

[0052]

[0053]

[0054]

[0055] Among them, x ij represents the period when the j-th unit of the ith power producer starts maintenance, and ∨ represents or.

[0056] Furthermore, the solution module is specifically used for:

[0057] A multi-objective particle swarm algorithm is used to solve the unit maintenance scheduling model;

[0058] Taking the unit output and maintenance status as the particle position and the objective function of the unit maintenance scheduling model as the fitness, the Pareto solution set is obtained according to the constraint-Pareto dominance principle, and the Pareto optimal solution set is selected from the Pareto solution set using the adaptive grid method.

[0059] Furthermore, the output module is specifically used to:

[0060] The improved approximate ideal solution sorting method is used to select the optimal dispatching solution from the Pareto solution set, including the output and status of the unit in each time period.

[0061] Compared with the prior art, the advantages of the present invention are:

[0062] The present invention provides a multi-objective unit maintenance scheduling optimization method, which introduces a carbon trading mechanism into the unit maintenance scheduling, and takes the optimal reliability target, the minimum system total cost target and the minimum regional carbon emission target as the unit maintenance scheduling optimization objective function, which reduces the carbon emissions of the power system to a certain extent and ensures the stable and economical operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The daily system power demand for 52 weeks (364 days) provided by the embodiment of the present invention;

[0064] Figure 2 A flow chart of a multi-objective particle swarm algorithm provided by an embodiment of the present invention;

[0065] Figure 3 A flow chart of an improved approach-to-ideal solution sorting method provided by an embodiment of the present invention;

[0066] Figure 4 The Pareto front between the system reliability target and the system total cost target provided by the embodiment of the present invention;

[0067] Figure 5 A Pareto front between a carbon trading cost target and a system total cost target provided for an embodiment of the present invention;

[0068] Figure 6 The Pareto front between the system reliability target and the carbon trading cost target provided in the embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention is further described below. The following examples are only used to more clearly illustrate the technical solution of the present invention, and are not intended to limit the protection scope of the present invention.

[0070] The present invention provides a multi-objective unit maintenance scheduling optimization method, comprising:

[0071] Construct a unit maintenance scheduling model with the goals of optimizing power system reliability, minimizing total system cost, and minimizing regional carbon emission cost;

[0072] Solve the constructed unit maintenance scheduling model and obtain the Pareto optimal solution set;

[0073] Select the optimal scheduling solution from the Pareto solution set.

[0074] As a preferred implementation, the total cost minimization target of the power system is defined in formula (1); the system reliability optimization target is defined in formula (2) and formula (3); the regional carbon emission minimization target is defined in formula (4),

[0075]

[0076] Minimize:F2=std(I(t,s)) t=1,2,3,...,N t ,s=1,2,3,...,N s (2)

[0077]

[0078]

[0079] Among them, F1 represents the total cost of the power system, F2 is the standard deviation of the power system reliability index, F3 is the regional power system carbon trading cost, I(t,s) is the system reliability index, N t Indicates the number of time periods included in a maintenance scheduling cycle, N s represents the number of sub-periods contained in a period, T(t,s) represents the number of hours in a sub-period, G i Indicates the number of units assigned to the i-th generator, N i represents the number of generators, is the output of the jth unit of the i-th generator in the s-th sub-period of the t-th period,

[0080] C 0ij , C 1ij , C 2ij represents the power generation cost coefficient of the j-th unit of the i-th power producer, represents the startup cost of the jth unit of the i-th generator, y ij(t,s) represents the startup status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the maintenance cost of the jth unit of the i-th generator, and represents the maximum and minimum output of the jth unit of the i-th generator, M ij (t) represents the maintenance status of the jth unit of the i-th generator in the t-th period, P D (t,s) represents the power demand of the sth sub-period in the tth period, Ct 0ij , Ct 1ij , Ct 2ij represents the carbon emission coefficient of the j-th unit of the ith power producer, λ represents the carbon trading price (assuming that the buying and selling prices of carbon emission rights are the same), and η represents the unit electricity emission factor, which is obtained by taking the weighted average of the marginal emission factor of electricity and the marginal emission factor of capacity in the system area.

[0081] In the present invention, carbon trading is taken as a separate optimization goal, and the goal of minimizing carbon emissions is converted into the goal of minimizing carbon trading costs. The carbon trading system encourages enterprises to reduce emissions in excess, sell remaining quotas, and obtain economic returns as an economic incentive for positive environmental externalities; conversely, enterprises with excess emissions need to purchase quotas or accept fines, and their expenses are essentially the cost paid for their negative externalities. There are two main initial allocation methods for carbon emission rights: free allocation and paid allocation. For the existing power industry, free allocation is easier to implement and accept. The present invention adopts a free initial carbon emission rights allocation method based on power generation, which links the allocation of carbon emission rights with the system power generation.

[0082] As a preferred implementation, the unit maintenance scheduling model also includes multiple constraints: minimum reserve constraint, maximum number of units under maintenance at the same time constraint, unit maximum output and minimum output constraints, maintenance and working state constraints and power balance constraints. Formula (5) ensures that the reserve of each sub-period of each time period is higher than the system minimum reserve, formula (6) ensures that the number of units under maintenance at the same time in the same unit does not exceed the upper limit, formula (7) indicates that the output power of each working unit should be between its maximum output and minimum output, formula (8) means that each unit will not be in maintenance and working state at the same time, and formula (9) indicates that the output power of each sub-period of each time period is equal to the demand power.

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] Among them, R Min (t,s) represents the minimum net reserve in the sth sub-period of the tth period, K i (t) represents the maximum number of units that the i-th generator can overhaul simultaneously in the t-th period, v ij (t,s) represents the working status of the jth unit of the i-th generator in the s-th sub-period of the t-th period.

[0089] As a preferred implementation, the variables used in the unit maintenance scheduling model are encoded, including: unit maintenance variables, unit working state variables and unit startup state variables as follows:

[0090]

[0091]

[0092]

[0093] Among them, x ij It represents the period when the j-th unit of the i-th power producer starts maintenance, and ∨ means or.

[0094] As a preferred implementation, a multi-objective particle swarm algorithm is used to solve the unit maintenance scheduling model. In the multi-objective particle swarm algorithm, the unit output variable and maintenance variable of each particle determine the position, and the particle speed determines the direction and distance of particle movement. Through the Pareto dominance relationship between the target vectors of each particle before and after, the non-dominated solution is selected as the individual optimal solution of the particle. A non-inferior solution can be selected based on the objective function value of the particle and the degree of constraint violation, and the adaptive grid method is used to select the global optimal solution of the multi-objective particle swarm from the non-inferior solution set. The specific process of the multi-objective particle swarm algorithm used in the present invention is as follows: Figure 2 The solution process of multi-objective particle swarm algorithm is as follows:

[0095] (1) First, initialize the particle swarm. Randomly initialize the position of the particle swarm between the upper and lower limits of the unit output and the upper and lower limits of the maintenance variable. The initial velocity of the particles is zero.

[0096] (2) Substitute the position of each particle into the objective function and constraints of the model to calculate the objective function value and constraint violation degree of the particle;

[0097] (3) Based on the objective function value and constraint violation degree of the particle and the constraint-Pareto dominance principle, the Pareto solution is obtained from the particle swarm by comparison, and the Pareto solution is stored in the Pareto solution set;

[0098] (4) Calculate and select the global optimal solution in the contemporary particle swarm;

[0099] (5) Calculate the congestion degree of the Pareto solution set and update the Pareto solution set.

[0100] (6) Calculate the individual optimal solution for each particle;

[0101] (7) Update the particle velocity, and the update formula is:

[0102] VEL'=W×VEL+C1×rand(1,nVAR)·(PBEST-POS)+C2×rand(1,VAR)·(GBEST-POS) where VEL' represents the updated velocity, VEL represents the original velocity, W is the inertia factor, C1 is the local velocity factor, C2 is the global velocity factor, nVAR is the dimension of the particle's position, rand(1,nVAR) represents a random number of nVAR dimension from 0 to 1, PBEST is the individual optimal solution of the particle, GBEST is the global optimal solution of the particle swarm, and POS represents the particle's position.

[0103] Update the position of the particle, the update formula is:

[0104] POS'=VEL'+POS, where POS' is the updated position. The number of iterations of the particle swarm increases once;

[0105] (8) Determine whether the number of iterations of the particle swarm has reached the maximum. If not, return to step (2) and continue the cycle. If the number of iterations has reached the maximum, output the Pareto solution set.

[0106] As a preferred implementation, the multi-objective particle swarm algorithm solution also includes: initializing basic data for unit maintenance scheduling, including unit power generation cost parameters, unit carbon emission parameters and daily electricity load data for unit maintenance.

[0107] As a preferred implementation, based on the obtained Pareto solution set, an improved sorting method approaching the ideal value is used to select the final scheduling solution therefrom.

[0108] In this embodiment, the improved approximate ideal solution ranking method in "Evaluation of the provincial competitiveness of the Chinese high-tech industry using an improved TOPSIS method" is used, where the weight solution formula is: Where N is the number of objective functions, and in the present invention, N=3. The traditional approach to the ideal solution sorting method uses the Euclidean distance to calculate the distance between each point and the positive ideal point and the negative ideal point. However, the Euclidean distance treats the differences between different attributes (i.e., each objective function) as equal, which sometimes cannot meet the actual requirements. The Mahalanobis distance is not affected by the dimension, and the Mahalanobis distance between two points is independent of the measurement unit of the original data, which can eliminate the interference of the correlation between variables. Therefore, improvements are made on this point. The specific improved approach to the ideal solution sorting method process is as follows: Figure 3 The specific steps of the approach to ideal solution sorting method are as follows:

[0109] (1) Calculate the normalized matrix: v ij =ω i (f i + -f ij ) / (f i + -f i - )i=1,2,...,N j=1,2,...,J,

[0110] Among them, f ij is the i-th objective function value of the j-th Pareto solution, N is the number of objective functions, J is the number of Pareto solutions, ω i is the weight of the objective function:

[0111] (2) Calculate the positive ideal solution and the negative ideal solution:

[0112] in, is a positive ideal solution, is a negative ideal solution;

[0113] (3) Calculate the Mahalanobis distance of each Pareto solution to the positive ideal solution and the negative ideal solution:

[0114]

[0115]

[0116] Among them, ∑ is the covariance matrix;

[0117] (4) Calculate the optimal distance ratio of each Pareto solution:

[0118]

[0119] (5) Select the optimal distance ratio R j The largest Pareto solution is the optimal solution, which is also the final scheduling solution.

[0120] As a preferred implementation, the Pareto frontier curves between the three objectives can be obtained based on the obtained Pareto optimal solution, the correlation between the three objectives can be analyzed, and the objectives with positive correlation can be found. Some objectives can be removed in subsequent maintenance work to simplify the maintenance model.

[0121] Example

[0122] Assume that the power system has 32 units and 3 generators, that is, G = 32, N i =3, and the maintenance scheduling is performed within 52 weeks, the period is set to one week, and the sub-period is set to one day, that is, N t =52,N s =7, T(t,s)=24. The population size and maximum number of iterations of the multi-objective particle swarm algorithm are set to 100 and 400 respectively. The continuous maintenance duration of each unit is set to two weeks. The unit parameters used in the system are taken from the literature "Research on Low-carbon Power Generation Dispatch Mode and Its Electricity Price Mechanism", see Table 1 and Table 2. The load demand in the system is taken from the literature "IEEE reliability test system" and the Spanish Electricity Energy Market (http: / / www.omel.es / / EN / Resultados / Resultados_index.htm), see Figure 1 .

[0123] According to the data in the document "Research on Low-carbon Power Dispatching Technology of Regional Power Grid Considering Carbon Trading", the carbon trading price λ is set to 3$ / ton, the regional electricity marginal emission factor in this model is set to 1.1171, and the capacity marginal emission factor is set to 0.4425. The weighted average of the two weights is 50% each, and the grid baseline emission factor η is 0.7798.

[0124] Table 1

[0125]

[0126]

[0127] Table 2

[0128]

[0129]

[0130] Figure 4 , Figure 5 and Figure 6 They represent the Pareto frontiers between multiple objectives respectively, and the solid points in the figure represent the final solutions in the Pareto solution set. Figure 4This is a comparison chart of the Pareto solution set between the minimum standard deviation target of the system reliability index and the minimum total system cost target. It can be seen from the figure that the larger the total system cost, the smaller the system reliability index. Conversely, as the total system cost decreases, the system reliability standard deviation index increases. Figure 6 This is a comparison chart of the Pareto solution set between the minimum target of system reliability index and the minimum target of carbon trading cost. It can be seen from the figure that the larger the carbon trading cost, the smaller the standard deviation of system reliability. Conversely, as the carbon trading cost decreases, the standard deviation of system reliability increases. Figure 5 This is a Pareto solution set comparison chart of the minimum system total cost target and the minimum carbon trading cost target. It can be seen from the figure that the minimum carbon trading cost target is positively correlated with the minimum total power generation cost target of the system. The power generation cost of most units in the system is positively correlated with carbon emissions. Units with high power generation costs also have high carbon emissions. Therefore, the minimum carbon trading cost target in the system is positively correlated with the minimum total power generation cost target. This is because carbon emissions will be minimized while power generation costs are minimized. Based on this, in subsequent maintenance work, such as the maintenance work of the next year, a maintenance model containing only the two objective functions F1 and F2 can be established, thereby simplifying the unit maintenance optimization scheduling model and improving work efficiency.

[0131] Another embodiment of the present invention further provides a multi-objective unit maintenance scheduling optimization device, comprising:

[0132] Model module, used to build a unit maintenance scheduling model with the goals of optimizing power system reliability, minimizing total system cost and minimizing regional carbon emission cost;

[0133] A solution module is used to solve the constructed unit maintenance scheduling model and obtain the Pareto optimal solution set;

[0134] as well as,

[0135] The output module is used to select the optimal scheduling solution from the Pareto solution set, including the output of the unit in each period and the state; the state includes the maintenance state, the startup state and the working state.

[0136] In this embodiment, the model module is specifically used to:

[0137] The unit maintenance scheduling model is constructed as follows:

[0138]

[0139] Minimize:F2=std(I(t,s)) t=1,2,3,...,N t ,s=1,2,3,...,N s ;

[0140]

[0141]

[0142] Among them, F1 represents the total cost of the power system, F2 is the standard deviation of the power system reliability index, F3 is the carbon trading cost of the power system, I(t,s) is the power system reliability index, N t Indicates the number of time periods included in a maintenance scheduling cycle, N s represents the number of sub-periods contained in a period, T(t,s) represents the number of hours in a sub-period, G i represents the number of units assigned to the i-th generator, N i represents the number of generators, is the output of the jth unit of the i-th generator in the s-th sub-period of the t-th period, C 0ij , C 1ij , C 2ij represents the power generation cost coefficient of the j-th unit of the i-th power producer, represents the startup cost of the jth unit of the i-th generator, y ij (t,s) represents the startup status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the maintenance cost of the jth unit of the i-th generator, represents the maximum output of the jth unit of the i-th generator, M ij (t) represents the maintenance status of the jth unit of the i-th generator in the t-th period, P D (t,s) represents the power demand of the sth sub-period in the tth period, Ct 0ij , Ct 1ij , Ct 2ij represents the carbon emission coefficient of the j-th unit of the i-th power producer, λ represents the carbon trading price, and η represents the emission factor per unit of electricity;

[0143] The unit maintenance scheduling model needs to meet the following constraints:

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] Among them, R Min (t,s) represents the minimum net reserve in the sth sub-period of the tth period, K i(t) represents the maximum number of units that the i-th generator can overhaul simultaneously in the t-th period, v ij (t,s) represents the working status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the minimum output of the jth unit of the i-th generator;

[0150] The status codes of the unit are as follows:

[0151]

[0152]

[0153]

[0154] Among them, x ij represents the period when the j-th unit of the ith power producer starts maintenance, and ∨ represents or.

[0155] In this embodiment, the solution module is specifically used to:

[0156] A multi-objective particle swarm algorithm is used to solve the unit maintenance scheduling model;

[0157] Taking the unit output and maintenance status as the particle position and the objective function of the unit maintenance scheduling model as the fitness, the Pareto solution set is obtained according to the constraint-Pareto dominance principle, and the Pareto optimal solution set is selected from the Pareto solution set using the adaptive grid method.

[0158] In this embodiment, the output module is specifically used to:

[0159] The improved approximate ideal solution sorting method is used to select the optimal dispatching solution from the Pareto solution set, including the output and status of the unit in each time period.

[0160] It is worth pointing out that the device embodiment corresponds to the above-mentioned method embodiment, and the implementation methods of the above-mentioned method embodiments are all applicable to the device embodiment and can achieve the same or similar technical effects, so they will not be repeated here.

[0161] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0162] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-objective unit maintenance scheduling optimization method, characterized in that: include: A unit maintenance scheduling model with the goals of optimizing power system reliability, minimizing total system cost, and minimizing regional carbon emission cost is constructed as follows: Minimize:F2=std(I(t,s))t=1,2,3,...,N t ,s=1,2,3,...,N s ; Among them, F1 represents the total cost of the power system, F2 is the standard deviation of the power system reliability index, F3 is the carbon trading cost of the power system, I(t,s) is the power system reliability index, N t Indicates the number of time periods included in a maintenance scheduling cycle, N s represents the number of sub-periods contained in a period, T(t,s) represents the number of hours in a sub-period, G i represents the number of units of the i-th generator, N i represents the number of generators, is the output of the jth unit of the i-th generator in the s-th sub-period of the t-th period, C 0ij , C 1ij , C 2ij represents the power generation cost coefficient of the j-th unit of the i-th power producer, represents the startup cost of the jth unit of the i-th generator, y ij (t,s) represents the startup status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the maintenance cost of the jth unit of the i-th generator, represents the maximum output of the jth unit of the i-th generator, M ij (t) represents the maintenance status of the jth unit of the i-th generator in the t-th period, P D (t,s) represents the power demand of the sth sub-period in the tth period, Ct 0ij , Ct 1ij , Ct 2ij represents the carbon emission coefficient of the j-th unit of the i-th power producer, λ represents the carbon trading price, and η represents the emission factor per unit of electricity; The unit maintenance scheduling model needs to meet the following constraints: Among them, R Min (t,s) represents the minimum net reserve in the sth sub-period of the tth period, K i (t) represents the maximum number of units that the i-th generator can overhaul simultaneously in the t-th period, v ij (t,s) represents the working status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the minimum output of the jth unit of the i-th generator; Solve the constructed unit maintenance scheduling model and obtain the Pareto optimal solution set; The optimal dispatching solution is selected from the Pareto optimal solution set, including the output of the unit in each time period and the state; the state includes the maintenance state, the startup state and the working state.

2. A multi-objective unit maintenance scheduling optimization method according to claim 1, characterized in that: The status of the unit is coded as follows: Among them, x ij represents the period when the j-th unit of the ith power producer starts maintenance, and ∨ represents or.

3. The multi-objective unit maintenance scheduling optimization method according to claim 1 is characterized in that: The unit electricity emission factor is obtained by weighted average of the marginal electricity emission factor and capacity marginal emission factor of the system area.

4. A multi-objective unit maintenance scheduling optimization method according to claim 2, characterized in that: A multi-objective particle swarm algorithm is used to solve the unit maintenance scheduling model; In the solution process, the unit output and maintenance status are used as particle positions, the objective function of the unit maintenance scheduling model is used as fitness, and the Pareto solution set is obtained according to the constraint-Pareto dominance principle. The Pareto optimal solution set is selected from the Pareto solution set using the adaptive grid method.

5. A multi-objective unit maintenance scheduling optimization method according to claim 4, characterized in that: The improved approximate ideal solution sorting method is used to select the optimal dispatching solution from the Pareto solution set, including the output and status of the unit in each time period.

6. A multi-objective unit maintenance scheduling optimization device, characterized in that: The device is used to implement the multi-objective unit maintenance scheduling optimization method according to any one of claims 1 to 5, comprising: The model module is used to construct a unit maintenance scheduling model with the goals of optimizing power system reliability, minimizing total system cost, and minimizing regional carbon emission cost, as follows: Minimize:F2=std(I(t,s))t=1,2,3,...,N t ,s=1,2,3,...,N s ; Among them, F1 represents the total cost of the power system, F2 is the standard deviation of the total reliability index of the power system, F3 is the total carbon trading cost of the power system, I(t,s) is the total reliability index of the power system, N t Indicates the number of time periods included in a maintenance scheduling cycle, N s represents the number of sub-periods contained in a period, T(t,s) represents the number of hours in a sub-period, G i represents the number of units of the i-th generator, N i represents the number of generators, is the output of the jth unit of the i-th generator in the s-th sub-period of the t-th period, C 0ij , C 1ij , C 2ij represents the power generation cost coefficient of the j-th unit of the i-th power producer, represents the startup cost of the jth unit of the i-th generator, y ij (t,s) represents the startup status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the maintenance cost of the j-th unit of the i-th generator, represents the maximum output of the jth unit of the i-th generator, M ij (t) represents the maintenance status of the jth unit of the i-th generator in the t-th period, P D (t,s) represents the power demand of the sth sub-period in the tth period, Ct 0ij , Ct 1ij , Ct 2ij represents the carbon emission coefficient of the j-th unit of the i-th power producer, λ represents the carbon trading price, and η represents the emission factor per unit of electricity; The unit maintenance scheduling model needs to meet the following constraints: Among them, R Min (t,s) represents the minimum net reserve in the sth sub-period of the tth period, K i (t) represents the maximum number of units that the i-th generator can overhaul simultaneously in the t-th period, v ij (t,s) represents the working status of the jth unit of the i-th generator in the s-th sub-period of the t-th period, represents the minimum output of the jth unit of the i-th generator; The status codes of the unit are as follows: Among them, x ij represents the period when the jth unit of the i-th power producer begins maintenance, ∨ represents or; A solution module is used to solve the constructed unit maintenance scheduling model and obtain the Pareto optimal solution set; as well as, The output module is used to select the optimal scheduling solution from the Pareto optimal solution set, including the output of the unit in each period and the state; the state includes the maintenance state, the startup state and the working state.

7. A multi-objective unit maintenance scheduling optimization device according to claim 6, characterized in that: The solution module is specifically used for: A multi-objective particle swarm algorithm is used to solve the unit maintenance scheduling model; Taking the unit output and maintenance status as the particle position and the objective function of the unit maintenance scheduling model as the fitness, the Pareto solution set is obtained according to the constraint-Pareto dominance principle, and the Pareto optimal solution set is selected from the Pareto solution set using the adaptive grid method.

8. The multi-objective unit maintenance scheduling optimization device according to claim 7 is characterized in that: The output module is specifically used for: The improved approximate ideal solution sorting method is used to select the optimal dispatching solution from the Pareto solution set, including the output and status of the unit in each time period.

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