Power system scheduling and maintenance decision optimization method and device considering carbon emission constraint, terminal equipment and storage medium

Abstract

The invention discloses an electric power system scheduling and maintenance decision optimization method and device considering a carbon emission constraint, terminal equipment and a storage medium, and belongs to the technical field of electric power system decision optimization. Constructing a power system maintenance scheduling optimization model and corresponding constraint conditions by taking power generation cost minimization, carbon emission minimization, power system reliability maximization and power transmission line safety margin maximization as targets; and then solving is carried out to obtain the generating power of the generator set, the running time of the generator set, the shutdown time of the generator set, the maintenance state of the generator set, the starting state of the generator set and the shutdown state of the generator set, and then the scheduling and maintenance decision of the electric power system is obtained. By implementing the scheduling decision optimization method and device, the problem that balance between environmental protection and economic benefits is difficult to obtain when scheduling decision optimization is carried out in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system decision optimization, and in particular, to a method, device, terminal device and storage medium for optimizing power system scheduling and maintenance decisions considering carbon emission constraints. Background Art

[0002] With the continuous expansion of the scale of modern power systems, the complexity of power scheduling and equipment maintenance has increased significantly. Traditional power system scheduling technologies are mostly based on optimization models, and the main goal is to ensure the stability and security of power supply by minimizing operating costs, maximizing power supply reliability or improving equipment utilization.

[0003] In the prior art, scheduling optimization problems are solved through mathematical models such as linear programming and mixed integer programming. Scheduling schemes consider various factors such as the on / off states of generating units, fluctuations in load demand, and electricity market prices. However, since the optimization goal of carbon emissions is not incorporated into the scheduling decision-making process, there is a problem that it is difficult to balance environmental protection and economic benefits in the scheduling results. Summary of the Invention

[0004] The present invention provides a method, device, terminal device and storage medium for optimizing power system scheduling and maintenance decisions considering carbon emission constraints, which can solve the problem in the prior art that when optimizing scheduling decisions, the optimization goal of carbon emissions is not considered, resulting in difficulty in balancing environmental protection and economic benefits in the scheduling results.

[0005] An embodiment of the present invention provides a method for optimizing power system scheduling and maintenance decisions considering carbon emission constraints, including:

[0006] Obtaining power data of the power system within a preset time period; wherein, the above power data includes: carbon emission quotas of generating units, start-up costs of generating units, power generation costs of generating units, maximum power generation capacities of generating units, load demands of the power system, minimum net reserve capacities of the power system, maximum line power flows, minimum power generation capacities of generating units, minimum running times of generating units, minimum shutdown times of generating units, minimum line power flows, line transmission powers, and the number of generating units that can be maintained simultaneously;

[0007] Based on the above power data, an optimal model for power system maintenance scheduling and its corresponding constraint conditions are constructed with the objectives of minimizing power generation cost, minimizing carbon emissions, maximizing power system reliability, and maximizing the safety margin of transmission lines. Among them, the above constraint conditions include: carbon emission constraint, power balance constraint, upper and lower limits of generator output constraint, minimum operation time and downtime constraint, branch power flow safety constraint, power system net reserve capacity constraint, simultaneously maintainable generator set constraint, generator output constraint, and operation and maintenance status constraint.

[0008] Under the above constraint conditions, the optimal model for power system maintenance scheduling is solved to obtain the power generation power, operation time, shutdown time, maintenance status, startup status, and shutdown status of the generator sets when the power generation cost is minimized, carbon emissions are minimized, power system reliability is maximized, and the safety margin of transmission lines is maximized. Then, the scheduling and maintenance decisions of the power system are obtained, and the power system is scheduled and maintained according to the above scheduling and maintenance decisions.

[0009] Furthermore, the objective function of the above optimal model for power system maintenance scheduling is:

[0010]

[0011] In the formula, G represents the set of generator sets, N t represents the total number of preset time periods, t represents the t-th time period, Z i represents the power generation cost of the i-th generator set, P Gi (t) represents the power generation power of the i-th generator set in the t-th time period, v i (t) represents the shutdown status variable of the i-th generator set in the t-th time period, represents the startup cost of the i-th generator set, y i (t) represents the startup status variable of the i-th generator set in the t-th time period, C Oi represents the preset first cost coefficient of the i-th generator set, C li represents the preset second cost coefficient of the i-th generator set, C 2i represents the preset third cost coefficient of the i-th generator set, η i represents the carbon emission coefficient of the i-th generator set, Rm(t) represents the net reserve capacity of the power system in the t-th time period, represents the maximum power generation power of the i-th generator set, M i (t) represents the maintenance status of the i-th generator set in the t-th time period, P D (t, s) represents the load demand of the power system in the s-th sub-time period within the t-th time period, Rm Min(t, s) represents the minimum net reserve capacity of the power system during the s-th sub-period within the t-th period, S l (t) represents the transmission line safety margin during the t-th period, P l Max represents the maximum power flow of the line, P l (t) represents the actual line power flow during the t-th period.

[0012] Furthermore, the above carbon emission constraint is:

[0013]

[0014] In the formula, E q represents the carbon emission quota of the above generator set;

[0015] The above power balance constraint is:

[0016]

[0017] In the formula, P D (t) represents the load demand of the power system during the t-th period;

[0018] The above upper and lower limit constraints of the generator set output are:

[0019]

[0020] In the formula, represents the minimum power generation of the i-th generator set;

[0021] The above minimum operating time and downtime constraints are:

[0022] (t i,on (t) - T i,U )(v i (t) - v i (t - 1)) ≥ 0

[0023] (t i,off (t) - T i,D )(v i (t - 1) - v i (t)) ≥ 0

[0024] In the formula, t i,on (t) represents the operating time of the i-th generator set during the t-th period, T i,U represents the minimum operating time of the i-th generating unit, v i (t - 1) represents the shutdown status variable of the i-th generator set during the previous period of the t-th period, t i,off (t) represents the shutdown time of the i-th generator set during the t-th period, T i,DDenote the minimum shutdown time of the $i$-th power generation unit;

[0025] The above branch power flow security constraint is:

[0026]

[0027] In the formula, $P$ l Min Denote the minimum power flow of the line, $G$ l-i Denote the power transfer distribution factor of the node where the $i$-th generator set is located and its line $l$, $G$ l-j Denote the power transfer distribution factor of the node where the $i$-th generator set is located and its line $j$, $D$ j $(t)$ denotes the transmission power of line $j$ in the $t$-th time period; $KP$ represents all nodes in the power system;

[0028] The above power system net reserve capacity constraint is:

[0029]

[0030] The above constraint for simultaneously maintainable generator sets is:

[0031]

[0032] In the formula, $K(t)$ represents the maximum number of generator sets that can be maintained simultaneously in the $t$-th time period;

[0033] The above generator set output constraint is:

[0034]

[0035] In the formula, $v$ i $(t, s)$ represents the shutdown state variable of the $i$-th generator set in the $s$-th sub-time period within the $t$-th time period, Denote the minimum power generation of the $i$-th generator set in the $s$-th sub-time period within the $t$-th time period, $P$ Gi $(t, s)$ represents the power generation of the $i$-th generator set in the $s$-th sub-time period within the $t$-th time period, Denote the maximum power generation of the $i$-th generator set in the $s$-th sub-time period within the $t$-th time period;

[0036] The above operation and maintenance state constraint is:

[0037] $M$ i $(t)+v$ i $(t, s)\leq1$.

[0038] Further, under the above-mentioned constraints, the above-mentioned power system maintenance scheduling optimization model is solved to obtain the power generation power, operating time, shutdown time, maintenance status, start-up status, and shutdown status of the generator sets when the power generation cost is minimized, the carbon emissions are minimized, the reliability of the power system is maximized, and the safety margin of the transmission line is maximized, including:

[0039] According to the above-mentioned constraints, a corresponding value range is generated for each parameter to be optimized; wherein, the parameters to be optimized are: the power generation power of the generator set, the operating time of the generator set, the shutdown time of the generator set, the maintenance status of the generator set, the start-up status of the generator set, and the shutdown status of the generator set;

[0040] A number of particles are generated according to the above value range; initially, each particle corresponds to an initial particle position and an initial particle velocity; the particle position is used to represent the values of the above-mentioned parameters to be optimized;

[0041] The optimization solution operation is repeatedly executed until the current iteration number is not less than the preset iteration threshold, or the difference between the current fitness and the fitness of the previous moment is less than the preset fitness threshold, to obtain the values of the optimized parameters, and the values of the optimized parameters are used as the power generation power, operating time, shutdown time, maintenance status, start-up status, and shutdown status of the generator sets when the power generation cost is minimized, the carbon emissions are minimized, the reliability of the power system is maximized, and the safety margin of the transmission line is maximized;

[0042] Wherein, the above optimization solution operation includes:

[0043] Obtain the current particle positions and current particle velocities of all particles, and calculate the current fitness of each particle according to the current particle positions of all particles; wherein, the initial particle position is the above-mentioned initial particle position, and the initial particle velocity is the above-mentioned initial particle velocity;

[0044] For each particle, compare the values of the parameters to be optimized within the current particle position with the corresponding value range, and determine the current latest individual optimal particle position and the current latest global optimal particle position according to the comparison result;

[0045] If the difference between the current fitness and the fitness of the previous moment is less than the preset fitness threshold, and the current iteration number is less than the preset iteration threshold, for each particle, update the inertial weight and learning factor of the corresponding particle at the previous moment according to the current fitness to obtain the current inertial weight and the current learning factor;

[0046] Calculate the particle velocity at the next moment based on the current particle position, current particle velocity, current inertia weight, current learning factor, current latest individual optimal particle position, and current latest global optimal particle position; calculate the particle position at the next moment based on the particle velocity at the next moment and the current particle position.

[0047] Further, calculating the current fitness of each particle according to the current particle positions of all particles includes:

[0048] For each particle, calculate the total power generation of the current generator set according to the power generation of the generator set in the current particle position;

[0049] Calculate the current power balance deviation according to the difference between the load demand of the power system and the total power generation of the current generator set;

[0050] Determine the current actual line power flow of the power system at the current particle position according to the current particle position, and calculate the current line over-limit amount according to the current actual line power flow and the maximum line power flow;

[0051] Determine the number of generator sets in the current maintenance state according to the maintenance state of the generator sets in the current particle position, and obtain the current maintenance over-limit amount according to the difference between the number of generator sets in the current maintenance state and the number of generator sets that can be maintained simultaneously;

[0052] Calculate the value of the current penalty term according to the current power balance deviation, current line over-limit amount, and current maintenance over-limit amount;

[0053] Calculate the current fitness of each particle according to the values of the parameters to be optimized in the current particle position, the power generation cost of the above generator set, the start-up cost of the above generator set, the maximum power generation of the generator set, the load demand of the above power system, the maximum line power flow, the current actual line power flow, and the value of the current penalty term.

[0054] Further, for each particle, comparing the values of the parameters to be optimized in the current particle position with the corresponding value ranges, and determining the current latest individual optimal particle position and current latest global optimal particle position according to the comparison results includes:

[0055] For each particle, judge whether the values of the parameters to be optimized in the current particle position all meet the corresponding value ranges;

[0056] If in the current particle position of each particle, at least one value of any parameter to be optimized does not meet the corresponding value range, then do not update the individual optimal particle position and the global optimal particle position;

[0057] Otherwise, particles whose values of each parameter to be optimized within the current particle position all satisfy the corresponding value range are used as the current selected particles, and based on the current fitness of the current selected particles, the current individual best particle position and the current global best particle position are updated.

[0058] Further, updating the current individual best particle position and the current global best particle position based on the current fitness of the current selected particles includes:

[0059] For each particle, compare the current fitness with the individual best fitness of the current individual best particle position; wherein, each particle corresponds to a current individual best particle position, and each individual best particle position corresponds to an individual best fitness;

[0060] In the case where the current fitness is less than the current individual best fitness, take the current particle position as the updated individual best particle position, and take the current fitness as the updated individual best fitness;

[0061] Extract the current minimum fitness from the current fitnesses of all particles, and compare the current minimum fitness with the global best fitness of the current global best particle position;

[0062] In the case where the current minimum fitness is less than the current global best fitness, take the particle position corresponding to the current minimum fitness as the updated global best particle position, and take the current minimum fitness as the updated global best fitness.

[0063] Based on the above method item embodiments, the present invention correspondingly provides device item embodiments;

[0064] The present invention provides an optimization device for power system scheduling and maintenance decision considering carbon emission constraints, including:

[0065] A power data acquisition module, an optimization model construction module, and an optimization model solution module;

[0066] The above-mentioned power data acquisition module is used to acquire power data of the power system within a preset time period; wherein, the above-mentioned power data includes: carbon emission quotas of generating units, start-up costs of generating units, power generation costs of generating units, maximum power generation capacities of generating units, load demands of the power system, minimum net reserve capacities of the power system, maximum line power flows, minimum power generation capacities of generating units, minimum running times of generating units, minimum shutdown times of generating units, minimum line power flows, line transmission powers, and the number of generating units that can be overhauled simultaneously;

[0067] The above optimization model construction module is used to construct an optimization model for the maintenance scheduling of the power system and corresponding constraint conditions with the goals of minimizing the power generation cost, minimizing the carbon emissions, maximizing the reliability of the power system, and maximizing the safety margin of the transmission line, based on the above power data; wherein, the above constraint conditions include: carbon emission constraint, power balance constraint, upper and lower limits constraint of the generator output, minimum operation time and shutdown time constraint, branch power flow safety constraint, power system net reserve capacity constraint, simultaneously maintainable generator set constraint, generator set output constraint, and operation and maintenance status constraint;

[0068] The above optimization model solving module is used to solve the above optimization model for the maintenance scheduling of the power system under each of the above constraint conditions, to obtain the power generation power, operation time, shutdown time, maintenance status, startup status, and shutdown status of the generator sets when the power generation cost is minimized, the carbon emissions are minimized, the reliability of the power system is maximized, and the safety margin of the transmission line is maximized, and then to obtain the scheduling and maintenance decisions of the power system, and to schedule and maintain the power system according to the above scheduling and maintenance decisions.

[0069] Based on the above method item embodiment, the present invention correspondingly provides a terminal device item embodiment;

[0070] The present invention provides a terminal device, including a processor, a memory, and a computer program stored in the above memory and configured to be executed by the above processor. When the above processor executes the above computer program, it implements an optimization method for the scheduling and maintenance decisions of a power system considering carbon emission constraints according to any one of the embodiments of the present invention.

[0071] Based on the above method item embodiment, the present invention correspondingly provides a storage medium item embodiment;

[0072] The present invention provides a storage medium, including a processor, a memory, and a computer program stored in the above memory and configured to be executed by the above processor. When the above processor executes the above computer program, it implements an optimization method for the scheduling and maintenance decisions of a power system considering carbon emission constraints according to any one of the embodiments of the present invention.

[0073] The embodiments of the present invention have the following beneficial effects:

[0074] The present invention provides an optimization method, device, terminal device and storage medium for power system scheduling and maintenance decision-making considering carbon emission constraints. The above method includes: first, obtaining power data of the power system within a preset time period; wherein, the above power data includes: carbon emission quotas of generating units, start-up costs of generating units, power generation costs of generating units, maximum power generation capacities of generating units, load demands of the power system, minimum net reserve capacities of the power system, maximum line power flows, minimum power generation capacities of generating units, minimum running times of generating units, minimum shutdown times of generating units, minimum line power flows, line transmission powers, and the number of generating units that can be overhauled simultaneously; subsequently, according to the above power data, aiming at minimizing power generation costs, minimizing carbon emissions, maximizing power system reliability, and maximizing the safety margin of transmission lines, an optimization model for power system maintenance scheduling and corresponding constraint conditions are constructed; wherein, the above constraint conditions include: carbon emission constraints, power balance constraints, upper and lower limits of generating unit output constraints, minimum running time and shutdown time constraints, branch power flow safety constraints, power system net reserve capacity constraints, constraints on generating units that can be overhauled simultaneously, generating unit output constraints, and operation and maintenance state constraints; finally, under the above constraint conditions, the optimization model for power system maintenance scheduling is solved to obtain the power generation powers, running times, shutdown times, maintenance states, start-up states, and shutdown states of generating units when the power generation costs are minimized, the carbon emissions are minimized, the power system reliability is maximized, and the safety margin of transmission lines is maximized, and then the scheduling and maintenance decisions of the power system are obtained, and the power system is scheduled and maintained according to the above scheduling and maintenance decisions. Therefore, when constructing the optimization model for power system maintenance scheduling, the present invention adds the optimization goal of minimizing carbon emissions while aiming at minimizing power generation costs. Therefore, the final obtained scheduling and maintenance decision results can reduce the problem of being difficult to balance between environmental protection and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the present application, the drawings required for implementation will be briefly introduced below. Obviously, the drawings in the following description are only some implementations of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0076] Figure 1 It is a flowchart of an optimization method for power system scheduling and maintenance decision-making considering carbon emission constraints provided by an embodiment of the present invention.

[0077] Figure 2 It is a structural diagram of an optimization device for power system scheduling and maintenance decision-making considering carbon emission constraints provided by an embodiment of the present invention. Detailed implementation manners

[0078] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion.

[0080] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of the present application, "a plurality of" means two or more, unless otherwise specifically defined.

[0081] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0082] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally means that the associated objects before and after are in an "or" relationship.

[0083] In the description of the embodiments of the present application, the term "a plurality of" means two or more (including two). Similarly, "a plurality of groups" means two or more groups (including two groups), and "a plurality of pieces" means two or more pieces (including two pieces).

[0084] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "connection", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral one; it can also be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to specific situations.

[0085] See Figure 1 , to solve the problem in the prior art that when optimizing the scheduling decision, the optimization goal of carbon emissions is not taken into account, resulting in the scheduling result being difficult to balance environmental protection and economic benefits. An embodiment of the present invention provides an optimization method for power system scheduling and maintenance decision-making considering carbon emission constraints, including:

[0086] Step S101: Obtain the power data of the power system within a preset time period; wherein, the above power data includes: carbon emission quotas of generator sets, start-up costs of generator sets, power generation costs of generator sets, maximum power generation capacities of generator sets, load demands of the power system, minimum net reserve capacities of the power system, maximum line power flows, minimum power generation capacities of generator sets, minimum operation times of generator sets, minimum shutdown times of generator sets, minimum line power flows, line transmission powers, and the number of generator sets that can be overhauled simultaneously;

[0087] Specifically, the above carbon emission quotas of generator sets are used to represent the maximum allowable carbon emissions of generator sets within a certain time period.

[0088] Step S102: According to the above power data, with the goals of minimizing power generation costs, minimizing carbon emissions, maximizing the reliability of the power system, and maximizing the safety margin of transmission lines, construct an optimization model for power system maintenance scheduling and the corresponding constraint conditions; wherein, the above constraint conditions include: carbon emission constraints, power balance constraints, upper and lower limits of generator set output constraints, minimum operation time and shutdown time constraints, branch power flow safety constraints, power system net reserve capacity constraints, constraints on generator sets that can be overhauled simultaneously, generator set output constraints, and operation and maintenance status constraints;

[0089] Preferably, the constructed power system maintenance scheduling optimization model comprehensively considers power generation cost, carbon emissions, system reliability, and transmission line safety margin. It is a multi-objective optimization model. Compared with traditional single-objective optimization methods, this model can not only reduce power generation cost but also achieve coordinated optimization in carbon emission control, improving system reliability, and enhancing line security. This advantage ensures the comprehensiveness and efficiency of the power system in actual operation. At the same time, it balances and optimizes the operation stability and economy of the power system, maximizing system reliability on the premise of ensuring power supply safety and reducing power generation cost through optimizing the scheduling plan. By effectively integrating multiple objectives, it ensures the optimized operation of the power system under multiple constraints. Especially in carbon emission control, the model flexibly adapts to different load demands and generator operating states through dynamic constraints. This comprehensive balance can effectively avoid conflicts between maintenance plans and scheduling plans in practical applications and improve the overall operation efficiency of the power system.

[0090] Preferably, in the prior art, carbon emission management is mostly static constraints and difficult to adapt to system load changes. Therefore, the present invention innovatively introduces dynamic carbon emission constraints and combines with the carbon trading mechanism, allowing generators to adjust their carbon emissions under market conditions. This design not only improves the flexibility of the scheduling system but also maximizes economic benefits through carbon trading.

[0091] Preferably, the power system maintenance scheduling optimization model proposed by the present invention can dynamically adapt to fluctuations in power demand and carbon emissions of generators. By reasonably allocating and scheduling the power generation tasks of generators, it can effectively respond to different loads and environmental conditions. Therefore, compared with the limitations of static planning in the prior art, this system has stronger flexibility and adaptability, can better handle complex situations in actual operation, and comprehensively considers the impact of maintenance plans on power generation scheduling. By reasonably arranging maintenance time, it ensures the optimal balance between system supply and demand and operation efficiency.

[0092] In a preferred embodiment, the objective function of the above power system maintenance scheduling optimization model is:

[0093]

[0094] In the formula, G represents the set of generators, N t represents the total number of preset time periods, t represents the t-th time period, Z i represents the power generation cost of the i-th generator, P Gi (t) represents the power generation power of the i-th generator in the t-th time period, v i (t) represents the shutdown state variable of the i-th generator in the t-th time period, represents the start-up cost of the i-th generator, yi (t) represents the start-up status variable of the i-th generating unit in the t-th time period, C Oi represents the preset first cost coefficient of the i-th generating unit, C li represents the preset second cost coefficient of the i-th generating unit, C 2i represents the preset third cost coefficient of the i-th generating unit, η i represents the carbon emission coefficient of the i-th generating unit, and Rm(t) represents the net reserve capacity of the power system in the t-th time period. represents the maximum power generation of the i-th generating unit, M i (t) represents the maintenance status of the i-th generating unit in the t-th time period, P D (t, s) represents the load demand of the power system in the s-th sub-time period within the t-th time period, Rm Min (t, s) represents the minimum net reserve capacity of the power system in the s-th sub-time period within the t-th time period, S l (t) represents the safety margin of the transmission line within the t-th time period, P l Max represents the maximum line power flow, P l (t) represents the actual line power flow in the t-th time period.

[0095] Specifically, by optimizing the generating unit combination and operation time, the generation cost of the power system can be reduced. Therefore, the objective function corresponding to minimizing the generation cost is:

[0096]

[0097] Among them, v i (t) ∈ {0, 1}, when it is 0, it represents the shutdown state, and when it is 1, it represents the non-shutdown state, y i (t) ∈ {0, 1}, when it is 0, it represents the non-start state, and when it is 1, it represents the start state.

[0098] Specifically, the carbon trading mechanism allows generating units to adjust their carbon emission allowances through market transactions. When the actual carbon emissions of a generating unit are lower than its quota, the remaining carbon emission rights can be sold to other units; if it exceeds the quota, additional carbon emission rights need to be purchased through the carbon market. Through this mechanism, the system can not only maximize economic benefits but also promote carbon emission reduction. Therefore, in order to make the optimized power system dispatching and maintenance decision results reduce the overall carbon emissions of the power system, the objective function of minimizing carbon emissions is constructed through the following formula:

[0099]

[0100] Specifically, maximizing the reliability of the power system aims to enhance the reliability of the power system under load fluctuations and faults. The net reserve capacity index is used to define the reliability of the power system. The net reserve capacity of the power system is the reserve power generation capacity that can be called upon at any time (including spinning reserve and non-spinning reserve), which needs to meet the minimum requirements to cope with sudden load fluctuations or unit failures. Therefore, the objective function for maximizing the reliability of the power system is constructed through the following formula:

[0101]

[0102] where M i (t) ∈ {0, 1}. When it is 0, it indicates that the maintenance status of the generator set is not under maintenance; when it is 1, it indicates that the maintenance status of the generator set is under maintenance.

[0103] Specifically, maximizing the safety margin of the transmission line aims to increase the line safety margin through reasonable scheduling to prevent overload. The safety margin of the transmission line is defined as the percentage margin between the actual line power flow and the maximum allowable power flow, which is used to reflect the line overload risk. Therefore, the objective function for maximizing the safety margin of the transmission line is constructed through the following formula:

[0104]

[0105] Preferably, the construction of the above-mentioned power system maintenance scheduling optimization model can solve the complex multi-objective optimization problem in power system scheduling.

[0106] In this preferred embodiment, a power system maintenance scheduling optimization model is constructed with the objectives of minimizing the generation cost, minimizing the carbon emissions, maximizing the reliability of the power system, and maximizing the safety margin of the transmission line.

[0107] In another preferred embodiment, the above carbon emission constraint is:

[0108]

[0109] where E q represents the carbon emission quota of the above generator set;

[0110] The above power balance constraint is:

[0111]

[0112] where P D (t) represents the load demand of the power system at the t-th time period;

[0113] The above upper and lower limits constraint of the generator set output is:

[0114]

[0115] In the formula, represents the minimum power generation of the i-th generating unit;

[0116] The above minimum operation time and shutdown time constraints are:

[0117] (t i,on (t) - T i,U )(v i (t) - v i (t - 1)) ≥ 0

[0118] (t i,off (t) - T i,D )(v i (t - 1) - v i (t)) ≥ 0

[0119] In the formula, t i,on (t) represents the operation time of the i-th generating unit at the t-th time period, and T i,U represents the minimum operation time of the i-th generating unit, and v i (t - 1) represents the shutdown state variable of the i-th generating unit at the previous time period of the t-th time period, and t i,off (t) represents the shutdown time of the i-th generating unit at the t-th time period, and T i,D represents the minimum shutdown time of the i-th generating unit;

[0120] The above branch power flow safety constraints are:

[0121]

[0122] In the formula, P l Min represents the minimum power flow of the line, G l-i represents the power transfer distribution factor of the node where the i-th generating unit is located and its line l, and G l-j represents the power transfer distribution factor of the node where the i-th generating unit is located and its line j, and D j (t) represents the transmission power of line j at the t-th time period, and KP represents all nodes in the power system;

[0123] The above power system net reserve capacity constraints are:

[0124]

[0125] The above constraints for units that can be overhauled simultaneously are:

[0126]

[0127] Wherein, K(t) represents the maximum number of generator sets that can be overhauled simultaneously in the t-th time period;

[0128] The above unit output constraint is:

[0129]

[0130] Wherein, v i (t, s) represents the shutdown state variable of the i-th generator set in the s-th sub-time period within the t-th time period, represents the minimum power generation of the i-th generator set in the s-th sub-time period within the t-th time period, P Gi (t, s) represents the power generation of the i-th generator set in the s-th sub-time period within the t-th time period, represents the maximum power generation of the i-th generator set in the s-th sub-time period within the t-th time period;

[0131] The above operation and maintenance state constraints are:

[0132] M i (t) + v i (t, s) ≤ 1.

[0133] Specifically, the unit maintenance plan is crucial for the safe and economic operation of the power system. Its core decision variable is the start time of the maintenance, and it is restricted by multiple problems. The goal of the unit maintenance plan is to complete the maintenance work as early as possible and prevent fault problems by arranging reasonable maintenance tasks in advance.

[0134] Preferably, after constructing the above various constraint conditions, it can be ensured that the obtained power system scheduling and maintenance decisions meet the operation requirements of the power system.

[0135] In this preferred embodiment, according to the obtained power data, the power balance constraint, the upper and lower limits of the generator set output, the minimum operation time and shutdown time constraints, the branch power flow safety constraint, the power system net reserve capacity constraint, the constraint of the generator sets that can be overhauled simultaneously, the unit output constraint, and the operation and maintenance state constraint corresponding to the power system maintenance scheduling optimization model are constructed.

[0136] Step S103: Under the above various constraint conditions, solve the above power system maintenance scheduling optimization model to obtain the power generation of the generator sets, the operation time of the generator sets, the shutdown time of the generator sets, the maintenance state of the generator sets, the startup state of the generator sets, and the shutdown state of the generator sets when the power generation cost is the smallest, the carbon emission is the smallest, the reliability of the power system is the largest, and the safety margin of the transmission line is the largest, and then obtain the scheduling and maintenance decisions of the power system, and schedule and maintain the power system according to the above scheduling and maintenance decisions.

[0137] Specifically, the generated power, operating time, shutdown time, maintenance status, startup status, and shutdown status of the generator set obtained from the final solution are used as the dispatching and maintenance decisions for the power system, and based on this, the generator sets within the power system are dispatched and maintained.

[0138] In a preferred embodiment, solving the power system maintenance scheduling optimization model under the above-mentioned constraint conditions to obtain the generated power, operating time, shutdown time, maintenance status, startup status, and shutdown status of the generator set when the power generation cost is minimized, the carbon emissions are minimized, the reliability of the power system is maximized, and the safety margin of the transmission line is maximized includes:

[0139] According to the above-mentioned constraint conditions, generate corresponding value ranges for each parameter to be optimized; where the parameters to be optimized are: the generated power of the generator set, the operating time of the generator set, the shutdown time of the generator set, the maintenance status of the generator set, the startup status of the generator set, and the shutdown status of the generator set;

[0140] Specifically, the particle swarm algorithm is used here. When building the model of the particle swarm algorithm in advance, the above-mentioned constraint conditions have been modeled and encoded in the algorithm, so that during the process of solving using the particle swarm algorithm, the particle positions obtained will be constrained by the above-mentioned constraint conditions.

[0141] Generate a number of particles according to the above value range; initially, each particle corresponds to an initial particle position and an initial particle velocity; the particle position is used to represent the values of the above-mentioned parameters to be optimized;

[0142] Specifically, each particle represents a specific solution for the dispatching and maintenance decision, and the particle velocity is used to represent the adjustment rate of this solution. As the iteration progresses, the particle position will be continuously updated to find the global optimal solution.

[0143] Specifically, since it is necessary to solve the values of the generated power, operating time, shutdown time, maintenance status, startup status, and shutdown status of the above-mentioned generator set, the position vector of the particle contains the following variables: v i (t) ∈ {0, 1}, when it is 0, it represents the shutdown state, and when it is 1, it represents the non-shutdown state; y i (t) ∈ {0, 1}, when it is 0, it represents the non-startup state, and when it is 1, it represents the startup state; M i (t) ∈ {0, 1}, when it is 0, it means that the maintenance status of the generator set is not maintained, and when it is 1, it means that the maintenance status of the generator set is under maintenance.

[0144] Schematically, assume that a power system has N generator sets, and each generator set needs to optimize the generator set power generation power, generator set operation time, generator set shutdown time, generator set maintenance status, generator set startup status, and generator set shutdown status for 24 hours. Then the dimension of the particle position vector is:

[0145]

[0146] In the formula, Dimensions represents the dimension of the particle position vector. The unit start-stop status represents the dimensions corresponding to the above generator set startup status and generator set shutdown status. The unit output represents the dimension corresponding to the above generator set power generation power. The maintenance time represents the dimension corresponding to the generator set maintenance status.

[0147] Specifically, when generating the particle positions of a number of initial particles, it is generated based on the value ranges of the parameters to be optimized. Therefore, each parameter to be optimized within these particle positions must conform to the above constraints. Preferably, for discretized variables, the continuous particle positions can be mapped to discrete values through a threshold method. For example, when the particle position value is not less than 0.5, the shutdown status variable therein is mapped to 1. For continuous variables, truncation operations can be performed to keep them within the allowable value ranges. For example, for the generator set power generation power, the truncation operation is achieved through the following formula:

[0148]

[0149] Preferably, if in the obtained particle positions, the shutdown status variable v i (t) = 1 of the generator set, and the maintenance status M i (t) = 1 of the generator set, then force the shutdown status variable v i (t) = 0 of the generator set, that is, if the obtained generator set is in a non-shutdown state and the maintenance status of the generator set is under maintenance, then force the generator set to be in a shutdown state.

[0150] Repeat the optimization solution operation until the current iteration number is not less than the preset iteration threshold, or the difference between the current fitness and the fitness at the previous moment is less than the preset fitness threshold, to obtain the values of each optimized parameter, and use the values of each optimized parameter as the generator set power generation power, generator set operation time, generator set shutdown time, generator set maintenance status, generator set startup status, and generator set shutdown status when the power generation cost is the minimum, the carbon emission is the minimum, the power system reliability is the maximum, and the transmission line safety margin is the maximum;

[0151] Among them, the above optimization solution operation includes:

[0152] Obtain the current particle positions and current particle velocities of all particles, and calculate the current fitness of each particle based on the current particle positions of all particles; wherein, the initial particle position is the above-mentioned initial particle position, and the initial particle velocity is the above-mentioned initial particle velocity;

[0153] For each particle, compare the values of the parameters to be optimized within the current particle position with the corresponding value ranges, and determine the current latest individual optimal particle position and the current latest global optimal particle position according to the comparison results;

[0154] If the difference between the current fitness and the fitness at the previous moment is less than the preset fitness threshold, and the current iteration number is less than the preset iteration threshold, for each particle, update the inertial weight and the learning factor at the previous moment of the corresponding particle according to the current fitness to obtain the current inertial weight and the current learning factor;

[0155] Calculate the particle velocity at the next moment based on the current particle position, the current particle velocity, the current inertial weight, the current learning factor, the current latest individual optimal particle position, and the current latest global optimal particle position; calculate the particle position at the next moment based on the particle velocity at the next moment and the current particle position.

[0156] Specifically, calculate the particle velocity at the next moment and the particle position at the next moment according to the following formula:

[0157]

[0158] In the formula, represents the particle position of the i-th particle at the next moment, represents the particle position of the i-th particle at the current t' moment, represents the particle velocity of the i-th particle at the current t' moment, represents the particle velocity of the i-th particle at the next moment, ω represents the current inertial weight, which is used to control the proportion of the particle maintaining the original velocity, c1 represents the learning factor that controls the weight of the individual experience, and c2 is the learning factor that controls the weight of the group experience, represents the current latest individual optimal position of the i-th particle, r1 and r2 represent two random numbers with different values, and their values are taken in the range [0, 1], represents the current latest global optimal position of the i-th particle.

[0159] Preferably, dynamically adjust the contraction and expansion parameters of the particle swarm through deep reinforcement learning, that is, learn and adjust the inertial weight and learning factor of the algorithm according to the current state of the particle swarm (i.e., the current fitness) to ensure that the particle swarm can effectively explore the solution space and accelerate convergence.

[0160] Preferably, traditional optimization algorithms such as linear programming or mixed integer programming are less efficient in dealing with multi-objective optimization problems and are prone to falling into local optimal solutions. By improving the particle swarm optimization algorithm and dynamically adjusting the algorithm parameters in combination with deep reinforcement learning, the present invention solves the problem that the traditional particle swarm algorithm is prone to falling into local optimal solutions when dealing with large-scale complex optimization problems, improves the global search ability and convergence efficiency of the algorithm, enables the system to obtain the optimal scheduling scheme more quickly, and then significantly improves the convergence speed and solution accuracy of the algorithm. Compared with the prior art, the present invention can more efficiently handle complex power dispatching and maintenance plan optimization problems.

[0161] In this preferred embodiment, the constructed power system maintenance scheduling optimization model is solved by the particle swarm algorithm, and the optimized generator power generation power, generator operation time, generator shutdown time, generator maintenance status, generator startup status, and generator shutdown status are obtained when the power generation cost is minimized, the carbon emission is minimized, the power system reliability is maximized, and the transmission line safety margin is maximized.

[0162] In another preferred embodiment, calculating the current fitness of each particle according to the current particle positions of all particles includes:

[0163] For each particle, according to the generator power generation power in the current particle position, calculate the total power generation power of the current generator set;

[0164] Specifically, there will be the generator power generation power corresponding to all generator sets in the particle position, so summing them up can obtain the total power generation power.

[0165] Calculate the current power balance deviation according to the difference between the load demand of the power system and the total power generation power of the current generator set;

[0166] According to the current particle position, determine the current actual line power flow of the power system at the current particle position, and calculate the current line overlimit according to the current actual line power flow and the line maximum power flow;

[0167] Specifically, according to the values of the parameters to be optimized in the current particle position, the current actual line power flow of the power system in this case can be obtained, and then combined with the known line maximum power flow, the current line overlimit can be obtained. Among them, the line overlimit is calculated by the following formula:

[0168] ΔD = 1000·max(0,|P l (t)-P l max |) 2

[0169] In the formula, ΔD represents the line overlimit quantity.

[0170] Determine the number of generator sets in the current maintenance state according to the maintenance state of the generator sets in the current particle position, and obtain the current maintenance overlimit quantity based on the difference between the number of generator sets in the current maintenance state and the number of generator sets that can be maintained simultaneously as described above;

[0171] Calculate the value of the current penalty term based on the current power balance deviation quantity, the current line overlimit quantity, and the current maintenance overlimit quantity;

[0172] Specifically, calculate the value of the penalty term according to the following formula:

[0173] F penalty = λ1·ΔP + λ2·ΔD + λ3·ΔM

[0174] In the formula, F penalty represents the value of the penalty term, λ1 represents the weight of the power balance deviation value, ΔP represents the power balance deviation value, λ2 represents the weight of the line overlimit quantity, λ3 represents the weight of the maintenance overlimit quantity, and ΔM represents the maintenance overlimit quantity.

[0175] Calculate the current fitness of each particle based on the values of the parameters to be optimized in the current particle position, the above-mentioned power generation cost of the generator sets, the above-mentioned start-up cost of the generator sets, the maximum power generation capacity of the generator sets, the load demand of the above-mentioned power system, the above-mentioned maximum line power flow, the current actual line power flow, and the value of the current penalty term.

[0176] Specifically, based on the values of the parameters to be optimized, the above-mentioned power generation cost of the generator sets, the above-mentioned start-up cost of the generator sets, the maximum power generation capacity of the generator sets, the load demand of the above-mentioned power system, the above-mentioned maximum line power flow, the current actual line power flow, and the formulas corresponding to each objective function, the current power generation cost, the current carbon emission, the current system reliability, and the current transmission line safety margin can be calculated. Then, perform weighted calculation according to the following formula to obtain the current fitness:

[0177] F obj = w1f cost + w2f emission + w3f reliability + w4f security + F penalty

[0178] In the formula, F obj represents the current fitness, w1 represents the weight corresponding to the power generation cost, f cost represents the current power generation cost, w2 represents the weight corresponding to the carbon emission, f emissionrepresents the current carbon emissions, w3 represents the weight corresponding to system reliability, f reliability represents the current system reliability, w4 represents the weight corresponding to the safety margin of the transmission line, f security represents the current safety margin of the transmission line.

[0179] Preferably, the fitness is calculated based on the objective function in the power system maintenance scheduling optimization model, so that the final solution obtained can meet the objective requirements of minimizing the power generation cost, minimizing the carbon emissions, maximizing the power system reliability, and maximizing the safety margin of the transmission line.

[0180] In this preferred embodiment, for each particle, comparing the values of the parameters to be optimized in the current particle position with the corresponding value ranges, and determining the current latest individual optimal particle position and the current latest global optimal particle position according to the comparison results includes:

[0181] For each particle, determining whether the values of the parameters to be optimized in the current particle position all satisfy the corresponding value ranges;

[0182] If in the current particle position of each particle, there is at least one value of the parameter to be optimized that does not satisfy the corresponding value range, then the individual optimal particle position and the global optimal particle position are not updated;

[0183] Specifically, in this case, the current latest individual optimal particle position is still the latest individual optimal particle position obtained at the previous moment, and the current latest global optimal particle position is still the latest global optimal particle position obtained at the previous moment.

[0184] Otherwise, the particles whose values of the parameters to be optimized in the current particle position all satisfy the corresponding value ranges are used as the currently selected particles, and the current individual optimal particle position and the current global optimal particle position are updated according to the current fitness of the currently selected particles.

[0185] Schematically, if the values of all the parameters to be optimized in the particle position of a certain particle all satisfy the corresponding value ranges, then the particle position of this particle can be marked as a "feasible solution", that is, it is used as the selected particle, and then participates in the update of the individual optimal particle position and the global optimal particle position.

[0186] In this preferred embodiment, by comparing the values of the parameters to be optimized in the current particle position with the corresponding value ranges, it is determined whether the individual optimal particle position and the global optimal particle position need to be updated.

[0187] In another preferred embodiment, according to the current fitness of the currently selected particle, the current individual best particle position and the current global best particle position are updated, including:

[0188] For each particle, compare the current fitness with the individual best fitness of the current individual best particle position; wherein, each particle corresponds to a current individual best particle position, and each individual best particle position corresponds to an individual best fitness;

[0189] In the case where the current fitness is less than the current individual best fitness, take the current particle position as the updated individual best particle position, and take the current fitness as the updated individual best fitness;

[0190] Specifically, the updated individual best particle position at this time is the current latest individual best particle position.

[0191] Specifically, in the case where the current fitness is not less than the current individual best fitness, do not update the current individual best particle position and the current individual best fitness. At this time, the current latest individual best particle position is still the latest individual best particle position obtained at the previous moment.

[0192] Extract the current minimum fitness from the current fitnesses of all particles, and compare the current minimum fitness with the global best fitness of the current global best particle position;

[0193] In the case where the current minimum fitness is less than the current global best fitness, take the particle position corresponding to the current minimum fitness as the updated global best particle position, and take the current minimum fitness as the updated global best fitness.

[0194] Specifically, in this case, the updated global best particle position is the current latest global best particle position.

[0195] Specifically, in the case where the current minimum fitness is not less than the current global best fitness, do not update the current global best particle position and the current global best fitness. At this time, the current latest global best particle position is still the latest global best particle position obtained at the previous moment.

[0196] In this preferred embodiment, by comparing the current fitness of the selected particle with the current individual best fitness and the current global best fitness, the current individual best particle position and the current global best particle position are updated.

[0197] Based on the above method item embodiments, the present invention correspondingly provides apparatus item embodiments.

[0198] As Figure 2 shown, an embodiment of the present invention provides an optimization device for power system scheduling and maintenance decision-making considering carbon emission constraints, including:

[0199] A power data acquisition module, an optimization model construction module, and an optimization model solution module;

[0200] The above-mentioned power data acquisition module is used to acquire power data of the power system within a preset time period; wherein, the above-mentioned power data includes: carbon emission quotas of generator sets, start-up costs of generator sets, power generation costs of generator sets, maximum power generation capacities of generator sets, load demands of the power system, minimum net reserve capacities of the power system, maximum line power flows, minimum power generation capacities of generator sets, minimum operation times of generator sets, minimum shutdown times of generator sets, minimum line power flows, line transmission powers, and the number of generator sets that can be overhauled simultaneously;

[0201] The above-mentioned optimization model construction module is used to construct an optimal model for power system maintenance scheduling and corresponding constraint conditions with the goals of minimizing power generation costs, minimizing carbon emissions, maximizing power system reliability, and maximizing the safety margin of transmission lines according to the above-mentioned power data; wherein, the above-mentioned constraint conditions include: carbon emission constraints, power balance constraints, upper and lower limits of generator set outputs, minimum operation time and shutdown time constraints, branch power flow safety constraints, power system net reserve capacity constraints, constraints on generator sets that can be overhauled simultaneously, generator set output constraints, and operation and maintenance status constraints;

[0202] The above-mentioned optimization model solution module is used to solve the above-mentioned optimal model for power system maintenance scheduling under each of the above-mentioned constraint conditions to obtain the power generation power, operation time, shutdown time, maintenance status, start-up status, and shutdown status of the generator sets when the power generation cost is minimized, the carbon emissions are minimized, the power system reliability is maximized, and the safety margin of the transmission line is maximized, and then obtain the scheduling and maintenance decisions of the power system, and schedule and maintain the power system according to the above-mentioned scheduling and maintenance decisions.

[0203] It should be noted that the device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative work. The above schematic diagram is only an example of a power system scheduling and maintenance decision optimization device considering carbon emission constraints, and does not constitute a limitation on a power system scheduling and maintenance decision optimization device considering carbon emission constraints. It may include more or fewer components than shown in the figure, or combine some components, or different components.

[0204] Based on the above method item embodiments, the present invention correspondingly provides terminal device item embodiments.

[0205] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the above memory and configured to be executed by the above processor. When the above processor executes the above computer program, it implements the method for optimizing power system scheduling and maintenance decisions considering carbon emission constraints in any one of the embodiments of the present invention.

[0206] Exemplarily, in this embodiment, the above computer program can be divided into one or more modules. The above one or more modules are stored in the above memory and executed by the above processor to complete the present invention. The above one or more module elements can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the above computer program in the above device;

[0207] The above terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The above device may include, but is not limited to, a processor and a memory;

[0208] The so-called processor may be a central processing module (Central Processing Unit, CPU), or it may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned device, and uses various interfaces and lines to connect all parts of the entire device;

[0209] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and by calling the data stored in the memory. The above-mentioned memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; in addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card (Flash Card), at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0210] Based on the above method item embodiments, the present invention correspondingly provides storage medium item embodiments.

[0211] Another embodiment of the present invention provides a storage medium. The above-mentioned storage medium includes a stored computer program, wherein when the above-mentioned computer program runs, it controls the device where the above-mentioned storage medium is located to execute the method for optimizing power system scheduling and maintenance decision-making considering carbon emission constraints in any one of the embodiments of the present invention.

[0212] In this embodiment, the above storage medium is a computer-readable storage medium, and the above computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The above computer-readable medium may include: any entity or device capable of carrying the above computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0213] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. An optimization method for power system scheduling and maintenance decision-making considering carbon emission constraints, characterized in that Including: Obtain the power data of the power system within a preset time period; wherein, the power data includes: carbon emission quotas of generator sets, start-up costs of generator sets, power generation costs of generator sets, maximum power generation capacities of generator sets, load demands of the power system, minimum net reserve capacities of the power system, maximum line power flows, minimum power generation capacities of generator sets, minimum running times of generator sets, minimum shutdown times of generator sets, minimum line power flows, line transmission powers, and the number of generator sets that can be overhauled simultaneously; According to the power data, with the goals of minimizing power generation costs, minimizing carbon emissions, maximizing the reliability of the power system, and maximizing the safety margin of transmission lines, construct an optimal model for power system maintenance scheduling and corresponding constraint conditions; wherein, the constraint conditions include: carbon emission constraints, power balance constraints, upper and lower limits constraints of generator set outputs, minimum running time and shutdown time constraints, branch power flow safety constraints, power system net reserve capacity constraints, constraints on generator sets that can be overhauled simultaneously, generator set output constraints, and operation and maintenance status constraints; Under each of the constraint conditions, solve the optimal model for power system maintenance scheduling to obtain the power generation powers of generator sets, running times of generator sets, shutdown times of generator sets, maintenance statuses of generator sets, start-up statuses of generator sets, and shutdown statuses of generator sets when the power generation costs are minimized, carbon emissions are minimized, the reliability of the power system is maximized, and the safety margin of transmission lines is maximized, and then obtain the scheduling and maintenance decisions of the power system, and perform scheduling and maintenance on the power system according to the scheduling and maintenance decisions.

2. The optimization method for power system scheduling and maintenance decision-making considering carbon emission constraints according to claim 1, characterized in that The objective function of the optimal model for power system maintenance scheduling is: where, \(G\) represents the set of generating units, \(N\) t represents the total number of preset time periods, \(t\) represents the \(t\)-th time period, \(Z\) i represents the power generation cost of the \(i\)-th generating unit, \(P\) Gi (\(t\)) represents the power generation power of the \(i\)-th generating unit at the \(t\)-th time period, \(v\) i (\(t\)) represents the shutdown state variable of the \(i\)-th generating unit at the \(t\)-th time period, represents the start-up cost of the \(i\)-th generating unit, \(y\) i (\(t\)) represents the start-up state variable of the \(i\)-th generating unit at the \(t\)-th time period, \(C\) Oi represents the preset first cost coefficient of the \(i\)-th generating unit, \(C\) li represents the preset second cost coefficient of the \(i\)-th generating unit, \(C\) 2i represents the preset third cost coefficient of the \(i\)-th generating unit, \(\eta\) i represents the carbon emission coefficient of the \(i\)-th generating unit, \(R_m(t)\) represents the net reserve capacity of the power system at the \(t\)-th time period, represents the maximum power generation power of the \(i\)-th generating unit, \(M\) i (\(t\)) represents the maintenance state of the \(i\)-th generating unit at the \(t\)-th time period, \(P\) D (\(t, s\)) represents the load demand of the power system in the \(s\)-th sub-time period within the \(t\)-th time period, \(R_m\) Min (\(t, s\)) represents the minimum net reserve capacity of the power system in the \(s\)-th sub-time period within the \(t\)-th time period, \(S\) l (\(t\)) represents the safety margin of the transmission line within the \(t\)-th time period, \(P\) l Max represents the maximum line power flow, \(P\) l (\(t\)) represents the actual line power flow at the \(t\)-th time period.

3. An optimization method for power system scheduling and maintenance decision-making considering carbon emission constraints according to claim 2, characterized in that The carbon emission constraint is: Where E q represents the carbon emission quota of the power generation unit; The power balance constraint is: where P D (t) represents the load demand of the power system at the t-th time period; The upper and lower limits constraints of generator set outputs are: In the formula, represents the minimum power generation of the i-th generator set; The minimum running time and shutdown time constraints are: (t i,on (t)-T i,U )(v i (t)-v i (t - 1)) ≥ 0 (t i,off (t)-T i,D )(v i (t - 1)-v i (t))≥0 where t i,on (t) represents the operating time of the i-th generating unit in the t-th time period, and T i,U represents the minimum operating time of the i-th power generation unit, and v i (t - 1) represents the shutdown state variable of the i-th generating unit in the time period preceding the t-th time period, and t i,off (t) represents the shutdown time of the i-th generating unit in the t-th time period, and T i,D represents the minimum shutdown time of the i-th power generation unit; The branch power flow safety constraint is: Wherein, P l Min represents the minimum power flow of the line, G l-i represents the power transfer distribution factor of the node where the ith generator set is located and its line l, G l-j represents the power transfer distribution factor of the node where the ith generator set is located and its line j, D j (t) represents the transmission power of line j at the t-th time period, and KP represents all nodes in the power system; The power system net reserve capacity constraint is: The constraints on generator sets that can be overhauled simultaneously are: In the formula, K(t) represents the maximum number of generator sets that can be overhauled simultaneously in the t-th time period; The generator set output constraint is: where v i (t, s) represents the shutdown state variable of the i-th generator set at the s-th sub-period within the t-th period, represents the minimum power generation of the i-th generator set at the s-th sub-period within the t-th period, P Gi (t, s) represents the power generation of the i-th generator set at the s-th sub-period within the t-th period, represents the maximum power generation of the i-th generator set at the s-th sub-period within the t-th period; The operation and maintenance status constraint is: M i (t) + v i (t, s) ≤ 1.

4. The optimization method for power system scheduling and maintenance decision-making considering carbon emission constraints according to claim 3, characterized in that The process of solving the optimal model for power system maintenance scheduling under each of the constraint conditions to obtain the power generation powers of generator sets, running times of generator sets, shutdown times of generator sets, maintenance statuses of generator sets, start-up statuses of generator sets, and shutdown statuses of generator sets when the power generation costs are minimized, carbon emissions are minimized, the reliability of the power system is maximized, and the safety margin of transmission lines is maximized includes: According to each of the constraint conditions, generate corresponding value ranges for each parameter to be optimized; wherein, the parameters to be optimized are: power generation powers of generator sets, running times of generator sets, shutdown times of generator sets, maintenance statuses of generator sets, start-up statuses of generator sets, and shutdown statuses of generator sets; Generate a number of particles according to the value ranges; initially, each particle corresponds to an initial particle position and an initial particle velocity; the particle position is used to represent the values of each parameter to be optimized; Repeat the optimization solution operation until the current iteration number is not less than the preset iteration threshold, or the difference between the current fitness and the fitness at the previous moment is less than the preset fitness threshold, to obtain the values of the optimized parameters, and use the values of the optimized parameters as the power generation power, operating time, shutdown time, maintenance status, startup status, and shutdown status of the generator set when the power generation cost is the lowest, the carbon emission is the lowest, the power system reliability is the highest, and the transmission line safety margin is the highest; Among them, the optimization solution operation includes: Obtain the current particle positions and current particle velocities of all particles, and calculate the current fitness of each particle according to the current particle positions of all particles; where the particle position at the initial time is the initial particle position, and the particle velocity at the initial time is the initial particle velocity; For each particle, compare the values of the parameters to be optimized in the current particle position with the corresponding value ranges, and determine the current latest individual optimal particle position and the current latest global optimal particle position according to the comparison results; If the difference between the current fitness and the fitness at the previous moment is less than the preset fitness threshold, and the current iteration number is less than the preset iteration threshold, for each particle, update the inertia weight and learning factor at the previous moment of the corresponding particle according to the current fitness to obtain the current inertia weight and the current learning factor; Calculate the particle velocity at the next moment according to the current particle position, current particle velocity, current inertia weight, current learning factor, current latest individual optimal particle position, and current latest global optimal particle position; calculate the particle position at the next moment according to the particle velocity at the next moment and the current particle position.

5. An optimization method for power system scheduling and maintenance decision-making considering carbon emission constraints according to claim 4, characterized in that The calculation of the current fitness of each particle according to the current particle positions of all particles includes: For each particle, calculate the total power generation of the current generator set according to the power generation power of the generator set in the current particle position; Calculate the current power balance deviation according to the difference between the load demand of the power system and the total power generation of the current generator set; Determine the current actual line power flow of the power system at the current particle position according to the current particle position, and calculate the current line overlimit amount according to the current actual line power flow and the line maximum power flow; Determine the number of generator sets in the current maintenance state according to the maintenance status of the generator sets in the current particle position, and obtain the current maintenance overlimit amount according to the difference between the number of generator sets in the current maintenance state and the number of generator sets that can be maintained simultaneously; Calculate the value of the current penalty term according to the current power balance deviation, current line overlimit amount, and current maintenance overlimit amount; Calculate the current fitness of each particle based on the values of the parameters to be optimized in the current particle position, the power generation cost of the generator set, the start-up cost of the generator set, the maximum power generation capacity of the generator set, the load demand of the power system, the maximum line power flow, the current actual line power flow, and the value of the current penalty term.

6. The optimization method for power system scheduling and maintenance decision-making considering carbon emission constraints according to claim 5, characterized in that For each particle, the step of comparing the values of the parameters to be optimized in the current particle position with the corresponding value ranges and determining the current latest individual optimal particle position and the current latest global optimal particle position includes: For each particle, determine whether the values of the parameters to be optimized in the current particle position all satisfy the corresponding value ranges; If in the current particle position of each particle, there is at least one value of the parameter to be optimized that does not satisfy the corresponding value range, then do not update the individual optimal particle position and the global optimal particle position; Otherwise, take the particles whose values of the parameters to be optimized in the current particle position all satisfy the corresponding value ranges as the currently selected particles, and update the current individual optimal particle position and the current global optimal particle position according to the current fitness of the currently selected particles.

7. A method for optimizing power system scheduling and maintenance decisions considering carbon emission constraints, as described in claim 6, characterized in that Updating the current individual optimal particle position and the current global optimal particle position according to the current fitness of the currently selected particles includes: For each particle, compare the current fitness with the individual optimal fitness of the current individual optimal particle position; where each particle corresponds to a current individual optimal particle position, and each individual optimal particle position corresponds to an individual optimal fitness; If the current fitness is less than the current individual optimal fitness, take the current particle position as the updated individual optimal particle position and take the current fitness as the updated individual optimal fitness; Extract the current minimum fitness from the current fitnesses of all particles, and compare the current minimum fitness with the global optimal fitness of the current global optimal particle position; If the current minimum fitness is less than the current global optimal fitness, take the particle position corresponding to the current minimum fitness as the updated global optimal particle position and take the current minimum fitness as the updated global optimal fitness.

8. An optimization device for power system scheduling and maintenance decision-making considering carbon emission constraints, characterized in that, Including: A power data acquisition module, an optimization model construction module, and an optimization model solving module; The power data acquisition module is used to acquire the power data of the power system within a preset time period; wherein the power data includes: the carbon emission quota of the generator set, the start-up cost of the generator set, the power generation cost of the generator set, the maximum power generation capacity of the generator set, the load demand of the power system, the minimum net reserve capacity of the power system, the maximum line power flow, the minimum power generation capacity of the generator set, the minimum operation time of the generator set, the minimum shutdown time of the generator set, the minimum line power flow, the line transmission power, and the number of generator sets that can be overhauled simultaneously; The optimization model construction module is used to construct an optimal model for the maintenance scheduling of the power system and the corresponding constraint conditions based on the power data, with the objectives of minimizing the generation cost, minimizing the carbon emissions, maximizing the reliability of the power system, and maximizing the safety margin of the transmission line; wherein, the constraint conditions include: carbon emission constraint, power balance constraint, upper and lower limits constraint of the generator set output, minimum running time and downtime constraint, branch power flow safety constraint, power system net reserve capacity constraint, simultaneously maintainable generator set constraint, generator set output constraint, and operation and maintenance status constraint; The optimization model solving module is used to solve the optimal model for the maintenance scheduling of the power system under each of the constraint conditions, to obtain the generated power, running time, shutdown time, maintenance status, startup status, and shutdown status of the generator sets when the generation cost is minimized, the carbon emissions are minimized, the reliability of the power system is maximized, and the safety margin of the transmission line is maximized, and then to obtain the scheduling and maintenance decisions for the power system, and to schedule and maintain the power system according to the scheduling and maintenance decisions.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements an optimization method for power system scheduling and maintenance decisions considering carbon emission constraints as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute an optimization method for power system scheduling and maintenance decisions considering carbon emission constraints as described in any one of claims 1 to 7.

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