Power system carbon reduction planning method, system and device based on litsea rotundifolia algorithm (DOA) and medium
By constructing an annual rolling optimization scheduling model and using the Dowry Optimization Algorithm (DOA) to solve the problem, the challenge of multi-dimensional carbon reduction planning in power systems was solved, and efficient search for the globally optimal path was achieved, thereby improving the carbon reduction and economic efficiency of the system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUIZHOU POWER GRID CO LTD
- Filing Date
- 2025-11-05
- Publication Date
- 2026-04-10
AI Technical Summary
Traditional methods for carbon decarbonization planning in power systems struggle to take into account multiple dimensions of factors, including power structure evolution, energy location, policy disturbances, carbon emission constraints, and unit decommissioning and new construction. Furthermore, existing heuristic algorithms are prone to getting trapped in local optima, have limited adaptability, and are unable to meet the solution requirements in complex scenarios.
A power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) is adopted to construct an annual rolling optimization scheduling model. Global optimization is performed through decision variables, objective function and constraints. Combined with the synergistic optimization of supply and demand balance, carbon emissions and investment costs, the Jackal Optimization Algorithm is used to solve the problem. The search, encirclement and attack behavior of the jackal swarm is simulated to find the optimal solution.
It enables efficient and globally optimal carbon reduction path planning for multi-year power systems, improves the operability and economy of system supply and demand balance and carbon emission compliance, and provides a comprehensive optimization tool for complex power systems.
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Figure CN121840686A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of power system planning and operation, and in particular to a power system carbon reduction planning method, system, device and medium based on DOA. BACKGROUND
[0002] In recent years, with the gradual improvement of carbon trading market and multi-incentive policy, the research on carbon reduction path of power system is developing in a diversified way. Some scholars introduce a multi-year rolling optimization framework, combined with demand response, tightening of carbon emission quota, and increase of renewable energy proportion, to build a low-carbon power planning model considering time sequence evolution. For example, a dynamic power structure optimization method based on heuristic algorithm was proposed in 2021, which can dynamically adjust the new construction, retirement and technical modification plan of generating units according to annual load forecast, carbon constraint boundary and regional resource endowment, to realize the multiple balance of carbon emission, economy and safety. In addition, some scholars deeply integrate geographic information system (GIS) and optimization scheduling model, aiming at regional differences and spatial resource distribution, to realize the joint optimization of power generation facility spatial layout and carbon reduction path. For example, in 2020, some researchers based on GIS and multi-objective evolutionary algorithm, carried out multi-energy coordinated site selection and capacity configuration research at provincial scale, and formulated differentiated power evolution and carbon reduction route for different regions, which significantly improved the overall flexibility and carbon reduction potential of power system. At the same time, some researchers proposed a modeling method based on life cycle analysis and phased retirement optimization for the problem of unit retirement and flexible modification.
[0003] With the continuous acceleration of low-carbon transformation of power system, the simulation and optimization of carbon reduction path are increasingly showing the characteristics of multi-dimension, high complexity and strong constraints. The traditional analytical optimization method and classical mathematical programming method (such as linear programming, mixed integer programming, etc.) often face the limitations of dimension disaster, difficulty in modeling and low convergence efficiency when dealing with multi-objective, multi-stage, large-scale, nonlinear and non-convex optimization problems, which makes it difficult to meet the solution requirements in actual complex scenarios.
[0004] Under this background, meta-heuristic algorithms (such as genetic algorithm, particle swarm optimization, ant colony algorithm, whale optimization, grey wolf optimization, etc.) have become an important tool in the field of power system optimization due to their global optimization ability in complex search space, good adaptability to nonlinear and multi-constraint problems. However, with the continuous expansion of actual application scenarios, the existing classical heuristic algorithms also face the problems of easy to fall into local optimum, limited adaptability to dynamic, time-varying and multi-scenario characteristics, and strong dependence on algorithm parameters. SUMMARY
[0005] In view of the above existing problems, the present application provides a power system carbon reduction planning method, system, device and medium based on DOA.
[0006] Therefore, the technical problem solved by the present application is: how to dynamically and collaboratively search for an optimal path for the multi-dimensional and complex decisions of the decommissioning-expansion-carbon constraint-investment of the power system in the future years; Traditional carbon emission reduction path simulation methods mainly focus on static power source structure optimization or single-year load adjustment, and it is difficult to consider multi-dimensional collaborative elements such as power source structure evolution, energy site selection, policy disturbance, carbon emission constraint, unit decommissioning and new construction, and cross-year dynamic evolution; The error caused by model assumptions and simplification needs to introduce more strict carbon emission constraints and policy constraints to build a refined mathematical model, so as to minimize the error as much as possible.
[0007] To solve the above technical problems, the present application provides the following technical solutions: a power system carbon reduction planning method based on DOA, comprising: constructing an annual rolling optimization scheduling model of the power system, the model including decision variables, an objective function and constraint conditions; The decision variables include annual decommissioning decisions of units and annual new installed capacity of new power sites; the objective function is a weighted sum of total carbon emissions and total annual new investment cost, and includes a constraint penalty term; the constraint conditions include supply and demand balance constraints, carbon emission constraints, new capacity upper and lower bound constraints and variable type constraints; The DOA is used to solve the model, and the decision vector is globally optimized, and the multi-year unit decommissioning and power expansion sequence that meets the constraint conditions and optimizes the objective function is output as the optimal carbon reduction path of the power system.
[0008] As a preferred scheme of the power system carbon reduction planning method based on DOA, the decision variables include a binary variable representing the decision of whether each existing unit is decommissioned in a specific year, wherein the first value represents decommissioning and the second value represents continuous operation; A continuous variable is introduced to represent the new installed capacity of each new power site in a specific year; All the binary variables and the continuous variables in the same planning year are combined to form a decision vector representing the annual configuration evolution of the system.
[0009] As a preferred scheme of the power system carbon reduction planning method based on DOA, the objective function includes two heterogeneous objectives, i.e., total carbon emissions and total annual new investment cost, which are fused into a single comprehensive optimization objective by weighted summation. By introducing a constraint penalty term, the degree of violation of supply and demand balance constraints, carbon emission constraints, and upper and lower bound constraints of new capacity are included in the objective function in the form of a penalty function. The penalty coefficient is used to convert the constraint violation into the increment of the objective function value, thus transforming the constrained optimization problem into an unconstrained optimization problem.
[0010] As a preferred embodiment of the power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) described in this invention, the constraints include setting supply and demand balance constraints to require that the total available installed capacity of the system is not less than the predicted load demand for the year. By setting carbon emission constraints, the total carbon emissions of the system are required not to exceed the annual carbon emission cap set based on policy. By setting upper and lower limits for new capacity, the newly added installed capacity of each new power station in a single year is limited to between zero and the maximum annual buildable capacity of the station determined based on resource assessment and policy.
[0011] As a preferred embodiment of the power system carbon reduction planning method based on the jackal optimization algorithm (DOA) described in this invention, the step of using the jackal optimization algorithm to solve the model includes, in the search phase, the jackal determines the target prey based on the current population optimal position and the global optimal position, and approaches the prey through a position update formula; During the encirclement phase, when the group is large, the jackals cooperate to surround the prey, adjusting their own position by randomly selecting the positions of other jackals. During the attack phase, the jackal launches attacks based on the size of its prey, simulating alternating attack strategies using cosine and sine functions, ultimately subduing the prey or adjusting its position to approach the optimal solution.
[0012] As a preferred embodiment of the power system carbon reduction planning method based on the Dowry Optimization Algorithm (DOA) described in this invention, the annual rolling optimization scheduling model of the power system includes, in, This represents the total carbon emissions of the system for this year. The annual carbon emission cap, For this year's load demand, For the first Maximum annual buildable capacity Indicates the first Will the Taiwanese crew be retired this year? Indicates the first The newly built power stations will have a new installed capacity this year.
[0013] As a preferred embodiment of the power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) described in this invention, the objective function further includes, considering the synergistic optimization of system carbon emissions and investment costs, constructing the following weighted objective function F. in, This represents the total carbon emissions of the system for this year. (Total cost of new investment in the year) To constrain penalties, This is the penalty coefficient.
[0014] This invention provides a power system carbon reduction planning system based on the Jackal Optimization Algorithm (DOA).
[0015] As a preferred embodiment of the power system carbon reduction planning system based on the Jackal Optimization Algorithm (DOA) described in this invention, it includes a data management module, an optimization modeling module, a core algorithm module, and a result output module. The data management module is used to acquire, store, and manage basic data and policy constraint data of the power system, and to provide data input for the optimization modeling module; The optimization modeling module is used to construct an annual rolling optimization scheduling model for the power system, which includes decision variables, objective functions, and multiple constraints, based on the data provided by the data management module. The core algorithm module is used to run the jackal optimization algorithm, solve the model constructed by the optimization modeling module, and output the optimal decision variable sequence by simulating the search, encirclement and attack behavior of the jackal pack. The result output module is used to receive and parse the decision variable sequence output by the core algorithm module to generate a visualized multi-year unit decommissioning plan, power expansion scheme and carbon emission path map.
[0016] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA).
[0017] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA).
[0018] The beneficial effects of this invention are as follows: This invention innovatively integrates the decision-making of unit retirement with the annual expansion capacity optimization of new energy in multiple locations into a unified model, and introduces rolling updates of multi-year supply and demand and carbon emission constraints, thereby realizing a global carbon reduction path solution that is more closely aligned with the actual evolution of the power system, laying the foundation for the scientific nature and feasibility of low-carbon transformation schemes.
[0019] An improved swarm intelligence algorithm, DOA, is used to efficiently find the global optimum for the mixed integer-continuous optimization model. It takes into account the coordinated scheduling of unit retirement and new energy expansion, significantly improving the optimal solution quality and convergence efficiency of large-scale multivariate low-carbon planning problems. It provides a powerful tool for the comprehensive optimization of carbon reduction and economic efficiency of complex power systems, and further enhances the operability and economic and environmental benefits of the planning scheme. Attached Figure Description
[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0021] Figure 1 This is a schematic diagram of a power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) provided in one embodiment of the present invention.
[0022] Figure 2 An annual carbon emission path map of a provincial power system provided as an embodiment of the present invention, based on the Jackal Optimization Algorithm (DOA) for power system carbon reduction planning.
[0023] Figure 3 A schematic diagram of the cumulative newly installed capacity at each site for each year of the power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) provided in an embodiment of the present invention. Detailed Implementation
[0024] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.
[0025] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA), including: S1: Construct an annual rolling optimization scheduling model for the power system. The model includes decision variables, objective function, and constraints.
[0026] S2: Decision variables include the annual decommissioning decision of generating units and the annual new installed capacity of newly built power stations; the objective function is the weighted sum of the total carbon emissions of the system and the total annual new investment cost, and includes a constraint penalty term; the constraints include supply and demand balance constraints, carbon emission constraints, upper and lower bound constraints on new capacity and variable type constraints.
[0027] S3: The Jackal optimization algorithm is used to solve the model, and the decision vector is globally optimized. The output is a multi-year sequence of unit decommissioning and power expansion that satisfies the constraints and optimizes the objective function, which serves as the optimal carbon reduction path for the power system.
[0028] It should be noted that this embodiment proposes a multi-year, dynamically constrained power system low-carbon evolution optimization method. By jointly considering multi-dimensional dynamic factors such as unit retirement, distributed renewable energy expansion, annual demand growth and carbon emission limits, an annual rolling optimization scheduling model for the power system is constructed. A Jackal Optimization Algorithm (DOA) is introduced to efficiently search for the globally optimal decision path, thereby obtaining high-quality scheduling results that balance system supply and demand, carbon emission compliance and investment economy.
[0029] The Jackal Optimization Algorithm (DOA) has significant advantages over traditional optimization algorithms such as Genetic Optimization (GA), Differential Optimization (DE), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in terms of exploring balance and utilization during the optimization process, convergence speed and robustness, ability to handle complex high-dimensional problems, and effectiveness in solving practical problems.
[0030] To achieve the optimal low-carbon development path planning for the power system over multiple years while meeting demand and carbon emission constraints, this paper introduces strategies such as unit decommissioning and new power plant construction into the optimization decision-making process. By constructing a mixed integer programming model, the carbon emissions and investment costs of the system are comprehensively weighed.
[0031] Example 2, refer to Figure 2 and Figure 3 As an embodiment of the present invention, based on the above embodiment, a power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) is provided.
[0032] Furthermore, in the embodiments of this application, step S1 constructs an annual rolling optimization scheduling model for the power system. The model includes decision variables, an objective function, and constraints, specifically including: Construct an annual rolling optimization scheduling model for the power system, which includes decision variables, objective function, and constraints. The decision variables include the annual decommissioning decision of generating units and the annual new installed capacity of newly built power stations; the objective function is the weighted sum of the total carbon emissions of the system and the total annual new investment cost, and includes a constraint penalty term; the constraints include supply and demand balance constraints, carbon emission constraints, upper and lower bound constraints on new capacity and variable type constraints.
[0033] The established optimization model for the low-carbon power system can be expressed as: in, This represents the total carbon emissions of the system for this year. The annual carbon emission cap, For this year's load demand, For the first Maximum annual buildable capacity Indicates the first Will the Taiwanese crew be retired this year? Indicates the first The newly installed capacity of the newly built power stations this year This represents the total cost of new investment for the year. To constrain penalties, This is the penalty coefficient.
[0034] Furthermore, in this embodiment, the decision variables in step S2 include the annual decommissioning decision of the generating units and the annual new installed capacity of the new power stations; the objective function is the weighted sum of the total carbon emissions of the system and the total annual new investment cost, and includes a constraint penalty term; the constraints include supply and demand balance constraints, carbon emission constraints, upper and lower bound constraints on new capacity, and variable type constraints, and the specific steps include S201-S204: S201: Decision Variables – The decision variables in this model include two parts: unit decommissioning decisions and new capacity construction decisions. Indicates the first Whether the Taiwanese generator unit will be decommissioned this year (1 indicates decommissioning, 0 indicates continued operation).
[0035] Indicates the first The newly installed capacity of each new power station this year (in MW).
[0036] The two types of variables together constitute the annual optimization decision vector. .
[0037] S202: Objective function construction. Considering the synergistic optimization of system carbon emissions and investment costs, the following weighted objective function is constructed: in, This represents the system's total carbon emissions for the year (unit: tons). The total cost of new investment for the year (unit: yuan). To constrain penalties, This is the penalty coefficient (usually a large value is taken to ensure that the constraint is satisfied first).
[0038] S203: Annual carbon emissions and installed capacity calculations: The decommissioning and new construction of units jointly determine the annual available installed capacity and emissions levels of the system. The annual available capacity and carbon emissions of existing units are as follows: in, For the first Rated capacity of the unit Marking the retirement of history For emission factors.
[0039] The annual new capacity is: in This represents the cumulative built capacity at the beginning of the year for each newly constructed site. This is a new addition this year.
[0040] Total annual system installed capacity: Annual investment cost: in, For the first Unit investment cost.
[0041] In an optional embodiment, the available capacity of the units can also be calculated using a continuous decommissioning rate. Specifically, a continuous decision variable for the decommissioning rate within the interval [0,1] is defined for each unit. The annual available capacity of the existing units is obtained by multiplying the rated capacity of each unit by its "availability rate" and then summing the results.
[0042] In another alternative embodiment, the available capacity of the units can also be calculated by grouping the units by their status. Specifically, based on the age, emission level or policy requirements of the units, they are pre-divided into different status groups. In the optimization model, only "flexibly dispatchable units" are introduced with binary decommissioning decision variables, while the capacity of other units is directly included or excluded from the available capacity according to their group rules.
[0043] S204: Constraints. Model constraints cover the following aspects: Supply and demand balance constraints: in, This represents the load demand for this year.
[0044] Carbon emission constraints: in, This is the annual carbon emission cap.
[0045] New capacity upper and lower bound constraints: in, For the first Maximum buildable capacity per year.
[0046] Variable type constraints: To enhance the feasibility of the model, a penalty function is used to incorporate constraint violations into the objective function. The penalty term is designed as follows: In an alternative embodiment, incorporating constraint violations into the objective function can also be achieved through constraint relaxation. Specifically, relaxation variables are introduced for strict constraints, allowing violations within a certain range, and these relaxation variables are included in the objective function for penalty.
[0047] In another alternative embodiment, incorporating constraint violations into the objective function can also be achieved through a feasible solution repair method. Specifically, during the optimization process, when a solution that does not satisfy the constraints is generated, the decision variables are adjusted according to specific rules to make them satisfy all constraints.
[0048] This model effectively integrates carbon reduction targets with economic constraints and is suitable for annual optimization analysis. Iterative solutions using the DOA algorithm can yield the optimal evolution path for system configuration in each year, providing scientific decision-making support for the green and low-carbon transformation of regional power systems.
[0049] Furthermore, in this embodiment, step S3 uses the Jackal optimization algorithm to solve the model, performs global optimization on the decision vector, and outputs a multi-year unit decommissioning and power source expansion sequence that satisfies the constraints and optimizes the objective function, serving as the optimal carbon reduction path for the power system. Specific details include: In the Jackal Optimization Algorithm (DOA), the jackal's position update process is divided into three different stages, and its design is inspired by the jackal's natural behavior.
[0050] Phase 1: Search Phase (Exploration) Before the search phase begins, the jackal needs to identify its target prey in the following manner: in, This indicates the optimal position in the current population. This indicates the optimal position obtained throughout the entire iteration process.
[0051] After finding prey, the jackal determines the size of its pack. If the pack size is less than 10 individuals and the jackal's vocalization signal (a random value) is less than 0.5, the jackal needs to search for and approach the prey to determine if it is safe to hunt. The mathematical model for this behavioral mechanism is as follows: in, and These represent the current iteration number and the current solution, respectively. This represents the solution obtained during the search phase. and These represent the next iteration number and a decreasing slope, respectively, as follows: The jackals' goal is to get close to their prey (i.e., the optimal solution in the search phase). They move closer to the prey, bringing the group members closer to the optimal solution, thereby improving the development capability of the DOA and accelerating the convergence speed of the algorithm.
[0052] Phase 2: Encirclement Phase (Exploration). When a jackal pack spots a potential target, they cooperate to encircle it. Group members utilize their agility and communication skills to strategically position themselves around the prey, forming a tight circle or semicircle. This coordinated encirclement is crucial for cutting off the prey's escape routes and reducing its chances of escape. Jackals begin encircling their prey when the vocal signal strength is less than 0.5 and the group size exceeds 10 members. The mathematical model is as follows: Where z represents another randomly selected jackal, determined by the following formula: During the encirclement phase, there is competition among the jackals, and the jackals... i Will be based on other jackals z Adjust its own position.
[0053] Phase 3: Attack Phase. During this phase, the jackals execute a coordinated and strategic attack when capturing prey. After successfully surrounding the target through stealthy approach and vocal communication, the jackals launch their attack. They act in unison, launching a series of rapid and precisely timed attacks, with each member taking turns to execute strategic actions. The jackals utilize their agility, speed, and sharp teeth to subdue their prey. When the vocal signal is greater than 0.5, the alpha jackal issues the attack command. The actions during the attack phase depend on the size of the prey; if the prey is too large, the jackals will launch a series of attacks until it is subdued. The prey size S is defined as follows: in, This represents the prey coefficient, which is a constant of 3 (representing the largest prey). and Let represent the fitness values of the i-th jackal and the location of the prey, respectively. The jackal's judgment of prey size is based on the maximum prey size. If This indicates that if the prey is too large, the jackal will attempt to harm it first. The formula for this behavior is as follows: in, This indicates a weakened prey. Once the prey is injured and weakened, a second and third attack will subdue it.
[0054] In an optional embodiment, the size of the prey can also be determined by the fitness difference. Specifically, the absolute difference between the current jackal's fitness value and the prey's fitness value is calculated. This difference is compared with a dynamic threshold preset based on the problem size and experience to determine the prey's state. If the difference is large, it indicates that the current solution is far from the optimal solution, i.e., the prey is too large; otherwise, it is considered that convergence can be achieved directly.
[0055] In another alternative embodiment, the size of the prey can be determined through the iteration process. Specifically, the determination of the size of the prey is associated with the iteration process of the algorithm. In the early stage of iteration, the prey is tended to be judged as too large to encourage the algorithm to conduct more diverse searches. In the later stage of iteration, the prey is tended to be judged as directly subdued to promote the convergence of the algorithm to the optimal solution.
[0056] The algorithm uses a combination of cosine and sine functions to simulate this alternating attack strategy. The formulas for a series of attacks are as follows: when At this time, the prey is small or weak enough to be subdued directly, and the formula is as follows: in, Let ps be the new position of the i-th jackal in the j-th dimension at the next iteration (t+1), where ps is the population size, rand is a random number, and π is the mathematical constant pi.
[0057] During the attack phase, the jackal will determine the size of its prey (S) and the local optimum. Different hunting techniques are employed. If the size and energy of the prey allow the jackal to subdue it, it will attack and subdue it. When S is too large, the jackal's approach deviates significantly from the optimal solution. It will be adjusted and brought closer to the prey. During the attack phase, DOA will strategically converge towards the optimal solution, thereby enhancing the algorithm's development capabilities and improving its convergence performance.
[0058] In an optional embodiment, the attack phase can also be implemented through random directional perturbations. Specifically, when the prey is too large, one or more random directional perturbations are superimposed on the basic direction of movement toward the prey. By controlling the amplitude of the perturbations, a more detailed search of the area surrounding the prey can be achieved within a local range.
[0059] In another alternative embodiment, the attack phase can also be implemented through historical path learning. Specifically, when the prey is too large, the direction and step size of the current attack are adjusted by referring to the movement direction and effect of the individual jackal or the entire population in previous iterations.
[0060] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that: This embodiment also provides a power system carbon reduction planning system based on the Jackal Optimization Algorithm (DOA), including: a data management module, an optimization modeling module, a core algorithm module, and a result output module; The data management module is used to acquire, store, and manage basic data and policy constraint data of the power system, and to provide data input for the optimization modeling module; The optimization modeling module is used to construct an annual rolling optimization scheduling model for the power system, which includes decision variables, objective functions, and multiple constraints, based on the data provided by the data management module. The core algorithm module is used to run the jackal optimization algorithm, solve the model constructed by the optimization modeling module, and output the optimal decision variable sequence by simulating the search, encirclement and attack behavior of the jackal pack. The results output module is used to receive and parse the decision variable sequence output by the core algorithm module to generate a visualized multi-year unit decommissioning plan, power expansion scheme and carbon emission path map.
[0061] This embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) proposed in the above embodiment.
[0062] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) proposed in the above embodiments.
[0063] The storage medium proposed in this embodiment and the method for implementing carbon reduction planning of power systems based on the Jackal Optimization Algorithm (DOA) proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.
[0064] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.
[0065] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A power system carbon reduction planning method based on the Dowry Optimization Algorithm (DOA), characterized in that: include, Construct an annual rolling optimization scheduling model for the power system, which includes decision variables, objective function, and constraints. Decision variables include the annual decommissioning decision of generating units and the annual new installed capacity of newly built power plants; The objective function is a weighted sum of the total carbon emissions of the system and the total annual new investment cost, and includes a constraint penalty term; The constraints include supply and demand balance constraints, carbon emission constraints, upper and lower bound constraints on new capacity, and variable type constraints; The model is solved using the Jackal optimization algorithm, which performs global optimization on the decision vector and outputs a multi-year sequence of unit decommissioning and power source expansion that satisfies the constraints and optimizes the objective function, serving as the optimal carbon reduction path for the power system.
2. The power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) as described in claim 1, characterized in that: The decision variables include the introduction of binary variables to represent the decision of whether each existing unit should be decommissioned in a specific year, where the first value indicates decommissioning and the second value indicates continued operation; By introducing continuous variables, the newly installed capacity of each newly built power station within a specific year can be represented; The binary variables and continuous variables within the same planning year are combined to form a decision vector characterizing the annual configuration evolution of the system.
3. The power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) as described in claim 2, characterized in that: The objective function includes merging the two heterogeneous objectives of total carbon emissions and total annual new investment cost into a single comprehensive optimization objective through a weighted summation method. By introducing a constraint penalty term, the degree of violation of supply and demand balance constraints, carbon emission constraints, and upper and lower bound constraints of new capacity are included in the objective function in the form of a penalty function. The penalty coefficient is used to convert the constraint violation into the increment of the objective function value, thus transforming the constrained optimization problem into an unconstrained optimization problem.
4. The power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) as described in claim 3, characterized in that: The constraints include setting supply and demand balance constraints, requiring that the total available installed capacity of the system is not less than the predicted load demand for the year; By setting carbon emission constraints, the total carbon emissions of the system are required not to exceed the annual carbon emission cap set based on policy. By setting upper and lower limits for new capacity, the newly added installed capacity of each new power station in a single year is limited to between zero and the maximum annual buildable capacity of the station determined based on resource assessment and policy.
5. The power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) as described in claim 4, characterized in that: The process of solving the model using the jackal optimization algorithm includes the following steps: In the search phase, the jackal determines the target prey based on the current optimal position of the population and the global optimal position, and approaches the prey using a position update formula. During the encirclement phase, when the group is large, the jackals cooperate to surround the prey, adjusting their own position by randomly selecting the positions of other jackals. During the attack phase, the jackal launches attacks based on the size of its prey, simulating alternating attack strategies using cosine and sine functions, ultimately subduing the prey or adjusting its position to approach the optimal solution.
6. The power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) as described in claim 5, characterized in that: The annual rolling optimization scheduling model for the power system includes, in, This represents the total carbon emissions of the system for this year. The annual carbon emission cap, For this year's load demand, For the first Maximum annual buildable capacity Indicates the first Will the Taiwanese crew be retired this year? Indicates the first The newly built power stations will have a new installed capacity this year.
7. The power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) as described in claim 6, characterized in that: The objective function also includes, considering the synergistic optimization of system carbon emissions and investment costs, constructing the following weighted objective function F: in, This represents the total carbon emissions of the system for this year. This represents the total cost of new investment for the year. To constrain penalties, This is the penalty coefficient.
8. A power system carbon reduction planning system based on the Jackal Optimization Algorithm (DOA), employing the power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) as described in any one of claims 1 to 7, characterized in that, include: The module comprises a data management module, an optimization modeling module, a core algorithm module, and a results output module. The data management module is used to acquire, store, and manage basic data and policy constraint data of the power system, and to provide data input for the optimization modeling module; The optimization modeling module is used to construct an annual rolling optimization scheduling model for the power system, which includes decision variables, objective functions, and multiple constraints, based on the data provided by the data management module. The core algorithm module is used to run the jackal optimization algorithm, solve the model constructed by the optimization modeling module, and output the optimal decision variable sequence by simulating the search, encirclement and attack behavior of the jackal pack. The result output module is used to receive and parse the decision variable sequence output by the core algorithm module to generate a visualized multi-year unit decommissioning plan, power expansion scheme and carbon emission path map.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) as described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power system carbon reduction planning method based on the Jackal Optimization Algorithm (DOA) as described in any one of claims 1 to 7.