A method and system for optimizing operation of an urban low-carbon energy system

Abstract

The present application relates to the technical field of energy management, in particular to a kind of urban low-carbon energy system operation optimization method and optimization system.The present application includes the following steps: setting multiple evaluation indexes of urban low-carbon energy system operation;Introduce improved intelligent algorithm, construct system operation control strategy generation model;Combining the system operation control strategy generation model and multiple evaluation indexes, obtain the evaluation index value of multiple system operation control strategies;According to the evaluation index value, the evaluation index is weighted distribution, and then the operation control objective function is obtained;Based on the operation control objective function and the system operation control strategy generation model, the optimal operation control strategy of the urban low-carbon energy system is solved.The present application carries out weight analysis to multiple evaluation indexes of urban low-carbon energy system, and then reasonably evaluates urban low-carbon energy system operation control strategy, improves the operation efficiency of urban low-carbon energy system.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, specifically to a method and system for optimizing the operation of a low-carbon energy system in a city. Background Technology

[0002] As the main bodies accommodating population and economic activities, cities account for approximately two-thirds of global primary energy consumption and over 70% of greenhouse gas emissions. Urban development is a powerful driving force for global economic green recovery and sustainable development; however, the accompanying long-term growth in energy consumption and the environmental problems caused by energy use remain significant challenges faced by countries worldwide. Traditional urban energy supply models can no longer meet the future needs of cities for sustainable development in terms of resources, environment, and economy. The green and low-carbon transformation of urban energy systems has become an inevitable requirement for my country to achieve its carbon peaking and carbon neutrality goals. Urban energy systems that are guided by low-carbon development and meet the needs of urban low-carbon transformation are defined as urban low-carbon energy systems.

[0003] Compared to traditional urban energy infrastructure, which operates independently by supply and demand sectors and involves unidirectional energy flow, urban low-carbon energy systems aim to simultaneously meet the diverse energy service needs of urban end-users. Due to their characteristics of cross-sectoral multi-energy complementarity and coordination, multi-technology coupling and linkage, and source-grid-load-storage supply-demand synergy, they are better suited to the needs of high-proportion renewable energy development and serve as an effective vehicle for achieving systemic energy conservation and emission reduction in urban energy systems. However, traditional energy management systems lack flexibility and have low levels of intelligence, making it difficult to effectively cope with energy demand fluctuations and achieve energy conservation and emission reduction targets. Summary of the Invention

[0004] To address the shortcomings of existing methods and the needs of practical applications, and in order to rationally evaluate the operation and control strategies of urban low-carbon energy systems and solve the problem of improving the operational efficiency of urban low-carbon energy systems, this invention provides a method for optimizing the operation of urban low-carbon energy systems. The method includes the following steps: setting multiple evaluation indicators for the operation of the urban low-carbon energy system; introducing an improved intelligent algorithm to construct a system operation and control strategy generation model; combining the system operation and control strategy generation model and the multiple evaluation indicators to obtain evaluation indicator values ​​for multiple system operation and control strategies; assigning weights to the evaluation indicators according to the evaluation indicator values ​​to obtain an operational control objective function; and solving for the optimal operational control strategy for the urban low-carbon energy system based on the operational control objective function and the system operation and control strategy generation model. This invention improves the operational efficiency of urban low-carbon energy systems by performing weight analysis on multiple evaluation indicators of the urban low-carbon energy system, thereby rationally evaluating the operational control strategies and obtaining the optimal operational control strategy.

[0005] Optionally, the evaluation indicators include economic evaluation indicators, environmental evaluation indicators, energy efficiency evaluation indicators, reliability evaluation indicators, external impact evaluation indicators, and consumer satisfaction evaluation indicators. The evaluation indicators adopted in this invention provide strong theoretical support and practical guidance for the coordinated optimization of energy systems, which is conducive to guiding the results of coordinated optimization of energy systems towards a more ideal direction.

[0006] Optionally, the improved intelligent algorithm includes the following steps:

[0007] This invention determines whether the intelligent algorithm is stuck in an iterative optimization deadlock; based on the determination result, the parameters of the intelligent algorithm are adjusted. By determining whether the intelligent algorithm is stuck in an iterative optimization deadlock and then adjusting its parameters, this invention solves the problem that intelligent algorithms are prone to getting trapped in local optima during iterative optimization, thus facilitating the rapid acquisition of the optimal operation and control strategy for urban low-carbon energy systems.

[0008] Optionally, the determination of whether the intelligent algorithm is stuck in an iterative optimization deadlock satisfies the following formula:

[0009]

[0010] in, Indicates the first The fitness value of the optimal solution in the next iteration. Indicates the first The fitness value of the 10th best solution in the next iteration. Indicates the first The fitness value of the 5th best solution in the next iteration. This represents the fitness value of the optimal solution in the iteration history.

[0011] Optionally, the parameters of the intelligent algorithm are adjusted based on the judgment result to satisfy the following formula:

[0012]

[0013] in, Indicates the adjusted number The optimal solution in the next iteration. express random numbers, Indicates the number before adjustment The optimal solution in the next iteration. This represents the optimal solution in the adjusted iteration history. Indicates the maximum number of iterations. Represent the adjustment factor and satisfy: , This represents the optimal solution in the iteration history before adjustment. This invention replaces the optimal solution in the current iteration and the iteration history in the intelligent algorithm, which helps to break out of deadlocks and obtain the globally optimal solution.

[0014] Optionally, the step of combining the system operation control strategy generation model and multiple evaluation indicators to obtain evaluation indicator values ​​for multiple system operation control strategies includes the following steps:

[0015] For each evaluation indicator, a corresponding system operation control strategy is obtained through the system operation control strategy generation model; based on the system operation control strategy, the evaluation indicator values ​​of other evaluation indicators are obtained. This invention obtains a corresponding system operation control strategy for each evaluation indicator, thereby obtaining the values ​​of other evaluation indicators. This facilitates subsequent steps in evaluating and analyzing the weights of the evaluation indicators, and further helps to obtain a more reasonable operation control objective function.

[0016] Optionally, the step of assigning weights to the evaluation indicators based on their values ​​to obtain the operational control objective function includes the following steps:

[0017] The evaluation indicators are coarsely weighted, and a coordinate system is obtained based on the coarse weighting result. The evaluation indicator values ​​are then standardized. Combining the standardization result and the evaluation indicator coordinate system, the evaluation indicators are finely weighted. Based on the fine weighting result, the operational control objective function is obtained according to the evaluation indicators. This invention comprehensively evaluates the evaluation indicators through a two-level weighting process, which helps improve the rationality of the operational control objective function.

[0018] Optionally, the evaluation index values ​​are standardized to satisfy the following formula:

[0019]

[0020] in, Indicates the first The standardized values ​​of the evaluation indicators. Indicates the first The evaluation index values ​​to be processed are as follows: Indicates the first The minimum evaluation index value for each evaluation indicator. Indicates the first The maximum evaluation index value of each evaluation index. This invention, by standardizing the evaluation index values, removes the influence of the dimensions of each evaluation index, which is beneficial to further improving the rationality of the operational control objective function.

[0021] Optionally, the evaluation indicators are precisely weighted by combining the standardization results and the evaluation indicator coordinate system, satisfying the following formula:

[0022]

[0023] in, Indicates the first The final weight of each evaluation indicator Indicates the number of evaluation indicators. Indicates according to the first The first evaluation index obtained The area of ​​each evaluation index value in the evaluation index coordinate system. This invention quantifies the final weight of each evaluation index through model formulas, which facilitates the rapid and accurate acquisition of the operational control objective function and improves the optimization efficiency of this invention.

[0024] Secondly, to efficiently execute the urban low-carbon energy system operation optimization method provided by this invention, this invention also provides an urban low-carbon energy system operation optimization system, including a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory stores a computer program containing program instructions. The processor is configured to call the program instructions to execute the urban low-carbon energy system operation optimization method as described in the first aspect of this invention. This urban low-carbon energy system operation optimization system of this invention has a compact structure and stable performance, and can stably execute the urban low-carbon energy system operation optimization method provided by this invention, further enhancing the overall applicability and practical application capability of this invention. Attached Figure Description

[0025] Figure 1 A flowchart of an urban low-carbon energy system operation optimization method provided in an embodiment of the present invention;

[0026] Figure 2 This is a system framework diagram for optimizing the operation of a low-carbon energy system in a city, provided as an embodiment of the present invention. Detailed Implementation

[0027] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0028] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0029] Please see Figure 1 To rationally evaluate the operation and control strategies of urban low-carbon energy systems and address the issue of improving their operational efficiency, this invention provides a method for optimizing the operation of urban low-carbon energy systems, such as... Figure 1 As shown, in one embodiment, the method includes the following steps:

[0030] S1. Set multiple evaluation indicators for the operation of urban low-carbon energy systems.

[0031] Urban low-carbon energy systems comprise the energy supply side, energy conversion side, and energy consumption side. During operation, the output power of each device on the energy conversion side and the energy supply situation on the energy supply side need to be adjusted according to the load demands for electricity, heat, cooling, and gas on the energy consumption side. It should be understood that scientifically and rationally adjusting the output power of each device on the energy conversion side and the energy supply situation on the energy supply side is crucial to the operational efficiency of the urban low-carbon energy system.

[0032] In this embodiment, the evaluation indicators include economic evaluation indicators, environmental evaluation indicators, energy efficiency evaluation indicators, reliability evaluation indicators, external impact evaluation indicators, and consumer satisfaction evaluation indicators.

[0033] Economic evaluation indicators are used to guide system operation to pursue maximum economic benefits. These indicators mainly include annualized investment cost and operating cost. Annualized investment cost helps assess the overall economic benefits of the investment; a lower value generally indicates a greater potential return in the long term. Operating cost refers to the operating costs required to ensure a stable power supply per unit operating cycle. This cost mainly consists of raw material costs and maintenance costs. It is an important indicator for measuring the economic operation of the system and a key factor determining investment returns.

[0034] Environmental assessment indicators refer to carbon dioxide emissions. Carbon dioxide constitutes a significant proportion of pollutants emitted by energy systems. These emissions primarily originate from two sources: firstly, the energy conversion process itself generates carbon dioxide emissions, such as gas turbines emitting carbon dioxide during power supply; secondly, the electricity purchased from the grid often originates from fossil fuels with high carbon content. Therefore, the equivalent carbon emissions from purchasing electricity from the grid are a crucial factor that cannot be ignored when evaluating the environmental performance of energy systems.

[0035] Energy efficiency assessment indicators refer to the evaluation of energy systems in terms of energy absorption and energy conversion efficiency during the energy consumption process. The main energy efficiency assessment indicators include renewable energy absorption rate, primary energy saving rate, and system energy spillover rate.

[0036] The renewable energy absorption rate refers to the proportion of renewable energy power generation actually absorbed by the energy system to the total renewable energy power generation.

[0037] Primary energy saving rate refers to the quantitative indicator of the reduction in the total primary energy consumption of a system achieved by constructing a scientific integrated energy system collaborative optimization strategy during the planning and design of an integrated energy system. By reducing the primary energy consumption of the energy system, not only can the tight supply of primary energy be effectively alleviated, but also significant benefits such as a reduction in carbon emissions can be brought about.

[0038] The energy supply overflow rate refers to the ratio of the difference between the energy effectively utilized by the system and the energy produced by the system to the energy effectively utilized by the system. It reflects the degree of effective utilization of various heterogeneous energy sources by the energy system, specifically including the energy supply overflow rate of the system when supplying electrical energy, heat energy and cold energy.

[0039] Reliability assessment metrics refer to the system's ability to provide a stable and reliable energy supply to users according to predetermined energy requirements under certain time and conditions. Reliability evaluation metrics are core indicators for assessing the quality of collaborative optimization results and are crucial for ensuring stable system operation. Relevant evaluation metrics for measuring energy supply reliability include average load interruption rate and load interruption frequency.

[0040] External impact assessment indicators: As a grid-connected energy system, the operation of urban low-carbon energy systems inevitably affects the stability and regulation of the upper-level power system. Therefore, the assessment of system performance should also comprehensively consider its potential impact on the upper-level power grid, specifically quantified as hourly electricity purchase levels and average electricity purchase fluctuation rates.

[0041] The hourly power purchase level represents the average power purchased by the system during the operating cycle. It is used to promote the energy system to achieve self-sufficiency in energy supply and reduce dependence on the large power grid. This not only helps to promote the local consumption of distributed energy, but also effectively reduces the peak-shaving and frequency regulation pressure of the large power grid.

[0042] Purchased electricity is an important energy source for urban low-carbon energy systems. Its rate of change has a significant impact on the immediate response and overall stability of the upper-level power grid dispatch. The average rate of change of purchased electricity is used to quantify the real-time changes in the system's purchased power, thereby reflecting the average impact level of the integrated energy system operation on the upper-level power grid.

[0043] Furthermore, the average rate of fluctuation in electricity purchases satisfies the following formula:

[0044]

[0045] in, This represents the average rate of fluctuation in electricity purchases. Indicates the calculation period. Indicates the first Electricity purchase during the time period Indicates the first Electricity purchased during a specific time period.

[0046] Consumer satisfaction assessment indicators include satisfaction with energy usage patterns and satisfaction with energy efficiency. Specifically, satisfaction with energy usage patterns refers to the degree of change in consumers' energy usage patterns after system adjustments. The greater the change in electricity and gas load, the more consumers adjust their energy usage behavior, and the lower the satisfaction with energy usage patterns. Satisfaction with energy efficiency indicates the efficiency of energy utilization during consumer use. Under the "dual carbon" target (carbon reduction and emission reduction), improving energy efficiency can significantly reduce carbon emissions. The energy efficiency satisfaction is obtained by calculating the natural gas utilization efficiency before and after system adjustments.

[0047] S2. Introduce improved intelligent algorithms to construct a system operation control strategy generation model.

[0048] In this embodiment, the intelligent algorithm is the particle swarm optimization algorithm.

[0049] Further improvements to the intelligent algorithm include the following steps:

[0050] S21. Determine whether the intelligent algorithm has fallen into an iterative optimization deadlock.

[0051] Specifically, the determination of whether the intelligent algorithm is stuck in an iterative optimization deadlock satisfies the following formula:

[0052]

[0053] in, Indicates the first The fitness value of the optimal solution in the next iteration. Indicates the first The fitness value of the 10th best solution in the next iteration. Indicates the first The fitness value of the 5th best solution in the next iteration. This represents the fitness value of the optimal solution in the iteration history.

[0054] If the intelligent algorithm encounters the above model conditions for the first or even second time during the iterative optimization process, it is considered to have fallen into an iterative optimization deadlock.

[0055] S22. Adjust the parameters of the intelligent algorithm based on the judgment result.

[0056] Specifically, the parameters of the intelligent algorithm are adjusted based on the judgment result to satisfy the following formula:

[0057]

[0058] in, Indicates the adjusted number The optimal solution in the next iteration. express random numbers, Indicates the number before adjustment The optimal solution in the next iteration. This represents the optimal solution in the adjusted iteration history. Indicates the maximum number of iterations. Represent the adjustment factor and satisfy: , This represents the optimal solution in the iteration history before adjustment.

[0059] When an intelligent algorithm gets stuck in an iterative optimization deadlock, the corresponding optimal solution can be replaced according to the above model to re-perform iterative optimization, thereby breaking out of the optimization deadlock and improving the quality of the optimal solution.

[0060] Furthermore, an improved intelligent algorithm is introduced to construct a system operation control strategy generation model, including the following steps:

[0061] First, mathematical models are established based on the city's low-carbon energy system, including mathematical models of energy supply side such as external power grid, natural gas supply and wind power and photovoltaic renewable energy, mathematical models of energy conversion side such as gas turbine, P2G, CCUS and other energy equipment, and load forecasting models of energy consumption side.

[0062] Secondly, based on the actual operating conditions of the urban low-carbon energy system, establish the corresponding mathematical model constraints, such as power balance constraints, gas turbine operation constraints, P2G electrolyzer operation constraints, P2G methane generator operation constraints, and P2G hydrogen fuel cell operation constraints.

[0063] Finally, based on the mathematical model and the corresponding constraints, the optimal solution is generated using an improved intelligent algorithm, which is the system operation control strategy.

[0064] It should be understood that the basic idea of ​​the particle swarm optimization algorithm is to find the optimal solution through cooperation and information sharing among individuals in the group. In the algorithm, each solution to the optimization problem is regarded as a "particle". These particles have a certain position and velocity in the solution space. By simulating the collective cooperative behavior of a flock of birds, the particles adjust their position and velocity based on their own experience and the best experience of their peers, thereby continuously approaching the optimal solution.

[0065] Its core elements are:

[0066] The fitness function evaluates how well a particle's current position fits the problem. A higher fitness function value indicates a better position for the particle and a greater attraction for it.

[0067] Velocity and position are two key properties of a particle. Velocity is a vector quantity, including its magnitude and direction, and is used to determine the direction and distance the particle moves in the solution space. Position represents the particle's current coordinates, i.e., one of the possible solutions to the problem.

[0068] Individual extrema and swarm extrema: Individual extrema are the optimal positions found by a particle during its search, while swarm extrema are the optimal positions found by the entire particle swarm. These two extrema are used to guide the particle's next move.

[0069] The specific algorithm process includes:

[0070] Population initialization involves randomly initializing the positions and velocities of particles in the solution space and velocity space.

[0071] Calculate the fitness value; calculate the fitness value for each particle based on the fitness function.

[0072] Update the individual extreme value and the group extreme value. Compare the current fitness value of a particle with the fitness value of the individual extreme value. If the current fitness value of a particle is better, update the individual extreme value. At the same time, compare the fitness values ​​of the individual extreme values ​​of all particles with the fitness values ​​of the group extreme value. If the current fitness value of a particle is better, update the group extreme value.

[0073] The velocity and position of particles are updated based on individual and group extreme values ​​and a certain update formula (usually including memory terms, individual cognitive terms, and group cognitive terms).

[0074] Iterate, repeating the above steps until the preset number of iterations is reached or other termination conditions are met.

[0075] Particle swarm optimization (PSO) does not require complex mathematical derivations or programming skills, making it easy to understand and implement. Through group collaboration and information sharing, PSO can quickly find a better solution in a large search space and usually converges to a good solution within a short number of iterations.

[0076] The performance of the particle swarm optimization (PSO) algorithm is influenced by several parameters, including swarm size, maximum velocity, inertia weight, individual cognitive factor, and swarm cognitive factor. Appropriately selecting the values ​​of these parameters can improve the algorithm's performance. For example, a larger inertia weight can enhance global search capability, while a smaller one helps with local convergence; the balance between the individual cognitive factor and the swarm cognitive factor can affect the particle's search behavior and convergence speed.

[0077] Furthermore, the improvement of the present invention is that, in the case of the optimal solution getting stuck in the iterative process, it can be adaptively modified to cope with other swarm intelligence optimization algorithms. Therefore, in other embodiments, the intelligent algorithm can also be other swarm intelligence optimization algorithms such as the firefly algorithm and the cuckoo algorithm.

[0078] S3. By combining the system operation control strategy generation model and the multiple evaluation indicators, the evaluation indicator values ​​of multiple system operation control strategies are obtained.

[0079] Specifically, the step of combining the system operation control strategy generation model and multiple evaluation indicators to obtain evaluation indicator values ​​for multiple system operation control strategies includes the following steps:

[0080] For each evaluation index, a corresponding system operation control strategy is obtained through the system operation control strategy generation model. Specifically, using the evaluation index as a fitness function, the optimal system operation control strategy for the corresponding evaluation index is obtained through the system operation control strategy generation model. This process is repeated for all evaluation indices to obtain their respective system operation control strategies.

[0081] Based on the system operation control strategy, the evaluation index values ​​of other evaluation indicators are obtained.

[0082] Analyze the operation and control strategies of each system to obtain the corresponding evaluation index values.

[0083] S4. Assign weights to the evaluation indicators based on their values ​​to obtain the operational control objective function.

[0084] In this embodiment, the step of assigning weights to the evaluation indicators based on their values ​​to obtain the operational control objective function includes the following steps:

[0085] S41. Perform a coarse weight allocation on the evaluation indicators, divide the coordinate system based on the coarse allocation result, and obtain the evaluation indicator coordinate system.

[0086] Specifically, the evaluation indicators are roughly weighted based on the actual situation using expert discussion, and the resulting rough weight allocation satisfies the following: , Indicates the first The coarse weights of various evaluation indicators This indicates the number of evaluation indicators.

[0087] Furthermore, the evaluation indicators are sorted from largest to smallest according to their coarse weights, and then... Using the x-axis as the angle, the coordinate system is divided counterclockwise from the positive x-axis to obtain the evaluation index coordinate system.

[0088] S42. Standardize the evaluation index values.

[0089] In this embodiment, the evaluation index values ​​are standardized to satisfy the following formula:

[0090]

[0091] in, Indicates the first The standardized values ​​of the evaluation indicators. Indicates the first The evaluation index values ​​to be processed are as follows: Indicates the first The minimum evaluation index value for each evaluation indicator. Indicates the first The maximum evaluation index value of each evaluation indicator.

[0092] S43. Combining the standardized processing results and the coordinate system of the evaluation indicators, perform precise weight allocation on the evaluation indicators.

[0093] Specifically, by combining the standardization results and the evaluation index coordinate system, the evaluation index is weighted and precisely allocated, satisfying the following formula:

[0094]

[0095] in, Indicates the first The final weights of the evaluation indicators are the result of the weight allocation. Indicates the number of evaluation indicators. Indicates according to the first The first evaluation index obtained The area of ​​each evaluation index value in the evaluation index coordinate system.

[0096] The corresponding graph of the evaluation index, formed in the evaluation index coordinate system after standardization of other evaluation index values ​​obtained based on the evaluation index, is used as the area of ​​the triangle formed by the origin, the evaluation index value, and the value of the next evaluation index in the evaluation index coordinate system.

[0097] S44. Based on the weighted fine allocation results, obtain the operation control objective function according to the evaluation index.

[0098] In this embodiment, by combining the evaluation indicators and corresponding weight values, the operational control objective function is obtained, which satisfies the following formula:

[0099]

[0100] in, This indicates the execution of the control objective function. Indicates the first The final weights of the evaluation indicators are the result of the weight allocation. Indicates the first The values ​​of various evaluation indicators.

[0101] S5. Based on the operational control objective function and the system operational control strategy generation model, solve for the optimal operational control strategy of the urban low-carbon energy system.

[0102] Specifically, using the operational control objective function as the fitness function, the optimal operational control strategy for the urban low-carbon energy system is obtained by iteratively solving the system operational control strategy generation model.

[0103] Please see Figure 2 In an embodiment, to efficiently execute the urban low-carbon energy system operation optimization method provided by the present invention, the present invention also provides an urban low-carbon energy system operation optimization system, including: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory contains program instructions used for the steps of the urban low-carbon energy system operation optimization method. The urban low-carbon energy system operation optimization system of the present invention has a compact structure and stable performance, and can stably execute the urban low-carbon energy system operation optimization method of the present invention, further improving the overall applicability and practical application capability of the present invention.

[0104] In embodiments, the processor may be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. Input devices can be used to acquire data information. Output devices can be used to output the results obtained by storing program instructions contained in a computer program in the memory provided by this invention. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory.

[0105] In one possible implementation, the memory may include a stored program area and a stored data area. The stored program area may store the operating system and applications required for at least one function; the stored data area may store data created during use. Furthermore, the memory may include read-only memory and random access memory, providing instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores the operating system and operating instructions, executable modules, or data structures, or subsets thereof, or extended sets thereof. The operating instructions may include various operation instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and handling hardware-based tasks.

[0106] The embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described urban low-carbon energy system operation optimization method.

[0107] The storage medium can include various media that can store program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] In summary, this invention improves the operational efficiency of urban low-carbon energy systems by performing weighted analysis on multiple evaluation indicators of urban low-carbon energy systems and then rationally evaluating the operation and control strategies of urban low-carbon energy systems to obtain the optimal operation and control strategy.

[0109] Therefore, this invention effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention.

Claims

1. A method for optimizing the operation of a low-carbon energy system in a city, characterized in that, The method for optimizing the operation of a city's low-carbon energy system includes the following steps: Establish multiple evaluation indicators for the operation of urban low-carbon energy systems; An improved intelligent algorithm is introduced to construct a system operation control strategy generation model; By combining the system operation control strategy generation model and the multiple evaluation indicators, evaluation indicator values ​​for multiple system operation control strategies are obtained; The evaluation indicators are weighted according to their values ​​to obtain the operational control objective function. Based on the operational control objective function and the system operational control strategy generation model, the optimal operational control strategy of the urban low-carbon energy system is solved. The improved intelligent algorithm includes the following steps: Determine if the intelligent algorithm has fallen into an iterative optimization deadlock; Adjust the parameters of the intelligent algorithm based on the judgment results; Whether the intelligent algorithm is stuck in an iterative optimization deadlock is determined by the following formula: in, Indicates the first The fitness value of the optimal solution in the next iteration. Indicates the first The fitness value of the 10th best solution in the next iteration. Indicates the first The fitness value of the 5th best solution in the next iteration. This represents the fitness value of the optimal solution in the iteration history; Based on the judgment result, the parameters of the intelligent algorithm are adjusted to satisfy the following formula: in, Indicates the adjusted number The optimal solution in the next iteration. express random numbers, Indicates the number before adjustment The optimal solution in the next iteration. This represents the optimal solution in the adjusted iteration history. Indicates the maximum number of iterations. Represent the adjustment factor and satisfy: , This represents the optimal solution in the iteration history before adjustment; The step of assigning weights to the evaluation indicators based on their values ​​to obtain the operational control objective function includes the following steps: The evaluation indicators are coarsely weighted, and a coordinate system is divided based on the coarse weighting results to obtain the evaluation indicator coordinate system. The evaluation index values ​​are standardized. Based on the standardized processing results and the coordinate system of the evaluation indicators, the evaluation indicators are precisely weighted. Based on the weighted fine allocation results, the operational control objective function is obtained according to the evaluation indicators. The evaluation indicators are then precisely weighted based on the standardized processing results and the evaluation indicator coordinate system, satisfying the following formula: in, Indicates the first The final weight of each evaluation indicator Indicates the number of evaluation indicators. Indicates according to the first The first evaluation index obtained The area of ​​each evaluation index value in the evaluation index coordinate system; By combining the evaluation indicators and their corresponding weights, the operational control objective function is obtained, which satisfies the following formula: in, This indicates the execution of the control objective function. Indicates the first The final weights of the evaluation indicators are the result of the weight allocation. Indicates the first The values ​​of various evaluation indicators.

2. The method for optimizing the operation of a low-carbon energy system in a city according to claim 1, characterized in that, The evaluation indicators include economic evaluation indicators, environmental evaluation indicators, energy efficiency evaluation indicators, reliability evaluation indicators, external impact evaluation indicators, and consumer satisfaction evaluation indicators.

3. The method for optimizing the operation of a low-carbon energy system in a city according to claim 1, characterized in that, The process of combining the system operation control strategy generation model and multiple evaluation indicators to obtain evaluation indicator values ​​for multiple system operation control strategies includes the following steps: For each of the evaluation indicators, the corresponding system operation control strategy is obtained through the system operation control strategy generation model; Based on the system operation control strategy, the evaluation index values ​​of other evaluation indicators are obtained.

4. The method for optimizing the operation of a low-carbon energy system in a city according to claim 1, characterized in that, The evaluation index values ​​are standardized to satisfy the following formula: in, Indicates the first The standardized values ​​of the evaluation indicators. Indicates the first The evaluation index values ​​to be processed are as follows: Indicates the first The minimum evaluation index value for each evaluation indicator. Indicates the first The maximum evaluation index value of each evaluation indicator.

5. A system for optimizing the operation of a low-carbon energy system in a city, characterized in that, The urban low-carbon energy system operation optimization system includes: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory includes program instructions, which are used to execute the urban low-carbon energy system operation optimization method according to any one of claims 1-4.

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