Urban low-carbon energy system operation optimization method and optimization system

Abstract

The invention relates to the technical field of energy management, in particular to an urban low-carbon energy system operation optimization method and optimization system. The method comprises the following steps: setting a plurality of evaluation indexes for the operation of the urban low-carbon energy system; introducing an improved intelligent algorithm, and constructing a system operation regulation and control strategy generation model; in combination with the system operation regulation and control strategy generation model and the plurality of evaluation indexes, obtaining evaluation index values of the plurality of system operation regulation and control strategies; carrying out weight distribution on the evaluation indexes according to the evaluation index values so as to obtain an operation regulation and control objective function; and based on the operation regulation and control objective function and the system operation regulation and control strategy generation model, solving an optimal operation regulation and control strategy of the urban low-carbon energy system. According to the method, the multiple evaluation indexes of the urban low-carbon energy system are subjected to weight analysis, so that the operation regulation and control strategy of the urban low-carbon energy system is reasonably evaluated, and the operation efficiency of the urban low-carbon energy system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an urban low-carbon energy system operation optimization method and optimization system. Background Art

[0002] As the main body of population and economic activities, cities’ primary energy consumption accounts for about two-thirds of global energy consumption, and their greenhouse gas emissions account for more than 70%. Urban development will be a strong driving force for the green recovery and sustainable development of the global economy. The accompanying long-term growth in energy consumption and environmental problems caused by energy utilization are still important problems faced by countries around the world. The traditional urban energy supply model can no longer meet the needs of sustainable development of cities in terms of resources, environment and economy in the future. The green and low-carbon transformation of urban energy systems has become an inevitable requirement for my country to achieve the goals of carbon peak and carbon neutrality. An urban energy system that is guided by low-carbon development and meets the needs of urban low-carbon transformation is an urban low-carbon energy system.

[0003] Compared with the traditional urban energy infrastructure where each supply and demand department operates independently and energy flows in one direction, the urban low-carbon energy system aims to meet the various energy service needs of urban end users at the same time. Because of its cross-departmental multi-energy complementary coordination, multi-technology coupling linkage, source-grid-load-storage supply and demand synergy and interaction, it can better adapt to the development needs of a high proportion of renewable energy and is an effective carrier for achieving systematic energy conservation and emission reduction in urban energy. However, the traditional energy management system lacks flexibility and has a low level of intelligence, making it difficult to effectively respond to fluctuations in energy demand and achieve energy conservation and emission reduction goals. Summary of the invention

[0004] In view of the shortcomings of existing methods and the needs of practical applications, in order to reasonably evaluate the operation and control strategies of urban low-carbon energy systems, the problem of improving the operation efficiency of urban low-carbon energy systems is solved. On the one hand, the present invention provides an urban low-carbon energy system operation optimization method, comprising the following steps: setting multiple evaluation indicators for the operation of urban low-carbon energy systems; 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 multiple evaluation indicators to obtain multiple evaluation indicator values ​​of system operation and control strategies; weighting the evaluation indicators according to the evaluation indicator values ​​to obtain the operation and control objective function; solving the optimal operation and control strategy of the urban low-carbon energy system based on the operation and control objective function and the system operation and control strategy generation model. The present invention improves the operation efficiency of the urban low-carbon energy system by performing weight analysis on multiple evaluation indicators of the urban low-carbon energy system, thereby reasonably evaluating the operation and control strategy of the urban low-carbon energy system to obtain the optimal operation and control strategy.

[0005] Optionally, the evaluation index includes an economic evaluation index, an environmental evaluation index, an energy efficiency evaluation index, a reliability evaluation index, an external impact evaluation index, and a consumer satisfaction evaluation index. The evaluation index adopted by the present invention provides a strong theoretical support and practical guidance for the collaborative optimization of the energy system, which is conducive to guiding the collaborative optimization results of the energy system to develop in a more ideal direction.

[0006] Optionally, the improved intelligent algorithm comprises the following steps: Determine whether the intelligent algorithm is stuck in an iterative optimization deadlock; adjust the parameters of the intelligent algorithm according to the judgment result. The present invention solves the problem that the intelligent algorithm is prone to fall into a local optimum during the iterative optimization process by determining whether the intelligent algorithm is stuck in an iterative optimization deadlock and then adjusting the parameters of the intelligent algorithm, which is conducive to the present invention to quickly obtain the optimal urban low-carbon energy system operation and control strategy.

[0007] Optionally, the determination of whether the intelligent algorithm is stuck in iterative optimization deadlock satisfies the following formula: in, Indicates The fitness value of the optimal solution at the iteration, Indicates The fitness value of the 10th best solution at the iteration, Indicates The fitness value of the fifth best solution in the iteration, Represents the fitness value of the optimal solution in the iteration history.

[0008] Optionally, the parameters of the intelligent algorithm are adjusted according to the judgment result to satisfy the following formula: in, Indicates the adjusted The optimal solution at the iteration time is express A random number, Indicates the adjustment before The optimal solution at the iteration time is represents the optimal solution in the adjusted iteration history, represents the maximum number of iterations, represents the adjustment factor and satisfies: , The invention replaces the optimal solution of the iteration time and the iteration history in the intelligent algorithm, which helps to get out of the deadlock and obtain the global optimal solution.

[0009] Optionally, combining the system operation regulation strategy generation model and the multiple evaluation indicators to obtain the evaluation indicator values ​​of multiple system operation regulation 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 indicator values ​​of other evaluation indicators are obtained. The present invention obtains the corresponding system operation control strategy for each evaluation indicator, and then obtains the values ​​of other evaluation indicators, which is beneficial to the subsequent step of evaluating and analyzing the weight of the evaluation indicator, and further beneficial to obtaining a more reasonable operation control objective function.

[0010] Optionally, the step of weighting the evaluation indicators according to the evaluation indicator values ​​to obtain an operation control objective function comprises the following steps: The evaluation index is roughly weighted, and the coordinate system is divided based on the rough weighting result to obtain the evaluation index coordinate system; the evaluation index value is standardized; the evaluation index is precisely weighted based on the standardized processing result and the evaluation index coordinate system; based on the weight precision weighting result, the operation control objective function is obtained according to the evaluation index. The present invention comprehensively evaluates the evaluation index by performing two-level weighting on the evaluation index, which is conducive to improving the rationality of the operation control objective function.

[0011] Optionally, the evaluation index value is standardized to satisfy the following formula: in, Indicates The evaluation index value after the evaluation index is standardized. Indicates The evaluation index values ​​to be processed for the evaluation index, Indicates The minimum evaluation index value of the evaluation index, Indicates The present invention removes the dimensional influence of each evaluation index by standardizing the evaluation index value, which is conducive to further improving the rationality of the operation and control objective function.

[0012] Optionally, the combination of the normalization processing result and the evaluation index coordinate system is used to precisely assign weights to the evaluation index, satisfying the following formula: in, Indicates The final weight of the evaluation index is represents the number of evaluation indicators, Indicates that according to The evaluation index obtained The present invention specifically quantifies the final weight of each evaluation index through a model formula, which is conducive to quickly and accurately obtaining the operation control objective function and improving the optimization efficiency of the present invention.

[0013] In the second aspect, in order 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 a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and 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 the present invention. 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 provided by the present invention, further improving the overall applicability and practical application ability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a method for optimizing the operation of a low-carbon energy system in an urban area provided by an embodiment of the present invention; Figure 2 A framework diagram of an urban low-carbon energy system operation optimization system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0016] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0017] See also Figure 1 In order to reasonably evaluate the operation and control strategy of the urban low-carbon energy system and solve the problem of improving the operation efficiency of the urban low-carbon energy system, the present invention provides an urban low-carbon energy system operation optimization method, such as Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Set up multiple evaluation indicators for the operation of urban low-carbon energy systems.

[0018] The urban low-carbon energy system includes the energy supply side, the energy conversion side and the energy consumption side. During operation, it is necessary to adjust the output power of each device on the energy conversion side and the supply situation on the energy supply side according to the load demand of 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 supply situation on the energy supply side is crucial to the operating efficiency of the urban low-carbon energy system.

[0019] In an embodiment, the evaluation index includes an economic evaluation index, an environmental evaluation index, an energy efficiency evaluation index, a reliability evaluation index, an external impact evaluation index and a consumer satisfaction evaluation index.

[0020] Economic evaluation indicators are used to guide the operation of the system to pursue the greatest economic benefits. Economic evaluation indicators mainly include annualized investment costs and operating costs. Annualized investment costs help to evaluate the overall economic benefits of investment. The lower the value, the greater the potential return of the investment in the long run. Operating costs refer to the operating costs required to ensure stable energy supply of the system during the unit operating cycle. This cost is mainly composed of raw material costs and maintenance costs. This cost is an important indicator for measuring the economic operation of the system and a key factor in determining investment returns.

[0021] Environmental protection evaluation index refers to the emission of carbon dioxide. Among the pollutants discharged by the energy system, the content of carbon dioxide accounts for a large proportion. The carbon dioxide emissions of the energy system mainly come from two aspects. On the one hand, the system will produce a certain amount of carbon dioxide emissions during the energy conversion process, such as the gas turbine will emit carbon dioxide during the energy supply process; on the other hand, in the process of purchasing electricity from the system, the electricity of the upper-level power grid often comes from fossil energy with a high carbon content. Therefore, the equivalent carbon emissions generated by purchasing electricity from the upper-level power grid is also one of the important factors that cannot be ignored when evaluating the environmental protection performance of the energy system.

[0022] Energy efficiency evaluation indicators refer to the evaluation of energy consumption and energy conversion efficiency achieved by energy systems in the process of energy consumption. Energy efficiency evaluation indicators mainly include renewable energy consumption rate, primary energy saving rate and system energy supply overflow rate.

[0023] The renewable energy absorption rate refers to the proportion of renewable energy generation actually absorbed in the energy system to the total renewable energy generation.

[0024] The primary energy saving rate refers to the quantitative indicator of the degree of reduction in the total primary energy consumption of the system achieved by building a scientific integrated energy system collaborative optimization strategy during the planning and design of the integrated energy system. By reducing the primary energy usage of the energy system, it can not only effectively alleviate the tight primary energy supply situation, but also bring positive benefits such as significant reduction in carbon emissions.

[0025] The energy 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 multiple types of heterogeneous energy by the energy system, specifically including the energy overflow rate of the system when supplying electricity, heat and cooling energy.

[0026] Reliability evaluation index refers to the ability of the system to provide users with stable and reliable energy supply according to the predetermined energy supply requirements under certain time and conditions. Reliability evaluation index is the core indicator for evaluating the quality of collaborative optimization results, which is of great significance for ensuring the stable operation of the system. Relevant evaluation indicators for measuring energy supply reliability include average load interruption rate and load interruption frequency.

[0027] External impact assessment indicators: As a grid-connected energy system, the operation status of the urban low-carbon energy system is bound to affect the stability and regulation of the upper power system. Therefore, the evaluation of system performance should also comprehensively consider its potential impact on the upper power grid, which can be quantified as the hourly power purchase level and the average power purchase fluctuation rate.

[0028] The hourly electricity purchase level represents the average electricity purchase power of 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 will not only help promote the local consumption of distributed energy, but also effectively reduce the peak and frequency regulation pressure of the large power grid.

[0029] As an important energy source for urban low-carbon energy systems, the rate of change of purchased electricity has a significant impact on the immediate response and overall stability of the superior power grid. The average purchased electricity fluctuation rate indicator is used to quantify the immediate changes in the system's purchased electricity power, thereby reflecting the average impact level of the integrated energy system operation on the superior power grid.

[0030] Furthermore, the average power purchase fluctuation rate index satisfies the following formula: in, represents the average power purchase fluctuation rate index, represents the calculation cycle, Indicates The amount of electricity purchased during the period, Indicates The amount of electricity purchased during the period.

[0031] Consumer satisfaction evaluation indicators include satisfaction with energy use methods and satisfaction with energy efficiency. Specifically, satisfaction with energy use methods refers to the degree of change in consumers' energy use methods after system operation adjustments. The greater the change in electricity load and gas load, the more consumers adjust their energy use behaviors, and the lower their satisfaction with energy use methods. Satisfaction with energy efficiency indicates the efficiency of energy use by consumers in the process of using energy. Improving energy efficiency in the context of the "dual carbon" goal can significantly reduce carbon emissions. Calculate the efficiency of natural gas use before and after system adjustment to obtain energy efficiency satisfaction.

[0032] S2. Introduce improved intelligent algorithms and build a system operation control strategy generation model.

[0033] In this embodiment, the intelligent algorithm is a particle swarm algorithm.

[0034] Further, the intelligent algorithm is improved, including the following steps: S21. Determine whether the intelligent algorithm is stuck in iterative optimization deadlock.

[0035] Specifically, the judgment of whether the intelligent algorithm falls into an iterative optimization deadlock satisfies the following formula: in, Indicates The fitness value of the optimal solution at the iteration, Indicates The fitness value of the 10th best solution at the iteration, Indicates The fitness value of the fifth best solution in the iteration, Represents the fitness value of the optimal solution in the iteration history.

[0036] During the iterative optimization process, if the above model conditions are met for the first or even second time, the intelligent algorithm is considered to be stuck in an iterative optimization deadlock.

[0037] S22. Adjust the parameters of the intelligent algorithm according to the judgment result.

[0038] Specifically, according to the judgment result, the parameters of the intelligent algorithm are adjusted to satisfy the following formula: in, Indicates the adjusted The optimal solution at the iteration time is express A random number, Indicates the adjustment before The optimal solution at the iteration time is represents the optimal solution in the adjusted iteration history, represents the maximum number of iterations, represents the adjustment factor and satisfies: , Represents the optimal solution in the iteration history before adjustment.

[0039] When the intelligent algorithm is stuck in iterative optimization, the corresponding optimal solution can be replaced according to the above model and iterative optimization can be carried out again, thereby breaking out of the optimization deadlock and achieving the goal of improving the quality of the optimal solution.

[0040] Furthermore, an improved intelligent algorithm is introduced to build a system operation control strategy generation model, including the following steps: First, a mathematical model is established based on the urban low-carbon energy system, including mathematical models of the energy supply side such as the external power grid, natural gas supply, wind power, and photovoltaic renewable energy; mathematical models of energy equipment on the energy conversion side such as gas turbines, P2G, CCUS, and load forecasting models on the energy consumption side.

[0041] Secondly, according to the actual operating conditions of the urban low-carbon energy system, the constraints of the corresponding mathematical model are established, such as electric power balance constraints, gas turbine operation constraints, P2G electrolyzer operation constraints, P2G methane generator operation constraints and P2G hydrogen fuel cell operation constraints.

[0042] Finally, based on the mathematical model and corresponding constraints, an improved intelligent algorithm is used to generate the optimal solution, which is the system operation control strategy.

[0043] It should be understood that the basic idea of ​​the particle swarm algorithm is to find the optimal solution through collaboration and information sharing among individuals in the group. In the algorithm, each solution to the optimization problem is regarded as a "particle" that has a certain position and speed in the solution space. By simulating the collective collaborative behavior of bird flocks, particles adjust their positions and speeds based on their own experience and the best experience of their companions, thereby continuously approaching the optimal solution.

[0044] Its core elements are: The fitness function is used to evaluate the adaptability of the particle's current position to the problem. The larger the fitness function value, the better the particle's position and the greater the attraction to the particle.

[0045] Speed ​​and position, particles have two key properties: speed and position. Speed ​​is a vector, including speed magnitude and direction, which is used to determine the direction and distance of the particle's movement in the solution space. Position represents the current coordinates of the particle, which is a possible solution to the problem.

[0046] Individual extremum and group extremum, the individual extremum is the best position found by the particle during the search process, and the group extremum is the best position found in the entire particle group. These two extremums are used to guide the next movement of the particle.

[0047] The specific algorithm process includes: Population initialization: randomly initialize the positions and velocities of particles in the solution space and velocity space; Calculate the fitness value, and calculate the fitness value of each particle according to the fitness function; Update individual extreme values ​​and group extreme values, compare the current fitness value of the particle with the fitness value of the individual extreme value, and update the individual extreme value if it is better; at the same time, compare the fitness values ​​of the individual extreme values ​​of all particles with the group extreme value, and update the group extreme value if it is better; Update speed and position: update the speed and position of particles according to individual extreme values ​​and group extreme values ​​and certain update formulas (usually including memory terms, individual cognitive terms and group cognitive terms); Iteration, repeat the above steps until the preset number of iterations is reached or other termination conditions are met.

[0048] The particle swarm algorithm does not require complex mathematical derivation and programming skills and is easy to understand and implement. Through group collaboration and information sharing, the particle swarm algorithm can quickly find a better solution in a larger search space and can usually converge to a better solution in a shorter number of iterations.

[0049] The performance of the particle swarm algorithm is affected by multiple parameters, including group size, maximum speed, inertia weight, individual cognitive factor, and group cognitive factor. Proper selection of the values ​​of these parameters can improve the performance of the algorithm. For example, a larger inertia weight can enhance global search capabilities, while a smaller one can help local convergence; the balance between individual cognitive factor and group cognitive factor can affect the search behavior and convergence speed of particles.

[0050] Furthermore, the improvement method of the present invention is to adapt the optimal solution to other swarm intelligence optimization algorithms in response to the situation where the optimal solution is deadlocked during the iteration process. Therefore, in other embodiments, the intelligent algorithm can also be other swarm intelligence optimization algorithms such as the firefly algorithm and the cuckoo algorithm.

[0051] S3. Combining the system operation regulation strategy generation model and the plurality of evaluation indicators, obtain evaluation indicator values ​​of a plurality of system operation regulation strategies.

[0052] Specifically, the combining the system operation control strategy generation model and the multiple evaluation indicators to obtain the evaluation indicator values ​​of multiple system operation control strategies includes the following steps: For each of the evaluation indicators, the corresponding system operation regulation strategy is obtained through the system operation regulation strategy generation model. Specifically, the evaluation indicator is used as the fitness function, and the system operation regulation strategy generation model is used to obtain the optimal system operation regulation strategy for the corresponding evaluation indicator. All evaluation indicators are traversed to obtain the corresponding system operation regulation strategies.

[0053] Based on the system operation control strategy, evaluation index values ​​of other evaluation indicators are obtained.

[0054] Analyze each system operation control strategy and obtain the evaluation index value of the corresponding evaluation index.

[0055] S4. Assign weights to the evaluation indicators according to the evaluation indicator values, thereby obtaining an operation control objective function.

[0056] In an embodiment, the step of weighting the evaluation indicators according to the evaluation indicator values ​​to obtain an operation control objective function comprises the following steps: S41 , roughly assigning weights to the evaluation indicators, and dividing the coordinate system based on the rough assignment result to obtain the evaluation indicator coordinate system.

[0057] Specifically, the expert discussion method is used to roughly allocate weights to the evaluation indicators according to the actual situation, and the obtained weight rough allocation results meet the following requirements: , Indicates The rough weights of the evaluation indicators, Indicates the number of evaluation metrics.

[0058] Furthermore, the evaluation indicators are sorted from large to small according to the size of the rough weights, and The angle is divided into two parts counterclockwise from the positive direction of the x-axis to obtain the evaluation index coordinate system.

[0059] S42: Standardize the evaluation index value.

[0060] In the embodiment, the evaluation index value is standardized to satisfy the following formula: in, Indicates The evaluation index value after the evaluation index is standardized. Indicates The evaluation index values ​​to be processed for the evaluation index, Indicates The minimum evaluation index value of the evaluation index, Indicates The maximum evaluation index value of the evaluation index.

[0061] S43, combining the normalization processing result and the evaluation index coordinate system, and performing precise weight allocation on the evaluation index.

[0062] Specifically, the combination of the normalization processing result and the evaluation index coordinate system is used to accurately allocate the weights of the evaluation indexes to satisfy the following formula: in, Indicates The final weight of the evaluation index is the result of the weight distribution. represents the number of evaluation indicators, Indicates that according to The evaluation index obtained The area of ​​the evaluation index value in the evaluation index coordinate system.

[0063] Based on other evaluation indicator values ​​obtained from the evaluation indicator, the corresponding graph of the evaluation indicator formed in the evaluation indicator coordinate system after standardization takes the area of ​​the triangle formed by the coordinate origin, the evaluation indicator value and the value of the next evaluation indicator as the area of ​​the evaluation indicator in the evaluation indicator coordinate system.

[0064] S44. Based on the result of the precise weight distribution, an operation control objective function is obtained according to the evaluation index.

[0065] In the embodiment, the operation control objective function is obtained by combining the evaluation index and the corresponding weight value, and satisfies the following formula: in, represents the operation control objective function, Indicates The final weight of the evaluation index is the result of the weight distribution. Indicates The value of the evaluation indicator.

[0066] S5. Based on the operation and control objective function and the system operation and control strategy generation model, solve the optimal operation and control strategy of the urban low-carbon energy system.

[0067] Specifically, the operation and control objective function is used as the fitness function, and an iterative solution is performed according to the system operation and control strategy generation model to obtain the optimal operation and control strategy of the urban low-carbon energy system.

[0068] See also Figure 2 In an embodiment, in order 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, wherein the input device, the output device, the processor, and the memory are interconnected, and the memory contains program instructions, and the program instructions are 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 ability of the present invention.

[0069] In an embodiment, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. An input device may be used to obtain data information. An output device may be used to output the results obtained by storing program instructions contained in a computer program in a memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0070] In one possible implementation, the memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0071] The embodiment also provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned urban low-carbon energy system operation optimization method are implemented.

[0072] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0073] In summary, the present invention performs weight analysis on multiple evaluation indicators of the urban low-carbon energy system, and then reasonably evaluates the operation and control strategy of the urban low-carbon energy system to obtain the optimal operation and control strategy, thereby improving the operation efficiency of the urban low-carbon energy system.

[0074] Therefore, the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope recorded in the present invention.

Claims

1. A method for optimizing the operation of an urban low-carbon energy system, characterized in that: The urban low-carbon energy system operation optimization method comprises the following steps: Set multiple evaluation indicators for the operation of urban low-carbon energy systems; Introduce improved intelligent algorithms and build a system operation control strategy generation model; Combining the system operation control strategy generation model and the plurality of evaluation indicators, obtaining evaluation indicator values ​​of a plurality of system operation control strategies; According to the evaluation index value, weights are assigned to the evaluation index, thereby obtaining an operation control objective function; Based on the operation and control objective function and the system operation and control strategy generation model, the optimal operation and control strategy of the urban low-carbon energy system is solved.

2. The urban low-carbon energy system operation optimization method according to claim 1 is characterized in that: The evaluation indicators include economic evaluation indicators, environmental protection 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 improved intelligent algorithm comprises the following steps: Determine whether the intelligent algorithm is stuck in iterative optimization deadlock; Adjust the parameters of the intelligent algorithm based on the judgment results.

4. The method for optimizing the operation of a low-carbon energy system in a city according to claim 3, characterized in that: The judgment of whether the intelligent algorithm falls into an iterative optimization deadlock satisfies the following formula: ， in, Indicates The fitness value of the optimal solution at the iteration, Indicates The fitness value of the 10th best solution at the iteration, Indicates The fitness value of the fifth best solution in the iteration, Represents the fitness value of the optimal solution in the iteration history.

5. The method for optimizing the operation of a low-carbon energy system in a city according to claim 3, characterized in that: According to the judgment result, the parameters of the intelligent algorithm are adjusted to satisfy the following formula: ， in, Indicates the adjusted The optimal solution at the iteration time is express A random number, Indicates the adjustment before The optimal solution at the iteration time is represents the optimal solution in the adjusted iteration history, represents the maximum number of iterations, represents the adjustment factor and satisfies: , Represents the optimal solution in the iteration history before adjustment.

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

7. The method for optimizing the operation of a low-carbon energy system in a city according to claim 1, characterized in that: The step of weighting the evaluation indexes according to the evaluation index values ​​to obtain an operation control objective function comprises the following steps: Roughly assigning weights to the evaluation indicators, dividing the coordinate system based on the rough assignment results, and obtaining the evaluation indicator coordinate system; Standardizing the evaluation index value; Combining the normalization processing result and the evaluation index coordinate system, accurately assigning weights to the evaluation index; Based on the precise weight distribution result, the operation control objective function is obtained according to the evaluation index.

8. The method for optimizing the operation of a low-carbon energy system in a city according to claim 7, characterized in that: The evaluation index value is standardized to satisfy the following formula: ， in, Indicates The evaluation index value after the evaluation index is standardized. Indicates The evaluation index values ​​to be processed for the evaluation index, Indicates The minimum evaluation index value of the evaluation index, Indicates The maximum evaluation index value of the evaluation index.

9. The method for optimizing the operation of a low-carbon energy system in a city according to claim 7, characterized in that: The combination of the normalization processing result and the evaluation index coordinate system is used to precisely allocate weights to the evaluation index, satisfying the following formula: ， in, Indicates The final weight of the evaluation index is represents the number of evaluation indicators, Indicates that according to The evaluation index obtained The area of ​​the evaluation index value in the evaluation index coordinate system.

10. An urban low-carbon energy system operation optimization system, characterized in that: The urban low-carbon energy system operation optimization system includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, and the program instructions are used to execute the urban low-carbon energy system operation optimization method according to any one of claims 1 to 9.

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