Comprehensive optimization method of low-carbon park energy system

Abstract

The invention discloses a comprehensive optimization method for a low-carbon park energy system, and the method comprises the steps: building an energy system simulation model based on the energy load demand prediction and energy system operation control principles of a low-carbon park; and performing multi-target energy system configuration parameter optimization through a particle swarm hybrid optimization algorithm of cubic mapping. According to the multi-objective optimization, evaluation factors such as the total cost of the energy system and economic operation and environmental influence of the energy system are considered, and an optimal capacity configuration scheme is formed. According to the overall scheme, optimal configuration of multiple types of energy systems can be systematically solved, and the optimal energy system form of the low-carbon park can be rapidly constructed. Meanwhile, the particle swarm optimization algorithm based on cubic mapping can ensure the global and local search optimization capability and the accuracy of the optimization result, and the performance of optimization configuration calculation of the park integrated energy system is improved.

Description

Technical Field

[0001] This invention relates to the field of low-carbon energy use optimization technology, and is particularly applicable to the comprehensive optimization method of energy systems in low-carbon industrial parks. Background Technology

[0002] With the deepening and orderly implementation of the "dual carbon" strategic goals, the construction concept of low-carbon and even zero-carbon building parks has gradually received widespread attention and entered the practical stage. Constructing a composite energy system for low-carbon parks and carrying out scientific and rational planning and configuration is of significant practical necessity for meeting diverse energy demands, improving comprehensive energy utilization efficiency, and promoting the large-scale consumption of renewable energy.

[0003] However, to improve the overall performance of the park's energy system, it is necessary to adopt systematic, integrated, and refined analysis methods at both ends of energy production and consumption, and comprehensively examine the energy flow and conversion process, thereby enhancing the system's robustness and overall energy efficiency.

[0004] In existing literature, patent CN119886852A establishes a visual interface for displaying park energy planning and configuration. It uses a genetic algorithm to determine the park's energy planning constraints based on the energy equipment information corresponding to the target energy equipment identifiers in the target park's energy system architecture. Based on the park's energy planning objective function, it determines the target energy planning scheme among candidate energy planning schemes, enabling convenient and accurate comprehensive energy planning for the park. Patent CN114971048A performs comprehensive carbon accounting for the park, analyzes the characteristics of wind and solar natural resources, and analyzes load characteristic data. With carbon emissions, investment costs, and new energy absorption rates as optimization objectives, it performs capacity planning and configuration for the energy system of cooling, heating, and electricity. Using clustering and NSGA algorithms, it forms the optimal capacity configuration and operation scheme.

[0005] However, models such as genetic algorithms and clustering algorithms have long training times and low learning efficiency. Their convergence speed is too slow, and they are prone to getting trapped in local minima, failing to reach the global optimum, which affects the performance of the model. Furthermore, their poor optimization performance is not conducive to target scheduling problems in scenarios with multiple data inputs. Summary of the Invention

[0006] The purpose of this invention is to provide a comprehensive optimization method for low-carbon park energy systems, which provides a multi-dimensional, globally optimal, and more accurate park energy system planning and configuration method with higher model operating efficiency.

[0007] To achieve the above objectives, the comprehensive optimization method for low-carbon industrial park energy systems described in this invention includes the following steps: S1, based on the historical energy load of the park, establish a prediction model for the park's cooling, heating and electricity loads, and predict the total hourly cooling, heating and electricity loads of the park's energy system throughout the year; S2, based on historical data of the park's energy system and the principles of energy system operation and control, constructs a simulation model of the park's energy system; S3, Construct an evaluation system of indicators that affect the configuration parameters of the park's energy system; S4. Initialize the energy system configuration parameters and their value ranges. Use the cubic mapping particle swarm optimization algorithm and a two-stage iterative optimization. First, based on the particle swarm algorithm and the dynamic simulation results of the simulation model, determine the configuration parameters with the optimal life cycle cost for each energy system. Then, using the cost-optimal configuration parameters as initial values, use cubic mapping to determine the configuration parameters of each energy system with the optimal comprehensive evaluation index. S5, until the comprehensive evaluation index no longer decreases, determine the final configuration parameters of each energy system.

[0008] Furthermore, step S1 specifically includes: according to the functional classification of the overall construction plan of the park, calculating the building area of ​​each functional category; based on the historical annual hourly cooling, heating and electricity load characteristics of each functional category of buildings, combined with the simultaneous use coefficient and building area of ​​each functional category of buildings, calculating the annual hourly cooling, heating and electricity load of each energy system in the park in the future.

[0009] Furthermore, the energy system includes solar energy, gas boilers, municipal heating, cooling, heating and power, auxiliary cold and heat sources, heat pumps, and energy storage systems.

[0010] Furthermore, in step S3, the indicator evaluation system includes primary indicators: system cost, economic operation, and environmental impact; secondary indicators belonging to the primary indicator system cost: computer room equipment costs, pipeline installation costs, computer room construction costs, and depreciation; secondary indicators belonging to the primary indicator economic operation: electricity prices, heat prices, comprehensive energy efficiency, and maintenance costs; and secondary indicators belonging to the primary indicator environmental impact: renewable energy utilization rate and carbon emission reduction.

[0011] Furthermore, the Monte Carlo simulation method was used to examine the impact of secondary indicators on the energy system configuration of the park. Based on the probability distribution of the impact of secondary indicators on the energy system configuration, the local weights of the secondary indicators were obtained. The scores of the secondary indicators were determined through historical data surveys and simulation models, and the scores of the corresponding primary indicators were obtained by weighted summation.

[0012] Furthermore, based on the global weights of the secondary indicators, the weights of the primary indicators are calculated, and the comprehensive evaluation index is obtained by weighted summation.

[0013] Furthermore, the simulation model needs to take into account outdoor meteorological parameters, energy system configuration parameters, and equipment performance during simulation calculations.

[0014] Furthermore, the total life-cycle cost of the energy system includes the initial investment cost, system operation and maintenance cost, and the net residual value of fixed costs.

[0015] The advantages of this invention lie in its foundation on energy load demand forecasting and energy system operation control principles for low-carbon industrial parks, constructing an energy system simulation model; and employing a cubic mapping-based particle swarm optimization algorithm to optimize energy system configuration parameters for multiple objectives. This multi-objective optimization considers the total cost of the energy system, as well as evaluation factors such as economic operation and environmental impact, resulting in an optimal capacity configuration scheme. The overall scheme can systematically solve the optimal configuration of multiple types of energy systems, quickly constructing the optimal energy system configuration for low-carbon industrial parks. Furthermore, the cubic mapping-based particle swarm optimization algorithm ensures global and local search optimization capabilities and the accuracy of optimization results, improving the performance of the comprehensive energy system optimization configuration calculation for industrial parks. Attached Figure Description

[0016] Figure 1 Flowchart of the comprehensive optimization method for the low-carbon industrial park energy system described in this invention.

[0017] Figure 2 The flowchart of the particle swarm optimization algorithm for cubic mapping in the method described in this invention. Detailed Implementation

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] The comprehensive optimization method for low-carbon industrial park energy systems described in this invention, such as... Figure 1 As shown, it includes the following steps: S1, based on the park's historical energy load, establishes a prediction model for the park's cooling, heating, and electricity loads, and predicts the park's total hourly cooling, heating, and electricity loads throughout the year.

[0020] First, based on the functional classification of the overall construction plan of the park, the building area of ​​each functional category is calculated. Then, based on the historical hourly cooling, heating, and electricity load characteristics of each functional category, and according to the building area of ​​each functional category, the hourly cooling, heating, and electricity load of each functional category is predicted for the entire year. Finally, considering the simultaneous use coefficient of each functional building within the park, the hourly cooling, heating, and electricity load of each energy system for the entire year can be predicted.

[0021] S2, based on historical data and operational control principles of the park's energy system, constructs a simulation model of the park's energy system. The energy system may include numerous systems such as solar energy, gas-fired boilers, municipal heating, cooling, heating, and power (CHP), auxiliary cold and heat sources, heat pumps, and energy storage systems. The simulation model also considers external input parameters, including indoor and outdoor meteorological parameters, energy system configuration parameters, and equipment performance. During simulation calculations, the model allocates the cooling, heating, and electrical loads to different energy systems based on the hourly cooling, heating, and electrical loads calculated for each energy system throughout the year, according to pre-set operational control principles, considering indoor and outdoor meteorological parameters, energy system configuration parameters, and equipment performance. The simulation outputs operational data such as dynamic load, energy consumption, and total lifecycle cost for different energy systems.

[0022] S3, Construct an evaluation system of indicators that affect the configuration parameters of the park's energy system.

[0023] The indicator evaluation system includes primary and secondary indicators. The primary indicators consist of three items: system cost, economic operation, and environmental impact. Each primary indicator contains multiple secondary indicators. The secondary indicators belonging to the primary indicator of system cost include: computer room equipment costs, piping installation costs, computer room construction costs, and depreciation. Secondary indicators belonging to the primary indicator of economic operation include: electricity price, heat price, comprehensive energy efficiency, and maintenance cost; secondary indicators belonging to the primary indicator of environmental impact include: renewable energy utilization rate and carbon emission reduction.

[0024] Monte Carlo simulation was used to examine the impact of secondary indicators on the energy system configuration of the industrial park. Based on the probability distribution of the impact of the secondary indicators on energy system configuration, the local weights of the secondary indicators were obtained. Then, through historical data surveys and simulation modeling, the scores of each secondary indicator were determined, and the scores of the primary indicators were obtained by weighted summation.

[0025] Based on the Monte Carlo simulation method, the weights of the primary indicators are calculated using the global weights of the secondary indicators, and then a comprehensive evaluation index is obtained through weighted summation. This comprehensive evaluation index is used to assess the merits of the current energy system configuration parameters.

[0026] S4. Initialize the energy system configuration parameters and their value ranges. Use a cubic mapping particle swarm optimization algorithm to iteratively adjust the configuration parameters of each energy system in the park until the optimal configuration parameters for each energy system are found.

[0027] like Figure 2 As shown, the particle swarm optimization algorithm of cubic mapping adopts a two-stage strategy of coarse-to-fine optimization, which balances computational efficiency and optimization accuracy, and achieves the optimization of configuration parameters of various energy systems in low-carbon parks.

[0028] In the first phase, a global exploration based on gridded particle swarm optimization is conducted to quickly locate the optimal configuration parameters for the total lifecycle cost of each energy system. Specifically, dynamic simulation using a simulation model is used to obtain the total lifecycle cost of each energy system corresponding to different configuration parameters. The optimal configuration parameters for the total lifecycle cost of each energy system are then identified. The total lifecycle cost of an energy system includes initial investment costs, system operation and maintenance costs, and the net residual value of fixed costs.

[0029] The optimal configuration parameters found in the first stage are used as the initial values ​​for the second node. In the second stage, cubic mapping is used to further optimize within the region of the configuration parameters that have the optimal life-cycle cost for each energy system. Using the comprehensive evaluation index as the standard, the optimal configuration parameters for each energy system are found where the comprehensive evaluation index no longer decreases, thus obtaining the optimal configuration parameters for each energy system.

Claims

1. A comprehensive optimization method for a low-carbon industrial park energy system, characterized in that, Includes the following steps: S1, based on the historical energy load of the park, establish a prediction model for the park's cooling, heating and electricity loads, and predict the total hourly cooling, heating and electricity loads of the park's energy system throughout the year; S2, based on historical data of the park's energy system and the principles of energy system operation and control, constructs a simulation model of the park's energy system; S3, construct an evaluation system for indicators affecting the configuration parameters of the park's energy system, and calculate the comprehensive evaluation index of the energy system configuration parameters; S4. Initialize the energy system configuration parameters and their value ranges. Use the cubic mapping particle swarm optimization algorithm and a two-stage iterative optimization. First, based on the particle swarm algorithm and the dynamic simulation results of the simulation model, determine the configuration parameters with the optimal life cycle cost for each energy system. Then, using the cost-optimal configuration parameters as initial values, use cubic mapping to determine the configuration parameters of each energy system with the optimal comprehensive evaluation index. S5, until the comprehensive evaluation index no longer decreases, determine the final configuration parameters of each energy system.

2. The comprehensive optimization method for low-carbon industrial park energy systems according to claim 1, characterized in that: Step S1 specifically includes: based on the functional classification of the overall construction plan of the park, calculating the building area of ​​each functional category; based on the historical hourly cooling, heating and electricity load characteristics of each functional category of buildings throughout the year, combined with the simultaneous use coefficient and building area of ​​each functional category of buildings, calculating the hourly cooling, heating and electricity load of each energy system in the park throughout the year.

3. The comprehensive optimization method for low-carbon industrial park energy systems according to claim 1, characterized in that: The energy system includes solar energy, gas boilers, municipal heating, cooling, heating and power, auxiliary cold and heat sources, heat pumps, and energy storage systems.

4. The comprehensive optimization method for low-carbon industrial park energy systems according to claim 1, characterized in that: In step S3, the indicator evaluation system includes primary indicators: system cost, economic operation, and environmental impact; secondary indicators belonging to the primary indicator system cost: computer room equipment cost, pipeline installation cost, computer room construction cost, and depreciation; and secondary indicators belonging to the primary indicator economic operation: electricity price, heat price, comprehensive energy efficiency, and maintenance cost. The secondary indicators belonging to the primary indicator of environmental impact are: renewable energy utilization rate and carbon emission reduction.

5. The comprehensive optimization method for the low-carbon industrial park energy system according to claim 4, characterized in that: The Monte Carlo simulation method was used to examine the impact of secondary indicators on the energy system configuration of the park. Based on the probability distribution of the impact of secondary indicators on the energy system configuration, the local weights of the secondary indicators were obtained. The scores of the secondary indicators were determined through historical data surveys and simulation models. The scores of the corresponding primary indicators were obtained by weighted summation.

6. The comprehensive optimization method for low-carbon park energy systems according to claim 5, characterized in that: Based on the global weights of the secondary indicators, the weights of the primary indicators are calculated, and the comprehensive evaluation index is obtained by weighted summation.

7. The comprehensive optimization method for low-carbon park energy systems according to claim 1, characterized in that: The simulation model needs to take into account outdoor meteorological parameters, energy system configuration parameters, and equipment performance during simulation calculations.

8. The comprehensive optimization method for low-carbon industrial park energy systems according to claim 1, characterized in that: The total life-cycle cost of the energy system includes the initial investment cost, system operation and maintenance cost, and the net residual value of fixed costs.

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