Collaborative planning method for regional integrated energy system

Abstract

The invention discloses a collaborative planning method for a regional integrated energy system, and the method comprises the steps: firstly collecting regional historical energy consumption data and various external factor data, and predicting an energy demand through employing a gray prediction and multiple linear regression combined algorithm; exploring existing energy supply facilities in the region and evaluating renewable energy potential; determining a candidate type of the integrated energy system component according to the result, establishing an optimal configuration model, and solving through a particle swarm optimization algorithm; constructing an energy network layout planning model, and determining an optimal topological structure and parameters of the network; establishing a dynamic operation model, and formulating a collaborative operation strategy by using a model prediction control algorithm; and finally, comprehensively evaluating the planning scheme, and analyzing and adjusting the scheme by utilizing sensitivity according to an evaluation result. According to the method, multiple factors are comprehensively considered, and the problems that in existing regional integrated energy system planning, demand prediction is not accurate, resource evaluation is not comprehensive, component configuration and network layout lack optimization collaboration, and the operation strategy is single are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy planning, and in particular to a method for collaborative planning of a regional integrated energy system. Background Art

[0002] In today's energy field, with the increasingly stringent requirements for improving energy utilization efficiency, enhancing energy supply stability and environmental protection, the construction of a regional integrated energy system has become an important development direction.

[0003] In the selection and configuration of integrated energy system components, traditional methods lack systematic optimization strategies. The equipment type and quantity are often determined based on experience or simple cost-benefit analysis, and a comprehensive multi-objective optimization model has not been established to comprehensively consider factors such as equipment investment, operation and maintenance, energy procurement, and environmental costs. At the same time, when solving optimization problems, the algorithms used are inefficient and prone to falling into local optimal solutions, making it impossible to obtain a truly global optimal configuration solution, resulting in high construction and operation costs for integrated energy systems and difficulty in improving energy efficiency.

[0004] In terms of energy network layout planning, most existing technologies design electricity, natural gas and thermal networks separately, without fully considering the synergy and coupling relationship between different energy networks. When planning the layout, the focus is mainly on reducing construction costs, ignoring the balance of key factors such as transmission loss and reliability. For example, when determining the network topology and pipeline and line parameters, there is a lack of effective combination optimization algorithms, making it difficult to achieve multi-objective optimization of network construction costs, transmission losses and reliability, thus affecting the overall performance and operational stability of the energy network.

[0005] In terms of collaborative operation strategy formulation, the traditional regional energy system operation control means are relatively simple and static. They fail to fully consider the dynamic characteristics of different energy conversion equipment and the real-time changes in energy demand, and lack effective prediction and dynamic adjustment mechanisms. For example, in the actual operation process, it is impossible to flexibly allocate energy according to the real-time energy supply and demand situation, resulting in low efficiency in energy conversion and distribution, difficulty in ensuring system reliability, and inability to adapt to complex and changing energy consumption demand scenarios.

[0006] In summary, the existing regional energy planning technology has defects in component selection and configuration, network layout planning, and collaborative operation strategies, making it difficult to achieve efficient, stable, economical and environmentally friendly operation of regional integrated energy systems. The present invention aims to provide a regional integrated energy system collaborative planning method to comprehensively and systematically solve the above technical problems, promote the development and progress of regional integrated energy system planning technology, and meet the needs of modern energy management and sustainable development. Summary of the invention

[0007] 1. Technical issues

[0008] The present invention aims to overcome the many defects existing in the existing regional integrated energy system planning technology and provide a comprehensive and efficient regional integrated energy system collaborative planning method. By integrating a variety of advanced technologies and innovative algorithms, it can accurately predict energy demand, scientifically evaluate energy supply resources, optimize system component selection and configuration, rationally plan energy network layout, and formulate dynamic collaborative operation strategies to achieve significant improvements in energy utilization efficiency, system reliability, economy, and environmental friendliness of the regional integrated energy system, and meet the growing and complex needs of modern society for regional energy integrated management.

[0009] (II) Technical solution

[0010] The technical solution of the present invention is: a method for collaborative planning of a regional integrated energy system, comprising the following steps:

[0011] Step 1: Energy demand analysis and forecasting:

[0012] Collect historical energy consumption data in the region, including hourly or daily consumption data of various energy sources such as electricity, natural gas, and heat, and construct an energy consumption time series data set;

[0013] A hybrid prediction algorithm based on the combination of grey prediction model and multiple linear regression analysis is used to predict various energy demands in the region. The grey prediction model is used to explore the inherent development trend of energy demand data, and the multiple linear regression analysis is used to consider various external factors that affect energy demand, such as weather factors, economic development indicators, and regional population density changes. The prediction formula is as follows:

[0014]

[0015] Among them, E t is the predicted energy demand at time t, T t is the average temperature at time t, GDP t is the regional GDP at time t, PD t is the regional population density at time t, GM i,t is the i-th energy demand trend component obtained by the grey prediction model, α0, α1, α2, α3, β i is the coefficient obtained by fitting historical data;

[0016] Step 2: Energy supply resource assessment and potential analysis:

[0017] Conduct detailed surveys of existing energy supply facilities in the region, including power plants, natural gas wells, and thermal power plants, to obtain information on their supply capacity, operating efficiency, and maintenance cost parameters;

[0018] Based on geographic information system (GIS) technology, the potential of renewable energy resources in the region, such as the distribution of solar radiation intensity and wind energy resources, is analyzed. The upper limit of the exploitable solar and wind energy installed capacity is determined using the solar radiation calculation model and the wind power generation potential assessment model. The solar radiation calculation model uses the astronomical radiation calculation formula combined with the regional atmospheric transmittance correction to obtain the regional hourly solar radiation. The wind power generation potential assessment model calculates the available power based on the regional wind speed Weibull distribution parameters and the wind turbine power curve.

[0019] Step 3: Integrated energy system component selection and configuration optimization:

[0020] Based on the energy demand forecast results and energy supply resource assessment, determine the candidate types of various energy conversion and storage equipment in the integrated energy system, such as gas turbines, heat pumps, batteries, and heat storage tanks;

[0021] An optimization configuration model with the lowest annual total cost of the integrated energy system as the objective function is established. The total cost includes equipment investment cost, operation and maintenance cost, energy procurement cost and environmental cost. The constraints include energy balance constraint, equipment capacity constraint and equipment operation status constraint. The particle swarm optimization algorithm is used to solve the model and obtain the optimal configuration and configuration scheme of various energy components. The objective function formula is as follows:

[0022] C totat =∑ j (C inv，j +C om，j +C en，j +C env，j )

[0023] Among them, C total is the total annual cost of the comprehensive energy system, C inv，j is the annual investment cost of the jth type of equipment, C om，j is the annual operation and maintenance cost of the jth type of equipment, C enj is the annual energy purchase cost of the jth type of equipment, C env，j is the annual environmental cost of the jth type of equipment;

[0024] Step 4: Energy network layout planning:

[0025] According to the configuration plan of the integrated energy system components, the layout planning model of the power network, natural gas network and thermal network is constructed. Considering the network construction cost, transmission loss and reliability factors, the minimum spanning tree algorithm is combined with the network flow optimization algorithm to determine the optimal topological structure of the energy network and the pipe and line diameter parameters. The minimum spanning tree algorithm is used to preliminarily determine the basic connection architecture of the network, and the network flow optimization algorithm is used to optimize the network flow distribution and pipe and line diameter selection under the premise of meeting the energy transmission demand and reliability requirements.

[0026] Step 5: Collaborative operation strategy formulation:

[0027] Based on the component configuration and network layout of the integrated energy system, a dynamic operation model of the integrated energy system is established, taking into account the coupling relationship between different energy sources, the dynamic characteristics of energy conversion equipment, and the real-time changes in user energy demand;

[0028] The model predictive control algorithm is used to formulate the coordinated operation strategy of the integrated energy system to achieve efficient energy distribution and conversion and improve the energy utilization efficiency and reliability of the system. The model predictive control algorithm predicts the system operation status in the time domain through rolling optimization and adjusts the control strategy in time according to the real-time measurement feedback information. The optimization formula is as follows:

[0029]

[0030] Among them, J is the performance index in the prediction time domain, C k is the operating cost at time k, E loss，k is the energy loss at time k, R k is the reliability index at time k, γ1, γ2, γ3 are weight coefficients, and N is the prediction time domain length;

[0031] Step 6: Planning scheme evaluation and adjustment:

[0032] Conduct a comprehensive evaluation of the regional integrated energy system collaborative planning scheme obtained, with evaluation indicators including energy utilization efficiency, system reliability, economic indicators, i.e., investment payback period, internal rate of return, and environmental impact indicators;

[0033] According to the evaluation results, the sensitivity analysis method is used to analyze the impact of changes in key parameters in the planning scheme on the evaluation indicators. If the evaluation indicators do not meet the expected requirements, the corresponding parts of the planning scheme are adjusted, and the process returns to step three to re-select components and optimize configurations, or to step four to re-plan the energy network layout until the planning scheme meets the requirements.

[0034] Furthermore, in step 1, the grey prediction model adopts a first-order univariate grey prediction model, and its basic differential equation is:

[0035]

[0036] Among them, a is the development grayscale, u is the endogenous control grayscale, x(1)(t) is the first-order cumulative generation sequence of the original energy demand data sequence x(0)(t), and a and u are solved by the least squares method to obtain the energy demand forecast value.

[0037] Furthermore, in step three, the position vector of the particle in the particle swarm optimization algorithm represents the configuration quantity or capacity of various types of equipment in the integrated energy system, and the velocity vector represents the rate of change of the configuration quantity or capacity of the equipment. The optimal solution is continuously searched through the position update and velocity update of the particle, and the position update formula is as follows:

[0038]

[0039] in, is the position of the d-th dimension of the ith particle in the k+1th iteration, is the position of the d-th dimension of the ith particle in the k-th iteration, is the velocity of the dth dimension of the ith particle in the k+1th iteration.

[0040] Furthermore, in step four, the network flow optimization algorithm is solved using a linear programming method, and its objective function is to minimize the sum of network construction cost and operation cost. Constraints include node flow balance constraints, pipeline capacity constraints, and pressure constraints.

[0041] Furthermore, in step 5, the prediction model of the model predictive control algorithm adopts a state space model, which is obtained by linearizing the integrated energy system, and its state equation and output equation are as follows:

[0042]

[0043] Among them, x k is the state vector of the system at time k, u k is the control input vector of the system at time k, y k is the output vector of the system at time k, A, B, C, D are coefficient matrices, and w k is the system state noise vector, v k is the measurement noise vector.

[0044] (III) Beneficial effects:

[0045] 1. The present invention uses a hybrid prediction algorithm based on a combination of a grey prediction model and multivariate linear regression analysis to predict energy demand, fully considering the comprehensive impact of various external factors on various energy demands, and significantly improving the accuracy of energy demand prediction. This enables energy supply to match actual demand more accurately, effectively avoids the phenomenon of excess or shortage of energy supply, reduces energy waste, and improves energy utilization efficiency. In the component selection and configuration optimization link of the integrated energy system, an optimization configuration model with the lowest annual total cost of the integrated energy system as the objective function is established, and the particle swarm optimization algorithm is used to solve it. The model comprehensively considers multiple objectives such as equipment investment cost, operation and maintenance cost, energy procurement cost, and environmental cost, and can obtain the optimal selection and configuration scheme of various energy components. This not only reduces the construction and operation costs of the system, but also improves the overall performance of the system and realizes the optimal allocation of resources.

[0046] 2. Based on the configuration scheme of the components of the integrated energy system, an energy network layout planning model is constructed, and the minimum spanning tree algorithm combined with the network flow optimization algorithm is used to determine the optimal topology and parameters of the energy network. This method fully considers multiple factors such as network construction cost, transmission loss, and reliability, and realizes multi-objective optimization of energy network construction. It can effectively reduce transmission loss, improve energy transmission efficiency, and enhance the reliability and stability of the energy network to ensure the safety and stability of regional energy supply. Based on the component configuration and network layout of the integrated energy system, a model predictive control algorithm is used to formulate a collaborative operation strategy. The algorithm predicts the system operation status in the time domain through rolling optimization, and adjusts the control strategy in time according to the real-time measurement feedback information, fully considering the coupling relationship between different energy sources, the dynamic characteristics of energy conversion equipment, and the real-time changes in user energy demand. In this way, efficient distribution and conversion of energy are realized, the energy utilization efficiency and reliability of the system are further improved, the adaptability of the system to complex and changeable energy consumption demand scenarios is enhanced, and strong support is provided for the intelligent operation of regional energy systems.

[0047] 3. From an economic perspective, accurate demand forecasting, optimized component configuration, reasonable network layout and efficient collaborative operation strategies work together to significantly reduce the full life cycle cost of the regional integrated energy system, including the cost of initial construction investment, mid-term operation and maintenance, and later energy procurement, and improve the project's return on investment and internal rate of return, with good economic feasibility and market competitiveness. In terms of environmental benefits, by optimizing energy configuration and collaborative operation, the utilization ratio of renewable energy in the integrated energy system is increased, and the dependence on traditional fossil energy is reduced, thereby reducing the emission of pollutants such as carbon dioxide, which helps to alleviate environmental pollution problems and promote the development of regional energy systems in a green and sustainable direction. It meets the strict requirements of today's society for environmental protection and has significant positive environmental externalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of a regional integrated energy system collaborative planning method of the present invention DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] like Figure 1 As shown, the present invention discloses a regional integrated energy system collaborative planning method, comprising the following steps:

[0051] Step 1: Energy demand analysis and forecasting:

[0052] Collect historical energy consumption data in the region, including hourly or daily consumption data of various energy sources such as electricity, natural gas, and heat, and construct an energy consumption time series data set;

[0053] A hybrid prediction algorithm based on the combination of grey prediction model and multiple linear regression analysis is used to predict various energy demands in the region. The grey prediction model is used to explore the inherent development trend of energy demand data, and the multiple linear regression analysis is used to consider various external factors that affect energy demand, such as weather factors, economic development indicators, and regional population density changes. The prediction formula is as follows:

[0054]

[0055] Among them, E t is the predicted energy demand at time t, T t is the average temperature at time t, GDP t is the regional GDP at time t, PD t is the regional population density at time t, GM i，t is the i-th energy demand trend component obtained by the grey prediction model, α0, α1, α2, α3, β i is the coefficient obtained by fitting historical data;

[0056] The grey prediction model adopts a first-order single variable grey prediction model, and its basic differential equation is:

[0057]

[0058] Among them, a is the development grayscale, u is the endogenous control grayscale, and x (1) (t) is the original energy demand data sequence x (0) The first-order accumulation sequence of (t) is used to solve a and u by the least square method to obtain the energy demand forecast value.

[0059] Step 2: Energy supply resource assessment and potential analysis:

[0060] Conduct detailed surveys of existing energy supply facilities in the region, including power plants, natural gas wells, and thermal power plants, to obtain information on their supply capacity, operating efficiency, and maintenance cost parameters;

[0061] Based on geographic information system (GIS) technology, the potential of renewable energy resources in the region, such as the distribution of solar radiation intensity and wind energy resources, is analyzed. The upper limit of the exploitable solar and wind energy installed capacity is determined using the solar radiation calculation model and the wind power generation potential assessment model. The solar radiation calculation model uses the astronomical radiation calculation formula combined with the regional atmospheric transmittance correction to obtain the regional hourly solar radiation. The wind power generation potential assessment model calculates the available power based on the regional wind speed Weibull distribution parameters and the wind turbine power curve.

[0062] Step 3: Integrated energy system component selection and configuration optimization:

[0063] Based on the energy demand forecast results and energy supply resource assessment, determine the candidate types of various energy conversion and storage equipment in the integrated energy system, such as gas turbines, heat pumps, batteries, and heat storage tanks;

[0064] An optimization configuration model with the lowest annual total cost of the integrated energy system as the objective function is established. The total cost includes equipment investment cost, operation and maintenance cost, energy procurement cost and environmental cost. The constraints include energy balance constraint, equipment capacity constraint and equipment operation status constraint. The particle swarm optimization algorithm is used to solve the model and obtain the optimal configuration and configuration scheme of various energy components. The objective function formula is as follows:

[0065] C total =∑ j (C inv，j +C om，j +C en，j +C env，j )

[0066] Among them, C total is the total annual cost of the comprehensive energy system, C inv，j is the annual investment cost of the jth type of equipment, C om，j is the annual operation and maintenance cost of the jth type of equipment, C en，j is the annual energy purchase cost of the jth type of equipment, C env，jis the annual environmental cost of the jth type of equipment;

[0067] The position vector of the particle in the particle swarm optimization algorithm represents the configuration quantity or capacity of various types of equipment in the integrated energy system, and the velocity vector represents the rate of change of the configuration quantity or capacity of the equipment. The optimal solution is continuously searched through the position update and velocity update of the particle. The position update formula is as follows:

[0068]

[0069] in, is the position of the d-th dimension of the ith particle in the k+1th iteration, is the position of the d-th dimension of the ith particle in the k-th iteration, is the velocity of the dth dimension of the ith particle in the k+1th iteration.

[0070] Step 4: Energy network layout planning:

[0071] According to the configuration plan of the integrated energy system components, the layout planning model of the power network, natural gas network and thermal network is constructed. Considering the network construction cost, transmission loss and reliability factors, the minimum spanning tree algorithm is combined with the network flow optimization algorithm to determine the optimal topological structure of the energy network and the pipe and line diameter parameters. The minimum spanning tree algorithm is used to preliminarily determine the basic connection architecture of the network, and the network flow optimization algorithm is used to optimize the network flow distribution and pipe and line diameter selection under the premise of meeting the energy transmission demand and reliability requirements.

[0072] The network flow optimization algorithm is solved by a linear programming method, and its objective function is to minimize the sum of network construction cost and operation cost. The constraints include node flow balance constraint, pipeline capacity constraint, and pressure constraint.

[0073] Step 5: Collaborative operation strategy formulation:

[0074] Based on the component configuration and network layout of the integrated energy system, a dynamic operation model of the integrated energy system is established, taking into account the coupling relationship between different energy sources, the dynamic characteristics of energy conversion equipment, and the real-time changes in user energy demand;

[0075] The model predictive control algorithm is used to formulate the coordinated operation strategy of the integrated energy system to achieve efficient energy distribution and conversion and improve the energy utilization efficiency and reliability of the system. The model predictive control algorithm predicts the system operation status in the time domain through rolling optimization and adjusts the control strategy in time according to the real-time measurement feedback information. The optimization formula is as follows:

[0076]

[0077] Among them, J is the performance index in the prediction time domain, Ck is the operating cost at time k, E loss，k is the energy loss at time k, R k is the reliability index at time k, γ1, γ2, γ3 are weight coefficients, and N is the prediction time domain length;

[0078] The prediction model of the model predictive control algorithm adopts a state space model, which is obtained by linearizing the integrated energy system. Its state equation and output equation are as follows:

[0079]

[0080] Among them, x k is the state vector of the system at time k, u k is the control input vector of the system at time k, y k is the output vector of the system at time k, A, B, C, D are coefficient matrices, and w k is the system state noise vector, v k is the measurement noise vector.

[0081] Step 6: Planning scheme evaluation and adjustment:

[0082] Conduct a comprehensive evaluation of the regional integrated energy system collaborative planning scheme obtained, with evaluation indicators including energy utilization efficiency, system reliability, economic indicators, i.e., investment payback period, internal rate of return, and environmental impact indicators;

[0083] According to the evaluation results, the sensitivity analysis method is used to analyze the impact of changes in key parameters in the planning scheme on the evaluation indicators. If the evaluation indicators do not meet the expected requirements, the corresponding parts of the planning scheme are adjusted, and the process returns to step three to re-select components and optimize configurations, or to step four to re-plan the energy network layout until the planning scheme meets the requirements.

[0084] The specific detailed operation process of the present invention is as follows:

[0085] 1. Implementation Area Selection

[0086] An emerging commercial area in a certain city was selected as the implementation area for the coordinated planning of the regional integrated energy system. It includes various building types such as commercial office buildings, hotels, shopping malls, and residential buildings. The energy demand is diverse and fluctuates greatly over time. It is typical and complex, and is suitable for the application and verification of the method of the present invention.

[0087] 2. Energy Demand Analysis and Forecast

[0088] 1. Data collection: Cooperate with local energy supply departments, meteorological agencies and statistical departments to collect historical energy consumption data of the region in the past five years, including hourly consumption data of various energy sources such as electricity, natural gas, and heat. At the same time, obtain weather data (such as daily average temperature, humidity, etc.), regional economic development indicators (such as annual GDP, industrial structure data, etc.) and regional population density change data for the same period.

[0089] 2. Application of hybrid prediction algorithm: First, the original energy demand data is preprocessed, including data cleaning (removing outliers, supplementing missing values, etc.) and normalization processing, so that it is suitable for the prediction model. The first-order univariate grey prediction model is used to explore the inherent development trend of energy demand data. Taking electricity demand as an example, the original electricity demand data sequence is constructed, and a new sequence is generated by first-order accumulation. The development grayscale and endogenous control grayscale parameters of the grey prediction model are solved by the least squares method, and then the trend component prediction value of electricity demand is obtained. At the same time, multiple linear regression analysis is used to consider the impact of external factors on energy demand. The average temperature, regional GDP, regional population density, etc. are used as independent variables, and energy demand is used as the dependent variable. The regression coefficient is obtained by fitting historical data. Combined with the trend component obtained by the grey prediction model, the predicted demand of various energy sources at different times is calculated according to the prediction formula in the patent claims. For example, after calculation, the power demand forecast curve of a typical day in the future in the region is obtained. Its peak period coincides with the commercial office hours and the peak of residential electricity consumption, and it shows the characteristics of the peak of air conditioning electricity consumption in summer due to the influence of temperature, which provides an accurate basis for subsequent energy supply planning.

[0090] III. Energy Supply Resource Assessment and Potential Analysis

[0091] 1. Survey of existing energy supply facilities: Organize a professional team to conduct a detailed survey of existing power plants, natural gas pressure regulating stations, thermal boiler rooms and other energy supply facilities in the region. Record the equipment parameters of each facility, such as the installed capacity, power generation efficiency, and operation and maintenance records of the power plant; the gas supply pressure, flow range, and equipment aging degree of the natural gas pressure regulating station; the heating capacity, thermal efficiency, fuel consumption and other information of the thermal boiler room. Through field measurements and data analysis, determine the actual supply capacity, operation efficiency, maintenance cost and other key parameters of each facility.

[0092] 2. Assessment of renewable energy resource potential: Based on geographic information system (GIS) technology, obtain geographic information data in the region, including topography, building distribution and other information. Use the solar radiation calculation model, combined with the regional atmospheric transmittance correction, to calculate the hourly solar radiation distribution in the region. For example, through analysis, it was found that a certain open roof area in the region has a high solar radiation intensity and has great potential for solar energy development. According to the regional meteorological data, determine the parameters of the wind speed Weibull distribution, and combine the wind turbine power curve to calculate the upper limit of the developable wind energy installed capacity using the wind power generation potential assessment model. According to the assessment, there is a certain scale of wind energy resources that can be developed in the open areas outside the region.

[0093] 4. Component selection and configuration optimization of integrated energy system

[0094] 1. Determination of candidate components: Based on the energy demand forecast results and energy supply resource assessment, determine the candidate types of various energy conversion and storage equipment in the integrated energy system. For example, considering the large regional electricity demand and the existence of a natural gas supply base, gas turbines are selected as power generation equipment, which have the advantages of fast startup, high efficiency and the ability to utilize natural gas resources; in response to the heating demand in the region, heat pumps are selected as heat supply equipment, which can effectively improve energy utilization efficiency; at the same time, batteries are equipped for power storage to balance fluctuations in power supply and demand, and heat storage tanks are used to store excess heat to improve the flexibility of thermal energy utilization.

[0095] 2. Solving the optimization configuration model: Establish an optimization configuration model with the lowest annual total cost of the integrated energy system as the objective function, and determine the calculation method and parameter values ​​of various costs in detail. For example, the equipment investment cost obtains the purchase price and installation cost of different equipment based on market research, and considers the depreciation calculation of the equipment service life; the operation and maintenance cost is calculated based on the equipment's operating hours, maintenance cycle and single maintenance cost; the energy procurement cost is calculated based on the energy market price forecast and energy demand forecast results; the environmental cost is determined by calculating the pollutant emissions and corresponding environmental governance costs during the use of different energy sources. The particle swarm optimization algorithm is used to solve the model. Set the parameters such as the size of the particle swarm, the number of iterations, and the learning factor, where the particle position vector represents the configuration quantity or capacity of various energy equipment, and the velocity vector represents the rate of change of the equipment configuration quantity or capacity. In the iterative process, the fitness value of each particle is calculated according to the objective function, and the optimal solution is continuously searched through the particle position update and velocity update. After multiple iterative calculations, the optimal configuration and configuration scheme of various energy components are obtained. For example, determine the specific parameter configurations such as the installed capacity of the gas turbine, the heating power of the heat pump, the capacity of the battery, the volume of the heat storage tank, so that the annual total cost of the integrated energy system can be minimized while meeting energy needs.

[0096] V. Energy network layout planning

[0097] 1. Network layout model construction: Based on the configuration plan of the integrated energy system components, the layout planning model of the power network, natural gas network and thermal network is constructed. The network construction cost is considered, including the laying costs of cables and pipelines, and the construction costs of nodes such as substations and voltage regulating stations; transmission loss is calculated based on the transmission characteristics and line length of different energy networks; reliability is improved by setting up redundant lines and backup equipment, and it is quantified as reliability indicators and included in the model.

[0098] 2. Application of optimization algorithm: The minimum spanning tree algorithm is used to preliminarily determine the basic connection architecture of the network. Taking the power network as an example, each power node in the area is regarded as a vertex in the graph, and the cable connection is regarded as an edge. The weight of the edge is a comprehensive indicator of construction cost and transmission loss. The minimum spanning tree algorithm is used to find the tree structure that connects all vertices and has the smallest weight, and determine the basic topology of the power network. On this basis, the network flow optimization algorithm is used to further optimize the network flow distribution and pipe diameter and line diameter selection. Taking the natural gas network as an example, the objective function of the network flow optimization algorithm is set to minimize the sum of network construction cost and operation cost. The constraints include node flow balance constraint (that is, the natural gas flow entering the node is equal to the flow out of the node), pipeline capacity constraint (ensuring that the natural gas flow does not exceed the maximum transmission capacity of the pipeline), pressure constraint (ensuring that the transmission pressure of natural gas in the pipeline is within a safe range), etc. By solving the linear programming problem, the diameter of each pipeline in the natural gas network and the flow distribution of natural gas are determined to achieve the best network performance.

[0099] 6. Collaborative Operation Strategy Formulation

[0100] 1. Establishment of dynamic operation model: Based on the component configuration and network layout of the integrated energy system, a dynamic operation model of the integrated energy system is established. The integrated energy system is linearized to obtain a state space model, in which the state vector includes the operating state parameters of each energy device (such as the speed, temperature, and power generation of the gas turbine; the heating coefficient and operating current of the heat pump, etc.), the storage capacity of the energy storage device (the power of the battery, the heat storage of the heat storage tank), and the flow, pressure and other parameters in the energy network; the control input vector includes the control instructions for each energy device (such as the fuel supply of the gas turbine, the operating frequency of the heat pump, etc.); the output vector is the actual energy supply output of the system (electricity, heat, etc.). Through the analysis of the physical process of the system and mathematical modeling, the coefficient matrix in the state equation and the output equation is determined, and the system state noise vector (such as random fluctuations in equipment operation, interference from environmental factors, etc.) and the measurement noise vector (error of the measuring instrument, etc.) are considered.

[0101] 2. Implementation of model predictive control algorithm: Use model predictive control algorithm to formulate collaborative operation strategy. Set the prediction time domain length, for example, 12 hours. At the beginning of each control cycle, use the dynamic operation model to predict the system operation status in the future prediction time domain based on the current system status and energy demand forecast information. Take energy utilization efficiency and system reliability as the main performance indicators, construct the performance indicator function in the prediction time domain, including operating cost (energy consumption cost, equipment operation and maintenance cost, etc.), energy loss (power transmission loss, heat loss, etc.) and reliability indicators (such as energy supply interruption probability, voltage fluctuation range, etc.), and set corresponding weight coefficients for each indicator to balance the relationship between different goals. The minimum value of the performance indicator function is solved by rolling optimization to obtain the optimal control sequence in the prediction time domain. In the control cycle, only the first control instruction is implemented, and then according to the actual operation feedback information of the system (real-time collection of system state parameters through sensors), at the beginning of the next control cycle, the above prediction and optimization process is repeated to adjust the control strategy in time to adapt to the real-time changes in energy demand and the dynamic fluctuations of the system operation status. For example, during peak commercial electricity consumption periods, the gas turbine's power generation capacity is increased according to real-time demand, while the battery discharge strategy is adjusted to coordinate the operation of the thermal system to ensure efficient distribution and conversion of energy and maintain stable operation of the system.

[0102] VII. Evaluation and Adjustment of Planning Schemes

[0103] 1. Determination of scheme evaluation indicators: A comprehensive evaluation is conducted on the obtained regional integrated energy system collaborative planning scheme, and the following evaluation indicators are determined: Energy utilization efficiency: It is measured by calculating the ratio of the effective energy actually output by the system to the total amount of input energy, reflecting the degree of energy utilization of the system. System reliability: Comprehensive evaluation is conducted using indicators such as the proportion of energy supply interruption time to total operating time, voltage qualification rate, and heating temperature stability, reflecting the system's ability to ensure a continuous and stable supply of energy. Economic indicators: Calculate the payback period, that is, the time required from the start of project construction to the cumulative net cash flow equal to zero; internal rate of return, the discount rate when the net present value of the project is zero. These indicators reflect the investment benefits and profitability of the project. Environmental impact indicators: Calculate the carbon dioxide emission reduction, compare the difference in carbon dioxide emissions between this planning scheme and the traditional energy supply scheme during energy production and consumption, and evaluate the improvement effect on the environment.

[0104] 2. Evaluation and adjustment: According to the above evaluation indicators, the planning scheme is evaluated in detail. For example, after calculation, it is found that the energy utilization efficiency of the regional comprehensive energy system is 10% higher than that of the traditional planning scheme, the investment recovery period is shortened by 5 years, the carbon dioxide emission reduction reaches 2 tons / year, and the system reliability index also meets the relevant standard requirements, indicating that the scheme has achieved significant optimization effects in many aspects. If the evaluation index does not meet the expected requirements, the sensitivity analysis method is used to analyze the impact of changes in key parameters (such as the number of energy equipment configurations, network layout parameters, control strategy parameters, etc.) in the planning scheme on the evaluation indicators. For example, it is found that the configuration quantity of a certain energy equipment has a great impact on the energy utilization efficiency and investment recovery period. If the energy utilization efficiency does not meet the standard, the configuration quantity of the equipment is appropriately increased, and the component selection and configuration optimization steps are returned to recalculate; if there is a problem with the system reliability index, the redundant line settings or spare equipment parameters in the energy network layout planning are adjusted, and the energy network layout planning step is returned to re-optimize until the planning scheme meets the requirements.

[0105] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A regional integrated energy system collaborative planning method, characterized in that: The following steps are involved: Step 1: Energy demand analysis and forecasting: Collect historical energy consumption data in the region, including hourly or daily consumption data of various energy sources such as electricity, natural gas, and heat, and construct an energy consumption time series data set; A hybrid prediction algorithm based on the combination of grey prediction model and multiple linear regression analysis is used to predict various energy demands in the region. The grey prediction model is used to explore the inherent development trend of energy demand data, and the multiple linear regression analysis is used to consider various external factors that affect energy demand, such as weather factors, economic development indicators, and regional population density changes. The prediction formula is as follows: Among them, E t is the predicted energy demand at time t, T t is the average temperature at time t, GDP t is the regional GDP at time t, PD t is the regional population density at time t, GM i,t is the i-th energy demand trend component obtained by the grey prediction model, α0, α1, α2, α3, β i is the coefficient obtained by fitting historical data; Step 2: Energy supply resource assessment and potential analysis: Conduct detailed surveys of existing energy supply facilities in the region, including power plants, natural gas wells, and thermal power plants, to obtain information on their supply capacity, operating efficiency, and maintenance cost parameters; Based on geographic information system (GIS) technology, the potential of renewable energy resources in the region, such as the distribution of solar radiation intensity and wind energy resources, is analyzed. The upper limit of the exploitable solar and wind energy installed capacity is determined using the solar radiation calculation model and the wind power generation potential assessment model. The solar radiation calculation model uses the astronomical radiation calculation formula combined with the regional atmospheric transmittance correction to obtain the regional hourly solar radiation. The wind power generation potential assessment model calculates the available power based on the regional wind speed Weibull distribution parameters and the wind turbine power curve. Step 3: Comprehensive energy system component selection and configuration optimization: Based on the energy demand forecast results and energy supply resource assessment, determine the candidate types of various energy conversion and storage equipment in the integrated energy system, such as gas turbines, heat pumps, batteries, and heat storage tanks; An optimization configuration model with the lowest annual total cost of the integrated energy system as the objective function is established. The total cost includes equipment investment cost, operation and maintenance cost, energy procurement cost and environmental cost. The constraints include energy balance constraint, equipment capacity constraint and equipment operation status constraint. The particle swarm optimization algorithm is used to solve the model and obtain the optimal configuration and configuration scheme of various energy components. The objective function formula is as follows: C total =∑ j (C inv,j +C om,j +C en,j +C env,j ) Among them, C total is the total annual cost of the comprehensive energy system, C inv,j is the annual investment cost of the jth type of equipment, C om,j is the annual operation and maintenance cost of the jth type of equipment, C en,j is the annual energy purchase cost of the jth type of equipment, C env,j is the annual environmental cost of the jth type of equipment; Step 4: Energy network layout planning: According to the configuration plan of the integrated energy system components, the layout planning model of the power network, natural gas network and thermal network is constructed. Considering the network construction cost, transmission loss and reliability factors, the minimum spanning tree algorithm is combined with the network flow optimization algorithm to determine the optimal topological structure of the energy network and the pipe and line diameter parameters. The minimum spanning tree algorithm is used to preliminarily determine the basic connection architecture of the network, and the network flow optimization algorithm is used to optimize the network flow distribution and pipe and line diameter selection under the premise of meeting the energy transmission demand and reliability requirements. Step 5: Collaborative operation strategy formulation: Based on the component configuration and network layout of the integrated energy system, a dynamic operation model of the integrated energy system is established, taking into account the coupling relationship between different energy sources, the dynamic characteristics of energy conversion equipment, and the real-time changes in user energy demand; The model predictive control algorithm is used to formulate the coordinated operation strategy of the integrated energy system to achieve efficient energy distribution and conversion and improve the energy utilization efficiency and reliability of the system. The model predictive control algorithm predicts the system operation status in the time domain through rolling optimization and adjusts the control strategy in time according to the real-time measurement feedback information. The optimization formula is as follows: Among them, J is the performance index in the prediction time domain, C k is the operating cost at time k, E loss,k is the energy loss at time k, R k is the reliability index at time k, γ1, γ2, γ3 are weight coefficients, and N is the prediction time domain length; Step 6: Planning scheme evaluation and adjustment: Conduct a comprehensive evaluation of the regional integrated energy system collaborative planning scheme obtained, with evaluation indicators including energy utilization efficiency, system reliability, economic indicators, i.e., investment payback period, internal rate of return, and environmental impact indicators; According to the evaluation results, the sensitivity analysis method is used to analyze the impact of changes in key parameters in the planning scheme on the evaluation indicators. If the evaluation indicators do not meet the expected requirements, the corresponding parts of the planning scheme are adjusted, and the process returns to step three to re-select components and optimize configurations, or to step four to re-plan the energy network layout until the planning scheme meets the requirements.

2. A regional integrated energy system collaborative planning method according to claim 1, characterized in that: In step 1, the grey prediction model adopts a first-order univariate grey prediction model, and its basic differential equation is: Among them, a is the development grayscale, u is the endogenous control grayscale, and x (1) (t) is the original energy demand data sequence x (0) The first-order accumulation sequence of (t) is used to solve a and u by the least square method to obtain the energy demand forecast value.

3. A regional integrated energy system collaborative planning method according to claim 1, characterized in that: In step 3, the position vector of the particle in the particle swarm optimization algorithm represents the configuration quantity or capacity of various types of equipment in the integrated energy system, and the velocity vector represents the rate of change of the configuration quantity or capacity of the equipment. The optimal solution is continuously searched through the position update and velocity update of the particle. The position update formula is as follows: in, is the position of the d-th dimension of the ith particle in the k+1th iteration, is the position of the d-th dimension of the ith particle in the k-th iteration, is the velocity of the dth dimension of the ith particle in the k+1th iteration.

4. A regional integrated energy system collaborative planning method according to claim 1, characterized in that: In step 4, the network flow optimization algorithm is solved using a linear programming method, and its objective function is to minimize the sum of network construction cost and operation cost. Constraints include node flow balance constraints, pipeline capacity constraints, and pressure constraints.

5. A regional integrated energy system collaborative planning method according to claim 1, characterized in that: In step 5, the prediction model of the model predictive control algorithm adopts a state space model, which is obtained by linearizing the integrated energy system, and its state equation and output equation are as follows: Among them, x k is the state vector of the system at time k, u k is the control input vector of the system at time k, y k is the output vector of the system at time k, A, B, C, D are coefficient matrices, and w k is the system state noise vector, v k is the measurement noise vector.

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