Energy consumption optimization method and optimization system for reducing loss of electrothermal integrated energy system
By dividing the energy consumption data of the integrated electric and thermal energy system into regions and time periods, and using the expectation-maximization algorithm to generate optimization measures, the problem of the lack of targeted energy consumption optimization in traditional systems is solved, and rapid and accurate identification of energy consumption anomalies and reduction of losses are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA THREE GORGES UNIV
- Filing Date
- 2023-02-21
- Publication Date
- 2026-06-26
Smart Images
Figure CN116258259B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of energy system optimization, specifically relating to an energy consumption optimization method and system for reducing losses in an integrated electrothermal energy system. Background Technology
[0002] Energy consumption, or energy expenditure, is a key indicator reflecting energy consumption levels and energy conservation efforts. The ratio of total primary energy supply to GDP is an indicator of energy efficiency. There are many types of energy consumption, each with different energy consumption levels. The consumption of electrical and thermal energy is called electrical and thermal energy consumption. To reduce electrical and thermal energy consumption, energy consumption optimization design is necessary.
[0003] Chinese patent CN111144642A, entitled "An Optimization Method and System for an Integrated Energy System," discloses an optimization method that includes a microgrid alliance partitioning module, an optimization scheduling model establishment module, an optimization scheduling model solving module, and a system optimization module. This method divides the integrated energy system into a dual alliance of power grid and heat network, with CHP as the alliance center. Optimization scheduling is then performed on each microgrid alliance to achieve the optimization of the integrated energy system containing large-scale distributed energy resources. However, traditional energy consumption optimization systems do not perform specific regional divisions of energy consumption, nor do they analyze energy consumption at specific time points. Therefore, they lack targeted optimization of energy consumption. Often, the energy consumption levels and categories in these regions differ significantly from other regions, and traditional optimization methods cannot fundamentally solve the problem. Summary of the Invention
[0004] The purpose of this invention is to address the aforementioned problems by providing an energy consumption optimization method and system for a comprehensive electrothermal energy system that reduces losses. This method analyzes energy consumption data from different regions to identify consumption sources. Using the normal distribution probability model of the expectation-maximization algorithm, it calculates benchmark data values for energy consumption data analysis and comparison. The method then optimizes energy consumption in different regions, generates suggestions for improving consumption sources (i.e., loss reduction measures), and implements energy consumption optimization.
[0005] The technical solution of this invention is an energy consumption optimization method for a comprehensive electrothermal energy system to reduce losses, comprising the following steps:
[0006] Step 1: Obtain energy consumption data for the integrated electric and thermal energy system;
[0007] Step 2: Process the acquired energy consumption data, categorize it according to the region of energy consumption, and divide it into time periods;
[0008] Step 3: Analyze the energy consumption data for different regions to identify the sources of energy consumption;
[0009] Step 4: Optimize energy consumption for different regions and generate suggestions for improving energy consumption sources, i.e., loss reduction measures;
[0010] Step 5: Implement loss reduction measures, analyze and compare the effects of loss reduction, and record the effective loss reduction measures for promotion and use.
[0011] In step 4, the energy consumption is optimized for different regions. The formula for calculating the energy source deviation is as follows:
[0012]
[0013] In the formula η t Indicates the deviation between energy consumption data and average energy consumption within a specific interval; A t Energy consumption data of the integrated energy system acquired over a time interval of t; This represents the average energy consumption over a 24-hour period.
[0014] The magnitude of energy consumption differences within different time intervals is determined using equation (1); the calculated η t The higher the value, the more severe the energy consumption anomaly during that period.
[0015] Analysis shows that energy consumption data A approximately follows a normal distribution, i.e.
[0016]
[0017] Similarly, A t The different monitoring values also follow a normal distribution, belonging to a subdistribution of A; σ represents the standard deviation;
[0018] Maximum likelihood estimation using the expectation-maximization algorithm:
[0019] Calculate the expectation of the subdistribution
[0020]
[0021]
[0022] In the formula: a i For different energy data A t The weight of the sub-distribution of A; n is the number of values; Indicates energy data A t The probability density function.
[0023] Find the maximum value of each parameter and perform iteration.
[0024]
[0025]
[0026] In the formula μi σ represents the expectation of the i-th normal distribution model; i This represents the standard deviation of the i-th model; This indicates taking the average value of the data in the normal distribution model. and the expectation μ of the normal distribution model i The maximum value in.
[0027] Repeat the above calculations to obtain different σ values. i Choose the value of σ with the smallest value. i The corresponding energy consumption data serves as a benchmark for energy consumption data comparison; A at different time intervals t The longer the monitoring period, the more normal energy consumption data can be obtained for reference, and the more accurate the monitoring of abnormal loss values becomes.
[0028] By comparing energy consumption data, the deviation between the actual energy consumption data of each node and the benchmark is calculated. The larger the deviation, the more serious the loss of the node. A threshold for the deviation is set, and node improvement suggestions, i.e. loss reduction measures, are generated for nodes that exceed the threshold.
[0029] Preferably, the threshold for deviation is set to 20% of the baseline data value.
[0030] Preferably, reactive power compensation is performed on nodes with severe power loss to reduce losses; for nodes with severe heat loss, heat loss is reduced by upgrading or replacing equipment or improving the lines.
[0031] The energy consumption optimization system of the above-mentioned integrated electric and thermal energy system includes an electric and thermal energy consumption data acquisition module, an energy consumption data processing and analysis module, and a system energy consumption optimization module.
[0032] The electric heating energy consumption data acquisition module is used to acquire data from each node in the integrated energy system.
[0033] The energy consumption data processing and analysis module is used to classify and analyze the acquired energy consumption data, and determine the time interval θ and time interval t.
[0034] The system energy consumption optimization module is used to calculate the acquired energy consumption data and generate suggestions for improving energy consumption.
[0035] Compared with the prior art, the beneficial effects of the present invention include:
[0036] 1) This invention acquires energy consumption data from different regions and time intervals, and identifies nodes with abnormal energy consumption in real time; it uses the normal distribution probability model of the expectation-maximization algorithm to calculate the optimal energy consumption data distribution and generate suggestions for improving consumption sources, thereby achieving energy consumption optimization.
[0037] 2) The optimization system of the present invention based on the energy consumption optimization method of the integrated electrothermal energy system with reduced loss is easy to be executed by computer, realizes real-time monitoring, and can more quickly and accurately find energy consumption anomalies. Attached Figure Description
[0038] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0039] Figure 1 This is a flowchart illustrating the energy consumption optimization method for an integrated electrothermal energy system according to an embodiment of the present invention.
[0040] Figure 2 This is a schematic diagram of the module structure of the energy optimization system according to an embodiment of the present invention.
[0041] Figure 3 This is a block diagram of the electrothermal energy consumption data acquisition module according to an embodiment of the present invention.
[0042] Figure 4 This is a block diagram of the energy consumption data processing and analysis module according to an embodiment of the present invention.
[0043] Figure 5 This is a block diagram of the system energy consumption optimization module in an embodiment of the present invention. Detailed Implementation
[0044] like Figure 1 As shown, the energy consumption optimization method for reducing losses in an integrated electrothermal energy system includes the following steps:
[0045] Step 1: Obtain energy consumption data for the integrated electric and thermal energy system;
[0046] Step 2: Process the acquired energy consumption data, categorize it according to the region of energy consumption, and divide it into time periods;
[0047] Step 3: Analyze the energy consumption data for different regions to identify the sources of energy consumption;
[0048] Step 4: Optimize energy consumption for different regions and generate suggestions for improving energy consumption sources, i.e., loss reduction measures;
[0049] Step 5: Implement loss reduction measures, analyze and compare the effects of loss reduction, and record the effective loss reduction measures for promotion and use.
[0050] In step 4, the energy consumption is optimized for different regions. The formula for calculating the energy source deviation is as follows:
[0051]
[0052] In the formula η i Indicates the deviation between energy consumption data and average energy consumption within the interval; A tEnergy consumption data of the integrated energy system acquired over a time interval of t; This represents the average energy consumption over a 24-hour period.
[0053] The magnitude of energy consumption deviation within different time intervals is determined by equation (1); the larger the calculated ηt value, the more severe the energy consumption anomaly within that period.
[0054] Analysis shows that energy consumption data A approximately follows a normal distribution, i.e.
[0055]
[0056] Similarly, A t The different monitoring values also follow a normal distribution, belonging to a subdistribution of A; σ represents the standard deviation;
[0057] Maximum likelihood estimation using the expectation-maximization algorithm:
[0058] Calculate the expectation of the subdistribution
[0059]
[0060]
[0061] In the formula: γ i a represents the probability that the i-th data point belongs to a normal distribution; i For different energy data A t The weight of the sub-distribution of A; n is the number of values; Indicates energy data A t The probability density function;
[0062] Find the maximum value of each parameter and perform iteration.
[0063]
[0064]
[0065] Where: μ i σ represents the expectation of the normal distribution model; i This represents the standard deviation of each model; This indicates taking the average value of the data in the normal distribution model. The expected value μ of the normal distribution model i The maximum value in.
[0066] Repeat the above calculations to obtain different σ values. i Choose the value of σ with the smallest value. i The corresponding energy consumption data serves as a benchmark for energy consumption data comparison; A at different time intervals tBy extending the monitoring period, more normal loss data can be referenced, and the monitoring of abnormal loss values becomes more accurate. By comparison, the deviation between the actual data and the optimized data of each node can be calculated. The larger the deviation, the more severe the loss of that node.
[0067] In this embodiment, nodes whose deviation from the baseline data value exceeds 20% are included in the list of nodes with abnormal energy loss. For nodes with severe power loss, reactive power compensation is performed to reduce losses; for nodes with severe heat loss, equipment upgrades and line improvements are implemented to reduce heat loss.
[0068] The energy consumption optimization system employing the above-mentioned energy consumption optimization method for integrated electric and thermal energy systems includes an electric and thermal energy consumption data acquisition module, an energy consumption data processing module, an energy consumption data analysis module, and a system energy consumption optimization module, such as... Figure 2 As shown.
[0069] The electric heating energy consumption data acquisition module is used to acquire data from each node in the integrated energy system.
[0070] The energy consumption data processing and analysis module is used to classify and analyze the acquired energy consumption data, and determine the time interval θ and time interval t.
[0071] The system energy consumption optimization module is used to calculate the acquired energy consumption data and generate suggestions for improving energy consumption.
[0072] like Figure 3 As shown, the electric heating energy consumption data acquisition module includes an electric heating energy consumption type acquisition submodule, an electric heating energy consumption data classification submodule, an invalid energy consumption data filtering submodule, and an energy consumption data integration submodule.
[0073] The electric heating energy consumption data acquisition submodule is used to acquire energy consumption data of each node in the system.
[0074] The electric heating energy consumption data classification submodule is used to classify energy consumption data into electrical energy loss data and thermal energy loss data by identification.
[0075] The invalid energy consumption data filtering submodule is used to eliminate data that does not conform to the pattern of energy consumption data.
[0076] The energy consumption data integration submodule is used to integrate the classified data to obtain energy consumption data A.
[0077] like Figure 4 As shown, the energy consumption data processing and analysis module includes a regional division submodule, a different regional numbering submodule, a time period acquisition submodule, an electric heating energy consumption data analysis submodule, and an electric heating energy consumption data export submodule within the region.
[0078] The region partitioning submodule is used to determine different time intervals θ.
[0079] The different region numbering submodule is used to number the time interval θ.
[0080] The time interval acquisition submodule is used to determine the time interval t, where t << θ.
[0081] The electrothermal energy consumption data analysis submodule is used to analyze the electrothermal data within the region and determine the power loss data and heat loss data through node consumption data.
[0082] The regional electric heating energy consumption data export submodule is used to determine the energy consumption data A that has been obtained.
[0083] like Figure 5 As shown, the system energy consumption optimization module includes a consumption source integration submodule, an optimization model establishment submodule, a consumption source standard calculation submodule, a consumption data comparison submodule, and a consumption source improvement suggestion generation submodule.
[0084] The energy consumption source integration submodule is used to determine the mean and standard deviation of energy consumption data, as well as the probability density function of energy consumption data;
[0085] The optimization model building submodule is used to build a normal distribution probability model for the expectation maximization algorithm;
[0086] The standard calculation submodule for the consumption source is used to calculate the probability γ. i The expected value μ of the normal distribution model i The standard deviation σ of the normal distribution model i ;
[0087] The consumption data comparison submodule is used to calculate the minimum standard deviation σ. i and σ i Comparison of corresponding energy consumption data with actual energy consumption data;
[0088] The Consumption Source Improvement Suggestion Generation Submodule is used to generate improvement suggestions, i.e. loss reduction measures, for nodes with abnormal energy consumption.
Claims
1. A method for optimizing energy consumption in a comprehensive electrothermal energy system to reduce losses, characterized in that, Includes the following steps: Step 1: Obtain energy consumption data for the integrated electric and thermal energy system; Step 2: Process the acquired energy consumption data, categorize it according to the region of energy consumption, and divide it into time periods; Step 3: Analyze the energy consumption data for different regions to identify the sources of energy consumption; Step 4: Optimize energy consumption for different regions and generate suggestions for improving energy consumption sources, i.e., loss reduction measures; Step 5: Implement loss reduction measures, analyze and compare the effects of loss reduction, and record the effective loss reduction measures for promotion and use; The energy consumption optimization method also includes an energy consumption optimization system, which includes an electric heating energy consumption data acquisition module, an energy consumption data processing and analysis module, and a system energy consumption optimization module. The electric heating energy consumption data acquisition module is used to acquire energy consumption data of each node in the integrated energy system; The energy consumption data processing and analysis module is used to classify and analyze the acquired energy consumption data, and determine the time interval. θ and time interval t ; The system energy consumption optimization module is used to calculate the acquired energy consumption data and generate suggestions for improving energy consumption. The electric heating energy consumption data acquisition module includes an electric heating energy consumption type acquisition submodule, an electric heating energy consumption data classification submodule, an invalid energy consumption data screening submodule, and an energy consumption data integration submodule. The electric heating energy consumption data acquisition submodule is used to acquire energy consumption data of each node in the integrated energy system; The electric heat energy consumption data classification submodule is used to divide energy consumption data into electrical energy loss data and heat energy loss data. The invalid energy consumption data filtering submodule is used to eliminate data that does not conform to the patterns of energy consumption data. The energy consumption data integration submodule is used to integrate the classified and processed data to obtain energy consumption data. A ; The energy consumption data processing and analysis module includes a region division submodule, a different region numbering submodule, a time period acquisition submodule, an electric heating energy consumption analysis submodule, and an electric heating energy consumption data export submodule within the region. The region division submodule is used to determine different time intervals. θ ; The different region numbering submodule is used for time intervals θ Number them; The time interval acquisition submodule is used to determine the time interval. t , t << θ ; The electric heating energy consumption data analysis submodule is used to analyze the electric heating data within the region and determine the power loss data and heat loss data through node consumption data. The regional electric heating energy consumption data export submodule is used to export the acquired energy consumption data. A .