Optimized scheduling method containing multi-energy coupling

Abstract

The invention discloses a multi-energy coupling-containing optimal scheduling method, which comprises the following steps: S1, system modeling and basic data preparation: establishing models of renewable energy sources, energy coupling equipment and energy storage equipment, collecting and processing basic data, modeling a multi-energy coupling system, and obtaining a multi-energy coupling system model; comprising the links of production, transmission, conversion, consumption and the like of various energy sources, and the model can accurately reflect the dynamic characteristics and constraint conditions of the system; and S2, load prediction and demand analysis. According to the optimal scheduling method containing multi-energy coupling, complementarity and collaboration among different energy sources can be fully considered, and collaborative utilization of various energy sources is realized through an optimal scheduling strategy, so that energy waste can be reduced, and the overall utilization efficiency of the energy sources can be improved, for example, in an electric power, natural gas and thermal power integrated energy system, the optimal scheduling method has a wide application prospect. By coordinating the operation of the subsystems, the energy utilization efficiency can be maximized on the premise of meeting the requirements.

Description

Technical Field

[0001] The present invention relates to the field of multi - energy coupling scheduling technology, and specifically to an optimal scheduling method including multi - energy coupling. Background Technique

[0002] In recent years, with the rapid development of the economy, many clean - energy power - generation technologies have become indispensable energy sources for the national economy. Among them, new - energy power plants include equipment such as photovoltaic power plants, wind power plants, and combined heat and power plants. These power plants jointly deliver electric energy to loads through a common bus. The demand response on the user side including air - source heat pumps and controllable loads helps wind and solar power plants to consume clean energy. However, the system operation cost remains high, and the power - grid peak - shaving capacity is insufficient, which has long been the focus of attention of power enterprises. At this time, it is necessary to coordinate the production, transmission, storage, and consumption of multiple energies such as electricity, heat, natural gas, and hydrogen energy to achieve the technical means of efficient, economic, and low - carbon operation of the energy system. The core lies in using the complementary characteristics of multiple energies to solve problems such as the volatility of renewable energy and the lack of system flexibility.

[0003] Existing various energies may be in an isolated state and cannot achieve complementary and collaborative utilization. This will lead to some energies not being effectively utilized when the supply is excessive, and being unable to meet the demand when the supply is insufficient, thus causing energy waste. In the power system, if the electric power generated by renewable energies such as wind energy and solar energy cannot be timely scheduled and utilized, it may be wasted due to the insufficient grid acceptance capacity. At the same time, it will lead to the segmentation and fragmentation of the energy market, making the energy price unable to fully reflect the supply - demand relationship, and then affecting the efficiency and fairness of the energy market. Therefore, we propose an optimal scheduling method including multi - energy coupling to solve the problems raised above. Summary of the Invention

[0004] The purpose of the present invention is to provide an optimal scheduling method including multi - energy coupling to solve the problem that existing various energies in the current market may be in an isolated state and cannot achieve complementary and collaborative utilization, which will lead to some energies not being effectively utilized when the supply is excessive, and being unable to meet the demand when the supply is insufficient, thus causing energy waste as mentioned in the above - mentioned background technique.

[0005] To achieve the above object, the present invention provides the following technical solutions: An optimized scheduling method with multi - energy coupling, comprising the following steps: S1. System modeling and basic data preparation: Establish models of renewable energy, energy coupling devices and energy storage devices. At the same time, collect and process basic data, and model the multi - energy coupling system, including various links such as production, transmission, conversion and consumption of various energies, and the model should be able to accurately reflect the dynamic characteristics and constraint conditions of the system; S2. Load forecasting and demand analysis: Conduct electricity, cooling, heating and gas load forecasting for a future time scale of 24 hours or longer, and analyze the characteristics of energy demand; S3. Day - ahead scheduling plan formulation: Establish a day - ahead scheduling model and solve the day - ahead scheduling model; S4. Intra - day rolling optimized scheduling: Establish an intra - day rolling optimized scheduling model and adjust the device output in real time; S5. Monitoring and evaluation: Monitor the system operation status in real time and evaluate the effect of the scheduling strategy; S6. Scheduling strategy optimization and iteration: First, collect feedback information, optimize the scheduling strategy, and perform iterative updates.

[0006] Preferably, the renewable energy includes wind power, photovoltaic, etc., and the energy coupling devices cover gas turbines, fuel cells, waste heat boilers, absorption chillers, gas boilers, electric chillers, electric boilers and power - to - gas, etc. And the energy storage devices include lead - acid batteries, ice - thermal energy storage, heat storage tanks and gas storage tanks, etc. Then, collecting and processing basic data includes collecting and processing basic data, and these data are used for subsequent load forecasting and scheduling strategy formulation. And for the power generation characteristics and prediction data of renewable energy (such as solar energy, wind energy, etc.), establish corresponding mathematical models, and these models need to be able to reflect the output changes of renewable energy and the power generation capacity under different weather conditions. At the same time, devices for converting and coupling different forms of energy, such as power converters, thermal couplers, etc.

[0007] Preferably, in the load forecasting and demand analysis, advanced forecasting algorithms and models are used to accurately predict the future energy demand of the system based on historical data and real - time information, and identify the distribution characteristics of different energy demands in time and space, as well as their interaction and coupling relationships. And through simulation software, the model is simulated and analyzed to simulate the system operation characteristics and optimization strategies under different scenarios, which helps to evaluate the effects of different scheduling schemes and provide a basis for subsequent optimized scheduling.

[0008] Preferably, in the formulation of the day-ahead scheduling plan, the scheduling duration is 24 hours a day, and the unit scheduling time is 1 hour or a shorter time interval. The predicted results of electricity, cooling, heating, and gas loads for the next 24 hours are substituted into the objective function and constraint conditions of the optimal scheduling model, and an optimization algorithm (such as the second-order cone optimization algorithm, Cplex optimizer, etc.) is used to solve the problem, obtaining the planned output values and operating states of the energy coupling equipment and energy storage equipment, the minimum operating cost value of the system, etc. According to the day-ahead scheduling plan, combined with real-time load data and prediction results, the system is subjected to intraday rolling optimal scheduling. The intraday rolling optimal scheduling is usually divided into three sub-layers: the slow control layer, the intermediate control layer, and the fast control layer, which are optimized according to the scheduling characteristics of cooling and heating energy, natural gas, and electric energy respectively. Among them, the slow control layer: adjusts the output of the energy coupling equipment and energy storage equipment according to the intraday changes in cooling and heating loads to meet the cooling and heating load requirements. The intermediate control layer: adjusts the operating strategy of the natural gas system according to the intraday changes in natural gas loads to ensure the stability and economy of natural gas supply. The fast control layer: performs real-time scheduling and optimization for the fast-changing characteristics of the electric energy system to ensure the stable operation and supply-demand balance of the power system.

[0009] Preferably, multiple objectives usually need to be considered simultaneously in the intraday rolling optimal scheduling, such as minimizing energy costs, reducing carbon emissions, and improving energy utilization efficiency. Therefore, a multi-objective optimization algorithm needs to be used to solve this problem, and intelligent optimization algorithms such as genetic algorithms, particle swarm algorithms, and ant colony algorithms have extensive applications in solving the optimal scheduling problem of multi-energy coupling systems. These algorithms can globally search for the optimal solution and have good robustness and adaptability.

[0010] Preferably, in the mathematical model of the intraday rolling optimal scheduling, the objective function is usually multi-objective optimization, and economic, environmental protection and other indicators need to be weighed. For example: min(Coperation + λ⋅Ccarbon), where Coperation is the operating cost and λ is the carbon emission penalty coefficient. The constraint conditions include energy balance, the supply-demand balance equations of each energy network, equipment physical limitations, CHP output range, energy storage charge and discharge rate, energy conversion efficiency, etc., network constraints, power grid power flow equations, heat network hydraulic-thermal coupling models, gas network pressure equations, etc., and operation safety, the upper and lower limits of parameters such as voltage, temperature, and pressure.

[0011] Preferably, in the monitoring and evaluation, the energy supply and demand situation, equipment operation status, etc. of the system are monitored in real time through sensors and data acquisition systems, and the collected data is transmitted to the central control system or data center for unified storage and management to ensure the integrity, accuracy, and timeliness of the data. At the same time, the collected data is cleaned, verified, and formatted to remove outliers, duplicate values, and missing values to improve the data quality. Moreover, through the visualization interface or monitoring system, the operation status and key parameters of the energy system, such as power load, natural gas flow, thermal temperature, etc., are monitored in real time, and warning thresholds are set. When the system parameters exceed the thresholds, the warning mechanism is triggered to notify the relevant personnel for handling in a timely manner. Then, the actual operation results are compared with the dispatching plan to evaluate the effectiveness and accuracy of the dispatching strategy, and the dispatching strategy is adjusted and optimized according to the evaluation results.

[0012] Preferably, in the optimization and iteration of the dispatching strategy, the results of the day-ahead dispatching and the intra-day rolling optimization dispatching are analyzed to evaluate the system stability and energy utilization efficiency. According to the analysis results, the dispatching strategy is adjusted, such as adjusting the output plan of the energy coupling equipment, the charge and discharge strategy of the energy storage equipment, etc. Then, the adjusted dispatching strategy is re-substituted into the model for solution to obtain a new dispatching result, and the above process is continuously repeated until the dispatching result meets the requirements of system stability and energy utilization efficiency, or reaches the predetermined number of iterations. At the same time, feedback information on energy supply and demand, equipment failures, market changes, etc. is collected during the system operation, and based on the feedback information, the dispatching strategy is adjusted and optimized to improve the economy and stability of the system, and the optimized dispatching strategy is applied to the subsequent system operation and continuously iteratively updated and improved.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: The optimization dispatching method with multi-energy coupling adopts a novel structural design, and the specific content is as follows: (1) It can fully consider the complementarity and synergy between different energies. By optimizing the dispatching strategy, the collaborative utilization of various energies can be realized, which helps to reduce energy waste and improve the overall energy utilization efficiency. For example, in the integrated energy system of electricity, natural gas, and heat, by coordinating the operation of each subsystem, the energy utilization efficiency can be maximized on the premise of meeting the demand.

[0014] (2) It can reduce the overall operation cost of the energy system. By accurately predicting energy demand and supply, and optimizing the energy distribution and conversion process, unnecessary energy losses and waste can be reduced, and at the same time, it also helps to achieve the efficient utilization of energy, thereby reducing energy costs and improving economic benefits.

[0015] (3) It can enhance the stability of the energy system. By coordinating the operation of different energy systems, it can balance the energy supply and demand relationship, reduce the instability of energy supply. When there are problems with certain energy supplies, the stability and security of energy supply can be ensured through the supplementation and synergy of other energy sources.

[0016] (4) It helps to reduce carbon emissions and environmental pollution. By improving energy utilization efficiency and optimizing the energy structure, it can reduce the dependence on fossil energy, thereby reducing carbon emissions. Brief Description of the Drawings

[0017] Figure 1 It is a schematic flow diagram of the structural optimization scheduling method of the present invention. Detailed Embodiments

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Please refer to Figure 1, the present invention provides a technical solution: an optimized scheduling method with multi - energy coupling, including the following steps: S1. System modeling and basic data preparation: Establish models for renewable energy, energy coupling devices, and energy storage devices. At the same time, collect and process basic data, and model the multi - energy coupling system, including various links such as the production, transmission, conversion, and consumption of various energies. And the model should be able to accurately reflect the dynamic characteristics and constraint conditions of the system. Renewable energy includes wind power, photovoltaic, etc. And energy coupling devices cover devices such as gas turbines, fuel cells, waste heat boilers, absorption chillers, gas boilers, electric chillers, electric boilers, and power - to - gas. And energy storage devices include batteries, ice - storage, heat storage tanks, and gas storage tanks, etc. Collecting and processing basic data includes collecting and processing basic data, and these data are used for subsequent load forecasting and scheduling strategy formulation. And establish corresponding mathematical models for the power generation characteristics and prediction data of renewable energy (such as solar energy, wind energy, etc.). These models need to be able to reflect the output changes of renewable energy and its power generation capacity under different weather conditions. At the same time, devices for converting and coupling different forms of energy, such as power converters, thermal couplers, etc.; S2. Load forecasting and demand analysis: Conduct electricity, cooling, heating, and gas load forecasting for a future time scale of 24 hours or longer, and analyze the characteristics of energy demand. In load forecasting and demand analysis, use advanced forecasting algorithms and models, based on historical data and real - time information, to accurately predict the future energy demand of the system, and identify the distribution characteristics of different energy demands in time and space, as well as their interaction and coupling relationships. And conduct simulation analysis on the model through simulation software to simulate the system operation characteristics and optimization strategies under different scenarios. This helps to evaluate the effects of different scheduling schemes and provides a basis for subsequent optimized scheduling;S3. Day-ahead scheduling plan formulation: Establish a day-ahead scheduling model and solve it. In the day-ahead scheduling plan formulation, the scheduling duration is 24 hours a day, and the unit scheduling time is 1 hour or a shorter time interval. Substitute the predicted results of electricity, cooling, heating, and gas loads for the next 24 hours into the objective function and constraint conditions of the optimal scheduling model, and use optimization algorithms (such as second-order cone optimization algorithm, Cplex optimizer, etc.) to solve, obtaining the planned output values and their operating states of energy coupling equipment and energy storage equipment, the minimum operating cost value of the system, etc. According to the day-ahead scheduling plan, combined with real-time load data and prediction results, conduct intraday rolling optimal scheduling for the system. Intraday rolling optimal scheduling usually consists of three sub-layers: slow control layer, intermediate control layer, and fast control layer, which optimize according to the scheduling characteristics of cooling and heating energy, natural gas, and electric energy respectively. Among them, the slow control layer: Adjust the output of energy coupling equipment and energy storage equipment according to the intraday changes in cooling and heating loads to meet the cooling and heating load demands. The intermediate control layer: Adjust the operating strategy of the natural gas system according to the intraday changes in natural gas loads to ensure the stability and economy of natural gas supply. The fast control layer: Conduct real-time scheduling and optimization for the fast-changing characteristics of the electric power system to ensure the stable operation and supply-demand balance of the power system; S4. Intraday rolling optimal scheduling: Establish an intraday rolling optimal scheduling model and adjust the equipment output in real time. In intraday rolling optimal scheduling, multiple objectives usually need to be considered simultaneously, such as minimizing energy cost, reducing carbon emissions, and improving energy utilization efficiency. Therefore, a multi-objective optimization algorithm needs to be used to solve this problem. And intelligent optimization algorithms such as genetic algorithm, particle swarm algorithm, and ant colony algorithm have extensive applications in solving the optimal scheduling problem of multi-energy coupling systems. These algorithms can globally search for the optimal solution and have good robustness and adaptability. In the mathematical model of intraday rolling optimal scheduling, the objective function is usually multi-objective optimization, and economic, environmental protection and other indicators need to be weighed. For example: min(Coperation + λ ⋅ Ccarbon), where Coperation is the operating cost and λ is the carbon emission penalty coefficient. The constraint conditions include energy balance, supply-demand balance equations of each energy network, equipment physical limitations, CHP output range, energy storage charge-discharge rate, energy conversion efficiency, etc., network constraints, power grid power flow equation, thermal network hydraulic-thermal coupling model, gas network pressure equation, etc., and operation safety, upper and lower limits of parameters such as voltage, temperature, and pressure;S5. Monitoring and Evaluation: Monitor the operation status of the system in real time and evaluate the effectiveness of the scheduling strategy. During monitoring and evaluation, use sensors and data acquisition systems to monitor the energy supply and demand situation, equipment operation status, etc. of the system in real time, and transmit the collected data to the central control system or data center for unified storage and management to ensure the integrity, accuracy, and timeliness of the data. At the same time, clean, verify, and format the collected data to remove outliers, duplicate values, and missing values to improve data quality. And through a visualization interface or monitoring system, monitor the operation status and key parameters of the energy system in real time, such as power load, natural gas flow, thermal temperature, etc., and set warning thresholds. When the system parameters exceed the thresholds, trigger the warning mechanism to notify relevant personnel for handling in a timely manner. Then compare the actual operation results with the scheduling plan to evaluate the effectiveness and accuracy of the scheduling strategy, and adjust and optimize the scheduling strategy according to the evaluation results; S6. Optimization and Iteration of Scheduling Strategy: First, collect feedback information and optimize the scheduling strategy, and at the same time perform iterative updates. During the optimization and iteration of the scheduling strategy, analyze the results of day-ahead scheduling and intra-day rolling optimization scheduling, evaluate the system stability and energy utilization efficiency, and adjust the scheduling strategy according to the analysis results, such as adjusting the output plan of energy coupling equipment, the charge and discharge strategy of energy storage equipment, etc. Then substitute the adjusted scheduling strategy back into the model for solution to obtain new scheduling results, and continuously repeat the above process until the scheduling results meet the requirements of system stability and energy utilization efficiency, or reach the predetermined number of iterations. At the same time, collect feedback information on energy supply and demand, equipment failures, market changes, etc. during system operation, and based on the feedback information, adjust and optimize the scheduling strategy to improve the economy and stability of the system, and apply the optimized scheduling strategy to subsequent system operations and perform continuous iterative updates and improvements.;

[0020] Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An optimization scheduling method involving multi-energy coupling, characterized in that: The following steps are involved: S1. System modeling and basic data preparation: Establish models of renewable energy, energy coupling equipment and energy storage equipment, collect and process basic data, and model multi-energy coupling systems, including the production, transmission, conversion and consumption of various energy sources, and the model should be able to accurately reflect the dynamic characteristics and constraints of the system; S2. Load forecasting and demand analysis: Carry out electricity, cooling, heating and gas load forecasts for the next 24 hours or longer, and analyze energy demand characteristics; S3. Day-ahead scheduling plan formulation: Establish a day-ahead scheduling model and solve the day-ahead scheduling model; S4. Intraday rolling optimization scheduling: Establish an intraday rolling optimization scheduling model and adjust equipment output in real time; S5. Monitoring and evaluation: Real-time monitoring of system operation status and evaluation of the effectiveness of scheduling strategies; S6. Scheduling strategy optimization and iteration: First collect feedback information, optimize the scheduling strategy, and perform iterative updates.

2. The optimization scheduling method involving multi-energy coupling according to claim 1 is characterized in that: In the system modeling and basic data preparation, renewable energy includes wind power, photovoltaics, etc., and energy coupling equipment covers gas turbines, fuel cells, waste heat boilers, absorption chillers, gas boilers, electric chillers, electric boilers and power-to-gas equipment, and energy storage equipment includes batteries, ice storage, heat storage tanks and gas storage tanks, etc., and then collects and processes basic data, including collecting and processing basic data, and these data are used for subsequent load forecasting and scheduling strategy formulation, and the power generation characteristics and forecast data of renewable energy (such as solar energy, wind energy, etc.), establish corresponding mathematical models, these models need to be able to reflect the output changes of renewable energy, as well as the power generation capacity under different weather conditions, and at the same time, equipment that converts and couples different forms of energy, such as power converters, thermal couplers, etc.

3. The optimization scheduling method involving multi-energy coupling according to claim 1 is characterized in that: The load forecasting and demand analysis utilizes advanced forecasting algorithms and models to accurately predict the future energy demand of the system based on historical data and real-time information, and to identify the distribution characteristics of different energy demands in time and space, as well as the interactions and coupling relationships between them. The model is simulated and analyzed through simulation software to simulate the system operation characteristics and optimization strategies under different scenarios, which helps to evaluate the effects of different scheduling schemes and provide a basis for subsequent optimization scheduling.

4. The optimization scheduling method involving multi-energy coupling according to claim 1 is characterized in that: In the formulation of the day-ahead dispatch plan, 24 hours a day is used as the dispatch time, and the unit dispatch time is 1 hour or less. The forecast results of the electricity, cold, heat and gas loads in the next 24 hours are substituted into the objective function and constraints of the optimization dispatch model, and the optimization algorithm (such as the second-order cone optimization algorithm, the Cplex optimizer, etc.) is used to solve the problem, and the planned output values ​​of the energy coupling equipment and the energy storage equipment and their operating status, the system minimum operating cost value, etc. are obtained. According to the day-ahead dispatch plan, combined with the real-time load data and the forecast results, the system is subjected to intraday rolling optimization dispatch. The intraday rolling optimization dispatch is usually divided into three sub-layers: the slow control layer, the intermediate control layer and the fast control layer, which are optimized for the dispatch characteristics of cold and hot energy, natural gas and electricity respectively. The slow control layer: according to the changes of the cold and hot loads during the day, the output of the energy coupling equipment and the energy storage equipment is adjusted to meet the cold and hot load requirements. The intermediate control layer: according to the changes of the natural gas load during the day, the operation strategy of the natural gas system is adjusted to ensure the stability and economy of the natural gas supply. The fast control layer: according to the rapid change characteristics of the electric energy system, real-time dispatch and optimization are carried out to ensure the stable operation and supply and demand balance of the power system.

5. The optimization scheduling method involving multi-energy coupling according to claim 1 is characterized in that: The intraday rolling optimization scheduling usually needs to consider multiple objectives at the same time, such as minimizing energy costs, reducing carbon emissions, improving energy utilization efficiency, etc. Therefore, a multi-objective optimization algorithm is needed to solve this problem. Intelligent optimization algorithms such as genetic algorithms, particle swarm algorithms, and ant colony algorithms are widely used in solving the optimization scheduling problems of multi-energy coupling systems. These algorithms can globally search for the optimal solution and have good robustness and adaptability.

6. The optimization scheduling method involving multi-energy coupling according to claim 1 is characterized in that: The objective function in the mathematical model of the intraday rolling optimization scheduling is usually target optimization, which requires weighing indicators such as economy and environmental protection, for example: min (Coperation + λ⋅C carbon), where Costation is the operating cost, λ is the carbon emission penalty coefficient, and the constraints include energy balance, supply and demand balance equations of various energy networks, physical limitations of equipment, CHP output range, energy storage charging and discharging rate, energy conversion efficiency, network constraints, power grid flow equation, heat network hydraulic and thermal coupling model, gas network pressure equation, etc., operation safety, upper and lower limits of parameters such as voltage, temperature, and pressure.

7. The optimization scheduling method involving multi-energy coupling according to claim 1 is characterized in that: In the monitoring and evaluation, the energy supply and demand situation of the system, the operating status of the equipment, etc. are monitored in real time through sensors and data acquisition systems, and the collected data are transmitted to the central control system or data center for unified storage and management to ensure the integrity, accuracy and timeliness of the data. At the same time, the collected data is cleaned, verified and formatted to remove abnormal values, duplicate values ​​and missing values ​​to improve data quality. The operating status and key parameters of the energy system, such as power load, natural gas flow, thermal temperature, etc., are monitored in real time through a visual interface or monitoring system, and early warning thresholds are set. When the system parameters exceed the threshold, the early warning mechanism is triggered, and the relevant personnel are notified in time to handle it. Then, the actual operating results are compared with the scheduling plan, the effectiveness and accuracy of the scheduling strategy are evaluated, and the scheduling strategy is adjusted and optimized according to the evaluation results.

8. The optimization scheduling method involving multi-energy coupling according to claim 1 is characterized in that: In the optimization and iteration of the dispatching strategy, the results of the day-ahead dispatching and the intraday rolling optimization dispatching are analyzed to evaluate the system stability and energy utilization efficiency. Based on the analysis results, the dispatching strategy is adjusted, such as adjusting the output plan of the energy coupling equipment, the charging and discharging strategy of the energy storage equipment, etc., and then the adjusted dispatching strategy is re-substituted into the model for solving to obtain a new dispatching result, and the above process is repeated until the dispatching result meets the requirements of system stability and energy utilization efficiency, or reaches a predetermined number of iterations. At the same time, feedback information on energy supply and demand, equipment failures, market changes, etc. is collected during system operation, and based on the feedback information, the dispatching strategy is adjusted and optimized to improve the economy and stability of the system, and the optimized dispatching strategy is applied to subsequent system operations, and continuous iterative updates and improvements are performed.

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