Integrated energy supply system oriented to wind, light, fire and storage collaborative management

Abstract

The invention relates to the technical field of wind, light and fire storage energy, in particular to an integrated energy supply system oriented to wind, light and fire storage collaborative management. The system comprises an integrated device, wherein the integrated device comprises a thermal power module, a wind power module, a photovoltaic module, a photo-thermal power generation module, an electric energy storage module, a coal hydrogen production module, an electric exchange module, an electric energy router, an electric hydrogen production module, an electric heating module, a heat storage module, a heat release module, an electric refrigeration module and a heat-cold conversion module; by arranging the modules in the integrated device, integrated integration and collaborative management of thermal power, new energy, stored energy and hydrogen energy of a region or a station are achieved, meanwhile, coupling interconnection, system complementation and collaborative management are formed among internal cold, heat, electricity and hydrogen, and integrated efficient energy supply of cold, heat, electricity and hydrogen is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated wind-solar-thermal-storage energy, and particularly relates to an integrated energy supply system for coordinated management of wind-solar-thermal-storage. Background Art

[0002] Driven by the "dual carbon" goal, the installed capacity of new energy power generation such as wind power and photovoltaic power has grown rapidly. Due to the influence of weather conditions, the power generation stability of wind power and photovoltaic power generation will be affected to a certain extent. At the same time, considering the economy and safety of thermal power under deep peak shaving, higher requirements are put forward for the stability and safety of the power grid.

[0003] Currently, conventional decentralized layout and control are adopted for integrated wind-solar-thermal-storage and new energy bases. Among them, wind power, photovoltaic power, thermal power, and energy storage are connected and grid-connected through independent devices and control systems. It is impossible to achieve the integrated integration and coordinated management of thermal power, new energy, energy storage, and hydrogen energy in a region or power station. At the same time, there is no internal coupling and interconnection, system complementarity, and coordinated management, and the integrated high-efficiency energy supply is not achieved. Summary of the Invention

[0004] In view of this, the present invention provides an integrated energy supply system for coordinated management of wind-solar-thermal-storage to solve the problem that the integrated high-efficiency energy supply cannot be achieved in the existing integrated wind-solar-thermal-storage and new energy bases.

[0005] In a first aspect, the present invention provides an integrated energy supply system for coordinated management of wind-solar-thermal-storage, characterized in that the energy supply system includes: an integration device, and the integration device includes a thermal power module, a wind power module, a photovoltaic module, a solar thermal power generation module, an electrical energy storage module, a coal-to-hydrogen module, an electrical energy exchange module, an electrical energy router, an electrolytic hydrogen module, an electrothermal module, a heat storage module, a heat release module, an electric refrigeration module, and a heat-cold conversion module;

[0006] The electrical energy exchange module is used to convert the electrical energy output by the thermal power module, the wind power module, the photovoltaic module, the solar thermal power generation module, and the electrical energy storage module and then output it to the electrical energy router, and / or input the electrical energy to the electrothermal module for heating; the electrical energy router is used to input the received electrical energy to the electrolytic hydrogen module for hydrogen production, input the electrical energy to the electric refrigeration module for refrigeration, and / or output the electrical energy from the energy supply system; the heat storage module is used to receive and store the thermal energy output by the thermal power module, the solar thermal power generation module, and the electrothermal module, and / or input the received thermal energy to the heat release module; the heat release module is used to release the thermal energy to the heat-cold conversion module, the electrical energy storage module, and / or output it from the energy supply system; the heat-cold conversion module is used to convert the received thermal energy into cold energy and input it to the electric refrigeration module for storage; the electric refrigeration module inputs the received cold energy and / or the generated cold energy to the electrical energy storage module, or outputs it from the energy supply system; the coal-to-hydrogen module is used to produce hydrogen by coal gasification and output the hydrogen energy generated by the electrolytic hydrogen module from the energy supply system.

[0007] In the present invention, by setting the above modules in the integrated device, the integrated integration and collaborative management of thermal power, new energy, energy storage, and hydrogen energy in a region or a station are realized. At the same time, coupling interconnection, system complementarity, and collaborative management are formed among the internal cooling, heating, electricity, and hydrogen, achieving the integrated and efficient energy supply of cooling, heating, electricity, and hydrogen.

[0008] In an alternative embodiment, the integrated device further includes a hydrogen output pipeline, an electricity output pipeline, a heat output pipeline, and a cold output pipeline; the hydrogen output pipeline is connected to the electrolytic hydrogen production module and the coal gasification hydrogen production module for outputting the generated hydrogen energy to the energy supply system; the electricity output pipeline is connected to the power router for outputting the surplus electric energy to the energy supply system; the heat output pipeline is connected to the heat release module and the heat-cold conversion module for outputting the surplus heat energy to the energy supply system; the cold output pipeline is connected to the electric refrigeration module for outputting the surplus cold energy to the energy supply system.

[0009] In the present invention, through the setting of the hydrogen output pipeline, the electricity output pipeline, the heat output pipeline, and the cold output pipeline, the output of various energy sources such as cooling, heating, electricity, and hydrogen is realized.

[0010] In an alternative embodiment, the energy supply system further includes: a heterogeneous energy management system, and the heterogeneous energy management system includes an energy management module. The energy management module is used for real-time monitoring of the working processes of the thermal power module, the wind power module, the photovoltaic module, the solar thermal power generation module, and the electrical energy storage module, and adjusting the working of the power router, the coal gasification hydrogen production module, the electrolytic hydrogen production module, the heat storage module, the heat release module, the heat-cold conversion module, and the electric refrigeration module according to the monitoring results.

[0011] In an alternative embodiment, the heterogeneous energy management system further includes: a prediction system module. The prediction system module is used for predicting the future power generation data of the thermal power module, the wind power module, the photovoltaic module, the solar thermal power generation module, and the electrical energy storage module by using support vector machine regression, convolutional neural network, and long short-term memory network based on numerical weather forecast data and historical power generation data.

[0012] In an alternative embodiment, the heterogeneous energy management system further includes: a quotation decision-making module. The quotation decision-making module is used for making quotation decisions for cost minimization or revenue maximization by using linear programming and mixed integer programming based on power generation data, cost parameters, nodal marginal electricity price, unified settlement price, and uncertainty model.

[0013] In the present invention, by setting up a heterogeneous energy management system, the on-body control of thermal power, wind power, photovoltaic power, and electrical energy storage is integrated, and the integrated collaborative management of multiple heterogeneous energies is realized through a prediction system module, a quotation decision-making module, and an energy management module. Through communication with modules such as an electricity exchange module, coal-to-hydrogen production, power-to-hydrogen production, heat storage and heat release, electric refrigeration, and heat / cold conversion, the electricity / electricity conversion, electricity / heat conversion, electricity / hydrogen conversion, heat / cold conversion, and electricity / cold conversion are realized.

[0014] In an alternative embodiment, the energy supply system further includes: an integrated collaborative optimization system for wind power, photovoltaic power, thermal power, and energy storage. The integrated collaborative optimization system for wind power, photovoltaic power, thermal power, and energy storage is configured to obtain various energy data, status signals, and alarm signals in the integrated device for status modeling and evaluation, and perform coordinated optimization on the integrated device according to the evaluation results.

[0015] In an alternative embodiment, the integrated collaborative optimization system for wind power, photovoltaic power, thermal power, and energy storage includes a status acquisition and data input unit, a status evaluation and modeling unit, and an autonomous coordination unit; the status acquisition and data input unit is configured to obtain various energy data, status signals, alarm signals, and meteorological data in the integrated device; the status evaluation and modeling unit is configured to model the adjustable power, capacity, rate, and adjustment duration in the integrated device, analyze uncertain conditions, external conditions, and heterogeneous energy coupling, and evaluate the energy data in the integrated device; the autonomous coordination unit is configured to perform model solution analysis, scheduling calculation analysis, and risk assessment analysis based on the output data of the status evaluation and modeling unit.

[0016] In an alternative embodiment, the status evaluation and modeling unit includes a status modeling unit, a status analysis unit, and a status evaluation unit; the status modeling unit establishes a mixed integer programming model for wind power, photovoltaic power, thermal power, and energy storage, a non-linear calculation model for cold, heat, and electricity energy flow, a non-linear efficiency curve, and an intelligent decision-making model for energy trading with complex constraints for the adjustable power, capacity, rate, and adjustment duration; the status analysis unit is configured to analyze the energy output range, uncertain conditions, external conditions, and heterogeneous energy coupling of the integrated device. The uncertain conditions include fuel price, light resources, wind resources, external trading electricity price, and equipment failure rate. The external conditions include external instruction requirements, scheduling requirements, and weather changes. The heterogeneous energy coupling analysis includes the analysis of electricity, heat, and cold energy, as well as the coupling and interconnection of multi-dimensional communication protocols; the status evaluation unit is configured to determine the control range value of the integrated device based on the evaluation of power generation, heat supply, and cold supply of the integrated device, and calculate the adjustable electric energy, heat energy, and power of the integrated device.

[0017] In an alternative embodiment, the autonomous coordination unit includes: a model solving unit, a scheduling calculation unit, and a risk assessment unit; the model solving unit is used to perform non-linear analysis modeling, stochastic optimization analysis modeling, and model solving to obtain non-linear conditions and stochastic optimization results; the scheduling calculation unit is used to perform scheduling of different time scales for wind, light, fire, and energy storage, real-time operation scheduling, and formulate an optimized power generation plan; the risk assessment unit is used to perform analysis of energy supply security risks, benefit balance risks, and load change risks.

[0018] In an alternative embodiment, the integrated optimization system for wind, light, fire, and energy storage also includes an optimization control unit, which is used to perform distributed coordination control, real-time control of wind, light, and energy storage, power operation mode switching management, edge AI analysis and decision-making, and perform integrated management optimization, multi-power coordination, intelligent optimization, and hierarchical management coordination.

[0019] In the present invention, an integrated optimization system for wind, light, fire, and energy storage is set up. Through data collection of multiple energy sources, followed by state assessment, modeling, and autonomous coordination, and then optimized control of cloud-edge coordination, integrated coordinated management of multiple energy sources is achieved. Description of the Drawings

[0020] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a structural block diagram of an integrated device in an integrated energy supply system for wind, light, fire, and energy storage collaborative management according to an embodiment of the present invention;

[0022] Figure 2 It is a structural block diagram of an integrated optimization system for wind, light, fire, and energy storage in an integrated energy supply system for wind, light, fire, and energy storage collaborative management according to an embodiment of the present invention. Detailed Embodiments

[0023] The following will clearly and completely describe the technical solutions of the present invention with reference to the drawings. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0024] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0025] In the description of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and can also be the communication inside two elements. It can be a wireless connection or a wired connection. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0026] In addition, the technical features involved in different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0027] An embodiment of the present invention provides an integrated energy supply system for coordinated management of wind, light, thermal power and energy storage, as Figure 1 shown. The energy supply system includes: an integration device, and the integration device includes a thermal power module 1, a wind power module 2, a photovoltaic module 3, a solar thermal power generation module 4, an electrical energy storage module 5, a coal-to-hydrogen module 6, an electrical exchange module 7, an electrical energy router 8, an electrolytic hydrogen module 9, an electric heating module 10, a heat storage module 11, a heat release module 12, an electric refrigeration module 13, and a heat-cold conversion module 14.

[0028] The electric power exchange module 7 is used to convert the electric energy output by the thermal power module 1, the wind power module 2, the photovoltaic module 3, the solar thermal power generation module 4, and the electric energy storage module 5 and then output it to the electric energy router 8, and / or input the electric energy into the electric heating module 10 for heating; the electric energy router 8 is used to input the received electric energy into the hydrogen production module 9 for hydrogen production, input the electric energy into the electric refrigeration module 13 for refrigeration, and / or output the electric energy to the energy supply system; the heat storage module 11 is used to receive and store the heat energy output by the thermal power module 1, the solar thermal power generation module 4, and the electric heating module 10, and / or input the received heat energy into the heat release module 12; the heat release module 12 is used to release the heat energy to the heat-cold conversion module 14, the electric energy storage module 5, and / or output it to the energy supply system; the heat-cold conversion module 14 is used to convert the received heat energy into cold energy and input it into the electric refrigeration module 13 for storage; the electric refrigeration module 13 inputs the received cold energy and / or the generated cold energy into the electric energy storage module 5, or outputs it to the energy supply system; the coal-based hydrogen production module 6 is used to produce hydrogen through coal-based hydrogen production and output the hydrogen energy generated with the hydrogen production module 9 to the energy supply system.

[0029] The integrated device further includes a hydrogen output pipeline 19, an electric output pipeline 20, a heat output pipeline 21, and a cold output pipeline 22; the hydrogen output pipeline 19 is connected to the hydrogen production module 9 and the coal-based hydrogen production module 6 and is used to output the generated hydrogen energy to the energy supply system; the electric output pipeline 20 is connected to the electric energy router 8 and is used to output the surplus electric energy to the energy supply system; the heat output pipeline 21 is connected to the heat release module 12 and the heat-cold conversion module 14 and is used to output the surplus heat energy to the energy supply system; the cold output pipeline 22 is connected to the electric refrigeration module 13 and is used to output the surplus cold energy to the energy supply system.

[0030] Specifically, the thermal power module includes a thermal power unit, which can generate electric energy by burning coal. At the same time, a large amount of waste heat will be generated during the process of generating electric energy, that is, the thermal power module has the functions of electric power output, thermal steam output, and coal gasification and coal combustion output. In addition, the thermal power module is connected to the coal-based hydrogen production module, which has the functions of gasifying coal and producing hydrogen. The wind power module includes a wind turbine, and the photovoltaic module has structures such as a photovoltaic array. Both the wind power module and the photovoltaic module have the function of outputting electric power for energy supply. The solar thermal power generation module uses a large-scale array of parabolic or dish-shaped mirrors to collect solar heat energy, provides steam through a heat exchange device, and combines the process of a traditional steam turbine generator to generate electricity. Therefore, the solar thermal power generation module has the functions of electric power output and heat output. The electric energy storage module adopts an electrochemical energy storage method, which realizes the storage and release of electric energy through the charging and discharging of the battery. At the same time, the electric energy storage module can also receive externally input heat energy to improve the use efficiency of electrochemical energy storage in low-temperature situations; in addition, when the renewable energy power is in excess in high-temperature situations, the electric energy storage module can also receive externally input cold energy to reduce the battery temperature, which not only improves the consumption of new energy but also reduces the air-conditioning energy consumption of the electric energy storage module. Therefore, the electric energy storage module has the functions of electric power output and heat input for heating.

[0031] Based on the electric energy generated by the thermal power module, wind power module, photovoltaic module, solar thermal power generation module, and electrical energy storage module, it can be connected to the electric energy exchange module. The electric energy exchange module has the functions of multi-port electric energy conversion and multi-directional flow, and can convert and output the electric energy output by the thermal power module, wind power module, photovoltaic module, solar thermal power generation module, and electrical energy storage module to the electric energy router. At the same time, the electric energy exchange module is connected to the electric heating module through a cable. The electric heating module is used to solve the remaining thermal power or new energy electric energy and has the function of converting electric energy into heat energy. Therefore, the electric energy exchange module can input the excess electric energy into the electric heating module for heating.

[0032] The electric energy router can realize the function of transmitting electric energy on demand, point by point, and quantitatively. The electric energy router is connected to the hydrogen production module by electrolysis of water and the electric refrigeration module. The hydrogen production module by electrolysis of water has the function of integrated hydrogen supply, and the electric refrigeration module can solve the remaining electric energy of thermal power or new energy and has the function of converting electric energy into cold energy. Among them, the hydrogen energy generated by the hydrogen production module by electrolysis of water and the hydrogen energy generated by the coal-to-hydrogen module are both output through the hydrogen output pipeline. The cold energy generated by the electric refrigeration module can be output to the electrical energy storage module to reduce the temperature of the battery in the electrical energy storage module; the excess cold energy can also be output through the cold output pipeline.

[0033] For the heat energy generated by the electric heating module, thermal power module, and solar thermal power generation module, it can be input into the heat storage module for storage, that is, the heat storage module has the function of multi-channel heat source input and storage. In addition, the heat storage module is connected to the heat release module through a pipeline and can input the stored heat energy into the heat release module. The heat release module can realize multi-channel heat source output to achieve the purpose of flexible heating. Specifically, the heat release module is connected to the heat-cold conversion module, electrical energy storage module, and heat output channel through pipelines. The heat-cold conversion module converts the received heat energy into cold energy, and this cold energy can be input into the electrical energy storage module at high temperatures to reduce the temperature of the battery in the electrical energy storage module; the heat energy output by the heat release module can be input into the electrical energy storage module at low temperatures to increase the battery temperature.

[0034] Moreover, it should be noted that the electric energy, heat energy, cold energy, and hydrogen energy generated in this energy supply system are all output through the electric output pipeline, heat output pipeline, cold output pipeline, and hydrogen output pipeline to supply the load for use. When there is excess energy, the above internal energy consumption process is carried out.

[0035] In an alternative embodiment, such as Figure 1As shown in the figure, the energy supply system further includes: a heterogeneous energy management system 18, which includes an energy management module 17. The energy management module 17 is used to monitor the working processes of the thermal power module 1, the wind power module 2, the photovoltaic module 3, the solar thermal power generation module 4, and the electrical energy storage module 5 in real time, and adjust the operations of the power router 8, the coal-to-hydrogen module 6, the electrolytic hydrogen module 9, the heat storage module 11, the heat release module 12, the heat-cold conversion module 114, and the electric refrigeration module 13 according to the monitoring results.

[0036] Specifically, the heterogeneous energy management system is communicatively connected to each module in the integrated device through communication lines. Among them, the energy management module can obtain the working conditions of the thermal power module, the wind power module, the photovoltaic module, the solar thermal power generation module, and the electrical energy storage module through the communication lines, such as obtaining their power supply and heat supply conditions, etc. At the same time, it can also regulate their working processes. For example, it can control the start and stop of each module according to the external demand for electric energy and heat energy. In addition, the energy management module can also regulate the working processes of the power router, the coal-to-hydrogen module, the electrolytic hydrogen module, the heat-cold conversion module, the heat storage module, the heat release module, and the electric refrigeration module according to the external demand for hydrogen energy, electric energy, heat energy, and cold energy.

[0037] Among them, the regulation process of the energy management module specifically includes the following steps:

[0038] 1. In the integrated device, the thermal power module and the coal-to-hydrogen module gasify coal and produce hydrogen, and the thermal power module outputs electric energy to the power exchange module. For this process, the energy management module can calculate according to boundary conditions such as the power consumption situation and electricity price, store the steam for heat storage, and improve the primary energy utilization rate and variable load operation ability of thermal power. Among them, when calculating, the following constraint conditions are first established: the resource distribution and output characteristics of wind and photovoltaic (wind speed, light intensity, seasonal changes); the regulation ability and operation characteristics of thermal power units such as the minimum technical output and ramp rate); the capacity, charge-discharge efficiency, and cycle life of the energy storage system; the grid transmission capacity limit (the carrying capacity of transmission lines); the grid stability requirements (frequency, voltage fluctuation range); the standby capacity demand (spinning reserve, fast frequency regulation ability); the electricity price formation mechanism (time-of-use electricity price, spot electricity price, subsidy policy); the power market trading rules (policies for preferentially consuming renewable energy). According to the boundary conditions, calculation basis and methods, and at the same time combining the comprehensive evaluation of the consumption capacity and electricity price level of large-scale wind-solar-thermal-energy storage bases, specific strategies and methods for reducing operating costs, improving the consumption capacity of new energy, and ensuring the economic and environmental benefits of the project.

[0039] For the specific calculation process, first establish a multi-objective optimization model (linear programming, mixed-integer programming, stochastic optimization). Among them, the objective function is to minimize the total cost or maximize the total revenue. The constraint conditions include resource constraints, power grid constraints, policy constraints, etc. Then, based on Monte Carlo simulation, generate uncertainty scenarios of wind and solar power output and load demand. Conduct optimization calculations for different scenarios to evaluate the system risks and benefits. At the same time, use digital twin technology and system simulation software methods to analyze the consumption capacity and economy under different dispatching strategies. In addition, the impact of key parameters (such as wind and solar resources, electricity prices, policies) on the system benefits can be analyzed. Identify the key driving factors for system optimization. Finally, combined with indicators such as economy, environmental protection, and reliability, comprehensively evaluate the operation effect of the large-scale wind-solar-thermal-energy storage base.

[0040] 2. The wind power module and the photovoltaic module supply electrical energy through the electrical exchange module. Based on this, the energy management module can allocate the energy supply between new energy sources such as the wind power module and the photovoltaic module and the thermal power module in real time, thereby improving the power generation efficiency and reducing the curtailment rate of wind and solar power. Among them, when allocating, the thermal power fuel price, the new energy resource situation in different time periods, and the electricity price situation are mainly considered. Most importantly, the power generation cost and the environmental protection cost are considered.

[0041] 3. The electrical energy of the solar thermal power generation module is supplied through the electrical exchange module. The thermal energy of the solar thermal power generation module, the thermal power module, and the electric heating module is sent to the heat storage module for storage. Based on this, the energy management module can calculate conditions such as the heat storage module and the heat load demand, realizing heat storage of solar heat, thereby improving the comprehensive utilization efficiency of solar energy at this site. Among them, when calculating, a mathematical model of the thermal power module, the heat storage module, and the heat load is established, including the output characteristics of thermal power, the charging and discharging efficiency and capacity limit of heat storage, and the spatio-temporal distribution characteristics of the heat load. Coordinate the thermal power output and the heat storage charging and discharging strategy through a multi-objective optimization algorithm (mixed-integer linear programming, stochastic optimization) to minimize the operating cost, maximize the consumption of renewable energy, and meet the heat load demand. Use prediction technology to accurately predict the heat load and the output of renewable energy, and dynamically adjust the operating strategy by combining real-time dispatching and rolling optimization. Through model analysis and sensitivity analysis, optimize the system parameters and operating strategy to achieve the efficient coordinated operation of thermal power, heat storage, and heat load.

[0042] 4. The energy management module can analyze the operating conditions of thermal power generation, the utilization rates of wind power and photovoltaic power, and perform combined regulation of thermal, wind, and photovoltaic power. At the same time, it comprehensively utilizes and regulates the thermal energy stored in thermal power generation and solar thermal power generation. In winter, it can heat the battery and regulate the refrigeration module to achieve cooling of the battery, so as to cool down in high temperature, comprehensively improving the conversion efficiency of electrical energy storage. Among them, during combined regulation, by establishing mathematical models of the above-mentioned various energy components, including output prediction models of wind power and photovoltaic power, output constraints and ramp rates of thermal power, charge-discharge efficiencies and capacity limitations of electrical energy storage and thermal energy storage, and demand characteristics of the load. Through model algorithms such as multi-objective optimization algorithms, it coordinates the output of each energy source and the charge-discharge strategy of energy storage to minimize the total system cost, maximize the consumption of renewable energy, and meet the load demand. Using methods such as deep learning and reinforcement learning to improve prediction accuracy and optimization efficiency, combining real-time data to roll-optimize the operation strategy, and at the same time introducing electrical energy storage and thermal energy storage as flexible regulation resources to suppress the volatility of wind and light output and the regulation pressure of thermal power, improving system stability and economy, and realizing the efficient, reliable and economic operation of the multi-energy system.

[0043] 5. Through the electric energy routing module, the free distribution of thermal power, wind power, photovoltaic power, and electrical energy storage can be realized. Through the analysis and decision-making of the energy management module, the excess electricity is used for hydrogen production, electric refrigeration, and electric heating, realizing the diversified utilization and efficient distribution of electric energy.

[0044] 6. Through the heat storage and heat release module, efficient storage of steam heat storage, electric heating, and preheating utilization of thermal power can be carried out. Through the heat release module, heating of electrical energy storage and heat / cold conversion can be realized, achieving the efficient utilization of primary energy and the function of multi-heat source supply.

[0045] In an optional implementation manner, as Figure 1 shown, the heterogeneous energy management system 18 further includes: a prediction system module 15, and the prediction system module 15 is used to predict the future power generation data of the thermal power module, wind power module, photovoltaic module, solar thermal power generation module, and electrical energy storage module based on numerical weather forecast data and historical power generation data by using support vector machine regression, convolutional neural network, and long short-term memory network. The heterogeneous energy management system 18 further includes: a quotation decision module 16, and the quotation decision module 16 is used to make quotation decisions for cost minimization or revenue maximization based on power generation data, cost parameters, nodal marginal electricity price, unified settlement price, and uncertainty model by using linear programming and mixed integer programming.

[0046] Specifically, the specific working process of this prediction system module is as follows:

[0047] Collect numerical weather prediction (NWP) data such as wind speed, irradiance, and temperature. Obtain historical power generation data corresponding to the time of the NWP data. These data record the actual power generation situation over a past period and reflect the output performance of each power generation module under different meteorological conditions and other factors. Clean and normalize the collected and obtained data.

[0048] When making power generation predictions, first adopt a prediction based on Support Vector Regression (SVR). Before prediction, select a suitable support vector regression model according to the data characteristics and problem requirements, such as linear SVR, polynomial SVR, or radial basis function SVR, etc., and initialize the parameters of the model, such as the penalty parameter C, kernel function parameters, etc. Select the features related to power generation from the processed data as the input of the SVR model, and use the historical power generation data as the output to train the SVR model. By adjusting the model parameters, make the model minimize the prediction error in the training data. Then input the processed NWP data and historical curve data into the trained SVR model to obtain the prediction result of power generation.

[0049] When making power generation predictions, a prediction based on CNN + LSTM can also be adopted. Before prediction, organize the processed data in the form of a time series to construct a data set suitable for the input of the CNN + LSTM model. Use the convolutional layer and pooling layer of the Convolutional Neural Network (CNN) to extract the spatial features in the data. Through convolutional operations, automatically learn the local features and patterns in the data, reduce the dimension of the data, and extract more representative features. Input the features extracted by the CNN into the Long Short-Term Memory Network (LSTM). The LSTM can effectively handle the long-term dependencies in time series data and capture the changing trends and laws of the data over time. Use the processed data as the training data set to train the CNN + LSTM model. Adjust the parameters of the model through the backpropagation algorithm and optimizers (such as Adam, SGD, etc.) to minimize the loss function and make the model better fit the training data. Input the processed NWP data and historical curve data into the trained CNN + LSTM model to obtain the prediction result of power generation.

[0050] Finally, the prediction results of the SVR and CNN + LSTM models can be fused. Methods such as simple average and weighted average can be used to comprehensively consider the advantages of the two models and obtain a more accurate prediction result.

[0051] Specifically, the specific working process of the quotation decision module is as follows:

[0052] Data acquisition and collection: Collect relevant information of power generation equipment, including power generation capacity, power generation efficiency, start-up and shutdown times, minimum operation time, and minimum shutdown time of different units. Organize historical power generation data and analyze power generation volume, power generation load rate, etc. in different time periods. Identify various costs, such as fuel cost, operation and maintenance cost, equipment depreciation cost, etc. Fuel cost may be related to fuel price and power generation consumption; operation and maintenance cost involves expenses for equipment maintenance, repair, etc.; equipment depreciation cost is calculated based on the purchase price and service life of the equipment. Consider other costs related to power generation, such as environmental protection cost, auxiliary service cost, etc., and comprehensively evaluate the cost composition of power generation. Obtain the variation law of Locational Marginal Price (LMP). LMP usually varies according to factors such as different geographical locations, power supply and demand situations, etc. Collect historical LMP data and analyze its fluctuations in different time periods and regions. Obtain the formulation rules and change trends of Pay-as-Bid (PAB).

[0053] Uncertainty module establishment. This uncertainty module includes a prediction error model and a market volatility model. Among them, establish a prediction error model, analyze the error data between historical power generation prediction and actual power generation, and determine the distribution characteristics and change rules of the errors. Construct a market volatility model, collect market price volatility data, power demand change data, etc., and analyze the amplitude and frequency of market volatility. Methods such as time series analysis and volatility models can be used to quantify market volatility and provide a basis for risk assessment in the bidding decision-making.

[0054] Establish an objective function based on cost minimization or revenue maximization. Among them, if the goal is cost minimization, the objective function is usually a linear combination of various costs. When the goal is revenue maximization, the objective function is the difference between the electricity price revenue (the product of electricity price and power generation volume) and the total cost. In addition, power generation capacity constraints, power balance constraints, market rule constraints, and uncertainty factor constraints can also be established based on the data obtained above.

[0055] For the objective function and constraint conditions, they can be converted into the standard form of a linear programming problem, and mature linear programming solution algorithms such as the simplex method and the interior point method can be used to find the solution that makes the objective function reach the optimum within the feasible region that satisfies the constraint conditions. This solution gives the optimal power generation plan and bidding strategy without considering integer variables. In addition, when there are some decision variables that must take integer values in the problem, such as the start-up and shutdown status of the unit (0 represents shutdown, 1 represents startup), mixed integer programming needs to be used. Incorporate these integer variables and other continuous variables into the model, and construct the objective function and constraint conditions considering the integer variables.

[0056] Analyze the obtained optimal solution to evaluate the degree of cost minimization or benefit maximization. Calculate various cost and benefit indicators under the optimal solution, compare them with historical data and expected goals, and judge the effectiveness of the decision-making. Analyze the rationality of the power generation plan in the optimal solution, including whether the power generation power distribution and start-stop time arrangement of each unit conform to the actual operation conditions and equipment characteristics. And give the final quotation decision.

[0057] In the present invention, the heterogeneous energy management system integrates the body control of thermal power, wind power, photovoltaic power, and electrical energy storage, and realizes the integrated collaborative management of multiple heterogeneous energies through the prediction system module, quotation decision module, and energy management module. Through communication with modules such as the power exchange module, coal-to-hydrogen, power-to-hydrogen, heat storage and heat release, electric refrigeration, and heat / cold conversion, the power / electricity conversion, power / heat conversion, power / hydrogen conversion, heat / cold conversion, and power / cold conversion are realized.

[0058] In an alternative embodiment, as Figure 2 shown, the energy supply system further includes: a wind-solar-thermal-storage integrated collaborative optimization system, which is used to obtain various energy data, status signals, and alarm signals in the integrated device for status modeling and evaluation, and coordinate and optimize the integrated device according to the evaluation results.

[0059] Specifically, as Figure 2 shown, the wind-solar-thermal-storage integrated collaborative optimization system includes a status collection and data input unit 30, a status evaluation and modeling unit 40, and an autonomous coordination unit 50; the status collection and data input unit 30 is used to obtain various energy data, status signals, alarm signals, and meteorological data in the integrated device; the status evaluation and modeling unit 40 is used to model the adjustable power, capacity, rate, and adjustment duration in the integrated device, analyze uncertain conditions, external conditions, and heterogeneous energy coupling, and evaluate the energy data in the integrated device; the autonomous coordination unit 50 is used to perform model solution analysis, scheduling calculation analysis, and risk assessment analysis based on the output data of the status evaluation and modeling unit.

[0060] Among them, the status collection and data input unit 30 includes a power generation unit data input unit 31 and a meteorological and prediction data input unit 32. The power generation unit data input unit 31 is used to monitor the energy output of the thermal power module, wind power module, photovoltaic module, solar thermal power generation module, and electrical energy storage module, and at the same time obtain status signals and alarm signals in the equipment. The status signals include analog signals, digital signals, switch signals, etc.; the alarm signals include the operation status alarms and internal fault alarms of electrical equipment such as inverters and converters in each module. The meteorological and prediction data input unit 32 is used to collect and analyze meteorology and power data of each module.

[0061] The status evaluation and modeling unit 40 can achieve the status collection and data input, status evaluation and modeling of multiple energy sources through status evaluation and modeling analysis by combining meteorological and prediction-related data. Among them, status evaluation and modeling mainly conduct multivariate uncertainty optimization scheduling analysis on the multi-link and multi-time-space cross-coupling of the source-network-load, and adopt algorithms such as stochastic optimization and robust optimization. Among them, when modeling, the coupled modeling C = Σ(Xij·Xij) / Σ(Xij) is used, where Xij represents the coupling value between the i-th and j-th things. In the study of the relationship between multiple energy inputs and multiple energy outputs, the formula for the coupling degree C may be C = [F(x)k×G(y)k] / [αF(x)+βG(y)] 2k , where F(x) and G(y) respectively represent the quantization indexes of multiple energy inputs and multiple energy outputs, k is an adjustment coefficient, and α and β are undetermined coefficients.

[0062] Specifically, the status evaluation and modeling unit 40 includes a status modeling unit 41, a status analysis unit 42, and a status evaluation unit 43. Among them, the status modeling unit 40 includes an adjustable power modeling unit 411, an adjustable heat modeling unit 412, an adjustable rate modeling unit 413, and an adjustable duration modeling unit 414. In these modeling units, relevant parameters and relevant models are used for modeling. The relevant parameters include parameters such as electricity, heat energy, cold energy, and efficiency. The electricity parameters include the power generation power and the power purchased from the power grid. The heat energy parameters include the output power of the heat storage module and the electric heating module. The cold energy parameters include the output power of the electric refrigeration and the heat-cold conversion module. The efficiency parameters include the power generation efficiency, the heating efficiency, and the refrigeration efficiency. The capacity parameters include the maximum / minimum output power of the equipment. The ramp rate parameters include the rate of power adjustment of the equipment. The start-stop characteristic parameters include the start-stop time and cost of the equipment. The power balance parameters include the supply-demand balance constraints of electricity, heat, and cold. The energy conversion coefficient parameters include the conversion efficiency between different energy forms (such as electricity to heat, heat to cold). The energy loss parameters include the energy loss during transmission and conversion. The network constraint parameters include the transmission capacity limits of the power grid and the heat network. The established models include a wind-solar-thermal-storage mixed integer programming model, a cold-heat-electricity energy flow nonlinear calculation model, a nonlinear efficiency curve, and an intelligent decision-making model for energy trading with complex constraints.

[0063] In the adjustable power modeling unit 411, a hybrid integer programming model for wind-solar-thermal-storage is used to comprehensively plan the power generation of wind-solar-thermal-storage. Taking the power generation of wind-solar-thermal-storage as decision variables, considering whether they are put into operation (represented by integer variables) and the magnitude of the operating power, with the goal of minimizing costs or maximizing benefits, while subject to constraints such as power supply-demand balance, equipment capacity, and grid transmission. The non-linear calculation model of cooling-heating-power energy flow is used to accurately calculate the energy conversion and flow relationship between electricity and heat and cooling energy. When considering the output power of the electro-thermal conversion module and the electric refrigeration power, this model can calculate the heat and cooling energy power generated under different electricity inputs, as well as the flow and distribution of heat and cooling energy in the system according to parameters such as the energy conversion coefficient, so as to ensure the energy balance of cooling-heating-power. The intelligent decision-making model for energy trading with non-linear efficiency curves and complex constraints uses non-linear efficiency curves to describe the non-linear relationships between the power generation efficiency of wind-solar-thermal-storage, electro-thermal / refrigeration efficiency, etc. and power. When making energy trading decisions, combined with factors such as the power purchase of the power grid and market prices, under complex market rules and system operation constraints, the optimal power trading strategy is determined.

[0064] In the adjustable heat modeling unit 412, when the hybrid integer programming model for wind-solar-thermal-storage considers the heat storage module and the electro-thermal heating module, the operating state of the heat storage module (on or off, represented by an integer) and the electro-thermal conversion power, etc. can be used as decision variables. With the goal of meeting the heat load demand and optimizing the system operation cost, considering constraints such as heat storage capacity and electro-thermal conversion efficiency, this model determines when to turn on or off the heat storage equipment and the magnitude of the electro-thermal conversion power to be input. The non-linear calculation model of cooling-heating-power energy flow is mainly used to calculate the flow and distribution of the heat output by the heat storage module and the heat generated by the electro-thermal conversion module in the heat network. Considering non-linear factors such as heat loss and heat load demands at different nodes, the heat energy flow is accurately calculated to ensure heat balance. The intelligent decision-making model for energy trading with non-linear efficiency curves and complex constraints is based on the non-linear heating efficiency curve, combined with complex constraints such as heat price, electricity price, and heat network transmission capacity, to formulate the optimal heat production and trading strategy.

[0065] In the adjustable rate modeling unit 413, when the integrated wind-solar-thermal energy storage hybrid integer programming model considers the ramping rates of each power generation unit, the speed levels of power adjustment for each unit (such as fast, medium, and slow, which can be encoded by integers) can be used as one of the decision variables. With the goals of stable system operation and minimum power adjustment cost, and subject to the constraints of grid transmission capacity and the ramping rate limits of each device, the model determines the power adjustment speeds that each power generation unit should adopt at different times. The cold-heat-electricity energy flow non-linear calculation model is used to analyze the dynamic changes in the cold-heat-electricity energy flow under different power adjustment rates. Considering the impact of the power adjustment rate on the energy conversion and transmission processes, such as non-linear factors like increased energy losses caused by rapid power adjustment, the model calculates the cold-heat-electricity energy flow distribution at different adjustment rates to provide a basis for reasonably controlling the adjustment rate. The non-linear efficiency curve and complex constraint-based intelligent decision-making model for energy trading formulates the optimal adjustment rate decision based on the efficiency change curves (non-linear) of each device at different adjustment rates, combined with market price fluctuations and grid operation constraints.

[0066] In the adjustable duration modeling unit 414, the integrated wind-solar-thermal energy storage hybrid integer programming model takes the start-stop times and operation durations of each wind-solar-thermal energy storage power generation unit as decision variables, with the comprehensive optimization goals of device operation cost, start-stop cost, and system power supply reliability, considering constraints such as the minimum operation duration, minimum shutdown duration, and load demand of the device. The model determines the start-stop plans and operation durations of each power generation unit in different time periods. The cold-heat-electricity energy flow non-linear calculation model is used to calculate the cumulative supply and consumption of cold-heat-electricity energy under different device operation durations, considering non-linear factors such as energy losses and storage changes during long-term operation. The non-linear efficiency curve and complex constraint-based intelligent decision-making model for energy trading formulates the optimal operation duration decision based on the efficiency change curves of the device at different operation durations, combined with market trading rules and system operation constraints.

[0067] The state analysis unit 42 is used to analyze the energy output range, uncertain conditions, external conditions, and heterogeneous energy coupling of the integrated device. The uncertain conditions include fuel prices, light resources, wind resources, external trading electricity prices, and equipment failure rates. The external conditions include external instruction requirements, dispatching requirements, and weather changes. The heterogeneous energy coupling analysis includes the analysis of the coupling and interconnection of electric energy, thermal energy, and cold energy, as well as multi-dimensional communication protocols.

[0068] Specifically, the status analysis unit 42 includes an adjustment range analysis unit 421, an uncertainty analysis unit 422, a heterogeneous energy coupling analysis unit 423, and an external condition analysis unit 424. The adjustment range analysis unit 421 is used to analyze and obtain the range values of the energy output by considering the health status, maintenance status, fatigue status, and external meteorological conditions of each power generation module. The uncertainty analysis unit 422 is used to analyze uncertain conditions such as fuel prices, light resources, wind resources, external trading electricity prices, and equipment failure rates. The heterogeneous energy coupling analysis unit 423 is used to analyze the energy data such as electric energy, thermal energy, and cold energy of wind-solar-thermal-storage, and at the same time analyze the coupling and interconnection of multi-dimensional communication protocols such as TCP, wireless WIFI, optical fiber, MODBUS, and IEC 61850; the external condition analysis unit 424 is used to analyze external conditions such as external instruction requirements, dispatching requirements, and weather changes.

[0069] The status evaluation unit 43 is used to determine the control range value of the integrated device based on the evaluation of the power generation, heating, and cooling of the integrated device, and calculate the adjustable electric energy, thermal energy, and power of the integrated device. Specifically, the status evaluation unit 43 includes a control energy quantification evaluation unit 431, an adjustable power and heat evaluation unit 432, an adjustable power range unit 433, and a power coupling model evaluation unit 434. Among them, the energy quantification evaluation unit 431 is an evaluation of the predicted energy output such as power generation, heating, and cooling of the energy supply unit, and uses a multi-dataset model, generalization training, robustness calculation, and verification to calculate the control range value of the energy supply unit. The adjustable power and heat evaluation unit 432 is mainly for the power evaluation of wind power, photovoltaic power, and thermal power, as well as the remaining power and health evaluation of the electrical energy storage. The heat is mainly for the thermal power output of thermal power and the heat output of the heat storage module, etc. The adjustable power range unit 433 is used to evaluate by considering comprehensive factors such as equipment status, health status, operating time, and fatigue status, and calculate the adjustable power range of the energy supply unit. The power coupling model evaluation unit 434 is used to calculate and evaluate the coupling power based on the power coupling relationship between different energy supply units, and at the same time consider the influence of factors such as equipment status, health status, operating time, and fatigue status on power coupling.

[0070] The autonomous coordination unit 50 analyzes the data output of state evaluation and modeling, and conducts model solving, scheduling calculation, and risk assessment. Among them, model solving mainly includes nonlinear analysis, stochastic optimization analysis, and model solving analysis. Scheduling calculation mainly calculates different time-scale scheduling models, heterogeneous energy real-time operation models, and optimization problem models, and evaluates the risks of energy supply security, benefit balance, and load fluctuation. Among them, the autonomous coordination unit 50 includes: a model solving unit 51, a scheduling calculation unit 52, and a risk assessment unit 53; the model solving unit 51 is used for nonlinear analysis modeling, stochastic optimization analysis modeling, and model solving to obtain nonlinear conditions and stochastic optimization results; the scheduling calculation unit 52 is used for scheduling different time scales of wind, light, fire, and storage, real-time operation scheduling, and formulating an optimized power generation plan; the risk assessment unit 53 is used for energy supply security risk analysis, benefit balance risk analysis, and load change risk analysis.

[0071] The autonomous coordination unit is also used for power and regulation modeling, including polynomial models: assuming that the power change has a polynomial relationship with multiple input variables, methods such as polynomial regression can be used to fit the model parameters. P = a0 + a1×x + a2×xn, where P is the power, x is the input variable, and a0 - an are the model parameters. Exponential model: assuming that the power change has an exponential relationship with the input variable, methods such as exponential regression can be used to fit the model parameters: P = a×e^(b×x), where P is the power, x is the input variable, and a and b are the model parameters.

[0072] Specifically, the model solving unit 51 includes a nonlinear analysis unit 511, a stochastic optimization analysis unit 512, and a model solving analysis unit 513. The nonlinear analysis unit 511 is mainly responsible for voltage stability analysis, frequency oscillation suppression, fault transient response, etc. It uses nonlinear modeling, dynamic characteristic simulation, and stability evaluation, and uses high-order numerical algorithms (such as adaptive step-size integration method) to improve the simulation accuracy, and constructs a surrogate model of the nonlinear system using data-driven technologies (such as deep learning). The stochastic optimization analysis unit 512 combines the fluctuations of renewable energy output, the randomness of load demand, and the changes in market electricity prices, etc. It has stochastic modeling, scenario generation, risk assessment, and optimization decision-making, and uses two-stage stochastic optimization and chance-constrained programming to handle uncertainties, and uses Monte Carlo simulation and scenario reduction techniques to improve the calculation efficiency to achieve the balance of system economy, reliability, and stability. The model solving analysis unit 513 uses heuristic algorithms (genetic algorithm, particle swarm optimization) and mathematical programming methods (mixed integer linear programming) to handle nonlinear and non-convex problems through model construction, algorithm selection, solution optimization, and result analysis.

[0073] In the non-linear analysis unit 511, the non-linear analysis modeling adopts the regression model and the logistic growth model algorithm. The modeling formula is: y = b0 + b1x + b2xn, where y is the target variable, x is the independent variable, b0, b1,..., bn are the regression coefficients, and n is the order of the polynomial. The non-linear model of the process with saturation phenomenon: A(t) = K / (1 + (K / A0 - 1) * exp(-r * t)), where P(t) is the population size at time t, K is the environmental carrying capacity (i.e., the maximum population size), P0 is the initial population size, and r is the growth rate.

[0074] The scheduling calculation unit 52 uses big data analysis and machine learning technologies to improve the load forecasting accuracy, and uses advanced optimization algorithms (mixed integer programming, stochastic optimization) to handle complex constraints and multi-objective problems; combines artificial intelligence (such as reinforcement learning) to implement an adaptive scheduling strategy for real-time scheduling and optimization of the power system to ensure the balance of power generation, transmission, and consumption. The scheduling calculation unit 52 includes the scheduling model unit 521 with different time scales, the real-time operation model unit 522 of heterogeneous energy sources, and the optimization problem model unit 523.

[0075] Among them, since the multi-energy system of wind, light, thermal, and energy storage undertakes multi-level optimization tasks from long-term planning to real-time operation, the main functions to be realized include resource capacity configuration, day-ahead scheduling plan, real-time power balance, and fast frequency modulation. Therefore, the scheduling model unit with different time scales includes models with different time scales. On the long-term scale, the model optimizes the capacity configuration of wind, light, thermal, and energy storage through mixed integer programming and scenario analysis method, taking into account both economy and reliability; in the day-ahead scheduling, based on stochastic optimization or robust optimization to handle the uncertainty of wind and light forecasting, formulate the start-stop plan of thermal power and the charge-discharge strategy of energy storage; in the real-time operation, adopt model predictive control and reinforcement learning to dynamically adjust the output to achieve second-level frequency modulation and minute-level power balance.

[0076] The real-time operation model unit 522 of heterogeneous energy sources combines model predictive control and rolling optimization of various types of data such as wind, light, thermal, and energy storage to adjust the output of wind, light, thermal, and energy storage in real time to cope with wind and light fluctuations and load changes; uses reinforcement learning to adaptively learn the optimal scheduling strategy to improve the robustness of the system to uncertainty; introduces distributed optimization algorithms and edge computing technologies to achieve multi-node collaborative control and reduce communication latency. Through real-time simulation and interaction with the physical system, it provides accurate state prediction and fault diagnosis. The optimization problem model unit uses mixed integer programming and stochastic optimization methods to comprehensively consider the uncertainty of wind and light forecasting, the start-stop constraints of thermal power units, and the charge-discharge characteristics of energy storage to formulate the optimal power generation plan, and uses reinforcement learning and deep reinforcement learning to adaptively adjust the scheduling strategy to improve the solution efficiency of complex non-linear problems and achieve the coordinated optimization of economy, environmental protection, and reliability of the multi-energy system (such as wind, light, thermal, and energy storage).

[0077] The risk assessment unit 53 comprehensively monitors and evaluates the system mainly through functions such as multi-source information monitoring technology, dynamic fault warning, automatic fault diagnosis, remaining life prediction, and power generation performance evaluation. The risk assessment unit includes an energy supply safety risk unit 531, a benefit balance risk unit 532, and a load change risk unit 533.

[0078] The core functions of the energy supply safety risk unit 531 include risk modeling, vulnerability analysis, real-time monitoring, early warning systems, and emergency response, etc. It uses big data analysis and artificial intelligence technologies (deep learning, natural language processing) to accurately identify and predict risk factors, and analyzes the vulnerability and cascading failure risks of the energy system using complex network theory, which is used to identify, evaluate, and respond to potential risks in energy supply to ensure the safety and reliability of the energy system.

[0079] The benefit balance risk unit 532 coordinates the operation of wind power, photovoltaic power, thermal power, and energy storage systems through energy collaborative optimization, benefit risk assessment, reserve capacity configuration, and real-time scheduling decisions, realizing multi-energy complementarity and maximizing benefits, while effectively managing the risks brought by uncertainties. The load change risk unit 533 uses information geometry decision theory to predict load deviation, optimizes equipment capacity configuration, reduces the impact of prediction errors on system operation, and effectively smooths the net load fluctuation and controls risks by constructing a flexible adjustment strategy based on deterministic parameters to optimize the start-stop state of equipment and uncertain variables.

[0080] In an alternative embodiment, the integrated wind-solar-thermal-storage collaborative optimization system further includes an optimization control unit 60. The optimization control analyzes the output results of autonomous coordinated model calculations and risk assessments, and through the optimization unit, it performs distributed coordinated control, real-time control of wind-solar-storage, power operation mode switching management, edge AI analysis and decision-making, and conducts integrated management optimization, multi-power coordination, intelligent optimization, and hierarchical management coordination. Finally, through cloud-edge collaboration and big data, it realizes the integrated intelligent collaborative management of multiple energies.

[0081] Among them, when performing real-time control of wind-solar-storage, the following real-time modeling and control model of wind-solar-thermal-storage is adopted: Ps = min[Aw∣ΔTw∣ + Av∣ΔTv∣ + Ah∣ΔTh∣ + Ab∣ΔTbi∣], where: Ps is the total cost of the real-time scheduling plan. Aw is the maintenance cost per unit output of the wind turbine. Av is the maintenance cost per unit output of the photovoltaic power generation. Ah is the maintenance cost per unit output of thermal power generation (when considering thermal power generation in the wind-solar-thermal-storage system). Ab is the maintenance cost per unit output of the energy storage battery. ∣ΔTw∣ is the increment of the wind turbine power generation. ∣ΔTv∣ is the increment of the photovoltaic power generation. ∣ΔTh∣ is the increment of the thermal power generation (when considering thermal power generation in the wind-solar-thermal-storage system). ∣ΔTbi∣ is the increment of the power generation of the i-th energy storage battery pack.

[0082] In addition, during the control process, it is also necessary to smooth the power fluctuations of wind-solar power generation. Usually, a real-time power smoothing algorithm is adopted: Psm′(k) = ∑i = qpwPw-so(k + i), where Psm′(k) is the smoothed power target at time k, Pw-so(k + i) is the actual output power of the wind-solar combined power generation system at time k + i, wi is the weight coefficient, and q and p are the parameters of the moving average algorithm.

[0083] Specifically, the optimization control unit 60 includes an edge control unit 61, a collaborative optimization unit 62, and a cloud management unit 63, which can realize the edge intelligent control of wind-solar-thermal energy storage, and through cloud-edge collaboration, achieve the collaboration, integrated optimization, and hierarchical management of heterogeneous energy sources. The edge control unit 61 includes a distributed coordination control unit 611, a wind-solar-thermal energy storage real-time control unit 612, a power supply operation mode control unit 613, and an edge AI analysis and decision-making unit 614; the collaborative optimization unit 62 includes an integrated management optimization unit 621, a multi-power supply collaborative optimization unit 622, an intelligent optimization control unit 623, and a hierarchical management optimization unit 624; the cloud management unit 63 includes a cloud-edge collaboration unit 631, an edge-side privacy and security protection unit 632, a cloud-edge data synchronization and feedback unit 633, and a cloud AI decision-making unit 634.

[0084] In the edge control unit 61, the distributed coordination control unit 611 is mainly responsible for the coordinated operation among multiple distributed power generation units, energy storage systems, and loads. By adopting distributed algorithms such as consensus algorithms and distributed optimization algorithms, fast and accurate global coordination can be achieved. The wind-solar-thermal energy storage real-time control unit 612 performs real-time control on wind power, photovoltaic power, thermal power, and energy storage systems. According to their actual operating conditions and external environment changes, it timely adjusts the operating parameters to ensure the stable and efficient operation of the wind-solar-thermal energy storage system, and realizes the real-time balance and optimal allocation of energy. The power supply operation mode control unit 613 is responsible for managing the operation mode switching of the power supply (i.e., the module that generates electric energy). According to the system requirements and the characteristics of the power supply, it reasonably controls the switching of the power supply between different operation modes, such as different output modes of thermal power and charge-discharge modes of energy storage, to meet the operating requirements of the system under different working conditions. The edge AI analysis and decision-making unit 614 takes advantage of edge computing to perform real-time analysis and processing on locally collected data, makes decisions using artificial intelligence algorithms, and provides intelligent support for other edge control units, such as predicting the output of wind-solar power generation and determining the best charge-discharge timing of energy storage, to improve the intelligent level of edge control.

[0085] In the collaborative optimization unit 62, the integrated management and optimization unit 621 manages and optimizes the system as a whole, considering all aspects of energy production, transmission, distribution, and use, coordinating various energy forms such as wind, solar, thermal, and energy storage, achieving the integrated operation of the system, and improving energy utilization efficiency and the overall performance of the system. The multi-power collaborative optimization unit 622 focuses on optimizing the collaborative work among multiple power sources. According to the characteristics and advantages of different power sources, it reasonably arranges their output and operation modes, realizes the complementarity and collaboration among multiple power sources, and improves the stability and reliability of the system. The intelligent optimization control unit 623 finds the optimal operation plan of the system through intelligent algorithms. For example, on the premise of meeting the load demand, it minimizes the energy cost, maximizes the energy utilization efficiency, etc., continuously optimizes the operation parameters and control strategies of the system, and makes the system in the best operation state. The hierarchical management and optimization unit 624 adopts a hierarchical management method according to the structural and functional characteristics of the system, optimizes and manages equipment and links at different levels, clarifies the responsibilities and tasks of each layer, realizes the orderly operation and efficient management of the system, and improves the scalability and flexibility of the system.

[0086] In the cloud management unit 63, the cloud-edge collaboration unit 631 is responsible for realizing the collaborative work between the cloud and the edge, coordinating the powerful computing power of the cloud and the real-time control ability of the edge, enabling efficient data interaction and instruction transmission between the two, and ensuring the overall optimization and collaborative operation of the system. The edge-side privacy and security protection unit 632 focuses on data privacy and system security on the edge side, adopts security technologies such as encryption and authentication to protect the security of edge devices and data, prevent data leakage and malicious attacks, and ensure the stable and reliable operation of the system. The cloud-edge data synchronization and feedback unit 633 realizes data synchronization between the cloud and the edge, ensuring the consistency and timeliness of data. At the same time, it feeds back the analysis results and optimization instructions of the cloud to the edge, providing decision-making support for edge control, so that the edge can adjust the control strategy according to the information of the cloud. The cloud AI decision-making unit 634 uses the powerful computing resources and massive data of the cloud to perform more complex and comprehensive artificial intelligence analysis and decision-making, such as trend prediction of the energy market, long-term planning of the system, etc., providing macro guidance and optimization strategies for the operation of the entire system.

[0087] In the present invention, the integrated collaborative optimization system of wind, solar, thermal power and energy storage can realize data interconnection, integrated management and control of thermal power, wind power, photovoltaic, solar thermal and electrical energy storage units; realize the integrated management and coordinated control of hydrogen production from coal and hydrogen production from electricity; realize the multi-condition integration and complementarity of thermal power and solar thermal power generation, electrical energy storage and electrical energy storage heat, improving the energy utilization rate of thermal power and the conversion efficiency of electrical energy storage; realize the complementarity of solar thermal power generation and electrical energy storage, improving the conversion efficiency of solar thermal power generation and electrical energy storage. It realizes the integrated management and supply of heating, power supply, cooling and hydrogen supply for wind, solar, thermal power and energy storage. It realizes the integrated cascade utilization of thermal energy of thermal power and solar thermal power, comprehensively providing comprehensive efficiency. According to multi-condition conditions, considering conditions such as wind and light abandonment, coal consumption of thermal power, on-grid electricity price, prices of cooling, heating, electricity and hydrogen, and losses of thermal power, flexibly formulate operation strategies for hydrogen production, power supply, heating and cooling; realize the multi-time, multi-condition management optimization of multiple energy sources and the efficient cloud-edge collaborative management.

[0088] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An integrated energy supply system for wind, solar, thermal and energy storage coordinated management, characterized in that: The energy supply system comprises: an integrated device, which comprises a thermal power module, a wind power module, a photovoltaic module, a solar thermal power generation module, an electric energy storage module, a coal-to-hydrogen module, an electric exchange module, an electric energy router, an electric hydrogen production module, an electric heating module, a heat storage module, a heat release module, an electric refrigeration module and a heat-to-cold conversion module; The electric exchange module is used to convert the electric energy output by the thermal power module, the wind power module, the photovoltaic module, the solar thermal power generation module and the electric energy storage module and output it to the electric energy router, and / or input the electric energy to the electric heating module for heating; The electric energy router is used to input the received electric energy into the electric hydrogen production module for hydrogen production, input the electric energy into the electric refrigeration module for refrigeration, and / or output the electric energy to the energy supply system; The heat storage module is used to receive the heat energy output by the thermal power module, the solar thermal power generation module and the electric heating module for storage, and / or input the received heat energy into the heat release module; The heat release module is used to release heat energy to the heat-to-cold conversion module, the electric energy storage module and / or the output energy supply system; The heat-to-cold conversion module is used to convert the received heat energy into cold energy and input it into the electric refrigeration module for storage; The electric refrigeration module inputs the received cold energy and / or generated cold energy into the electric energy storage module, or outputs the energy supply system; The coal-to-hydrogen module is used to produce hydrogen from coal and output the hydrogen energy generated by the electric hydrogen module to the energy supply system.

2. The system according to claim 1, characterized in that The integrated device also includes a hydrogen output pipeline, an electricity output pipeline, a heat output pipeline and a cold output pipeline; The hydrogen output pipeline is connected to the electric hydrogen production module and the coal hydrogen production module, and is used to output the generated hydrogen energy to the energy supply system; The electric output pipeline is connected to the electric energy router and is used to output the excess electric energy to the energy supply system; The heat output pipeline is connected to the heat release module and the heat-to-cold conversion module to output excess heat energy to the energy supply system; The cold output pipeline is connected to the electric refrigeration module and is used to output excess cold energy to the energy supply system.

3. The system according to claim 1, characterized in that The energy supply system also includes: a heterogeneous energy management system, which includes an energy management module. The energy management module is used to monitor the working process of the thermal power module, the wind power module, the photovoltaic module, the solar thermal power generation module and the electric energy storage module in real time, and adjust the work of the electric energy router, the coal-to-hydrogen module, the electric hydrogen production module, the heat storage module, the heat release module, the heat-to-cold conversion module and the electric refrigeration module according to the monitoring results.

4. The system according to claim 3, characterized in that The heterogeneous energy management system also includes: a prediction system module, which is used to use support vector machine regression, convolutional neural network and long short-term memory network to predict the future power generation data of thermal power modules, wind power modules, photovoltaic modules, solar thermal power generation modules and electric energy storage modules based on numerical weather forecast data and historical power generation data.

5. The system according to claim 3, characterized in that The heterogeneous energy management system also includes: a quotation decision module, which is used to make quotation decisions when minimizing costs or maximizing benefits based on power generation data, cost parameters, node marginal electricity prices, unified settlement prices and uncertain models using linear programming and mixed integer programming.

6. The system according to claim 1, characterized in that The energy supply system also includes: an integrated wind, solar, thermal and storage collaborative optimization system, which is used to obtain various energy data, status signals and alarm signals in the integrated device for status modeling and evaluation, and coordinate optimization of the integrated device based on the evaluation results.

7. The system according to claim 6, characterized in that The wind, solar, thermal and storage integrated collaborative optimization system includes a state acquisition and data input unit, a state evaluation and modeling unit and an autonomous coordination unit; The state acquisition and data input unit is used to obtain various energy data, state signals, alarm signals and meteorological data in the integrated device; The state evaluation and modeling unit is used to model the adjustable power, capacity, rate and adjustment duration in the integrated device, analyze uncertain conditions, external conditions and heterogeneous energy coupling, and evaluate energy data in the integrated device; The autonomous coordination unit is used to perform model solution analysis, scheduling calculation analysis and risk assessment analysis based on the output data of the state assessment and modeling unit.

8. The system according to claim 7, characterized in that The state assessment and modeling unit includes a state modeling unit, a state analysis unit and a state assessment unit; The state modeling unit establishes a wind-solar-thermal-storage mixed integer programming model, a nonlinear calculation model of cold, heat and electricity energy flow, a nonlinear efficiency curve and an intelligent decision model for energy trading with complex constraints for adjustable power, capacity, rate and adjustment duration; The state analysis unit is used to analyze the energy output range, uncertain conditions, external conditions and heterogeneous energy coupling of the integrated device. The uncertain conditions include fuel prices, light resources and wind resources, external transaction electricity prices and equipment failure rates. External conditions include external command requirements, scheduling requirements and weather changes. The heterogeneous energy coupling analysis includes analysis of electric energy, thermal energy and cold energy and multi-dimensional communication protocol coupling and interconnection. The state evaluation unit is used to determine the control range value of the integrated device based on the evaluation of power generation, heating and cooling of the integrated device, and calculate the adjustable electric energy, thermal energy and power of the integrated device.

9. The system according to claim 7, characterized in that The autonomous coordination unit includes: a model solving unit, a scheduling calculation unit and a risk assessment unit; The model solving unit is used to perform nonlinear analysis modeling, random optimization analysis modeling and model solving to obtain nonlinear conditions and random optimization results; The dispatch calculation unit is used to dispatch wind, solar, thermal and energy storage at different time scales, perform real-time operation dispatch, and formulate an optimized power generation plan; The risk assessment unit is used to perform energy supply safety risk analysis, benefit balance risk analysis and load change risk analysis.

10. The system according to claim 7, characterized in that The wind, solar, thermal and storage integrated collaborative optimization system also includes an optimization control unit, which is used to perform distributed coordinated control, real-time control of wind, solar and storage, power supply operation mode switching management, edge AI analysis and decision-making, and integrated management optimization, multi-power supply collaboration, intelligent optimization and hierarchical management collaboration.

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