Real-time energy regulation and control system based on data-driven scene prediction

Abstract

The invention discloses a real-time energy regulation and control system based on data-driven scene prediction, and the system comprises a data obtaining module which is used for obtaining the operation data of a target scene; the model processing module is used for calculating the operation data by utilizing a preset mapping model corresponding to the energy production unit, the energy conversion unit and the energy storage unit, and outputting an optimal parameter based on an optimization target; and the regulation and control module is used for regulating and controlling each energy unit in the power grid based on the optimal parameters. According to the method, the mapping models of the energy production unit, the energy conversion unit and the energy storage unit are constructed, so that accurate modeling of each link of the energy system is realized. The system can acquire the operation data of the target scene in real time, perform calculation and output optimal parameters. The optimal parameters are then transmitted to a regulation and control module to realize real-time regulation and control of each energy unit in the power grid.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a real-time energy control system based on data-driven scenario prediction. Background Art

[0002] With the rapid development of energy internet and smart grid, the complexity and dynamics of energy systems are increasing. Traditional energy control systems often rely on static models and manual experience, which makes it difficult to cope with the changing and complex actual operation scenarios, resulting in low energy control efficiency and serious energy waste. Especially in the integrated energy system where multiple links such as energy production, conversion, storage and consumption are intertwined, how to accurately predict future scenarios and adjust energy distribution in real time has become a technical problem that needs to be solved urgently.

[0003] Most existing energy control systems use rule-based scheduling methods, which lack flexibility and adaptability. At the same time, there is a lack of effective processing and utilization methods for large-scale and multi-type energy data, resulting in low accuracy and efficiency of information processing. In addition, traditional systems often ignore the synergy between different energy links and cannot achieve global optimal energy control. Therefore, it is of great practical significance and application value to develop a system that can achieve real-time energy control based on data-driven scenario prediction. Summary of the invention

[0004] In view of the problem that the existing technology ignores the synergy between different energy links and cannot achieve global optimal energy regulation, the present invention provides a real-time energy regulation system based on data-driven scenario prediction. By constructing a mapping model of energy production units, energy conversion units and energy storage units, accurate modeling of each link of the energy system is achieved. The system can obtain the operating data of the target scenario in real time, perform calculations, and output the optimal parameters. These optimal parameters are then passed to the regulation module to achieve real-time regulation of each energy unit in the power grid.

[0005] The following is the technical solution of the present invention.

[0006] Real-time energy control system based on data-driven scenario prediction, including: A data acquisition module is used to obtain the operation data of the target scene; A model processing module is used to calculate the operation data using the mapping models corresponding to the preset energy production unit, energy conversion unit and energy storage unit, and output the optimal parameters based on the optimization target; The control module controls each energy unit in the power grid based on the optimal parameters.

[0007] Preferably, the model processing module includes a mapping model building submodule, a scene building submodule, and a target calculation submodule.

[0008] Preferably, the mapping model construction submodule comprises a first processing area for constructing an energy production unit mapping model, a second processing area for constructing an energy conversion unit mapping model and a third processing area for constructing an energy storage unit mapping model.

[0009] Preferably, the energy production unit mapping model constructed in the first processing area includes: Power grid: Where: P grid (t) represents the output power value of the power grid at time t (kW), P equip_i (t) represents the power consumed by each type of equipment at time t (kW), P loss (t) represents the transmission loss power of the power grid (kW), P shortage (t) represents the power shortage of the power grid (kW); natural gas station: Where: G gas_station (t) represents the gas power value (kW) output by the natural gas station at time t, d represents the diameter of the natural gas pipeline (m), ν represents the flow rate of the natural gas in the pipeline (m / s), H VCI Indicates the lower calorific value of natural gas combustion (KJ / m 3 ).

[0010] Preferably, the energy conversion unit mapping model constructed in the second processing area includes: gas turbine: Where: and represents the power generation, heat generation and natural gas consumption of the i-th gas turbine at time t (kW, kW, m 3 ), and Indicates the power generation efficiency and gas loss coefficient of the gas turbine (%), Indicates the rated power generation of the gas turbine (kW); Gas boiler: Where: and represents the heat production and natural gas consumption of the i-th gas boiler at time t (kW, m 3 ), and Indicates the heating efficiency and gas loss coefficient of the gas boiler (%), H VCI Indicates the lower calorific value of natural gas combustion (KJ / m 3 ); Heat pump: Where: and Indicates the heating / cooling power (kW) generated by the heat pump when heating / cooling. and Indicates the electrical power consumed by the heat pump when heating / cooling (kW), and Indicates the power loss coefficient (%) when the heat pump is heating / cooling. and Indicates the energy efficiency ratio of the heat pump when heating / cooling (%); Heat Exchanger: Where: and represents the heat power absorbed and released by the i-th heat exchanger at time t (kW), Indicates the heat energy conversion coefficient of the heat exchanger (%); Power-to-gas: Where: and G P2G Indicates the amount of electricity consumed and natural gas produced by the power-to-gas equipment (kW, m 3 ), P2G and η P2G Indicates the power loss coefficient and energy conversion efficiency (%) of the power-to-gas equipment. and Indicates the mass of CO2 consumed by the power-to-gas equipment in the process of synthesizing natural gas and the CO2 conversion efficiency (%); air conditioner: Where: and represents the electrical power consumed and cooling power generated by the i-th air conditioner at time t (kW), and Indicates the power loss coefficient and refrigeration energy efficiency ratio of the air conditioner (%); Electric Refrigerator: Where: and represents the electrical power consumed and cooling power generated by the i-th electric refrigerator at time t (kW), and Indicates the power loss coefficient and refrigeration energy efficiency ratio of the electric refrigerator (%); Absorption Chiller: Where: and represents the heat power consumed and cooling power generated by the i-th absorption chiller at time t (kW), and Indicates the heat loss coefficient and refrigeration energy efficiency ratio of the absorption refrigerator (%); Hydrogen production by electrolysis: Where: and Indicates the power consumed by the electrolytic hydrogen production equipment and the amount of hydrogen produced (kW, m 3 ), and Indicates the power loss coefficient and electricity-hydrogen conversion efficiency (%) of electrolytic hydrogen production equipment, Indicates the lower calorific value of hydrogen combustion (KJ / m 3 ); Hydrogen fuel cells: Where: Indicates the amount of hydrogen input to the fuel cell (m 3 ), represents the output power of the fuel cell (kW), Indicates the hydrogen loss coefficient of the hydrogen fuel cell (%), Indicates the hydrogen-electricity conversion efficiency of hydrogen fuel cells (kW / m 3 ); Carbon capture equipment: Where: P CC (t) and represents the electric power consumed by the carbon capture equipment and the amount of CO2 produced at time t (kWh, kg), Indicates regional carbon emission intensity (kgCO2 / kWh), and represents the amount of CO2 input to the carbon capture equipment and the total amount of CO2 captured at time t (kg), η CC represents the efficiency of carbon capture equipment (%), δ CC Indicates the energy consumption per unit CO2 captured by the equipment (kWh / kgCO2), represents the fixed energy consumption of the carbon capture equipment at time t (kWh).

[0011] Preferably, the energy storage unit mapping model constructed in the third processing area includes: Electricity storage: Where: E ES (t) represents the storage capacity of the battery at time t (kWh), P ch (t) and P dis (t) represents the charging and discharging power of the energy storage at time t (kW), τ represents the charging flag of the energy storage, and Indicates the charging and discharging efficiency of the energy storage (%); Heat storage: Where: H ES (t) represents the heat storage capacity of the heat storage at time t (kWh), h ch (t) and h dis (t) represents the heat storage and heat release power of the heat storage at time t (kW), τ H Indicates the heat storage flag of the heat storage. and It represents the heat storage and heat release efficiency of the heat storage (%); Cold storage: Where: C ES (t) represents the cold storage capacity at time t (kWh), c ch (t) and c dis (t) represents the cold storage and cooling power of the cold storage at time t (kW), τ c Indicates the cold storage flag bit of cold storage. and Indicates the cold storage and refrigeration efficiency of cold storage (%); Natural gas storage: Where: G gas (t) represents the natural gas storage capacity at time t (m 3 ), g ch (t) and g dis (t) represents the gas storage and release rate of natural gas storage at time t (m 3 / h), τ gas Indicates the gas storage flag of natural gas storage. and Indicates the storage and release efficiency of natural gas storage (%); Hydrogen storage: Where: Indicates the hydrogen storage capacity at time t (m 3 ), Hy ch (t) and Hydis (t) represents the storage and release rate of hydrogen storage at time t (m 3 / h), Indicates the hydrogen storage flag bit of hydrogen storage. and Indicates the storage and release efficiency of hydrogen storage (%).

[0012] Preferably, the scene construction submodule is used to perform: Build and train the WGAN model, use the generator of the WGAN model to sample random vectors from a simple known distribution as needed, and convert them into system operation scenarios.

[0013] Preferably, the target calculation submodule is used to execute: Construct the objective function, substitute the running data into the mapping model for calculation, and continuously iterate to select the optimal parameter output.

[0014] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the steps of the above-mentioned real-time energy control system based on data-driven scene prediction are implemented.

[0015] The present invention also provides a storage medium, in which computer executable instructions are stored. When the computer executable instructions are loaded and executed by a processor, the steps of the above-mentioned real-time energy control system based on data-driven scenario prediction are implemented.

[0016] The substantial effects of the present invention include: The real-time energy control system based on data-driven scenario prediction proposed in this invention comprehensively covers the key links of the energy system by constructing a sophisticated mapping model of energy production units, energy conversion units and energy storage units. These mapping models are not only based on in-depth physical principles, but also combined with actual operation data to ensure the high accuracy and practicality of the model.

[0017] The system first uses the data acquisition module to capture the operating data of the target scenario in real time, providing a solid foundation for subsequent prediction and regulation. The model processing module uses these real-time data, combined with the preset mapping model, to perform efficient calculations and output the optimal parameters. This process not only takes into account the current state of the energy system, but also uses the WGAN model through the scenario construction submodule to predict possible future operating scenarios, thereby realizing a forward-looking regulation strategy.

[0018] Based on these optimal parameters, the control module accurately controls each energy unit in the power grid to ensure the stable operation and efficient output of the energy system. This real-time and dynamic control method not only improves the accuracy and efficiency of energy control, but also greatly enhances the flexibility and adaptability of the system.

[0019] In summary, the present invention realizes the global optimization and coordinated operation of the energy system through data-driven scenario prediction and real-time regulation, significantly improves energy utilization efficiency and reduces energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution will be clearly and completely described below in combination with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] It should be understood that in various embodiments of the present invention, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0023] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0024] It should be understood that in the present invention, "plurality" refers to two or more than two. "And / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "Contains A, B and C", "Contains A, B, C" means that A, B, and C are all included, "Contains A, B or C" means that one of A, B, and C is included, and "Contains A, B and / or C" means that any one, any two, or any three of A, B, and C are included.

[0025] The technical solution of the present invention is described in detail with specific embodiments below. The embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0026] Example: Real-time energy control system based on data-driven scenario prediction, such as Figure 1 As shown, including: A data acquisition module is used to obtain the operation data of the target scene; A model processing module is used to calculate the operation data using the mapping models corresponding to the preset energy production unit, energy conversion unit and energy storage unit, and output the optimal parameters based on the optimization target; The control module controls each energy unit in the power grid based on the optimal parameters.

[0027] The model processing module includes a mapping model building submodule, a scene building submodule, and a target calculation submodule.

[0028] Specifically, the mapping model construction submodule includes a first processing area for constructing an energy production unit mapping model, a second processing area for constructing an energy conversion unit mapping model, and a third processing area for constructing an energy storage unit mapping model.

[0029] According to different functions, the equipment in IES can be divided into three categories: energy production unit, energy conversion unit and energy storage unit. Among them, the energy production unit is the core component of IES, which is responsible for converting various natural resources into usable energy forms. The energy conversion unit is the key link to realize the conversion of different forms of energy, which can realize the conversion between various energy forms such as electrical energy and thermal energy, electrical energy and mechanical energy, etc. The energy storage unit plays an important role in balancing energy supply and demand and improving system stability in IES. Together, they build a complete and efficient energy utilization chain.

[0030] The energy production unit mapping model constructed in the first processing area includes: Power grid: Where: P grid (t) represents the output power value of the power grid at time t (kW), P equip_i (t) represents the power consumed by each type of equipment at time t (kW), P loss (t) represents the transmission loss power of the power grid (kW), P shortage (t) represents the power shortage of the power grid (kW); natural gas station: Where: G gas_station(t) represents the gas power value (kW) output by the natural gas station at time t, d represents the diameter of the natural gas pipeline (m), ν represents the flow rate of the natural gas in the pipeline (m / s), H VCI Indicates the lower calorific value of natural gas combustion (KJ / m 3 ).

[0031] For wind power and photovoltaic power generation, this embodiment uses a scenario generation method based on a generative adversarial network to describe the output uncertainty of wind power and photovoltaic power generation. This method does not require the prior assumption that wind power and photovoltaic power generation data conform to certain specific probability distributions, so there is no need to perform mathematical modeling on the two.

[0032] The energy conversion unit mapping model constructed in the second processing area includes: gas turbine: Where: and represents the power generation, heat generation and natural gas consumption of the i-th gas turbine at time t (kW, kW, m 3 ), and Indicates the power generation efficiency and gas loss coefficient of the gas turbine (%), Indicates the rated power generation of the gas turbine (kW); Gas boiler: Where: and represents the heat production and natural gas consumption of the i-th gas boiler at time t (kW, m 3 ), and Indicates the heating efficiency and gas loss coefficient of the gas boiler (%), H VCI Indicates the lower calorific value of natural gas combustion (KJ / m 3 ); Heat pump: Where: and Indicates the heating / cooling power (kW) generated by the heat pump when heating / cooling. and Indicates the electrical power consumed by the heat pump when heating / cooling (kW), and Indicates the power loss coefficient (%) when the heat pump is heating / cooling. and Indicates the energy efficiency ratio of the heat pump when heating / cooling (%); Heat Exchanger: Where: and represents the heat power absorbed and released by the i-th heat exchanger at time t (kW), Indicates the heat energy conversion coefficient of the heat exchanger (%); Power-to-gas: Where: and Indicates the amount of electricity consumed and natural gas produced by the power-to-gas equipment (kW, m 3 ), P2G and η P2G Indicates the power loss coefficient and energy conversion efficiency (%) of the power-to-gas equipment. and Indicates the mass of CO2 consumed by the power-to-gas equipment in the process of synthesizing natural gas and the CO2 conversion efficiency (%); air conditioner: Where: and represents the electrical power consumed and cooling power generated by the i-th air conditioner at time t (kW), and Indicates the power loss coefficient and refrigeration energy efficiency ratio of the air conditioner (%); Electric Refrigerator: Where: and represents the electrical power consumed and cooling power generated by the i-th electric refrigerator at time t (kW), and Indicates the power loss coefficient and refrigeration energy efficiency ratio of the electric refrigerator (%); Absorption Chiller: Where: and represents the heat power consumed and cooling power generated by the i-th absorption chiller at time t (kW), and Indicates the heat loss coefficient and refrigeration energy efficiency ratio of the absorption refrigerator (%); Hydrogen production by electrolysis: Where: and Indicates the power consumed by the electrolytic hydrogen production equipment and the amount of hydrogen produced (kW, m 3 ), and Indicates the power loss coefficient and electricity-hydrogen conversion efficiency (%) of electrolytic hydrogen production equipment, Indicates the lower calorific value of hydrogen combustion (KJ / m 3 ); Hydrogen fuel cells: Where: Indicates the amount of hydrogen input to the fuel cell (m 3 ), represents the output power of the fuel cell (kW), Indicates the hydrogen loss coefficient of the hydrogen fuel cell (%), Indicates the hydrogen-electricity conversion efficiency of hydrogen fuel cells (kW / m 3 ); Carbon capture equipment: Where: P CC (t) and represents the electric power consumed by the carbon capture equipment and the amount of CO2 produced at time t (kWh, kg), Indicates regional carbon emission intensity (kgCO2 / kWh), and represents the amount of CO2 input to the carbon capture equipment and the total amount of CO2 captured at time t (kg), η CC represents the efficiency of carbon capture equipment (%), δ CC Indicates the energy consumption per unit CO2 captured by the equipment (kWh / kgCO2), represents the fixed energy consumption of the carbon capture equipment at time t (kWh).

[0033] The energy storage unit mapping model constructed in the third processing area includes: Electricity storage: Where: E ES (t) represents the storage capacity of the battery at time t (kWh), P ch (t) and P dis (t) represents the charging and discharging power of the energy storage at time t (kW), τ represents the charging flag of the energy storage, and Indicates the charging and discharging efficiency of the energy storage (%); Heat storage: Where: H ES (t) represents the heat storage capacity of the heat storage at time t (kWh), h ch (t) and h dis (t) represents the heat storage and heat release power of the heat storage at time t (kW), τ HIndicates the heat storage flag of the heat storage. and It represents the heat storage and heat release efficiency of the heat storage (%); Cold storage: Where: C ES (t) represents the cold storage capacity at time t (kWh), c ch (t) and c dis (t) represents the cold storage and cooling power of the cold storage at time t (kW), τ c Indicates the cold storage flag bit of cold storage. and Indicates the cold storage and refrigeration efficiency of cold storage (%); Natural gas storage: Where: G gas (t) represents the natural gas storage capacity at time t (m 3 ), g ch (t) and g dis (t) represents the gas storage and release rate of natural gas storage at time t (m 3 / h), τ gas Indicates the gas storage flag of natural gas storage. and Indicates the storage and release efficiency of natural gas storage (%); Hydrogen storage: Where: Indicates the hydrogen storage capacity at time t (m 3 ), Hy ch (t) and Hy dis (t) represents the storage and release rate of hydrogen storage at time t (m 3 / h), Indicates the hydrogen storage flag bit of hydrogen storage. and Indicates the storage and release efficiency of hydrogen storage (%).

[0034] In this embodiment, the scene construction submodule is used to perform: Build and train the WGAN model, use the generator of the WGAN model to sample random vectors from a simple known distribution as needed, and convert them into system operation scenarios.

[0035] The generator and discriminator of this embodiment are trained using RMSprop and the learning rate η G and η D Set to 10 respectively -4 and 5×10 -5The mini-batch size N is set to 64. discri Set to 5, which means that the discriminator will be trained 5 times for every generator training in each training cycle. In order to make WGAN meet Lipschitz continuity and promote its stable training, the discriminator parameters are pruned during training and the pruned parameter φ is set to 0.02. All other training parameters are set to the default parameters of the Pytorch toolkit. The input implicit vector of the generator is sampled from a uniform distribution of [-1,1].

[0036] The code involved in the training process includes: Input: learning rate η G and η D , batch size N, pruning parameter φ, number of discriminator iterations n discri , training cycle n epoch ; Initialization: Initialize the generator weights θ (G) and the discriminator weights θ (D) ; for iteration=1,...,n epoch do for n=1,...,n epoch do # Update the discriminator parameters Sample N samples x from historical data; Randomly sample N vectors z from a uniform distribution in [-1,1]; θ (D) ←clip(θ (D) ,-φ,φ); end #Update generator parameters end In order to achieve reliable prediction of system operation scenarios, we must first train WGAN well, so as to construct an unsupervised representation of the statistical distribution of system operation scenarios to generate sufficiently realistic operation scenarios that can reflect the system operation characteristics. To this end, we first evaluate the performance of system operation scenario generation based on WGAN. Wasserstein distance can provide an intuitive standard for measuring the difference between two statistical distributions.

[0037] This embodiment adds a Sigmoid activation function to the last layer of the discriminator and uses the Adam optimizer. In addition, the network structure and training parameters of JSD-GAN are consistent with those of WGAN. As the alternating competitive training of the generator and the discriminator reaches equilibrium, the Wasserstein distance of WGAN gradually decreases and converges to a minimum value close to 0 after about 150 training cycles. At this time, the statistical distribution of the scenes generated by WGAN is almost the same as the statistical distribution of the historical scenes, thereby obtaining a well-trained generator that can generate sufficiently realistic scenes. In contrast, the Wasserstein distance of JSD-GAN has always been large and cannot converge, which means that the scenes generated by JSD-GAN are not realistic enough, so that the statistical distribution of the generated scenes is quite different from that of the historical scenes.

[0038] The target calculation submodule is used to execute: constructing the target function, substituting the operating data into the mapping model for calculation, and continuously iterating to select the optimal parameter output.

[0039] The iterative process includes: Input: pre-trained WGAN; observations s obse ; Point forecast value and the corresponding confidence level α; weight parameters γ, κ and ν; learning rate η; number of iterations; number of prediction scenarios m; Output: Prediction scenario

[0040] After testing, the hyperparameters γ, κ, and ν are set to 0.03, 0.05, and 0.05, respectively. Point prediction information can be provided by any prediction method. Since this is beyond the scope of this chapter, point prediction data is obtained by adding random errors to real data. Considering that the prediction error will increase with the increase of the prediction time scale, the random error is added by assuming that the maximum prediction error for a prediction time range of 24 hours is 30%, the prediction error for the most recent moment is 10%, and the prediction error for the intermediate moment is distributed equally between 10% and 30%.

[0041] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the steps of the above-mentioned real-time energy control system based on data-driven scenario prediction are implemented.

[0042] This embodiment also provides a storage medium, in which computer executable instructions are stored. When the computer executable instructions are loaded and executed by a processor, the steps of the above-mentioned real-time energy control system based on data-driven scenario prediction are implemented.

[0043] The substantial effects of this embodiment include: The real-time energy control system based on data-driven scenario prediction proposed in this embodiment comprehensively covers the key links of the energy system by constructing a sophisticated mapping model of energy production units, energy conversion units, and energy storage units. These mapping models are not only based on in-depth physical principles, but also combined with actual operation data to ensure the high accuracy and practicality of the model.

[0044] The system first uses the data acquisition module to capture the operating data of the target scenario in real time, providing a solid foundation for subsequent prediction and regulation. The model processing module uses these real-time data, combined with the preset mapping model, to perform efficient calculations and output the optimal parameters. This process not only takes into account the current state of the energy system, but also uses the WGAN model through the scenario construction submodule to predict possible future operating scenarios, thereby realizing a forward-looking regulation strategy.

[0045] Based on these optimal parameters, the control module accurately controls each energy unit in the power grid to ensure the stable operation and efficient output of the energy system. This real-time and dynamic control method not only improves the accuracy and efficiency of energy control, but also greatly enhances the flexibility and adaptability of the system.

[0046] In summary, this embodiment achieves global optimization and coordinated operation of the energy system through data-driven scenario prediction and real-time regulation, significantly improves energy utilization efficiency and reduces energy waste.

[0047] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0048] In the embodiments provided in the present application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the embodiments of the structure described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another structure, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, structures or units, which can be electrical, mechanical or other forms.

[0049] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0050] In addition, each functional unit in the embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0051] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0052] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A real-time energy control system based on data-driven scenario prediction, characterized in that: include: A data acquisition module is used to obtain the operation data of the target scene; A model processing module is used to calculate the operation data using the mapping models corresponding to the preset energy production unit, energy conversion unit and energy storage unit, and output the optimal parameters based on the optimization target; The control module controls each energy unit in the power grid based on the optimal parameters.

2. The real-time energy control system based on data-driven scenario prediction according to claim 1 is characterized in that: The model processing module includes a mapping model building submodule, a scene building submodule, and a target calculation submodule.

3. The real-time energy control system based on data-driven scenario prediction according to claim 2 is characterized in that: The mapping model construction submodule includes a first processing area for constructing an energy production unit mapping model, a second processing area for constructing an energy conversion unit mapping model, and a third processing area for constructing an energy storage unit mapping model.

4. The real-time energy control system based on data-driven scenario prediction according to claim 3 is characterized in that: The energy production unit mapping model constructed in the first processing area includes: Power grid: Where: P grid (t) represents the output power value of the power grid at time t (kW), P equip_i (t) represents the power consumed by each type of equipment at time t (kW), P loss (t) represents the transmission loss power of the power grid (kW), P shortage (t) represents the power shortage of the power grid (kW); natural gas station: Where: G gas_station (t) represents the gas power value (kW) output by the natural gas station at time t, d represents the diameter of the natural gas pipeline (m), ν represents the flow rate of the natural gas in the pipeline (m / s), H VCI Indicates the lower calorific value of natural gas combustion (KJ / m 3 ).

5. The real-time energy control system based on data-driven scenario prediction according to claim 3 is characterized in that: The energy conversion unit mapping model constructed in the second processing area includes: gas turbine: Where: and represents the power generation, heat generation and natural gas consumption of the i-th gas turbine at time t (kW, kW, m 3 ), and Indicates the power generation efficiency and gas loss coefficient of the gas turbine (%), Indicates the rated power generation of the gas turbine (kW); Gas boiler: Where: and represents the heat production and natural gas consumption of the i-th gas boiler at time t (kW, m 3 ), and Indicates the heating efficiency and gas loss coefficient of the gas boiler (%), H VCI Indicates the lower calorific value of natural gas combustion (KJ / m 3 ); Heat pump: Where: and Indicates the heating / cooling power (kW) generated by the heat pump when heating / cooling. and Indicates the electrical power consumed by the heat pump when heating / cooling (kW), and Indicates the power loss coefficient (%) when the heat pump is heating / cooling. and Indicates the energy efficiency ratio of the heat pump when heating / cooling (%); Heat Exchanger: Where: and represents the heat power absorbed and released by the i-th heat exchanger at time t (kW), Indicates the heat energy conversion coefficient of the heat exchanger (%); Power-to-gas: Where: and G P2G Indicates the electric power consumed and natural gas produced by the power-to-gas equipment (kW, m 3 ), P2G and η P2G Indicates the power loss coefficient and energy conversion efficiency (%) of the power-to-gas equipment. and Indicates the mass of CO2 consumed by the power-to-gas equipment in the process of synthesizing natural gas and the CO2 conversion efficiency (%); air conditioner: Where: and represents the electrical power consumed and cooling power generated by the i-th air conditioner at time t (kW), and Indicates the power loss coefficient and refrigeration energy efficiency ratio of the air conditioner (%); Electric Refrigerator: Where: and represents the electrical power consumed and cooling power generated by the i-th electric refrigerator at time t (kW), and Indicates the power loss coefficient and refrigeration energy efficiency ratio of the electric refrigerator (%); Absorption Chiller: Where: and represents the heat power consumed and cooling power generated by the i-th absorption chiller at time t (kW), and Indicates the heat loss coefficient and refrigeration energy efficiency ratio of the absorption refrigerator (%); Hydrogen production by electrolysis: Where: and Indicates the power consumed by the electrolytic hydrogen production equipment and the amount of hydrogen produced (kW, m 3 ), and It represents the power loss coefficient and electricity-hydrogen conversion efficiency (%) of the electrolytic hydrogen production equipment. VCIH_2 Indicates the lower calorific value of hydrogen combustion (KJ / m 3 ); Hydrogen fuel cells: Where: Indicates the amount of hydrogen input to the fuel cell (m 3 ), represents the output power of the fuel cell (kW), Indicates the hydrogen loss coefficient of the hydrogen fuel cell (%), Indicates the hydrogen-electricity conversion efficiency of hydrogen fuel cells (kW / m 3 ); Carbon capture equipment: Where: P CC (t) and represents the electric power consumed by the carbon capture equipment and the amount of CO2 produced at time t (kWh, kg), Indicates regional carbon emission intensity (kgCO2 / kWh), and represents the amount of CO2 input to the carbon capture equipment and the total amount of CO2 captured at time t (kg), η CC represents the efficiency of carbon capture equipment (%), δ CC Indicates the energy consumption per unit CO2 captured by the equipment (kWh / kgCO2), represents the fixed energy consumption of the carbon capture equipment at time t (kWh).

6. The real-time energy control system based on data-driven scenario prediction according to claim 3 is characterized in that: The energy storage unit mapping model constructed in the third processing area includes: Electricity storage: Where: E ES (t) represents the storage capacity of the battery at time t (kWh), P ch (t) and P dis (t) represents the charging and discharging power of the energy storage at time t (kW), τ represents the charging flag of the energy storage, and Indicates the charging and discharging efficiency of the energy storage (%); Heat storage: Where: H ES (t) represents the heat storage capacity of the heat storage at time t (kWh), h ch (t) and h dis (t) represents the heat storage and heat release power of the heat storage at time t (kW), τ H Indicates the heat storage flag of the heat storage. and It represents the heat storage and heat release efficiency of the heat storage (%); Cold Storage: Where: C ES (t) represents the cold storage capacity at time t (kWh), c ch (t) and c dis (t) represents the cold storage and cooling power of the cold storage at time t (kW), τ c Indicates the cold storage flag bit of cold storage. and Indicates the cold storage and refrigeration efficiency of cold storage (%); Natural gas storage: Where: G gas (t) represents the natural gas storage capacity at time t (m 3 ), g ch (t) and g dis (t) represents the gas storage and release rate of natural gas storage at time t (m 3 / h), τ gas Indicates the gas storage flag of natural gas storage. and Indicates the storage and release efficiency of natural gas storage (%); Hydrogen storage: Where: Indicates the hydrogen storage capacity at time t (m 3 ), Hy ch (t) and Hy dis (t) represents the storage and release rate of hydrogen storage at time t (m 3 / h), Indicates the hydrogen storage flag bit of hydrogen storage. and Indicates the storage and release efficiency of hydrogen storage (%).

7. The real-time energy control system based on data-driven scenario prediction according to claim 2 is characterized in that: The scene construction submodule is used to perform: Build and train the WGAN model, use the generator of the WGAN model to sample random vectors from a simple known distribution as needed, and convert them into system operation scenarios.

8. The real-time energy control system based on data-driven scenario prediction according to claim 2 is characterized in that: The target computing submodule is used to perform: Construct the objective function, substitute the running data into the mapping model for calculation, and continuously iterate to select the optimal parameter output.

9. An electronic device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the steps of the real-time energy control system based on data-driven scenario prediction as described in any one of claims 1 to 8 are implemented.

10. A storage medium, characterized in that: The storage medium stores computer executable instructions, which, when loaded and executed by a processor, implement the steps of a real-time energy control system based on data-driven scenario prediction as described in any one of claims 1 to 8.

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