193 results about "Energy balance" patented technology

The invention relates to the technical field of power system dispatching, and discloses a multi-agent reinforcement learning active power distribution network area coordination method and system, and the method comprises the steps: dividing a power distribution network into a plurality of areas, and each area is managed by an agent; collecting observation information; inputting the observation information into an upper reinforcement learning strategy network, and outputting control parameters; inputting the control parameters into a target function of the lower-layer local physical optimization model, and solving an output setting point of the equipment under the condition of meeting the safety operation constraint; constructing a De-POMDP problem, and obtaining a reward signal of each agent; a sequential updating mechanism is introduced, global network parameters are optimized, and corresponding decisions are obtained; and inputting the multi-agent decision into the global active power distribution network model to obtain the total operation cost, feeding back the total operation cost as an award to the reinforcement learning strategy network, updating global network parameters, and converging to obtain an optimal decision. According to the invention, regional wind-solar-storage multi-energy scheduling can be effectively optimized, and energy balance in the region is realized.

The invention discloses an airport energy balance management method, system and device and a medium, and relates to the field of energy management. The method comprises the following steps: constructing an energy topological graph according to multi-source data; predicting energy load demands of the airport in different time periods according to the energy topological graph, determining the supply amount of renewable energy in different time periods according to the predicted illumination intensity and wind power, and determining a time-sharing energy supply and demand balance probability according to the energy load demands and the supply amount; determining a preliminary energy management plan according to the cost parameter, the time-sharing energy supply and demand balance probability and the equipment operation constraint, wherein the preliminary energy management plan comprises an energy storage system charging and discharging time sequence, a standby generator starting and stopping plan and a power grid electricity purchasing proportion; and executing the preliminary energy management plan through the digital twin of the airport to obtain first energy execution data, and adjusting the preliminary energy management plan through the difference between the first energy execution data and the second energy execution data at the current moment. By implementing the technical scheme provided by the invention, the energy cost is reduced.

The invention discloses a balance scheduling system for energy management and a balance scheduling method thereof, and relates to the technical field of energy management scheduling, and the balance scheduling system comprises an energy data acquisition module, an energy demand trend analysis module, an energy supply and demand dynamic prediction module, an energy balance scheduling decision module, a scheduling execution module and a performance monitoring module. After the energy supply and demand dynamic prediction module integrates real-time external factor data of the energy data acquisition module and a long-term historical rule of the energy demand trend analysis module, a reference prediction baseline based on a long-term trend is generated, and then a dynamic correction value is superposed to respond to short-term disturbance. And meanwhile, through an error traceability mechanism for separating a trend baseline from external correction, the module performance can be optimized in a targeted manner, if an error is derived from long-term trend deviation, a trend analysis model is calibrated, and if external data is lagged, priority adjustment of a data acquisition module is triggered, so that system self-diagnosis and prediction precision closed-loop improvement are realized.

The invention discloses a modern agricultural park comprehensive energy optimization scheduling method considering extreme temperature. According to the method, an extreme air temperature scene is determined based on a meteorological data set and an extreme air temperature event screening index, and energy supply and demand are predicted by using an energy supply-meteorological and agricultural load-meteorological coupling model. And constructing a weekly-ahead and day-ahead two-stage optimization scheduling model, and calculating an optimization scheduling result by using a commercial solver in combination with constraint conditions such as energy balance and the like. Meanwhile, adjustable resources such as a battery energy storage system and a demand response reservoir are considered, and the flexibility and stability of the system are improved. According to the method, the accuracy of source load prediction of the comprehensive energy system of the modern agricultural park at the extreme temperature is effectively improved, unbalanced cost deviation of different time scales is relieved, optimal scheduling of the comprehensive energy system of the agricultural park is achieved, stable supply of agricultural production energy is guaranteed, the energy utilization efficiency is improved, and the method has important application value.

The invention discloses a method for processing carbon flux data obtained by a vortex correlation method. The method comprises the steps of data acquisition, original data preprocessing, data post-processing, data interpolation, data storage and additional description. The method comprises the following steps: capturing a signal by adopting an eddy correlation method, and recording original data in real time through a Smart Flux collector; in the preprocessing stage, invalid data, abnormal values, rainfall influence data and the like are removed by using Eddypro software, and necessary instrument error and inclination correction is carried out; in the post-processing stage, Eddypro and TOVI software are combined for further processing, including energy balance correction, quality control and the like; for missing data, adopting a linear interpolation method and other interpolation methods; the processed data can be stored in an Excel mode, an R language mode or a programming mode. According to the method, the carbon flux data quality can be effectively improved, and a solid foundation is provided for subsequent scientific research and data analysis.

The invention provides an ocean satellite Internet of Things multi-modal data narrow-band transmission scheduling method and device, belongs to the technical field of ocean satellite Internet of Things, and innovatively provides a multi-modal data narrow-band transmission scheduling strategy for challenges of ocean observation equipment in an energy-limited and dynamic satellite channel environment. The core of the method comprises the following steps: constructing a multi-modal data dynamic transmission model, and carrying out data value evaluation based on space-time intelligence; designing a congestion perception and hierarchical congestion control strategy to guarantee data timeliness; the transmission fairness is optimized in combination with the data value weight factor, the PAoI and the Jain fairness index; an energy dynamic evolution model is constructed, and long-term energy revenue and expenditure balance of equipment is guaranteed; based on a deep Q network and imitation learning, in combination with a security reinforcement learning framework, information timeliness, energy consumption and congestion are jointly optimized. According to the method, the deep and far sea observation and detection data transmission time delay, energy consumption and the congestion occurrence rate are remarkably reduced, and the information timeliness and the multi-modal data transmission scheduling fairness are improved.

The invention relates to an industrial park-based comprehensive energy flow direction on-line monitoring system and method, and the system comprises an energy modeling module which is used for representing instrument information, carrying out the modeling of collection labels and collection frequencies of different types of instruments, constructing nodes of different energy layered structures, carrying out the modeling of a directed flow direction relation between the nodes, and carrying out the modeling of the directed flow direction relation between the nodes; binding the logic relationship between the node and the instrument; the data acquisition module is used for performing data acquisition through an instrument, performing edge processing and encrypted transmission through communication equipment, and storing the data in a database; the energy monitoring module is used for monitoring and counting the state of the instrument and monitoring the energy flow direction of the enterprise in real time according to the directed flow direction of the node; and the statistical analysis module is used for providing energy balance analysis for different links of the enterprise energy in the conversion and use process according to the nodes, evaluating the energy consumption loss degrees of the different links, visualizing the nodes with abnormal loss and giving an alarm. Through the real-time and accurate calculation of the energy loss among the nodes at all levels, the backtracking of the nodes with abnormal loss and the timely pushing of the alarm information, the enterprise can timely discover the problems occurring in the energy supply, and can quickly find out the corresponding solution, reduce the energy consumption loss and guarantee the robustness of the energy flow network.

The embodiment of the invention provides a thermal power plant waste heat gradient utilization management method and system, belongs to the field of energy and power engineering, and aims at solving the problems that in the prior art, waste heat gradient matching is unreasonable, the low-temperature waste heat recovery efficiency is low, and the system integration degree is insufficient. The method comprises the following steps: constructing a multi-physics field coupling digital twinborn model and performing real-time correction; establishing a dynamic matching matrix of the residual heat source and the heat consumption load based on balance analysis; solving a Pareto optimal operation scheme through a multi-objective optimization model; and generating and issuing a cooperative control instruction. The system comprises a panoramic data sensing module, a digital twin modeling module, a balance and optimization decision module and a cooperative control instruction generation module. Through global optimization and dynamic regulation and control, the waste heat utilization efficiency, the economical efficiency and the equipment reliability are remarkably improved.

The invention relates to an anti-reflux control method based on time interval distribution. The method comprises the following steps: acquiring a multi-source fused operation data set; obtaining a trend prediction result set according to the operation data set; according to the trend prediction result set, dividing the prediction time window into a plurality of continuous control time periods, and based on the power generation prediction value, the load prediction value and the uncertainty index of each time period, calculating a corresponding time period countercurrent risk level, and generating a time period risk assessment result; obtaining a time period budget allocation table according to a time period risk assessment result; according to the time period budget distribution table, performing time-division power adjustment on the photovoltaic inverter, the energy storage unit and the controllable load, and realizing smooth transition between adjacent time periods through a cross-time period energy balance mechanism; and monitoring the grid-connected point power and the equipment operation state in real time, and when power deviation or load sudden change is detected, dynamically correcting the current output according to the residual capacity of the time period budget allocation table. According to the invention, the photovoltaic anti-reflux control effect can be improved.

The invention provides a multi-source complementary intelligent power supply management method and system in a mobile square cabin. The method comprises the following steps: collecting real-time state data of multi-type energy supply equipment, and generating a comprehensive energy management data set; based on the comprehensive energy management data set, dynamically evaluating real-time efficiency by adopting an interval optimization algorithm based on an evolutionary strategy, and generating an energy combination strategy report by applying a Markov decision process; based on the energy combination strategy report, adopting a gradient descent optimization algorithm to adjust working parameters of the multi-type energy supply equipment, and applying a real-time energy balance technology to generate an optimized energy configuration scheme; and based on the optimized energy configuration scheme, monitoring the operation condition of a power supply system, optimizing working parameters, and generating an intelligent power supply management parameter set. According to the technical scheme, the intelligent power supply management effect can be effectively improved.

The invention relates to the technical field of intelligent water affairs, in particular to a three-level combined processing method for urban water consumption prediction, which comprises the following steps: S1, data restoration: correcting abnormal values in an urban water consumption time sequence by using a dynamic threshold correction method based on multi-scale wavelet decomposition; s2, performing feature decoupling; and S3, performing prediction fusion. According to the method in the technical scheme, a dynamic threshold correction method based on multi-scale wavelet decomposition is adopted, signal hierarchical analysis is achieved through discrete wavelet transformation, a dynamic threshold mechanism is constructed in combination with MAD robust statistics, and abnormal points are effectively recognized and corrected; constructing a dynamic frequency band division joint model of variational mode decomposition and spectrum energy balance, proposing a dynamic frequency band recombination strategy guided by amplitude ranking, and performing energy balance division and inverse transformation reconstruction on VMD high-frequency sub-components through fast Fourier transform spectrum analysis; fine separation and multi-band phase synchronization of high-frequency abrupt change characteristics and periodic fluctuation are realized; and finally, constructing a long-short-term memory network to predict and reconstruct all sub-components.

The invention provides a multi-microgrid energy collaborative optimization method and system. The method comprises the following steps: S1, establishing an energy collaborative network topology structure comprising a plurality of microgrid nodes; s2, constructing an energy balance model based on the real-time power demand, the new energy output prediction and the power grid dispatching instruction; s3, taking the minimization of the total operation cost of the multi-microgrid system as a target to form an optimization function; s4, an improved whale optimization algorithm is adopted to solve the optimization function, and an energy scheduling scheme of each microgrid in each time period is obtained; s5, regulating and controlling an energy storage device, a charging pile and an adjustable load in each micro-grid according to the energy scheduling scheme, and coordinating the energy flow direction and the transmission power between the micro-grids; and S6, monitoring the actual operation state and the energy interaction effect of each micro-grid, and when it is detected that the predicted deviation exceeds a preset threshold value, re-executing the steps S2 to S5 based on the real-time data. According to the invention, energy collaboration among multiple microgrids can be realized, the new energy consumption capability is improved, the operation cost is reduced, and the economy and stability of power grid operation are enhanced.

The invention relates to the technical field of power systems and energy storage, and discloses a new energy and pumped storage coordinated cross-time energy balance method and system. The method comprises the following steps: performing feature extraction and dynamic modeling on wind power output night monitoring data and solar waste heat power generation night monitoring data to obtain a new energy excess power prediction value; performing water level trend analysis and energy storage capability evaluation on the real-time water level data of the pumped storage upper reservoir and the watershed incoming water forecast data to obtain a pumped storage acceptance power upper limit value; performing power adjustment on the new energy power generation end based on a comparison result to obtain new energy actual output data; performing threshold judgment and response coefficient calculation on the supply-demand deviation value to obtain a demand side adjustment scheme; and performing dynamic planning optimization and constraint condition correction on the time-phased power data sequence to obtain a multi-energy collaborative power regulation instruction sequence covering the whole time period. According to the method, accurate response to new energy output fluctuation and efficient utilization of pumped storage resources are achieved.

The invention discloses an electric power system resource scheduling method and device, and belongs to the technical field of electric power systems, and the method comprises the steps: obtaining the resource data of an electric power system when the natural climbing amount of the electric power system is not greater than the climbing demand of the electric power system, and taking the minimization of the total operation cost of the electric power system and the maximization of the climbing service demand response as targets; constructing an objective function, a system power balance constraint, a transmission capacity constraint, an energy storage charging and discharging constraint, an energy storage energy balance constraint, a climbing ability constraint and a climbing service demand constraint; and solving the target function under each constraint to obtain the output of each thermal power generating unit and each energy storage system in each scheduling period and the up-climbing demand quantity and the down-climbing demand quantity of each flexible climbing price interval in each scheduling period, and scheduling the resources of the power system according to the solving result. By implementing the invention, the problem that the power supply and demand of the power system are unbalanced when the natural climbing amount is not greater than the climbing demand in the prior art can be solved, and the operation stability of the power system is improved.

The invention discloses an energy-saving QoS (Quality of Service) routing protocol implementation method based on ant colony optimization, which aims at solving the problems of low routing efficiency and high path energy consumption in a UANET (User Ad Hoc Network) based on RF / FSO (Radio Frequency / Free Space Optimization) communication, and realizes an energy-saving QoS routing protocol based on ant colony optimization. An energy-saving QoS routing algorithm based on ant colony optimization is provided, and node neighborhood energy density is considered in a path transition probability model, so that the search efficiency of the ant colony algorithm is improved, and energy balance in the network is ensured; energy and distance factors are added into a pheromone updating rule, and the difference between the optimal path and other paths is increased; a self-adaptive pheromone volatilization mechanism is added, and the volatilization rate of pheromones is dynamically adjusted according to the real-time state of the network; and a cross-layer data exchange interface is added, so that real-time updating of link interaction data is ensured, and stable communication in a dynamic network environment is supported.

The invention provides a solar energy-radio frequency energy dual-driven low-altitude polarization switching dynamic power management system, and relates to the technical field of low-altitude flight. The system integrates a high-efficiency solar energy collection unit and a radio frequency energy collection unit with dynamic polarization adaptive switching capability, is assisted by a high-performance hybrid energy storage unit, and carries out unified coordination control through an intelligent power management unit. The radio frequency energy collection unit adopts a reconfigurable antenna technology, can sense the polarization characteristic of an environment radio frequency signal in real time, and dynamically switches the polarization state of an antenna to maximize the instantaneous energy capture efficiency; the intelligent power management unit executes a dynamic and collaborative energy flow scheduling strategy based on comprehensive analysis of solar irradiance, environment radio frequency spectrum and polarization characteristics, energy storage unit state, future energy income and expenditure prediction and real-time load demand; and the energy self-sustaining capability, the task endurance time and the working stability of low-altitude equipment in a complex and changeable environment are improved.

A method of real-time energy-tracking including: measuring the energy produced by an energy generator and the energy consumed by an energy consumer; collecting generated energy measurements and consumed energy measurements over a specified time period; receiving data packets containing these measurements along with associated temporal metadata, wherein by aligning the generated energy measurements with the consumed energy measurements based on this temporal data, the method creates matched sets of measurements, wherein for each matched set, it assesses the energy balance by comparing the generated energy to the corresponding consumed energy, thereby identifying any real-time energy deficit or excess.

The invention discloses an intelligent calibration method and system for the electric quantity of an instrument, and relates to the technical field of electric energy metering of an electric power system.The intelligent calibration method comprises the steps that an electric meter network diagram with a main meter and sub-meters as nodes is constructed, and robust composite correlation indexes are calculated on the basis of reading increments in a sliding time window; the interference of common background load and instantaneous noise is suppressed by using a threshold clipping and main table purification mechanism, so that the depiction capability of the correlation graph on a physical connection relationship is improved fundamentally, and the phase to which the sub-table belongs can be identified more accurately in a stock area with unknown phase or incomplete topology; a graph neural network model is introduced based on the network graph, electric meter embedding representation with physical meaning is obtained through neighborhood information aggregation, phase grouping is achieved in combination with unsupervised clustering, split-phase energy balance loss is designed in the training stage, and the difference between the reading of a main meter and the sum of the reading of sub-meters in each phase group is used as a constraint. Alignment of the embedding space and the energy conservation relation is pushed in a self-supervised mode.

The invention relates to a source-grid-load-storage integrated optimal configuration method of an enterprise power grid, and belongs to the technical field of power system planning of the enterprise power grid. The invention aims to realize source network load storage collaborative configuration through a multi-dimensional data processing and optimization model. The method comprises the following steps: generating a year-round wind and light output per unit value based on wind and light historical data, wind speed or illumination data; constructing annual typical load data of the enterprise through load curve generation, k-means clustering and random sampling; a mixed integer linear programming model is established in combination with constraints such as investment operation cost and energy balance; key indexes such as green electricity proportion, energy abandoning and electricity purchasing cost are calculated through the model; and performing scheme checking on the maximum / minimum load day and the maximum / minimum green power ratio day. According to the method, through data driving and optimization modeling, the green power consumption efficiency can be effectively improved, the wind curtailment and light curtailment amount is reduced, meanwhile, the investment and operation cost is optimized, and scientific support is provided for source network load storage integrated configuration of an enterprise power grid.

The invention provides a three-layer nested IES collaborative optimization method fusing semantic evolution and reinforcement learning, and belongs to the technical field of integrated energy system scheduling, and the method comprises the steps: constructing an IES mathematical model, an energy balance constraint and an optimization target, and abstracting a scheduling task into a Markov decision process MDP quintuple; building an ARDRL model containing a semantic knowledge layer, an evolutionary optimization layer and a distributed evaluation layer, generating a candidate reward function population through a large language model, and quantifying the fitness through Thompson sampling optimization and SAC intelligent agent distributed evaluation; and finally, carrying out iterative optimization by taking the fitness as feedback, and outputting an optimal reward function to guide IES collaborative scheduling. According to the method, a reward function does not need to be designed manually, the robustness and comprehensive performance of a scheduling strategy are improved, and economic, safe and low-carbon multi-target optimal balance is realized.

The invention discloses an optimal scheduling method for an electrical integrated energy system, and relates to the technical field of power systems. Comprising the following steps: data collection and system modeling: comprehensively collecting related data of energy production, load, equipment parameters, market and the like, and constructing a mathematical model of energy production, conversion, storage and load to accurately describe system characteristics; target setting: determining optimal scheduling targets of lowest cost and maximum benefit in economy, emission reduction and improvement of renewable energy consumption in environment, and guarantee of stable energy supply in reliability according to requirements; and constraint condition determination: determining conditions in aspects of energy balance, equipment operation limitation, network transmission capability and policies and regulations, and ensuring that a scheduling scheme is within a feasible range. According to the method, complex problems can be better solved by selecting the proper optimization algorithm, the solving efficiency and quality are improved, different optimization algorithms have different characteristics and application ranges, and the solving precision can be improved by selecting the proper algorithm according to the specific condition of the system.

The invention provides a hydrogen-electricity hybrid energy storage capacity optimization method and device applied to an integrated energy system, and belongs to the field of integrated energy configuration optimization. The method provided by the invention comprises the following steps: adopting a self-adaptive time sequence compression algorithm to perform self-adaptive grouping, weighted fusion and iterative updating processing on annual original time sequence data of the integrated energy system, and compressing to obtain representative time sequence data; based on the representative time sequence data, constructing a capacity planning model, and solving to obtain a capacity pre-planning value of the system core equipment; the capacity pre-planning value is substituted into the original time sequence data, the capacity planning model is reused, the hydrogen energy storage capacity is checked and corrected through full-time sequence energy balance simulation, and the final capacity configuration of the system core equipment is obtained; and based on the final capacity configuration, simulating system operation, extracting an electric and thermal power unbalance residual sequence, extracting a residual fluctuation scene and a corresponding probability, respectively constructing independent optimization models of electric energy storage and thermal energy storage, and solving to obtain the optimal capacity of the electric energy storage and the thermal energy storage.

The invention relates to the technical field of photovoltaic power generation, in particular to a regional autonomous regulation and control method and system for distributed photovoltaic groups and user side energy storage. The method comprises the following steps: obtaining photovoltaic group output data and user side load data; performing energy balance evaluation according to the photovoltaic group output data and the user side load data to determine a regional energy gap; performing energy storage autonomous adjustment analysis based on the regional energy gap to obtain energy storage autonomous adjustment data; performing peak clipping and valley filling identification based on the energy storage autonomous adjustment data to obtain peak clipping and valley filling data; positioning an energy mutual aid path by using peak clipping and valley filling data; performing power grid stability detection based on the energy mutual aid path to obtain power grid stability data; predicting a critical instability state according to the power grid stability data; and adjusting energy storage parameters according to the critical instability state to obtain energy storage adjustment parameters. Based on the photovoltaic power generation technology, the energy dispatching precision and the power grid operation stability of the regional photovoltaic and energy storage system are improved.

The invention discloses a full-process control method and system for galvanization processing, and relates to the technical field of Internet of Things production control. The invention discloses a full-flow control system for galvanization processing. The full-flow control system comprises a data acquisition module, a micro-flow field observation module, a risk calculation module, a parameter decision module, a comprehensive optimization module and a data positioning module. According to the method, a physical information neural network containing electrochemical constraint and thermal-fluid conservation constraint is introduced into a CFD digital twinborn model, and assimilation is carried out on micro-flow field observation data, so that micro-flow field state and dynamic evolution prediction synchronous with a production line are obtained; on the premise of ensuring mass balance and energy balance, the physical consistency and precision of flow field reconstruction and prediction are improved, and credible flow field state representation is provided for defect risk assessment and control decision based on the micro-flow field.

The invention discloses a hybrid energy system optimization scheduling method and system considering carbon emission of a thermal power plant, and belongs to the technical field of power system optimization scheduling. The method comprises the following steps: firstly, acquiring operating parameters and carbon emission coefficients of a thermal power generating unit, renewable energy sources, electric energy storage equipment and an external power grid in the hybrid energy system, and establishing a system energy balance model and an operating cost model containing carbon emission constraints; then, a multi-objective optimization algorithm is adopted, operation economy and carbon emission minimization serve as optimization objectives, and an optimal scheduling scheme of the hybrid energy system is obtained; and finally, real-time control and dynamic adjustment are realized through a scheduling instruction issuing module. The system comprises a data identification module and a parameter distribution module. According to the method, on the premise that system safety and power supply reliability are guaranteed, the total carbon emission amount of the thermal power generating unit is reduced, the renewable energy consumption level is improved, and coordinated optimization of economic benefits and environmental benefits is achieved.

The invention relates to the field of comprehensive energy system optimization, in particular to a multi-park collaborative comprehensive energy system optimization method considering stepped carbon transactions. The method comprises the following steps: firstly, acquiring multi-source data including energy prices, carbon transaction rules, equipment parameters and park interaction limits; constructing a target function with the purpose of minimizing the total operation cost of the system, wherein the target function covers the energy purchasing cost, the stepped carbon transaction cost and the energy abandoning punishment cost; designing composite constraint conditions of energy balance, node constraint, multi-energy conversion, energy storage dynamics, carbon emission segmented pricing and inter-park energy interaction; and finally, solving and generating a multi-park cooperative operation scheme through a state optimization algorithm. According to the invention, through integration of a stepped carbon transaction mechanism and multi-park dynamic energy flow allocation, the problems of extensive carbon cost accounting and insufficient consideration of an inter-park energy complementation strategy in an existing method are solved, and collaborative optimization of economical efficiency and low-carbon performance is realized.

The invention discloses a partition optimization scheduling control method and system for an energy storage power station, and relates to the technical field of power grid load prediction scheduling, and the method comprises the following steps: constructing a multi-scale dynamic baseline of load data, carrying out the phase calibration of a short-time fluctuation signal and a long-term trend signal under a unified time coordinate, and obtaining a multi-scale dynamic baseline; generating a cross-scale coupling mapping matrix by using a time embedding operator based on a calibration result; and establishing a gradient phase tracking network based on the cross-scale coupling mapping matrix. According to the method, time alignment and self-adaptive correction of short-time fluctuation and long-term trend signals are achieved through a multi-scale dynamic baseline and gradient phase tracking mechanism, weight energy balance is kept, and prediction continuity and accuracy are remarkably improved. Meanwhile, a causal residual error playback chain and time scale self-folding mapping enable the model to have self-correction and self-stabilization capabilities, sub-models can be automatically split during phase resonance, multi-scale response coordination is ensured, and the stability and operation efficiency of power grid load prediction scheduling are improved.

The invention discloses an energy-saving and consumption-reducing method for a sewage treatment plant based on four-level diagnosis and three-level optimization, and aims to solve the problems of high energy consumption and high medicine consumption in the existing sewage treatment technology. According to the method, data such as water quality, water quantity and energy consumption in 3-5 years are collected through a five-level technology cooperation system of historical data quantitative diagnosis, equipment energy efficiency evaluation and updating, facility function detection and transformation, technological parameter simulation regulation and control and intelligent closed-loop control, and key indexes are checked to locate anomalies; evaluating and updating energy consumption equipment, and detecting and transforming a structure, a pipeline and an aeration system; technological parameters are optimized in combination with material / energy balance, simulation analysis and small-scale tests; a four-stage intelligent control system is built to realize closed-loop regulation and control; after application, the energy consumption and the chemical consumption are obviously reduced, and the sewage treatment cost is effectively reduced; meanwhile, it is guaranteed that the effluent quality stably reaches the standard, extra treatment cost and environmental protection risks caused by the fact that the water quality does not reach the standard are avoided, the operation economical efficiency and environmental protection benefits of a sewage treatment plant are comprehensively improved, and sustainable development of the industry is promoted.

An electric power and electric quantity balancing method for a clean energy base containing a high-energy load comprises the steps that firstly, the fluctuation rule of the high-energy load is recognized based on the historical operation condition of the high-energy load, and a high-energy load model is established; secondly, according to the operation mechanism of the clean energy base, establishing an output model for each new energy unit in the base, and predicting the output and load demand of the new energy units in combination with weather forecast and historical data; then, according to the characteristics of a high-energy load and a conventional load, establishing a load management system, introducing a demand response mechanism, constructing a flexible scheduling resource platform considering different time scales by relying on the load management system and an adjustable power supply in a base, and setting priorities and adjusting costs for all adjustable resources in the flexible scheduling resource platform; and different adjustment resources can be reasonably selected in the scheduling process of the power grid. And finally, establishing a high-energy load-containing optimal scheduling model considering electric power and electric quantity balance, and ensuring that the electric power and electric quantity balance on the two sides of the source load can be realized in the scheduling of the power grid on different time scales.

The invention relates to the technical field of integrated energy system dispatching, in particular to an integrated energy system low-carbon dispatching method considering fuel gas hydrogen mixing and carbon transaction, which comprises the following steps of: constructing an integrated energy system model containing electric power, thermal power, natural gas and coupling equipment, and defining an electric hydrogen production process and an electric methane production process of power-to-gas equipment by the coupling equipment model; a fuel gas mixed hydrogen energy flow calculation model is constructed, a decomposition solution method is adopted for iterative convergence and dynamic calculation of a mixed fuel gas real-time heat value, and energy balance constraints are improved; constructing an optimal scheduling model considering the stepped carbon transaction, and taking the minimization of the total operation cost as a target; and applying hydrogen mixing proportion upper limit constraint, and cooperatively deciding power distribution and a hydrogen storage tank charging and discharging strategy through an optimization algorithm to solve a target function. According to the method, the energy flow calculation precision and the renewable energy consumption capability are improved, and collaborative optimization of system economy and environmental protection benefits is realized.