517 results about "Energy demand" patented technology

The invention belongs to the technical field of power grid regulation and control, and particularly relates to a park-level adjustable load-oriented resource regulation and control method and system, and the method comprises the steps: formulating an initial energy scheduling scheme according to the predicted energy demand in combination with the output characteristics of distributed energy in a park and the load condition of a power grid; and training a load regulation and control strategy model by adopting a reinforcement learning algorithm based on the initial energy scheduling scheme, and generating a personalized load regulation and control instruction for each energy consumption unit. According to the scheme, the real-time energy consumption data of the plurality of energy consumption units in the park, the output characteristics of the distributed energy and the power grid load condition can be comprehensively considered, a scientific and reasonable energy scheduling and load regulation and control strategy is formulated, and efficient utilization and green low-carbon development of park energy are realized. Meanwhile, the method also needs to have the characteristics of data sharing, accurate prediction, intelligent scheduling, rapid response and the like so as to adapt to complex and changeable energy requirements and environmental protection requirements of the park.

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses an energy management method of an electric bicycle system and the electric bicycle system.The method comprises the steps that battery parameters, motor parameters and whole vehicle kinetic parameters of an electric bicycle are obtained, and digital mapping processing is carried out to obtain a digital twin system; acquiring operation parameters of the battery, the motor and the whole vehicle, and performing distributed feature extraction processing to obtain feature data; training the bionic optimization model fusing the genetic algorithm and the ant colony algorithm to obtain a scenarized energy optimization strategy; performing hierarchical regulation and control processing on the control system to obtain a hierarchical control instruction sequence; and performing dynamic regulation and control processing on energy distribution to obtain a real-time energy optimization control instruction. According to the technical scheme of the invention, intelligent prediction and dynamic optimization distribution of energy demands are realized.

The invention discloses a park energy storage intelligent scheduling system and method based on big data, and relates to the technical field of big data park energy storage intelligent scheduling, and the method comprises the following steps: S100, collecting the real-time operation data of multiple types of energy systems in a park, constructing a time sequence database, and carrying out the preprocessing; s200, predicting load change based on an energy demand scene, and constructing a polymorphic energy storage model in combination with dynamic characteristics of energy storage equipment; s300, defining a multi-objective optimization function taking energy loss, renewable energy utilization rate and energy storage efficiency as cores, establishing a total energy balance model, and setting constraint conditions of charging and discharging and heat and cold storage power of energy storage equipment; and S400, updating the load demand and the energy storage state by using the real-time data, calculating the load deviation and correcting the target function, and readjusting the charging and discharging of the energy storage equipment and the heat storage and cold storage power distribution according to the corrected target function.

The invention belongs to the technical field of energy storage resource regulation and control, and particularly relates to a distributed energy storage network resource regulation and control decision-making method, which comprises the following steps of: collecting and processing multi-level data; a two-way interaction mechanism with a user side is established, the electricity demand and the energy storage intention of the user are collected, and a demand response signal is sent to the user to encourage the user to adjust the electricity consumption behavior when the electricity is insufficient or the electricity price fluctuates; and based on the multilevel data and the user demand response result, constructing a dynamic energy routing model of the distributed energy storage network, and predicting future energy demand and supply conditions. According to the method, multi-level data can be comprehensively utilized, the accuracy of energy demand prediction is improved, the flexibility and the intelligent level of a regulation and control strategy are enhanced, a global optimization method is adopted for resource regulation and control, and the robustness and the reliability of the system are improved.

The invention discloses a carbon emission real-time regulation and control method and system based on multi-source data fusion and AI decision, and relates to the field of data processing systems or methods specially suitable for administrative, commercial, financial, management, supervision or prediction purposes, in the method, real-time production data is collected through Internet of Things equipment, and the real-time carbon emission intensity is calculated in combination with a carbon accounting engine. And constructing an AI optimization layer comprising a process knowledge graph, a long-short-term memory network and a reinforcement learning agent, converting process constraints into mathematical boundary conditions, predicting energy demands, carrying out iterative optimization by taking carbon emission intensity minimization as a target, generating a dynamic scheduling strategy, and realizing real-time adjustment of operation parameters of production equipment. The method and the device are used for improving the accuracy of carbon emission monitoring data on production scheduling and reducing the risk of high-carbon-intensity production caused by improper time period selection of enterprises.

The invention relates to the technical field of industrial waste heat recovery optimization, in particular to an industrial circulating cooling water waste heat multi-source coupling system based on data driving, and the system comprises a waste heat collection unit, a data processing unit and an intelligent coupling regulation and control unit. After the data processing unit preprocesses data, high, medium and low temperature layers are divided according to temperature, a clustering algorithm is combined to subdivide a calorific value sub-layer, a dynamic characteristic file containing a parameter change curve, fluctuation amplitude and duration is established, the intelligent coupling regulation and control unit compares the file with pre-stored user heat demand characteristics, the matching degree is calculated preferentially according to the temperature contact ratio, and the matching degree is calculated preferentially. A single or multi-source cooperative coupling path is generated, target thermal parameters are synthesized through thermal capacity weighting in a multi-source mode, the supply and demand matching precision and the utilization efficiency of waste heat are improved, diversified energy consumption requirements of industrial scenes are met, and energy conservation and emission reduction are facilitated.

The invention relates to the technical field of electric power energy monitoring, and discloses a remote monitoring analysis method and system based on electric power equipment operation data, and the method comprises the steps: collecting the multi-element energy operation parameters of a multi-source energy system and the multi-element energy use parameters of a user side in real time; after the collected data are preprocessed, the preprocessed equipment operation state data are evaluated, and an electric power equipment operation state index is obtained; whether equipment faults exist or not is judged according to power equipment operation state indexes, virtual mapping of a multi-source energy system is constructed, and system operation conditions under different energy scheduling strategies are simulated in real time by combining user energy demand prediction and external environment changes; when potential energy supply abnormity or equipment failure is detected, a multi-stage early warning mechanism is triggered, and a decision report including abnormity positioning, reason analysis, energy scheduling optimization and equipment maintenance disposal suggestions is generated, so that potential problems can be found in time, and the stability and reliability of energy system operation are improved.

The invention discloses a micro-grid park integrated energy system planning method and device, a terminal and a medium. The method comprises the following steps: acquiring energy demand data, time-of-use electricity price information, natural gas price and meteorological information of a park to be planned, and equipment parameters, equipment investment cost and equipment maintenance cost in an integrated energy system of the park to be planned; the influence of temperature change on thermodynamic characteristics of steam is considered, and equivalent energy storage of a steam heat accumulator of the microgrid park is modeled by considering valley electricity heat storage; setting constraint conditions of a planning model according to the acquired data information, and taking the minimum comprehensive cost as an objective function, thereby constructing a cold-hot electro-optical storage and charging integrated microgrid park planning model considering electric power-steam multi-energy complementation; and solving the planning model, and outputting a planning scheme about whether each device in the integrated energy system is configured, the configuration capacity and the operation output curve of each configured device. According to the invention, the energy structure of the park is optimized, the resource utilization efficiency is improved, and the stability of the system is enhanced.

The invention belongs to the technical field of intelligent energy control, and particularly relates to an energy equipment control method based on regional people number distribution, which comprises the following steps: acquiring real-time people number distribution data, environmental parameter data, historical energy consumption data and historical people flow characteristic data, and predicting regional people number distribution data in different time periods; based on historical energy consumption data, historical people flow characteristic data, real-time people number distribution data and environmental parameter data, a dynamic energy demand model is constructed, regional people number distribution data in different time periods are combined, a people number density thermodynamic diagram is generated, regional energy equipment load change is predicted, and a regional energy demand model is established according to the load change and the people number density thermodynamic diagram. A differential control strategy is generated, and operation parameters of the energy equipment are dynamically adjusted; and regulating and controlling the energy equipment according to the differentiated control strategy, synchronously collecting regulated and controlled regional environment feedback data and equipment energy consumption data, and generating an energy efficiency evaluation report. Therefore, the problems of high response delay, limited parameter matching precision and the like in the prior art are solved.

The invention relates to the technical field of mine energy loss cost statistics, in particular to a cost statistics method based on mine energy loss balance, which comprises the following steps: deploying multiple types of sensors in mine equipment and areas to collect data, and carrying out anomaly detection, interpolation supplement and classified storage processing; constructing an energy consumption model by integrating equipment power, load and environmental factors, accounting electric power, fuel and total energy cost according to the energy consumption model, and analyzing the proportion; a genetic algorithm, a particle swarm algorithm and an LSTM model are used to optimize equipment parameters and an energy distribution path, and energy demand and cost are predicted; developing a visual interface to display energy consumption and cost data, generating a decision report, and pushing the decision report to a manager; comparing actual and predicted data regularly, collecting equipment, process and market information, and optimizing an energy loss and cost accounting model and algorithm; according to the method, fine energy management is realized, the utilization efficiency is improved, the cost is reduced, scientific decision support is provided for mine energy management, and the system adaptability and sustainability are enhanced.

The invention discloses a collaborative planning method for a regional integrated energy system, and the method comprises the steps: firstly collecting regional historical energy consumption data and various external factor data, and predicting an energy demand through employing a gray prediction and multiple linear regression combined algorithm; exploring existing energy supply facilities in the region and evaluating renewable energy potential; determining a candidate type of the integrated energy system component according to the result, establishing an optimal configuration model, and solving through a particle swarm optimization algorithm; constructing an energy network layout planning model, and determining an optimal topological structure and parameters of the network; establishing a dynamic operation model, and formulating a collaborative operation strategy by using a model prediction control algorithm; and finally, comprehensively evaluating the planning scheme, and analyzing and adjusting the scheme by utilizing sensitivity according to an evaluation result. According to the method, multiple factors are comprehensively considered, and the problems that in existing regional integrated energy system planning, demand prediction is not accurate, resource evaluation is not comprehensive, component configuration and network layout lack optimization collaboration, and the operation strategy is single are effectively solved.

The invention discloses an urban carbon emission peak prediction method, and particularly relates to the technical field of carbon emission prediction. A carbon emission data set with high time resolution is constructed, and first K influence factors with the highest contribution degree to a carbon emission peak value are screened based on an adaptive weight attribution mechanism, including extraction of heating system response time and energy scheduling hysteresis characteristics, construction of a carbon emission peak value prediction model, and when a prediction value exceeds a set threshold value, calculation of the carbon emission peak value prediction model is carried out. The method comprises the following steps: automatically adjusting the influence weight of a key factor, calculating a peak confidence interval on a test set, evaluating and optimizing the model performance, finally deploying an optimized model to an urban carbon emission monitoring system, and dynamically updating in combination with a real-time data stream to ensure the accuracy and stability of prediction; according to the method, the accuracy of urban carbon emission peak prediction is remarkably improved, the capability of responding to sudden energy demand increase in extreme weather is enhanced, and scientific support is provided for carbon neutralization policies, energy regulation and control and environment management.

The invention discloses a production process control system for functional food, and belongs to the technical field of production equipment control systems, and the system specifically comprises a parameter collection module which is used for collecting raw material characteristic parameters of functional food raw materials, and the raw material characteristic parameters comprise rheological characteristic parameters, thermal stability parameters and structural strength parameters; the process adaptation module is used for constructing a dynamic process adaptation model and mapping the raw material characteristic parameters into an equipment control instruction set; dividing energy demand levels according to raw material processing stages, and generating an on-demand energy supply tree structure; the equipment adjusting module is used for monitoring operation state data of the production equipment in real time; triggering a process parameter dynamic compensation instruction based on the equipment operation state data; the equipment feedback module is used for comparing the deviation between the actual product characteristics of the production line and the target characteristics through the closed-loop verification unit, and iteratively optimizing the dynamic process adaptation model; precise production, energy conservation and efficiency improvement of functional food are achieved through the multi-module cooperation technology.

The invention provides a ship shaft power generation and lithium battery coordinated multi-working-condition energy optimization management system, which is applied to the technical field of ship power systems and energy management and comprises a data acquisition module, an energy optimization processing module and a power regulation and control module, wherein the energy optimization processing module is provided with a prediction unit, a working condition identification unit, a loss evaluation unit and a decision-making unit, the prediction unit is used for predicting a ship load demand in a preset period, the working condition identification unit is used for identifying a ship operation working condition, and the loss evaluation unit is used for determining a life loss factor by combining a prediction result and an energy storage unit state; the decision-making unit generates a regulation and control instruction based on the life loss factor and the state of the power generation unit to realize power distribution of the power generation unit and the energy storage unit; according to the system, multi-working-condition energy consumption requirements of the ship are met, meanwhile, comprehensive optimization of fuel consumption cost and energy storage unit service life loss cost is achieved, the energy utilization rate is increased, the service life of the energy storage unit is prolonged, and the full-life-cycle operation cost of the ship is reduced.

The invention relates to the technical field of monitoring control, and discloses a comprehensive energy management method and system, and the method comprises the steps: obtaining historical energy data, equipment operation parameters and internal and external environment data of a target smart entity, the internal and external environment data comprising building characteristic data, season data, building internal temperature data and building internal humidity data; inputting the historical energy data, the equipment operation parameters and the internal and external environment data into a trained first adjustment model, and outputting a first energy demand result; inputting the building characteristic data and the first energy demand result into a trained second adjustment model, and outputting a second energy demand result; and formulating an energy adjustment strategy based on the second energy demand result. By implementing the method and the system, response and adjustment can be quickly made when environment and energy demand changes, challenges brought by changes of operating environment and building characteristics are effectively handled, and efficient energy utilization and sustainable development of the smart entity are realized.

The invention discloses a regional integrated energy system optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.

A federated learning framework including household agents configured to continuously learn model parameters for managing charging periods and discharging periods of household batteries, and microgrid agents to maximize use of local energy based on a pricing policy, including accessing power from other microgrids when there is insufficient local energy to cover local demand. and selling surplus energy to the other microgrids when power generation by the microgrid surpasses the local demand. Each household machine learning agent is configured to control household energy demand from and supply to a microgrid which they are connected in order to minimize household energy cost while adapting to changes in the energy price that is determined based on the pricing policy of the microgrid agent that encourages reduction of carbon emission. A federated learning engine combines the model parameters from the household machine learning agents to update a global household machine learning agent.

The invention discloses a multi-energy energy management method and a ship, the method is applied to a multi-energy power system comprising a fuel cell, a battery pack and photovoltaic power generation, an energy management optimization model based on model prediction control is established, and the energy management optimization model comprises a prediction model, rolling optimization and a reference trajectory. An energy management strategy is optimized in real time through model predictive control (MPC), conversion and distribution among different energy sources are intelligently managed according to actual energy source requirements and availability, and therefore stable supply of power output is achieved. A multi-energy complementary hybrid power platform is constructed by integrating a hydrogen fuel cell, a lithium battery and a photovoltaic power generation technology, an energy management strategy is optimized in real time through model predictive control (MPC), and efficient and stable use of energy under various navigation conditions is ensured.

The invention relates to the technical field of feed efficiency optimization, in particular to a feed efficiency optimization system based on a growth model, and the system comprises a data collection unit which is used for collecting and associating individual identities, individual weight data, time-phased feed intake data, environment data and environment forecast data; the model calibration unit is used for calibrating individual energy conversion efficiency parameters, generating an environment dynamic correction factor by using the environment data and generating a group classification label based on historical individual performance data; and the decision and execution unit operates a double-cycle cooperation mechanism based on pre-judgment and execution. By dynamically calibrating the individual energy conversion efficiency and combining with the environment forecast, real-time calculation and differentiated feeding regulation and control of the individual energy demand can be realized.

Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.

Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.

The invention discloses a preheating treatment carbon dioxide adsorption system and an operation method thereof, and belongs to the technical field of energy storage and chemical process equipment. The problems of high heat consumption, low heat conductivity and slow heating of the adsorbent in the prior art are solved. The technical key points are as follows: the temperature of the system is slowly increased at the initial stage of heating, part of carbon dioxide is desorbed, the carbon dioxide is heated by high-temperature heat exchange equipment and then flows back to the tower body of the adsorption tower, and the heating process can be completed in idle time except energy storage-energy release due to the fact that the compressed gas energy storage system implements discontinuous energy storage-energy release. Good adsorption and desorption effects can be realized in a relatively low temperature range, so that the energy demand in the heating and cooling process is reduced, the overall energy consumption of the system is reduced, the heat exchange pressure of the heating system is relieved, and the demand on the heat exchange power of heating equipment is relieved; the gas pressure in the adsorption tower body is slightly increased, so that continuous and stable release and supply of carbon dioxide of an adsorption system are guaranteed, and the method has a good industrial application prospect.

The embodiment of the invention provides a novel energy management system, and relates to the field. The novel energy management system comprises a data acquisition module; the intelligent analysis module is connected with the data acquisition module and is used for performing dynamic calculation and predictive analysis on the real-time data; the self-adaptive scheduling module is connected with the intelligent analysis module and is used for carrying out automatic switching and load balancing among various energy supply paths according to the optimization strategy output by the analysis module; the user interaction module communicates with the self-adaptive scheduling module and the intelligent analysis module; and the remote connection module is connected with the user interaction module. According to the method, high-precision prediction can be carried out on energy fluctuation in a complex energy utilization environment, and potential energy consumption fluctuation can be identified in advance. In this way, the system can carry out more accurate energy supply optimization under the condition that the energy demand fluctuates or is uncertain, and energy waste or shortage caused by surplus or insufficient demand is avoided.

The embodiment of the invention relates to the technical field of electronic atomization, and discloses a control method and an aerosol generating device, the whole baking process is divided into a plurality of time periods according to the energy demand characteristics in the aerosol forming substrate baking process, and corresponding energy supply is set for each time period, so that the aerosol forming substrate baking efficiency is improved. Therefore, the aerosol forming substrate receives heat output by the heater in different time periods, and the aerosol generated after baking can rapidly reach a smokable state and maintain the smokable state. On the other hand, starting from the heat conduction characteristic, for each first time period, energy is continuously supplied in the first time period, and energy supply is stopped in the second time period, so that the temperature is firstly increased to the highest temperature and then reduced to the lowest temperature in the first time period, and the temperature difference between the two is 15 DEG C or above; the heat provided by the heater can be absorbed by the aerosol forming matrix in a large proportion when the energy is supplied in the next time period, so that the effects of improving the energy utilization rate and reducing the energy consumption are achieved.

The invention discloses a building cluster demand response coordinated multi-objective optimization scheduling method and system, and the method comprises the steps: predicting the energy demand and supply information of a building cluster, and building an energy transaction model of a building unit and the cluster based on renewable energy supply and battery storage electric energy; the building cluster is integrated into a single entity, the energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, the battery capacity is the total battery capacity of all buildings, and the energy transaction model takes a cost function of the whole cluster as a target function; and optimizing the overall cost function of the building cluster through an iterative demand response algorithm, calculating to obtain an optimal operation parameter, controlling the operation of each building unit according to the optimal operation parameter of the cluster by using a nonlinear programming algorithm, and carrying out multi-target optimization scheduling on the building cluster. Flexible resources in the building cluster can be fully utilized, and efficient utilization and supply and demand balance of energy are achieved.

An energy requirement analyzing arrangement for analyzing production and energy requirements of an industrial process operated at a production site by a local production system using energy generated by a local energy supply system obtains (S100) energy consumption data and dynamics for the industrial process and the local energy generation at the production site, generates (S110) a prediction of energy generation and a prediction of energy consumption in a time window; estimates (S120) probabilities of energy surplus or energy shortage at different points in time in the time window based on the predictions of energy generation and energy consumption; generates (S130) an impact evaluation, which quantifies the impact of the probability of energy surplus and energy shortage at the production site, determines (S140) an activity for the local energy supply system and / or local production system that reduces the impact on the industrial process and applies (S150) the determined impact reducing activity in the local energy supply system and / or local production system.

The invention relates to the technical field of control systems, in particular to a hydrogen-doped natural gas regulation and control system and method based on data analysis, and the system comprises a data preprocessing module which collects original hydrogen-doped natural gas process data, divides gas flow, pressure and temperature parameters according to a time sequence, and calculates an average value and maximum and minimum values in each time period; according to the method, short-term fluctuation of gas flow and pressure is extracted through a convolution kernel in the feature extraction stage, local feature vectors are generated, accurate input is provided for time sequence modeling, the time sequence modeling captures trend change based on long-term dependency, a stable time sequence model is established, and the prediction precision of the hydrogen doping proportion is improved; in the prediction optimization stage, the proportion is adjusted under the constraint of energy demand, carbon emission and other targets, multi-target optimization is carried out, deviation is corrected in real time in the dynamic adjustment stage, the adaptability and flexibility of the system are ensured, the response capability of the model is enhanced through optimization of multiple links, and the adaptability to complex environment changes is improved.

The invention discloses a carbon emission prediction and novel power system planning method and system in the urban power field. The method comprises the following steps: calculating the energy demand quantity of each department according to the urban total production value GDP, population, industrial structure and energy consumption characteristic data; calculating the total carbon emission amount of the electric power department based on the energy demand in combination with the carbon emission factor; on the basis of the total carbon emission amount of an electric power department, urban energy endowment, emission reduction tasks, emission reduction policies and power generation technology improvement are considered, and on the basis of economy, population and electricity consumption parameters of an electric power demand end, various development situations of low carbon, zero carbon and negative carbon of an electric power system are designed; predicting carbon emission and carbon emission reduction corresponding to the demand end in each scene, and obtaining an emission reduction path from the demand end; and establishing a power system multi-scene optimization planning model by taking the lowest net present value of the total cost of the system as an objective function and taking urban power related constraints as constraint conditions, and calculating a power generation installed capacity development path meeting the system cost and the related constraints.

The invention provides a method for optimizing reported capacity before participation of fused magnesium group furnaces in primary frequency modulation, belongs to the field of primary frequency modulation of a power system, and is applied to participation of high-energy-consumption fused magnesium furnace loads in primary frequency modulation of a power grid. The method comprises the following steps: 1) establishing an objective function, and taking overall revenue maximization as an objective; 2) determining constraint conditions, and considering frequency modulation requirements, energy demand limitation and product quality; and 3) processing the uncertainty of the adjustment capability of the electric smelting magnesium group furnace, and allowing optimization solution to not meet the constraint among part of cells so as to realize efficient processing of the uncertainty. According to the method, the uncertainty of the adjustment capability of the fused magnesium group furnace is reflected in a probability form through a chance constraint method, and the optimal report capacity participating in primary frequency modulation of a power grid in each day-ahead time period is solved. And a support is provided for a fused magnesium enterprise as a load side demand response resource, reasonable participation in primary frequency modulation of a power grid and day-ahead declaration of frequency modulation capacity.