509 results about "Renewable power generation" patented technology

The invention relates to the technical field of hydrogen production, in particular to a micro-grid hydrogen production system based on dynamic optimization control and a multi-mode energy scheduling method. Comprising a renewable energy power generation unit which comprises at least two distributed power supplies with different power generation characteristics; the multi-stage hydrogen production device is configured to receive the electric energy output of the renewable energy power generation unit and perform proton exchange membrane water electrolysis hydrogen production; the hybrid energy storage system comprises a super capacitor bank and a lithium ion battery pack which are connected in parallel to the direct current bus through a bidirectional DC / DC converter; the dynamic optimization controller is internally provided with a multi-time-scale coordination algorithm and is used for acquiring meteorological prediction data, load demand data and an energy storage SOC state in real time; the multi-mode scheduling module comprises three working modes including an island operation mode, a grid-connected operation mode and an emergency standby mode; according to the scheme, hydrogen production economy, equipment durability and power supply reliability can be considered at the same time.

The invention discloses a renewable energy power generation power prediction and power dispatching method and system, and the method comprises the steps: collecting the historical power generation data and real-time meteorological data of renewable energy power generation, carrying out the linear interpolation of the historical power generation data and the real-time meteorological data, and carrying out the missing value filling and box plot anomaly detection, obtaining a normalized training data set; constructing a hybrid prediction model by using the normalized training data set and adopting a neural symbol acceleration technology with time logic constraints, extracting medium and long term space time features, and generating a renewable energy power generation power prediction result; and according to the renewable energy power generation power prediction result and the system constraint condition, adopting a linear one-dimensional projection constrained distribution robust control method to formulate a scheduling strategy, and utilizing the scheduling strategy to solve an optimal scheduling scheme through mixed integer linear programming. According to the method, the renewable energy power generation power prediction precision and the power dispatching robustness are remarkably improved.

The invention provides a data center hybrid energy storage and renewable energy intelligent scheduling control method and system, and relates to the technical field of data energy storage and resource scheduling, and the method comprises the steps: carrying out the load prediction through a double-layer time sequence memory network in combination with a multi-head attention mechanism, building a hybrid energy storage optimization scheduling model based on a renewable energy generation power prediction result, and carrying out the optimization scheduling of the hybrid energy storage. And a double-layer model predictive control framework is adopted to output a corrected charging and discharging power instruction, so that optimal control of the hybrid energy storage system is realized, the energy utilization efficiency is improved, and the operation cost of a data center is reduced.

The invention discloses a distributed energy system source load coordinated optimization method based on a quasi-potential game method, and the method comprises the steps: constructing a distributed energy system model, inputting system parameters, and predicting renewable energy power generation and initial load demands. In a source side optimization stage, a leader layer potential function of a quasi-potential game is established by taking minimization of source side cost # imgabs0 # as a target, and an initial power generation plan is generated by comprehensively considering economical efficiency and carbon emission constraints; and then, based on a scheduling result, calculating a carbon potential epsilon t of each node and a dynamic carbon emission factor # imgabs1 # of each stage, optimizing a load side response based on an LCDR scheme, constructing a local potential function of a follower layer, reflecting a relationship between a user profit maximization target and carbon emission, and adjusting user behaviors through a distributed decision. And the updated load demand is fed back to the source side, and the source side optimizes the output plan of each unit based on the updated load, so that an iterative process of source side potential function optimization-load side equilibrium response is formed, and an optimal scheduling strategy and scheduling result of the energy supply side are obtained. According to the framework, the global consistency requirement of a traditional potential game is relaxed, independent optimization of source-load two sides under the guidance of respective potential functions is allowed, and a Nash equilibrium state is finally achieved only by ensuring monotonous convergence of total potential energy of a system in an iteration process. According to the method, the convergence advantage of the potential game is reserved, the method is also adapted to the characteristics of a source-load heterogeneous decision subject, efficient consumption of renewable energy and collaborative optimization of carbon emission are realized through bidirectional transmission of the carbon potential signal, and the overall efficiency of the system is remarkably improved.

The invention discloses a fuel cell coupled flow battery energy supply system and an operation control method thereof. The energy supply system comprises a basic energy supply unit, an energy storage peak regulation unit, a renewable energy power generation unit, an electrolytic hydrogen production unit, a hydrogen storage unit, a first-stage waste heat recycling unit, a second-stage waste heat recycling unit and a heat storage unit. The basic energy supply unit is used for providing a basic power load of the target building group; the energy storage peak regulation unit is used for providing fluctuation power load of the target building group; the first-stage waste heat recycling unit receives first-stage waste heat generated by power generation of the fuel cell and is used for indoor heating or indoor refrigeration. And the second-stage waste heat recycling unit directly receives the first-stage waste heat or second-stage waste heat from the first-stage waste heat recycling unit and is used for preheating the electrolyzed water and / or the hydrogen storage unit. By arranging the first-stage waste heat recycling unit and the second-stage waste heat recycling unit, waste heat is utilized in a multi-stage mode according to requirements, and the waste heat utilization rate is high.

The invention provides a multi-data center computing power-electric power cooperative scheduling and demand response declaration capacity optimization method, which comprises the following steps of: firstly, acquiring multi-dimensional historical time sequence data of a data center; a machine learning algorithm is used to predict the workload of each data center in each time period of a future single day in a future scheduling period and the power market price of the place where each data center is located, and then a space-time coupling-oriented multi-data center computing power-power cooperation model considering uncertainty is constructed; the collaborative model comprises a joint optimization framework of computing power scheduling and power scheduling, and takes actual profit maximization as a target, then the established collaborative model is converted into a mixed integer linear programming problem and solved, and the optimal declaration capacity of each data center participating in demand response is obtained; and each data center carries out scheduling according to the task load of space migration and time migration, the charging and discharging power of an energy storage system and the power generation amount of renewable energy sources, so that overall optimization of demand response declaration capacity of the multiple data centers is realized.

Disclosed in the present invention is a deep learning-based power distribution network load prediction method, comprising: acquiring regional load data and renewable energy power generation data to form a data set, and preprocessing the data set to obtain a first data set; using a convolutional neural network to extract a time series feature in the first data set, and converting the time series feature into a data form of a deep learning model by means of an embedding layer to obtain one-dimensional time series data; performing fast Fourier transform on the one-dimensional time series data to obtain a frequency curve, extracting amplitude values on the frequency curve to calculate corresponding periods, and selecting the corresponding periods to slice the one-dimensional time series data and form same into two-dimensional matrixes; using a two-dimensional convolutional network to perform feature extraction, reshaping the two-dimensional matrixes that have undergone feature extraction into one-dimensional arrays, and performing adaptive fusion on the one-dimensional arrays to obtain a first time series feature; and inputting the first time series feature into a fully connected layer for weight calculation and linear transformation to obtain a power distribution network load prediction result, thereby improving the stability and reliability of electric power supply.

The invention discloses a hydrogen energy park planning method and system based on a heuristic algorithm, and relates to the technical field of smart energy management, and the method comprises the steps: constructing a mathematical model of a park comprehensive energy system which is composed of a renewable energy power generation unit, an energy conversion device, a carbon capture and gas power device and a multi-type energy storage unit; based on the mathematical model, with the minimum annual comprehensive cost of the park comprehensive energy system as a target, establishing a cost target function, setting constraint conditions, and formulating an energy management strategy; and according to an energy management strategy, solving according to an improved taboo horse racing optimization algorithm THRO, and planning the hydrogen energy park according to a solving result. According to the invention, not only is the interior of the exterior of the environment realized, but also the maximization of economic benefits is realized.

The invention provides a control method of a four-level hybrid clamping converter, and the core principle is to select a switching state which minimizes a cost function through a model predictive control (MPC) strategy. The topological structure and the working principle of the 4L-HCC converter are deeply researched, a traditional MPC control strategy is adopted, the charging and discharging process of the capacitors is accurately controlled, voltage fluctuation of the direct-current bus capacitor and the flying capacitor at low frequency and zero frequency is effectively restrained, safe operation of the capacitors, switches and other elements is guaranteed, and the stability and reliability of the converter are improved. By reasonably designing the cost function, the converter can output high-quality electric energy under different working conditions, the distortion of load voltage and current is reduced, the requirements of application scenes with high requirements on electric energy quality, such as motor driving and renewable energy power generation grid connection, are met, and the performance of the system is comprehensively improved.

The invention provides a day-ahead-real-time cooperative scheduling system for an electric energy replacement load and an energy storage system, and relates to the field of power management. Respectively inputting the load prediction model and the power generation prediction model to obtain a day-ahead load prediction result and a day-ahead power generation prediction result, generating a day-ahead scheduling plan based on the obtained prediction results and a preset optimization target, and then obtaining actual electric energy replacement load and renewable energy power generation data; deviation information of actual data and predicted data is calculated according to a day-ahead scheduling plan, and finally the charging and discharging power of the energy storage system is adjusted according to the deviation information. Therefore, the coordinated operation efficiency of the electric energy replacement equipment cluster and the renewable energy sources can be effectively improved, and the economic operation, safety and stability of the system are effectively ensured.

The invention relates to the technical field of energy storage load scheduling, in particular to an energy storage load scheduling method and system based on multi-objective optimization, and the method comprises the steps: obtaining historical electricity price, load power and renewable energy power generation power data of each time period before the current time as input variables, and carrying out the normalization processing to obtain input data; inputting the input data into an LSTM-Transformer prediction model, and predicting to obtain the power grid electricity price, the load demand and the renewable energy power generation amount in a future time period; establishing a multi-objective optimization model including electric charge cost, operating profit and energy storage life; substituting the prediction data into the multi-objective optimization model, and solving a Pareto frontier based on a multi-objective optimization algorithm to obtain a non-inferior solution set satisfying the multi-objective optimization model; and taking each solution in the non-inferior solution set as input, and screening the solution with the maximum Sharpley value as an optimal energy storage charging and discharging adjustment strategy. By accurately controlling the working state of the energy storage system, the interactive operation mode of the energy storage system and the power grid is optimized.

The invention relates to the technical field of data processing, and particularly provides a low-altitude aircraft power supply facility cooperative control method, system and device and a medium, and the method comprises the steps: predicting the renewable energy power generation power of a power supply facility and the charging load power of a low-altitude aircraft based on obtained multi-source information, and generating a prediction scene set representing uncertainty; based on the prediction scene set, through a multi-time scale optimization strategy, solving a scheduling model with optimal operation cost as a target, and successively obtaining a pre-scheduling plan of a day-ahead stage and a real-time control instruction of an intra-day stage; the multi-time scale optimization strategy comprises random optimization based on a scene and feedback optimization based on a rolling time domain; according to the real-time control instruction, power distribution is conducted on the hybrid energy storage system, and a power type energy storage unit preferentially responds to the transient power requirement for charging of the low-altitude aircraft. The comprehensive operation cost is effectively reduced, and particularly, the service life of equipment is prolonged through quantitative energy storage degradation.

A method of power management of a hybrid power plant includes at least one type of renewable power generation equipment and an energy storage system, including: before a first dispatching time range: using first forecasts of energy production capability and energy price, and in particular hybrid plant status and plant parameters, to derive a first power generation schedule defining power generation in the first dispatching time range; at a dispatch point in time within the first dispatching time range: using second forecasts of energy production capability and energy price, and in particular plant status and plant parameters, to derive a second power generation schedule defining power generation in a second dispatching time range; and calculating an optimal power injection value for the dispatch point in time based on the first power generation schedule and the second power generation schedule taking into account at least one constraint.

The invention provides an unmanned aerial vehicle electric power inspection electric energy guarantee planning method and device in a multi-weather scene, and aims to solve the problem of uncertainty of unmanned aerial vehicle energy consumption and renewable energy power generation in the multi-weather scene in the prior art. Weather scenes are divided into multiple types, the influence of the weather scenes on the energy consumption of the unmanned aerial vehicle power inspection system is quantified, and a system load model is established; establishing a two-stage micro-grid planning model by using the output of the power generation equipment unit; constructing a fuzzy set based on a multi-discrete scene method, and converting the micro-grid planning model into a distributed robust optimization model by using the fuzzy set; a differential evolution-column and constraint generation algorithm is utilized to solve the distributed robust optimization model, the solving time is remarkably shortened, the solution quality is ensured, simulation experiments are utilized to verify that the method can obtain an unmanned aerial vehicle electric power inspection electric energy guarantee system planning scheme considering weather uncertainty, and economy, reliability and efficiency are balanced.

The invention relates to the technical field of hydrogen production, in particular to a micro-grid hydrogen production system and method based on fuzzy logic control. Comprising a renewable energy power generation unit, an electrolytic cell array, a multi-type energy storage device, a power divider and a fuzzy logic controller, the renewable energy power generation unit is connected with the electrolytic cell array and the power divider through a direct-current bus, and the output end of the power divider is connected with the storage battery pack and the super-capacitor bank. The fuzzy logic controller collects the bus voltage fluctuation ratio, the electrolytic cell temperature gradient and the energy storage charge state in real time to serve as input variables, and outputs a multi-target optimization instruction to the power divider. Wherein the system dynamically adjusts the start-stop combination and the energy storage charge-discharge priority of the electrolytic cell array. According to the scheme, the mindset that fluctuation suppression and life management are separated in the prior art can be broken through, and a new paradigm of multi-parameter cooperative control is established.

The invention relates to the technical field of renewable energy power generation, and provides a photovoltaic power prediction method, device and equipment, and the method comprises the steps: obtaining historical power data and meteorological factor data of a target photovoltaic power station, and carrying out the preprocessing; dividing the historical days into different meteorological categories by adopting a clustering algorithm, and constructing a training set corresponding to the meteorological categories; performing correlation analysis on each training set to obtain core meteorological features and form a core feature sequence, and performing parallel decomposition on the core feature sequence by adopting three modal decomposition algorithms to form an enhanced feature set; constructing a double-flow neural network model, and taking the enhanced feature set as the input of the double-flow neural network model to obtain an optimized feature vector; and inputting the optimized feature vector into at least one full connection layer to obtain a final short-term photovoltaic power prediction value. According to the method, the prediction task is simplified, and the prediction accuracy and stability of the short-term photovoltaic power are remarkably improved in combination with the double-current neural network model.

The invention discloses a dynamic power distribution method and device for a direct-current micro-grid hybrid energy storage system, and relates to the technical field of electric energy storage management, and the method comprises the steps: collecting the power parameters, load demand fluctuation data and renewable energy power generation output data of each energy storage unit in the hybrid energy storage system in real time, calculating a multi-dimensional power entropy index, and calculating the power entropy index of the hybrid energy storage system; based on the indexes, power distribution weight coefficients are dynamically generated, power dynamic distribution is carried out, a charging and discharging strategy is generated, charging and discharging control of the hybrid energy storage system is executed, and power distribution is dynamically adjusted in combination with a real-time feedback mechanism. The technical problems that an existing energy storage system power distribution method lacks flexibility and cannot effectively cope with load fluctuation and unstable renewable energy sources, so that the system operation efficiency and stability are poor are solved, the charging and discharging strategy of the energy storage unit is dynamically adjusted through the multi-dimensional power entropy index and the real-time feedback mechanism, and the energy storage efficiency is improved. The technical effect of efficient and stable operation of the energy storage system is achieved.

The invention relates to the technical field of multi-park integrated energy system scheduling, in particular to a low-carbon scheduling method and system for a multi-park integrated energy system, and the method comprises the steps: obtaining renewable energy historical output data and historical meteorological parameters of each park, and generating a renewable energy generation power prediction sequence through a pre-trained generative adversarial network; constructing a comprehensive energy optimization model, inputting a renewable energy power generation power prediction sequence, and outputting an equipment scheduling instruction and an energy interaction strategy of each park; executing the equipment scheduling instruction, monitoring actual renewable energy power generation power data, and calculating a renewable energy power generation power prediction deviation; and calculating the energy interaction cost and the energy sharing income of each park, and calculating the actual marginal contribution value of each park by adopting an improved Shapley value algorithm based on the renewable energy power generation power prediction deviation, the energy interaction cost and the energy sharing income. According to the invention, prediction precision and scheduling reliability can be improved, cost and carbon emission are optimized, and earnings are distributed fairly.

The invention provides a power dynamic balance regulation and control method and system for renewable energy hydrogen-based energy, and relates to the technical field of energy system control, and the method comprises the steps: obtaining renewable energy generation power, an energy storage state and load demand data, building a dynamic balance equation, calculating a surplus power value, collecting equipment parameters, and calculating the operation efficiency; the method comprises the following steps: constructing a multi-scale space-time attention network, predicting power and efficiency data through an adaptive convolution kernel and a position weighted attention mechanism, calculating a power distribution scheme based on the predicted data, carrying out deep reinforcement learning by adopting a Gaussian process to model uncertainty and predict risks, and obtaining control parameters in combination with dynamic planning; and monitoring efficiency and determining an optimal power distribution scheme.

The invention provides a low-carbon integrated energy system modeling method based on a hybrid energy storage system. And constructing a multi-dimensional constraint and cost optimization objective function, and establishing a global optimization framework of the integrated energy system. Wherein the balance constraint and the assembly constraint ensure dynamic matching of electric / hot / cold / gas multi-energy flow and cooperative operation of equipment, and the problem of unbalanced supply and demand of a traditional system is solved. The carbon emission constraint is divided through a dynamic carbon valence interval, the carbon emission and the carbon cost are in nonlinear correlation, a low-carbon technology path is guided, the green certificate constraint converts excess renewable energy power generation into economic benefits, and dual excitation of environmental protection and economy is formed. And the target function comprehensively optimizes equipment investment, maintenance cost and market income, and drives multi-energy complementation and full life cycle cost optimization. The system is adaptive to renewable energy fluctuation, load change and energy price fluctuation through optimal configuration of system output and equipment capacity, and economical and efficient operation under the low-carbon target is achieved.

The invention relates to the technical field of nuclear energy comprehensive utilization, and discloses an energy system with nuclear energy as base load and a control method.The system comprises a nuclear power secondary loop system, a renewable energy system, an energy storage system, a steam supply system, a seawater desalination system, a hydrogen production system, a heating system and a refrigeration system; the heat of the reactor core enters the steam generator to generate main steam; the photovoltaic module and the wind turbine generator set carry out renewable energy power generation; a storage battery is used for storing renewable energy electric power, and a molten salt heat storage system is used for storing heat energy; the steam supply heat exchanger is used for heating fresh water produced by the seawater desalination system to obtain superheated steam; the low-temperature multi-effect evaporation system and the reverse osmosis membrane method seawater desalination system generate fresh water; the electrolytic bath generates hydrogen and oxygen; the heating system and the refrigerating system are used for refrigerating in summer and heating in winter, and nuclear energy, wind energy, solar energy and the like are coupled and utilized in a stepped mode.

A method for a renewable energy generating system is provided. The renewable energy generating system includes the following steps: —determining, by a first energy meter, a produced energy amount of the renewable energy generating system in the predetermined period of time; —verifying the produced energy amount of the renewable energy generating system in the predetermined period of time; —issuing, by a computing unit, an energy certificate for the renewable energy generating system for the predetermined period of time, where the energy certificate at least comprises a time stamp indicating the predetermined period of time, an identifier indicating the one or group of renewable energy generators having produced the energy amount, and the produced energy amount; —encrypting, by the computing unit, the energy certificated; and —adding, by the computing unit, the encrypted energy certificate to a block of a blockchain or a distributed ledger application as a digital output.

The invention discloses a zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling, and the system comprises a multi-energy flow coupling architecture which constructs a closed-loop system formed by connecting a renewable energy power generation module, a green hydrogen preparation module, a methanol synthesis module and a carbon capture module; the intelligent optimization model is deployed in a system control layer and comprises a multi-target collaborative optimization algorithm execution unit; the self-adaptive regulation and control system comprises a weather prediction module, a load demand analysis module and an equipment parameter regulation module; the system comprises a digital management platform, three-dimensional visualization, an integrated server and block chain nodes. Therefore, according to the system, a multi-energy-flow closed-loop architecture is constructed, energy flow efficient cooperation is achieved through intelligent optimization and self-adaptive regulation and control, the resource configuration precision is improved in combination with digital management, the utilization rate of renewable energy sources can be effectively increased, and the system flexibility is enhanced.

The invention discloses a power grid intelligent planning and load prediction integration method. The method comprises the following steps: S1, acquiring load data of a power system; s2, acquiring historical load data, and predicting load data by adopting an LSTM neural network according to the historical load data and the acquired load data; s3, acquiring renewable resource generating capacity, historical meteorological data and meteorological data predicted by weather forecast, and predicting the renewable resource generating capacity by adopting a random forest according to the renewable resource generating capacity, the historical meteorological data and the meteorological data predicted by the weather forecast; s4, evaluating demand data of the power system for the energy storage system according to the predicted renewable energy power generation amount and load data; and S5, according to the demand data, generating a power grid energy storage system planning strategy including charging and discharging time and power distribution. By optimizing the operation strategy of the energy storage system, the power grid can more flexibly cope with load change and fluctuation of renewable energy sources, and the operation risk of the power grid is reduced.

A load frequency controller for the integration of a thermal power generator with renewable energy generators includes first and second power systems situated in separate geographic regions, interconnected through an inter-area tie-line. Each power system incorporates a 3DOF-FOPIDN controller, programmed with a salp swarm algorithm to adjust the controller gain parameters. A model predictive controller (MPC) slaved to the 3DOF-FOPIDN controller generates droop correction values then transmitted to the energy generators. The power deviations from the resources, the tie-line power deviations and a load center power deviation are combined to generate a combined power deviation signal. A frequency generator converts the combined power deviation signal to a frequency deviation value for each area. Feedback loops channel the frequency deviation value to bias factor generator in each area. An ACE signal is generated, which is utilized by the 3DOF-FOPIDN-MPC controller to modulate frequency and maintain grid stability.

The invention relates to the technical field of hydrogen production, in particular to an off-grid micro-grid hydrogen production system containing hybrid energy storage and a residual electricity consumption method. Comprising a renewable energy power generation unit, a hybrid energy storage unit, a hydrogen production unit, a standby load unit and a central controller, the renewable energy power generation unit is connected with the hybrid energy storage unit through a direct current bus, the hybrid energy storage unit comprises a super capacitor bank and a lithium ion battery pack, the super capacitor bank and the lithium ion battery pack are connected to the direct current bus in parallel through a bidirectional DC / DC converter, and power distribution of the super capacitor bank and the lithium ion battery pack is dynamically complementary based on real-time fluctuation frequency; the hydrogen production unit comprises an electrolytic cell and a hydrogen storage device, the electrolytic cell is connected with the direct current bus through a first DC / DC converter, and the standby load unit is connected with the direct current bus through a second DC / DC converter; the scheme can accurately quantify the polarization effect and dynamically correct the hydrogen production power.

The invention discloses a zero-carbon intelligent electrified highway transportation system, and relates to the technical field of highway transportation green low-carbon and new energy automobiles, and the system comprises the following components: an energy supply and management module which supplies power through the combination of an external power grid and a renewable energy power generation micro-grid, carries out the voltage reduction and rectification of a traction substation, and outputs the power to a power supply module; a renewable energy power supply proportion dynamic adjustment formula is utilized, and the total central control platform dynamically adjusts the power supply proportion according to the power consumption demand and the energy reserve, so that stable power supply is guaranteed; through close cooperation of the energy supply and management module and the vehicle energy utilization module, electric power is obtained from the overhead contact line system in real time in the driving process, the problems that a new energy heavy truck is short in driving mileage and low in energy supplementing efficiency are effectively solved, the transportation efficiency is greatly improved, the requirement for long-distance freight can be better met, and the transportation cost is reduced. And the road freight industry is powerfully promoted to develop in a more efficient and more convenient direction, and the competitiveness of transportation enterprises in the market is remarkably enhanced.

The invention discloses an industrial park comprehensive energy management method and system based on a hierarchical system, and belongs to the technical field of energy management and automatic control. The method comprises the following steps: integrating external data, and predicting load and renewable energy power generation power by using a machine learning algorithm; making a start-stop and output plan of each energy device in the park through an MILP model; and based on short-term prediction and an intra-day scheduling plan, establishing an MPC model, solving a real-time control instruction of each energy device, and controlling operation of various energy production, conversion and storage devices. According to the method, an algorithm combining reinforcement learning and model prediction control is creatively applied, the prediction model and the cost function of MPC are adaptively adjusted on line through reinforcement learning, the equipment power is dynamically adjusted in combination with short-term rolling prediction, uncertainty is effectively dealt with, more accurate second-level power balance is achieved, and the reliability of the system is improved. And the adaptability and the operation economy of the system to high-proportion renewable energy sources are effectively improved through hierarchical collaborative optimization.

The invention relates to the technical field of energy engineering, in particular to an energy system optimization method based on mine compressed air energy storage, and the method comprises the following steps: S1, constructing a multi-energy-flow network: constructing the multi-energy-flow network comprising waste mine renewable energy power generation, compressed air energy storage, mining mountain combined heat and power generation and ORC devices in combination with the full life cycle of a mine, a waste-recovery coupling energy topology of electricity-heat-cold-gas cooperative energy supply is formed; s2, multi-energy flow system modeling: establishing a mining mountain side equipment model and an abandoned mine side compressor, turbine power generation, gas storage and heat storage tank model; and S3, optimization scheduling modeling: introducing a CVaR index, constructing an optimization objective function containing energy purchase, operation and maintenance and energy abandoning costs, setting system operation and equipment constraints, and forming a risk-considered CMIES optimization scheduling model. According to the invention, the operation cost of the energy system in the mining area is reduced, renewable energy sources are efficiently consumed, and resources in the whole life cycle of the mine are cooperatively utilized.

The invention discloses an electric energy dispatching method based on railway mobile energy storage, and relates to the field of rail transit power grids. The method comprises the following steps: acquiring an energy change parameter of each node in a power grid, wherein the energy change parameter comprises at least one of an energy injection amount and an energy output amount; according to the respective energy change parameters of the two adjacent nodes, energy interaction parameters between the two adjacent nodes are determined, so that the regional energy balance degree of the two adjacent nodes is obtained, and the larger the energy interaction parameters are, the higher the regional energy balance degree is; determining the regional frequency deviation degree of each node according to the energy injection amount and the energy output amount of each node; and based on the regional energy balance degree and the regional frequency deviation degree, obtaining starting and ending points of electric energy transportation in the power grid. The method can effectively improve the adaptability and stability of the power grid to renewable energy power generation volatility, and enhance the reliability of power supply.