5095 results about "Power station" patented technology

The embodiment of the invention provides a new energy optical power correction method and system based on weather abrupt change prediction. The method comprises the following steps: identifying a cloud layer type contained in a target area, and obtaining a cloud layer type coverage rate; if the cumulonimbus coverage rate exceeds a first threshold value, predicting a cloud layer movement path in combination with the cloud layer movement vector field to obtain a first cloud layer coverage change sequence; if the layer cloud coverage rate exceeds a second threshold value, an irradiance attenuation coefficient is predicted based on the cloud layer thickness in the meteorological data and the solar altitude angle, and a second cloud layer coverage change sequence is obtained in combination with the ground aerosol optical thickness and the corrected irradiance attenuation coefficient; and monitoring real-time operation data of the power station, combining the first / second cloud layer coverage change sequence, predicting a matching relationship between a cloud layer moving direction and a power station power reduction area by adopting a double-flow Transform architecture, calculating a power fluctuation range corresponding to each power station power reduction area, dynamically adjusting power grid operation parameters of a target area, and determining the power grid operation parameters of the target area. The power grid stability is ensured.

The invention belongs to the technical field of power station boiler waste heat recycling energy-saving equipment and relates to a plate-and-shell type coal economizer. The plate-and-shell type coal economizer comprises a corrugated plate heat exchange core body (1), a heat exchanger shell (2), a first and a second water inlet / outlet water tank (3, 3') and a first and a second exhaust gas inlet / outlet pipeline (4, 4'), wherein the corrugated plate heat exchange core body (1) is mounted in the heat exchanger shell (2), the first and second water inlet / outlet water tanks (3, 3') are respectively arranged at the upper and lower ends of the front side of the corrugated plate heat exchange core body (1), and the first and second exhaust gas inlet / outlet pipelines (4, 4') are respectively arranged at the upper and lower sides of the corrugated plate heat exchange core body (1). The plate-and-shell type coal economizer solves the problem that the length of a water passage needs to be increased to solve the problem of unapparent water heating due to the great difference between an exhaust gas density and a water density, and meanwhile, a corrugated passage at the exhaust gas side enables the exhaust gas flow turbulivity to be increased and the heat transfer efficiency to be improved; the blockage due to ash accumulation happens rarely, even if the ash is accumulated, the cleaning is easy to finish.

The invention discloses a lithium battery thermal runaway control and safety response strategy system, which realizes the real-time monitoring and prediction of the internal temperature and voltage state of a battery through thermal-electric coupling modeling and multi-sensing data fusion, and realizes the real-time monitoring and prediction of the internal temperature and voltage state of the battery based on a model prediction control and threshold enhancement strategy. And triggering graded safety response in the early stage of thermal runaway. The system adopts a multi-stage response mechanism including measures of primary early warning, active cooling intervention, emergency power-off fire extinguishing and the like, a gas suppression path and cooling mode switching function is designed, and heat diffusion and spreading and toxic gas harm are effectively suppressed. Compared with a method depending on complex digital twinning or deep learning, the method is simple in structure, rapid in response and low in data dependence, has good system stability and engineering applicability, and can be widely applied to the field of lithium battery safety management of electric vehicle battery packs, energy storage power stations and the like.

The invention relates to a boiler combustion optimization control system of a thermal power plant, relates to the technical field of thermal power generation control, and aims to solve the problems of inaccurate multi-source data perception, slow control response and insufficient multi-target collaborative optimization capability under fire coal quality fluctuation and load change. The system comprises a data perception and fusion module, a working condition self-adaptive identification module, a multi-target dynamic optimization decision module and a distributed execution control module, and through multi-source data acquisition and feature extraction, depth time sequence mode identification, multi-target optimization model solving and distributed coordination control, the multi-target dynamic optimization decision module and the distributed execution control module are subjected to multi-source data fusion. Dynamic balance of boiler heat efficiency improvement and nitrogen oxide emission reduction is achieved, and adaptability and control precision of the system under complex working conditions are enhanced.

The invention relates to the technical field of new energy intelligent decentralized control, and discloses a distributed photovoltaic power station intelligent monitoring method and system, and the method comprises the steps: collecting electrical state parameters, and carrying out the normalization processing; performing anomaly detection and island detection based on a multi-dimensional deviation fusion mechanism; generating a power support parameter through a self-adaptive reactive power injection mechanism; under the island mode, loading voltage frequency double-droop combined control; and after the main network is recovered, grid-connected reconnection is realized based on a phase progressive phase locking mechanism. Compared with the prior art that multi-dependence centralized master station judgment and scheduling cannot realize fault isolation and independent operation in time especially under the condition that main network communication is abnormal or frequency fluctuation occurs in a far-end island edge node, the technical problem that fault isolation and independent operation cannot be realized in time in the prior art is solved. Autonomous monitoring of an individual power station level and stable operation of an island are realized, and the stability of the system in a weak network or island operation scene is improved.

System and method embodiments are described for solar panel protection against severe environmental impacts. A tracker controller may be configured to rotate a solar tracker comprising a group of solar panels to a safe position to diminish hail impact during a hailstorm. Such a rotation may be implemented using one or more motors to drive arc gears coupled to a pair of supporting purlins that support the group of solar panels. The tracker may comprise a bumper rail placed on the top when the tracker is at the safe position for hail impact absorption. Implementing solar panel protection provides a practical solution to protect solar panels from damage. Thus, the need for costly repair or replacement may be avoided, minimized, or decreased. As a result, the economic efficiency of solar power plant operations may be improved significantly.

The invention discloses a network construction type energy storage capacity optimal matching method based on a frequency response model, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: S1, quantifying the power vacancy of a typical station under an extreme short-circuit fault, determining a virtual inertia constant and a virtual droop coefficient of network construction type energy storage, and carrying out the calculation of the virtual inertia constant and the virtual droop coefficient; setting an initial output coefficient and adjusting a step length; s2, constructing and integrating a system frequency response model based on the inertia and frequency modulation characteristics of the conventional unit, the wind turbine generator and the network construction type energy storage; s3, applying power disturbance to the system, gradually increasing an initial output coefficient and performing simulation, and recording the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation; and S4, when the maximum frequency deviation, the maximum frequency change rate and the quasi-steady-state frequency deviation are respectively smaller than or equal to set threshold values, the energy storage capacity proportion corresponding to the current initial output coefficient is the optimal proportion. According to the method, the problem of accurately measuring the optimal configuration proportion of the network construction type energy storage in the new energy station is solved.

The invention relates to the technical field of new energy storage regulation and control, and discloses an electricity-hydrogen coupling energy storage system and method for a new energy field station. The system comprises a feature analysis unit, a topology construction unit, a regulation and control domain positioning unit and a decision optimization unit. The feature analysis unit receives operation condition information of the new energy station, and generates a condition feature vector according to real-time load fluctuation, a historical output curve and an equipment operation database by using a deep feature extraction network; the topology construction unit constructs an energy topological graph for distributed energy storage equipment and hydrogen production equipment in the electricity-hydrogen coupling system, dynamically updates a graph structure and a node state based on a real-time monitoring mechanism, and deploys the graph structure and the node state in a heterogeneous computing framework; a regulation and control domain positioning unit positions a matched regulation and control domain in a dynamic representation learning mode according to the working condition feature vector; and the decision optimization unit utilizes a multi-source decision fusion algorithm to screen regulation and control instructions in a regulation and control domain and generate a scheduling sequence, so that the adaptability and the operation efficiency of the system to working conditions are improved.

The invention provides an energy storage power station optimization operation mode decision-making method and system, and relates to the technical field of energy storage power station optimizing.A hybrid prediction model is constructed to realize high-precision decomposition prediction of power load, and meanwhile, the internal resistance of a battery is estimated in real time by adopting a recursive least square method; and the battery capacity and internal resistance parameters are dynamically corrected in combination with a temperature compensation mechanism. Through health state multi-index fusion evaluation, self-adaptive distribution of charging and discharging power is achieved, and compared with the prior art, the problem that a traditional static model cannot adapt to complex environment changes is solved. The battery capacity fading risk can be predicted in advance by introducing a double-compensation mechanism of an environmental influence index and an electric power influence index. According to the scheme, the response speed and economical efficiency of energy storage in a high fluctuation load scene are remarkably improved, and a reliable dynamic optimization decision support system is provided.

The invention discloses a method for effectively studying and judging false alarm of a fire alarm system in a transformation power station in a multi-dimensional mode, and the method comprises the steps: collecting and fusing multi-channel sensor data, such as smoke concentration, temperature, humidity, electromagnetic field intensity, grounding state and the like, and constructing a scenarized data set in combination with an environment label and an operation and maintenance log; through multi-level feature processing such as normalization, wavelet denoising and dimension reduction, the data quality and discriminability are improved; according to the method, the space-time diagram neural network is used for achieving multi-period environment subarea and typical signal mode division, multi-period fusion confidence judgment is conducted on alarm signals based on the fuzzy set theory and Bayesian reasoning, dynamic judgment of false alarms and real alarms is achieved, and the accuracy and robustness of alarm judgment in the complex environment are effectively improved.

The invention discloses an energy storage battery health feature extraction and state evaluation method based on transfer learning. The method comprises the following steps: S1, constructing a source domain health feature library and pre-training a model; according to the method, dependence on complete cyclic data is broken through, high precision and robustness are still achieved under the conditions of data sparsity and working condition difference, and the method is suitable for intelligent operation and maintenance and predictive maintenance of an energy storage power station. And meanwhile, common incomplete and partial charge and discharge data fragments under actual working conditions can be directly utilized for feature extraction and state evaluation, dependence on complete charge and discharge cycles is avoided, and the application scene of the data driving method is greatly widened.

The invention relates to the technical field of wind-solar storage power station operation control, and discloses a wind-solar storage multi-energy complementary power station cooperative operation method and system. The method comprises the following steps: constructing a wind-light fluctuation rate phase plane, defining a coupling stable elliptical region and generating a multi-source coupling state tag; an energy storage response performance plane is constructed, a dynamic dead zone threshold parameter is calculated, a dynamic respiration hexagonal boundary is established, and the penetration depth is calculated to generate an energy storage effective action instruction; and constructing a virtual energy gravitational potential energy field by utilizing an energy storage effective action instruction, calculating virtual potential energy heights of independent physical nodes and nonlinear flow resistance characteristics of a virtual energy transmission pipeline, and analyzing a virtual potential energy height difference and a virtual energy flow velocity to obtain a final power distribution scheme. Precise classification of fluctuation modes is achieved through geometric topology, the dynamic breathing hexagon is utilized to consider battery life and stability control requirements, the problem of lagging of multi-target optimization calculation is solved based on the physics field theory, and the operation efficiency of a power station and the friendliness of a power grid are remarkably improved.

The invention discloses a multi-mode hybrid energy storage cooperative control method and system for a megawatt-level network-building type energy storage power station, and the method comprises the steps: S1, collecting the frequency, voltage amplitude and phase of a power grid, and the charge state and temperature of each energy storage branch, and generating real-time monitoring data; s2, determining an operation mode of the energy storage power station based on a comparison result between the frequency deviation and the voltage deviation in the real-time monitoring data and a preset threshold value; s3, based on the operation mode of the energy storage power station, performing double-layer decomposition on the power instruction through a high-pass filter and a low-pass filter to generate a super-capacitor power instruction and a lithium battery power instruction; s4, generating an internal potential amplitude and a phase angle by adopting a virtual synchronous machine algorithm; and S5, on the basis of the amplitude and the phase angle of the internal potential and the detection result of the power grid connection state, executing switching control of a network construction type control mode or a network following type control mode. The millisecond-second level multi-time scale power optimal distribution is realized; self-adaptive inertia and voltage support is carried out; and island / grid-connected seamless switching is realized.

The invention discloses a battery health management system and method, and belongs to the technical field of battery management, the system comprises an implantable sensor network, an edge and cloud collaborative computing platform, a multi-modal prediction model and a dynamic optimization execution unit; the implantable sensor network integrates various sensors to collect multi-physical field parameters in a battery, and the energy collection unit supplies power through cross-shielding communication transmission. The edge and cloud collaboration platform processes data, trains a model and carries out block chain evidence storage; the multi-modal prediction model is fused with space-time double-flow Transform and causal reasoning, SOH and RUL are predicted, and thermal runaway is early warned; the dynamic optimization execution unit realizes efficient control through layered equalization and self-adaptive thermal management; the method comprises an initialization stage, an operation stage and a maintenance stage to form a closed loop. The method breaks through the traditional limitation, improves the safety, reliability and economy of the battery, and is suitable for electric vehicles, energy storage power stations and other scenes.

A rock mass mechanical parameter prediction method based on an underground powerhouse of a pumped storage power station relates to the technical field of mechanical parameter prediction, and comprises the following steps: identifying an embedding position and distribution characteristics of a sulfur-containing shale interlayer by constructing a three-dimensional geologic structure model and lithologic information of a construction area, and establishing a structural domain partition parameter prediction area system; constructing a nonlinear regression function model taking infrared spectrum characteristic factors and environment control parameters as input and taking elasticity modulus and the like as output by combining infrared spectrum characteristic data and a historical evolution track; the model is embedded into a regional system for dynamic assignment, and a parameter space-time change atlas is constructed; the laser point cloud data and the micro-seismic monitoring data are fused to invert the actual response of the surrounding rock; when the monitoring value deviates from the prediction map, triggering parameter degradation function re-calibration to complete parameter dynamic updating and prediction closed loop; according to the method, the accuracy of rock mass mechanical parameter prediction of the pumped storage power station underground powerhouse can be improved.

The invention provides a mountain power station current collection system state online monitoring method and device, and relates to the field of data processing. According to the method, distributed optical fiber data, environmental meteorological data and electrical measurement data are acquired to construct a multi-mode time sequence matrix, an operation state diagram is formed through graph structure representation and graph signal processing, a working condition mode cluster is obtained by combining dynamic clustering, a constraint diagram optimization framework is input, thermal, electrical and mechanical constraints are introduced, and an operation state track is generated. And constructing and fusing a non-stationary degradation path library and environment exposure history to form a dynamic degradation evolution model, and finally outputting in-service reliability and predicting residual life by a risk inference engine to realize online monitoring of the state of the current collection system of the mountain power station. By implementing the technical scheme provided by the invention, the accuracy of online monitoring of the state of the current collection system of the mountain power station is improved.

The invention discloses a thermal power plant whole-plant collaborative optimization system and method based on digital twin and AI algorithms, and belongs to the field of thermal power plant optimization control. The invention discloses a thermal power plant whole-plant collaborative optimization system and method based on digital twinborn and AI algorithms. The system comprises a data fusion processing module, a digital twinborn body construction module, a collaborative optimization and decision module, a strategy decomposition and execution module and an online learning and updating module. According to the method, the problems that the existing thermal power plant optimization control lacks global collaboration and is difficult to adapt to dynamic complex working conditions, and online self-evolution of a model and a strategy cannot be realized are solved; and a deep reinforcement learning algorithm is utilized to carry out multi-target collaborative optimization on the whole plant level, so that a global optimal control strategy which comprehensively considers the operation cost, the energy efficiency, the equipment service life and the environmental protection constraint can be dynamically generated, and the limitation of traditional decentralized control and static optimization is effectively overcome.

The invention relates to a peak shaving power station downstream non-safe navigable water area identification method and system and a medium, and the method comprises the steps: building a peak shaving power station downstream river channel one-dimensional and two-dimensional coupling hydrodynamic mathematical model; determining a combination mode of a proposed condition and a water flow boundary condition of a typical peak regulation process of the downstream of the hydropower station; simulating and calculating downstream on-way hydrodynamic conditions of the power station under different peak shaving processes; calculating a downstream non-safe navigation index of the peak regulation power station; and identifying the downstream non-safe navigable water area of the peak shaving power station. According to the method, on one hand, the change condition of downstream hydrodynamic force of the peak regulation power station can be analyzed, on the other hand, the influence condition of the peak regulation process on downstream navigation conditions can be recognized, non-safe navigation water areas of the peak regulation power station in different discharge flow processes can be judged, and a research basis is provided for hub power generation and navigation scheduling of mountain rivers.

The invention relates to the technical field of thermal management of energy storage power stations, and discloses an immersion phase change and jet flow cooperative reinforcement thermal management system of an energy storage power station. An immersed phase-change cooling tank body of the system contains an energy storage battery pack and a low-boiling-point fluorinated liquid cooling liquid, so that the whole battery pack is immersed in the immersed phase-change cooling tank body; the condensation recovery device is connected with a steam outlet condensation vaporization medium; the liquid storage distribution unit comprises a main liquid storage tank and a special liquid storage tank, the main liquid storage tank is connected with the condensation recovery device, and the special liquid storage tank is connected with the bottom of the tank body through an electromagnetic drain valve; the multi-layer jet strengthening module comprises a top-layer annular jet pipeline, a bottom-layer annular jet pipeline and a middle-layer annular jet pipeline, the top-layer annular jet pipeline and the bottom-layer annular jet pipeline are connected with a main liquid storage tank and reflow through a circulating pump, and the middle-layer annular jet pipeline is connected with a special liquid storage tank driven by a high-pressure pump and provided with The temperature monitoring array acquires the real-time surface temperature of the battery pack in a distributed manner The dynamic control center is connected with all the modules. According to the system, efficient and uniform heat management of the energy storage battery pack is realized through cooperation of phase change and jet flow.

The invention provides an electric energy storage power station virtual inertia optimization method considering inertia space-time distribution characteristics, and the method comprises the steps: carrying out the virtual inertia optimization of an electric energy storage power station under the scene of considering the non-uniform inertia distribution of an electric power system under the high-proportion new energy access; constructing a source measurement frequency response characteristic model considering load power mapping and a load node frequency response characteristic model considering source measurement frequency mapping; the system small interference stability based on the characteristic root method is used as a frequency safety constraint condition; taking the minimum inertia and frequency modulation energy of the energy storage power station as a target, considering the physical constraint, the frequency security constraint and the system stability constraint of the energy storage power station, and proposing a network tracking / constructing energy storage power station inertia coordination optimization strategy considering the frequency dynamic space-time distribution; wherein the objective function is that the sum of the integral quantity of the load node frequency deviation and the inertia kinetic energy of the following / constructing network energy storage power station is minimum. According to the method, aiming at the characteristic of frequency space distribution differentiation in a novel power system, it is ensured that all nodes can meet double security constraints of frequency deviation and frequency change rate.

The invention provides an intelligent fire prevention and control fire-fighting management system and method for a lithium battery energy storage power station, and relates to the technical field of data processing, and the method comprises the steps: arranging a multi-level temperature sensor, a gas detector and a smoke detector in and outside a battery module, and collecting and forming multi-source monitoring data; performing time sequence rate analysis, and if a preset condition is met, generating early warning data; in combination with sensor matrix distribution in the battery module, spatial cross comparison is executed to obtain abnormal positioning data, and false anomalies are eliminated through multi-point consistency verification; generating a directional processing instruction corresponding to the injection unit, and setting injection data in the processing instruction; the spraying unit is controlled to release an atomized fire extinguishing medium, so that the medium forms a permeation flow field in the gaps of the battery cells, and treatment feedback data is acquired and formed; performing trend analysis, and if the trend is not enough to recover to the preset safety range, generating a secondary processing instruction; according to the invention, the autonomy and accuracy of fire control management are improved.

The invention discloses an oil product deterioration intelligent analysis and operation and maintenance evaluation system of power plant host equipment, and relates to the technical field of state monitoring and fault prediction of power plant key equipment. Comprising a multi-mode sensing module, an edge preprocessing unit, a dynamic reference model building module, a residual analysis engine, a mirror image diagnosis engine, a fault tracing module and a self-evolution digital twin platform, the device is used for collecting physical characteristic parameters, pollution state parameters, electrochemical characteristic parameters and wear information parameters of oil in real time. According to the oil degradation intelligent analysis and operation and maintenance evaluation system of the power plant host equipment, high-precision real-time monitoring of multiple parameters of the oil state is achieved, environment interference data are effectively recognized and filtered, the accuracy and reliability of fault diagnosis are ensured, the recognition time of the abnormal state of the equipment is advanced, and the false alarm condition is effectively controlled.

The invention provides a photovoltaic power station equipment health state assessment method, which belongs to the technical field of photovoltaic power stations, and comprises the following steps of: after performing wavelet transform decomposition and Kalman filtering noise reduction processing on power data, establishing a dynamic statistical baseline and extracting a power attenuation trend index; converting real-time power into standard test condition power by using an environment normalization model so as to eliminate the influence of environmental factors, constructing a game optimization model, obtaining an optimal detection threshold and a sensitivity parameter through iterative solution of upper-layer detection optimization and lower-layer threshold optimization, and outputting an equipment degradation type and degree score in combination with a health state recognition model. And finally, aggregating the health indexes of the equipment through an analytic hierarchy process to form a power station level comprehensive health index, thereby solving the technical problem of high false alarm rate caused by difficulty in balancing environmental factor interference and early fault detection sensitivity in a photovoltaic power station equipment health state evaluation process.

The invention relates to a pumped storage and compressed air energy storage coupled scheduling method, which comprises the following steps of: establishing a multi-stage data system based on input data of multiple time scales; constructing a multi-time scale scheduling model; when the load of the pumped storage mixed compressed air energy storage power station is lower than a first preset threshold value, electricity is abandoned preferentially, water pumping and compression are conducted, and when the load is larger than a second preset threshold value, the pumped storage mixed compressed air energy storage power station is controlled to conduct combined energy release. The nonlinear relation between the pumped storage efficiency and the water level and the nonlinear relation between the compressed air energy storage power and the pressure are converted into linear constraints, solving is conducted by dividing the water level interval and the pressure level, and a scheduling strategy is obtained. Therefore, the problems of relatively low energy storage efficiency, insufficient scheduling refinement, relatively high system standby cost and the like caused by the problems that pumped storage water scheduling and CAES energy management are separated, a standby capacity coordination mechanism is lacked and the like in a hybrid system scheduling method in related technologies are solved.

The invention discloses an energy storage power station multi-agent collaborative management method based on deep reinforcement learning, and relates to the technical field of intelligent control. The energy storage power station multi-agent collaborative management method based on deep reinforcement learning comprises the following steps: S1, acquiring energy storage collaborative perception data, and preprocessing the energy storage collaborative perception data; s2, dividing three types of control units, evaluating the operation coordination deviation degree of the energy storage power station, dynamically selecting the action strategy of each control unit, and constructing an action value evaluation model; s3, constructing a state, action and reward triple sample, and iteratively optimizing the action value evaluation model round by round; s4, deploying each control unit into a simulation operation environment, and executing action decision and state interaction feedback; after each action decision is executed, whether a strategy correction mechanism is triggered or not is judged. The problems that subsystems in an existing energy storage power station are not coordinated in time, battery state estimation errors are difficult to feed back in time, and then strategy lag and equipment operation imbalance are caused are solved.

The invention discloses a system safety assessment method and system for an energy storage power station containing a network-building type energy storage converter, and the method comprises the following steps: S1, collecting converter control parameters, power grid dynamic data and lithium battery mechanism data through multi-source heterogeneous data fusion, and carrying out the preprocessing through noise elimination and a normalization algorithm; s2, constructing a network construction characteristic quantitative evaluation system including indexes such as dynamic stability and voltage supporting capability; s3, establishing a three-layer evaluation system of an equipment layer, a control layer and a system layer, and calculating a total risk value of the system by using a weight distribution algorithm; s4, verifying system adaptability and optimizing parameters through time domain simulation in scenes such as a strong power grid and a weak power grid; and S5, generating an evaluation report containing parameter optimization suggestions, triggering graded early warning based on an index threshold value, and automatically generating a closed-loop control instruction. According to the invention, multi-dimensional safety assessment of the energy storage power station is realized, and systematic technical support is provided for safe operation of the network-forming type energy storage power station.

The invention relates to the technical field of power station operation and maintenance, and discloses a new energy power station intelligent operation and maintenance system based on big data analysis. The system comprises a data acquisition module used for acquiring and preprocessing operation data and maintenance logs of power station equipment; the diagnosis evidence generation module is used for generating diagnosis evidences of three dimensions of performance degradation, mechanical abnormity and repeated fault risk through parallel analysis; the data analysis module maps the diagnosis evidence to a state space to construct an equipment health track, calculates a health index based on a mahalanobis distance and predicts a change trend; the operation and maintenance decision module is used for performing significance verification on the health trend, generating a dynamic risk score, automatically matching a maintenance strategy and establishing a feedback optimization mechanism; according to the invention, accurate evaluation and predictive maintenance of the equipment health state are realized, and the operation and maintenance efficiency and the equipment reliability of the new energy power station are effectively improved.

Provided is a method of operating a hybrid power plant, including energy generating units including a wind park and at least one unit of another type of renewable energy source and including an energy storage system, the method including: creating a power production schedule based on an actual forecast of power production, an actual forecast of energy price and actual hybrid power plant status, the power production schedule including at least scheduled power for points in time in the future; and controlling, during the points in time in the future the energy generating units and the energy storage system based on the power production schedule, the hybrid power plant status at the points in time in the future, in particular energy storage system status, such as to dispatch power according to the power production schedule, but complying with any grid operator reference at the points in time in the future.

The invention relates to the technical field of power station operation and maintenance, and discloses a power station multi-robot collaborative inspection and task optimization scheduling system, which comprises a digital twinborn modeling module used for establishing a digital twinborn model of a power station environment, equipment and multi-type inspection robots, and realizing real-time mapping of a power station physical space and a virtual space through state synchronization; a task modeling and decomposition module; a task and capability matching scheduling module; a dynamic rescheduling module; a cloud edge cooperative control module; a data acquisition and feedback module; and a man-machine interaction and visualization module. Through integration of task node graph construction, robot capability vector modeling, local hot start rescheduling and a digital twin closed-loop self-learning mechanism, distribution continuity can be maintained when tasks change or robot states are updated, and parameters are continuously corrected based on operation data. And cooperative improvement of inspection task execution efficiency, scheduling response speed and system stability is realized.

The invention discloses an underground chamber structure of a compressed air energy storage power station, a construction method and a parameter optimization design method, and belongs to the field of compressed air energy storage and underground space engineering. A hard rock stratum building chamber is determined through systematic site selection investigation, a high-performance concrete lining (containing fiber reinforcement) and a polyurethane buffer layer are adopted to synergistically improve the stability, and a vinyl ester resin-based glass fiber reinforced plastic three-layer composite structure serves as a sealing layer (including a bonding layer, a reinforcement layer and an impermeable layer); the processes of sand blasting pretreatment, stepped joint and the like are combined to guarantee the sealing performance; meanwhile, on the basis of a finite element model, structural parameters are optimized by taking the lowest whole life cycle cost as a target and taking strength, deformation, leakage rate and the like as constraints, and a recommended parameter combination of each structural layer is obtained in combination with an orthogonal experimental design and an improved genetic algorithm. The method is suitable for various geological conditions, the safety, economical efficiency and adaptability of the compressed air energy storage power station are effectively improved, and the method is suitable for large-scale application and popularization.