447 results about "Renewable power generation" patented technology

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 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 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 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 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 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 provides a hydrogen energy system and power grid collaborative complementary adjustment method and system, and relates to the technical field of energy management, and the method comprises the steps: obtaining a power grid load and renewable energy power generation data, calculating a supply-demand difference curve, switching the hydrogen energy system to a hydrogen production mode in a first target time period, and adjusting the hydrogen energy system to target hydrogen production power, switching to the power generation mode in the second target time period, adjusting the output power according to the supply-demand difference value, the hydrogen pressure and the working state of the battery, and recording the operation parameters to generate a cooperative operation evaluation report, thereby achieving the efficient complementation of the power grid and the hydrogen energy system, improving the energy utilization efficiency, and enhancing the power grid adjustment capability.

The invention discloses a grid-connected inverter power and current coordination control method under a harmonic power grid and a related device, and relates to the technical field of renewable energy power generation, and the method comprises the steps: obtaining a two-phase power grid AC voltage based on a three-phase power grid AC voltage; calculating a reference current under a direct power control strategy based on the active power reference value, the reactive power reference value and the two-phase power grid AC voltage; harmonic waves in the reference current under the direct power control strategy are filtered out, and a fundamental component and a harmonic component are obtained; based on a preset adjustment coefficient, the fundamental component and the harmonic component, obtaining a reference current with an adjustable harmonic content; performing zero static error control on the reference current with the adjustable harmonic content to obtain a modulation voltage; and performing sine pulse width modulation on the modulation voltage to obtain a grid-connected current. According to the invention, coordinated control of power constancy and current balance of the grid-connected inverter can be realized.

The invention belongs to the technical field of fused salt energy storage, and particularly relates to a thermal power generating unit deep peak shaving system and method of a fused salt heat storage coupling high-temperature heat pump. In the high-generation time period of renewable energy sources, the load of a thermal power generating unit needs to be reduced, at the moment, a boiler operates at the lowest stable combustion load, a fused salt heat storage system is added, all main steam is extracted to enter the heat storage system to heat fused salt, a high-pressure cylinder of a steam turbine is stopped, and an intermediate-pressure cylinder and a low-pressure cylinder of the steam turbine operate under the low-load working condition; the output load of the unit only meets the plant power, and the load is not output to the power grid; in the load rising time period, heat stored by the fused salt heat storage system is used for generating steam in the unit load rising stage to increase the unit steam turbine load rising rate and peak power generation; the problems of operation safety and low energy grade in the unit peak regulation process are solved. In the peak period of electricity utilization, heat stored in the fused salt heat storage system is released into the unit, and the output load of the unit is rapidly increased. An outlet of the deaerator serves as a water taking point, part of supplied water is extracted to pass through the preheater, the evaporator and the superheater to generate high-temperature and high-pressure steam, the high-temperature and high-pressure steam enters a high-pressure cylinder of the steam turbine to do work, and therefore the output load of the steam turbine is rapidly increased. The invention provides a thermal power generating unit deep peak regulation system and method of a fused salt heat storage coupling high-temperature heat pump, and aims to realize deep peak regulation of a unit and power generation by absorbing renewable energy sources such as wind and light.

The invention relates to the technical field of system dynamics, and discloses an electricity certificate carbon market price analysis method and system based on system dynamics, and the method comprises the steps: S1, determining the main body of a green certificate transaction market and the main body of a carbon emission permit transaction market, and determining a renewable energy power generation mode; s2, constructing a TCC cost control mechanism; s3, TCC cost is calculated according to a TCC cost control mechanism; s4, establishing stock flow dynamic model frames of the green certificate market and the electricity market and the carbon emission permit trading market and the electricity market, and combining the two dynamic model frames to generate an electricity certificate carbon market stock flow dynamic model frame; and S5, analyzing the electricity evidence carbon market price. The system comprises an information input unit, a mechanism construction unit, a calculation and analysis unit and a system dynamics model framework construction unit. According to the method, the system dynamics and the method for controlling and optimizing the TCC cost are combined, and the analysis accuracy and reliability are improved.

The invention relates to the technical field of photovoltaic heating, in particular to a variable-power wind-solar complementary energy supply system and method for distinguishing renewable energy intensity, and the system comprises a system control module which is used for formulating a variable-power consumption strategy for distinguishing the renewable energy intensity, and the renewable energy power input into the energy consumption module is dynamically allocated based on a variable power consumption strategy, so that wind-solar complementary energy supply is performed on the energy consumption module, and the renewable energy power generation power and the power of the resistive heating tail end in the energy consumption module are matched in real time. The variable-power wind-solar complementary energy supply system capable of distinguishing the renewable energy intensity is constructed by utilizing the characteristics that the resistive heating tail end can work with variable heat production power and the storage battery can be efficiently charged under the low renewable energy intensity, and a control strategy of storage power consumption in weak wind and weak irradiation time periods and energy consumption consumption in strong wind and strong irradiation time periods is put forward; the situation that low voltage of the system is converted into invalid low-grade heat energy is avoided, and the utilization efficiency of renewable energy sources is improved.

The invention discloses a renewable energy power generation uncertainty tracking method based on a recursive Gaussian mixture model, and the method comprises the steps: 1, obtaining a renewable energy power generation certainty prediction curve, and forming a joint observation set containing predicted and actual power generation power; 2, setting a cluster number, and executing a k-means algorithm to obtain a cluster; 3, calculating an initial parameter of each Gaussian component in the Gaussian mixture model based on the clusters; 4, updating Gaussian mixture model parameters by using an expectation maximization algorithm to obtain initial optimal parameters; 5, constructing a recursive updating probability model, and performing recursive updating on the probability model based on a newly coming sample; and 6, calculating conditional probability distribution of actual power under a given latest predicted power condition by using the updated Gaussian mixture model so as to realize real-time quantification and tracking of renewable energy power generation uncertainty.

The invention discloses a hydrogen-electricity hybrid seaport logistics-energy collaborative optimization scheduling device and method.The scheduling device comprises a hydrogen-electricity energy network system and a hydrogen-electricity logistics system, the hydrogen-electricity energy network system comprises a renewable energy generator, a power distribution network and a hydrogen energy unit, and the hydrogen energy unit comprises an electrolytic cell, a hydrogen storage tank and a fuel cell; two sides of the hydrogen storage tank are respectively connected with the electrolytic cell and the fuel cell, the electrolytic cell and the fuel cell are respectively connected with one node of the power distribution network, and one side of the hydrogen storage tank is connected with the hydrogen refueling station. And a logistics-energy cooperative scheduling scheme is obtained. By adopting the device and the method, the hydrogen logistics equipment and the hydrogen energy equipment are introduced, and when the load level of the power distribution network is relatively high, the logistics operation can be completed by the hydrogen logistics equipment, so that the energy supply pressure of the power distribution network on the logistics load is effectively transferred.

The invention provides a maritime distributed green ammonia production scheduling system and method based on intelligent management, and relates to the technical field of maritime renewable energy utilization, and the system comprises a distributed ammonia production platform which is used for the distributed production of maritime green ammonia. A renewable energy power generation device, water electrolysis hydrogen production equipment, a synthetic ammonia reactor, a green ammonia energy storage device and an ammonia production platform sensor network are configured; the multifunctional ship node is used for production, transportation and consumption of green ammonia and is provided with a green ammonia transportation carrier, an offshore production node expansion unit, a green ammonia fuel consumption terminal and a ship state sensor; the intelligent green ammonia management platform integrates a data transmission module, a multi-target optimization scheduling module, a fault prediction and emergency response module and a supply chain management module, and is used for communicating with an ammonia production platform and ship nodes through the Internet of Things technology, so that intelligent closed-loop management of green ammonia production, transportation and consumption can be realized; the production scheduling efficiency and the energy utilization rate are improved, and the transportation cost is reduced.

The invention provides a micro-grid management method and system based on source grid load storage, and relates to the technical field of micro-grids, and the method comprises the steps: S1, obtaining renewable energy power generation data in a micro-grid, the state of charge (SOC) of an energy storage device, a load demand, and power grid operation state information; and S2, performing real-time prediction on the load demand by using an intelligent algorithm and generating a load scheduling plan. According to the micro-grid management method and system based on source grid load storage, load prediction is carried out by introducing an intelligent algorithm, power scheduling between renewable energy sources and an energy storage device is optimized by a dynamic coordination mechanism, and resources are allocated by a multi-objective optimization algorithm, so that efficient management of the micro-grid is successfully realized. By acquiring various data in the micro-grid in real time and combining with a dynamic adjustment scheduling strategy, the method can effectively deal with the uncertainty of load fluctuation and renewable energy output, optimize the operation efficiency of the power grid, and ensure the stability and economy of the system.

A dynamic frequency coordination control method for asynchronous regional power grids during wet and dry seasons is provided. By using frequency synchronous control in VSC-HVDC, frequency consistency and synchronization between the sending-end and receiving-end grids can be ensured within the range of power adjustment, enabling real-time and automatic sharing of frequency regulation resources between the two grids. Through the use of bilateral frequency limiting control in LCC-HVDC, the frequency regulation effect is enhanced, balancing frequency fluctuations between the sending-end and receiving-end grids. VSC-HVDC adaptive reactive power support control is employed to provide reactive power support to nearby LCC-HVDC, avoiding frequent operations of LCC-HVDC converter transformers and filters during frequency limiting control actions, thereby extending equipment lifespan. Furthermore, by utilizing grid-following and grid-forming control in renewable energy grid integration with additional frequency control, the frequency support capacity of renewable energy generation is improved.

The invention belongs to the technical field of micro-grids, and discloses a zero-carbon park distributed energy management system based on a micro-grid technology, and the system comprises a distributed renewable energy power generation unit, a multi-type energy storage unit, a flexible adjustable load unit, and an energy management master control system. According to the invention, by integrating renewable energy power generation, multi-type energy storage and flexible load, an efficient and collaborative energy system is constructed; the system adopts an advanced model prediction control algorithm to realize source-network-load-storage optimal scheduling, so that the utilization rate of renewable energy sources is remarkably improved, and the carbon emission intensity is effectively reduced; a multi-time-scale energy storage architecture ensures the stability and reliability of system operation, and can be quickly switched to an island mode when a power grid has a fault; the intelligent digital twin platform provides accurate fault diagnosis and operation and maintenance support, and the modular design endows the system with good expansibility.