835 results about "Renewable power generation" patented technology

The present invention relates to a production and energy system based on a new energy-powered submerged arc furnace, and a related control method. The production and energy system comprises a power transmission and distribution system, a renewable energy power generation system, an energy storage system, and a submerged arc furnace-based production system. The power transmission and distribution system is respectively connected to the renewable energy power generation system, the energy storage system, and the submerged arc furnace-based production system. The production and energy system further comprises an energy control system. The energy control system comprises: an integrated regulation and control system used for performing integrated regulation and control on the production and energy system by means of the power transmission and distribution system; a renewable energy power generation control system used for controlling operation of the renewable energy power generation system; an energy storage control system used for controlling operation of the energy storage system; and a submerged arc furnace-based production control system used for controlling operation of the submerged arc furnace-based production system. Compared with the prior art, the present invention has the advantages of optimizing energy utilization, achieving environmental protection and low-carbon development, improving the stability and reliability of the energy system, etc.

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 distributed energy storage output scheduling optimization method, medium and system, and belongs to the technical field of distributed energy storage output scheduling, and the method comprises the steps: firstly, collecting the electrical parameters, load parameters, renewable energy power generation parameters and energy storage equipment parameters of a transformer area, and building an energy storage grid-connected equation set which comprehensively considers the factors; and predicting a load, renewable energy output and energy storage demand curve in a period of time in the future according to historical data. Then, a multi-time-period energy storage scheduling optimization model is constructed, and objectives comprise operation cost minimization, renewable energy consumption maximization and power grid fluctuation minimization; and solving the multi-objective optimization problem by adopting an improved particle swarm optimization algorithm to obtain a plurality of candidate schemes. And finally, comprehensively evaluating the candidate schemes by using a multi-objective decision-making method, and selecting an optimal energy storage scheduling scheme. The technical problem that in the prior art, only parameters of an energy storage system are considered, and influences of power grid input electrical parameters and load parameters are ignored is solved.

The invention discloses an energy storage scheduling method and system based on a service area load and renewable energy power generation prediction, and the method carries out the prediction of the load demand of a service area based on a long short-term memory (LSTM) network, and combines the renewable energy power generation prediction with a power grid electricity price signal to dynamically adjust the charging and discharging strategy of an energy storage unit. By predicting the fluctuation of the load in the service area and the power generation condition of the renewable energy source, the optimal energy storage scheduling scheme can be automatically generated in different time periods, and the method is particularly suitable for scenes with large traffic flow fluctuation and complex power demand in the service area. According to the method, the dependence of the service area on the external power grid is effectively reduced, and the stability and economic benefits of system operation are 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 relates to a multi-power-supply power dispatching strategy optimization method and system and a storage medium, and the method comprises the following steps: carrying out the power grid aggregation and power supply cluster division processing of a multi-power-supply power dispatching system, and setting a working state constraint condition for each divided power supply; based on constraint conditions set by various power supplies, a power dispatching strategy optimization model with the highest source load matching degree of the various power supplies and the maximization of the renewable energy generating capacity as objective functions is constructed; solving the power dispatching strategy optimization model to obtain a strategy optimization result, a source load matching degree and a renewable energy power generation solving result; and finally, performing simulation checking calculation and index evaluation on a strategy optimization result. According to the strategy optimization method provided by the invention, the model construction complexity and the calculation difficulty are reduced, and the deviation between an actual optimization result and an expected result is effectively reduced.

The invention discloses a low-carbon building carbon emission optimization system, which relates to the technical field of building energy conservation, and comprises the following steps: collecting energy consumption data and environmental parameters of a building energy system, preprocessing the collected data, extracting data features, and identifying a typical mode of the building energy system; based on the typical mode of the building energy system, constructing a multi-objective optimization model including renewable energy power generation prediction, load demand prediction and carbon emission prediction, and determining constraint conditions and objective functions of the multi-objective optimization model; and generating an energy optimization scheduling strategy and a carbon capture control scheme execution optimization scheduling strategy considering carbon emission intensity according to an objective function of the multi-objective model, collecting an abnormal condition execution recovery strategy, and performing effect evaluation and strategy dynamic adjustment in real time. According to the method, economy, environmental protection and reliability of the system are taken into consideration, effective control over carbon emission is achieved through the improved multi-objective optimization algorithm, and the operation reliability and adaptability of the system are enhanced.

The invention relates to the technical field of battery intelligent charging, in particular to an intelligent charging system for a solid-state lead battery energy storage power station. The method comprises the following steps: acquiring a real-time impedance characteristic parameter set and a real-time battery operation parameter set, and determining a multi-dimensional battery state data set according to the real-time impedance characteristic parameter set and the real-time battery operation parameter set; obtaining a renewable energy power generation data set, and based on the renewable energy power generation data set and according to the multi-dimensional battery state data set, carrying out evolution analysis on solid electrolyte impedance fluctuation, and constructing a solid electrolyte impedance evolution model; determining an adaptive battery charging strategy according to the solid electrolyte impedance evolution model; and according to the self-adaptive battery charging strategy, power station charging parameters are adjusted, and a charging adjustment report is determined and output. The passivation speed of the solid electrolyte in the solid lead battery can be slowed down under the condition of intermittent fluctuation of renewable energy power generation, and the service life of the battery is prolonged.

The invention relates to the technical field of power systems, and particularly discloses a multi-energy coupling system optimal scheduling method considering grid-connected safety, and the method comprises the steps: constructing a multi-energy coupling system architecture model; the multi-energy coupling system architecture model comprises a renewable energy power generation unit, a traditional energy power generation unit, an energy storage unit, an energy conversion unit and a combined cooling heating and power generation unit; constructing a distributed energy storage optimization operation model based on the multi-energy coupling system architecture model; the constraint conditions comprise charging and discharging constraint, charge state constraint and peak clipping constraint of battery energy storage; carrying out output constraint on the energy conversion equipment; electric vehicle charging station operation constraint; new energy power generation constraint; power balance constraint; public network line transmission power constraint; constraining transmission power of an internal distribution network line; and solving the distributed energy storage optimization operation model by adopting an optimization algorithm to obtain an optimization scheduling scheme. The method has the advantage that scientific and reliable technical support is provided for efficient operation of the multi-energy coupling system.

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 photovoltaic hybrid hydrogen production system and a capacity configuration optimization method. The system is formed by sequentially connecting a renewable energy power generation unit, an electrolytic hydrogen production unit and a hydrogen energy storage unit. Wherein the power generation unit outputs electric energy by means of a photovoltaic array; aLK and PEM are used in the water electrolysis hydrogen production unit, through cooperative coupling, the adaptability of the hydrogen production process to unstable solar power generation is enhanced, and efficient and stable preparation of green hydrogen under driving of fluctuating photovoltaic electric energy is enhanced. According to the invention, the two types of electrolytic cells are combined and researched, and the two types of electrolytic cells are combined for hydrogen production to form an electrolytic hydrogen production unit so as to give full play to respective advantages. And the economy of the whole hydrogen production system is improved by determining the optimal ratio of the two components in the system. And the prepared hydrogen can be conveyed to a user side or stored in a hydrogen storage tank through a high-pressure storage technology and the like, so that cross-regional transmission and utilization of renewable energy sources are realized.

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 discloses a capacity configuration optimization method for preparing methanol by coupling wind-solar hydrogen production with biomass gasification, belongs to the field of methanol preparation, and develops a comprehensive optimization framework to evaluate the technical economic feasibility of renewable methanol production. According to the framework, renewable energy power generation, an energy storage technology, hydrogen production through water electrolysis and methanol synthesis are integrated together, so that a production system with cost effectiveness is constructed. And establishing an optimization model to determine the optimal investment and operation strategy of green methanol production. According to the model, investment and operation decisions of a plurality of system components are considered, and a series of decision variables are set. Through the solution of the optimization model, the problem of insufficient economic evaluation and comparison technology of synthesizing methanol from renewable energy sources is solved, a scientific basis is provided for a project in planning, design and decision-making stages, and the development of the field of synthesizing methanol from renewable energy sources is promoted.

The invention discloses a multi-energy collaborative complementary power grid access capability assessment method, and relates to the technical field of electric power. The multi-energy collaborative complementary power grid access capability assessment method comprises the three steps of S1 data collection and preprocessing, S2 characteristic analysis module analysis and S3 access capability comprehensive assessment, and by constructing a renewable energy power generation facility characteristic library, collecting output sample information, load sample information and the like, the output characteristics and load characteristics of different renewable energy power supplies are analyzed, and the access capability of the power grid is assessed. The transmission capability, the operation characteristics and the like of the power grid are analyzed, development potential evaluation, access capability evaluation, future output and load prediction, equipment bearing upper limit determination and power grid weak link identification are finally carried out, and development potential evaluation comprises illumination resource endowment evaluation, geographic attribute constraint evaluation and economic cost constraint evaluation. The multi-energy collaborative complementary power grid access capability assessment method has the beneficial effects of multiple functions, accurate assessment and high practicability.

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 smart power grids and renewable energy power generation, and particularly provides an optical storage and charging station optimization control method and device, and the method comprises the steps: constructing a target function which takes the maximization of the total revenue when an optical storage and charging station participates in auxiliary service as an optimization target, the objective function comprises earnings and weights of all subsystems of the optical storage and charging station; performing preliminary calculation on the target function to obtain a preliminary total income; carrying out multi-objective optimization calculation on the objective function on the basis of the initial total income, and obtaining a weight meeting an optimization objective; and determining the priority of each subsystem of the optical storage and charging station according to the weight meeting the optimization target, and controlling each subsystem of the optical storage and charging station based on the priority. Therefore, the weight meeting the optimization target is obtained through multi-target optimization calculation, then the priority among the subsystems is determined according to the weight, optimization control is carried out on the optical storage and charging station according to the priority, and the optimization target of maximizing the total revenue can be achieved.

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.

The invention discloses a multi-energy complementary distributed micro-grid energy storage coordination control method, and relates to the technical field of micro-grids, and the method comprises the steps: collecting and preprocessing data of renewable energy sources and energy storage units, monitoring and predicting the load change of a micro-grid and the fluctuation of the power generation amount of the renewable energy sources in real time, and generating a short-term charging and discharging plan; the power distribution proportion of the energy storage units is adjusted in real time through a virtual navigator algorithm, the charging and discharging rates of the energy storage units are dynamically adjusted, and the voltage and frequency between different energy storage units are synchronized after the charging and discharging rates are adjusted. Through combination of the LSTM model and the ARIMA model, the accuracy of power and load prediction is improved, the power distribution proportion and the charge and discharge rate of the energy storage unit are dynamically adjusted through the virtual navigator algorithm, stable operation of the system under the condition of complex load fluctuation is ensured, the system can adapt to real-time fluctuation of the load and the power generation amount, and the real-time performance of the system is improved. And the operation efficiency of the energy storage system and the stability of the whole microgrid are improved.

The invention discloses a self-adaptive source network load storage integrated intelligent scheduling system and method, and the system comprises a data collection module, a prediction module, a multi-objective optimization module, an optimization algorithm module, a self-adaptive parameter adjustment module, a constraint processing module, and a result output module. The future power demand is predicted through the power demand prediction model, the future renewable energy power generation amount is predicted through the renewable energy power generation prediction model, and a multi-target model with cost, benefit and greenhouse gas emission and model constraint conditions are constructed; the multi-target model is optimized through a hybrid optimization algorithm model combining a genetic algorithm and a particle swarm optimization algorithm, algorithm parameters are dynamically adjusted, a global optimal solution is searched, the optimized power generation equipment output power, the optimized energy storage equipment charging and discharging power and the optimized power grid interaction power are output, and a scheduling decision is guided; according to the method, the optimization efficiency and robustness are improved, and the adaptive capacity and the multi-target optimization capacity of a power grid system are enhanced.

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 discloses a spatio-temporal feature enhancement prediction method and device with wide applicability, and the method comprises the steps: obtaining the pure time series data of renewable energy power generation and load through setting a database, and carrying out the normalization processing of the pure time series data; constructing a spatial-temporal feature enhancer, and performing reconstruction processing on the normalized pure time sequence data to obtain enhanced feature data; designing a dynamic peak-valley weighted loss function according to the horizontal coordinate difference of the uncertain extreme fluctuation values in the sequence; training a set neural network prediction model through a lightweight neural network architecture integrated with an attention mechanism according to the dynamic peak valley weighted loss function, the training data and the test data; inputting the plurality of pure time sequence data into a set neural network prediction model for prediction processing to obtain a plurality of prediction values; and verifying the performance of the set neural network prediction model according to the set evaluation index. According to the method, prediction of the uncertain power can be accurately optimized, and reliable technical support is provided for stable operation of a power grid.

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 provides a multi-type power supply collaborative optimization method based on complementary characteristics, and relates to the technical field of power generation. Obtaining a predicted renewable power generation parameter sequence; obtaining an optimal power generation control parameter; and controlling the traditional power supply to perform power generation control in a future preset time range by adopting the optimal power generation control parameter as a collaborative optimization result. According to the invention, the technical problem that the complementary effect between a traditional power supply and a renewable power supply is not good and the stability of power grid operation is influenced because the influence of meteorological data change is not fully considered because of the dependence on a static or simple optimization model in the prior art can be solved; through calculation of a historical complementary coefficient sequence and meteorological data prediction, in combination with renewable power generation parameter prediction, deviation between a complementary coefficient and a preset complementary coefficient is reduced, power generation control optimization of a traditional power supply is realized, and the technical effects of improving power grid stability and energy utilization efficiency are achieved.