386 results about "Renewable energy consumption" patented technology

The invention relates to a power distribution system optimization method considering a space-time game under vehicle-station-network interaction, and the method employs a double-layer optimization framework integrating the space-time game and dynamic electricity price, an upper decision maker is a power distribution system operator, and the power distribution system operation cost is minimized and the renewable energy consumption rate is maximized. Generating a dynamic electricity price signal in a time-sharing and partition manner; the lower-layer main body comprises an electric vehicle user and a charging station operator, and the electric vehicle user optimizes a charging time period and a charging station and forms charging demand distribution through a non-cooperative game based on a dynamic electricity price signal and a charging service charge by taking the maximum self-charging decision utility as a target; and a charging station operator optimizes and adjusts a charging service fee decision by taking charging demand distribution as input and taking revenue maximization as a target. Compared with the prior art, the method can collaboratively optimize the economic operation of the power grid and the consumption of renewable energy sources, promotes the efficient flow and balanced distribution of resources among regions, and provides decision support for the dispatching of the power distribution network.

The invention discloses a green data center scheduling optimization method under computing power and electric power coupling, data load types of a data center comprise a transferable load, a reducible load and a rigid load, and the method comprises the following steps: constructing a double-target scheduling optimization model by taking the minimum power consumption cost of the data center and the maximum green power fitting degree as targets; the power consumption cost of the data center is used for representing the sum of expenses generated by the data center for purchasing power from a power grid in order to meet the power required for processing the data load in each time period, and the green power fitting degree is used for evaluating the matching degree between the power consumption demand of the data center and the renewable energy source supply; solving the dual-target scheduling optimization model by using a multi-target dogvessel swarm algorithm to obtain a Pareto frontier; and determining an optimal scheduling scheme based on the Pareto frontier, wherein the optimal scheduling scheme comprises a data center power consumption curve, an energy storage power station charging and discharging curve and a transferable load scheduling strategy. According to the invention, the power consumption cost of the data center can be reduced, and the renewable energy consumption capability can be improved.

The invention discloses a coordinated optimization method for a wind power photovoltaic energy storage ratio, and belongs to the field of power distribution network optimization, and the method comprises the following steps: S1, multi-dimensional data collection and preprocessing; s2, constructing a robustness uncertainty set; s3, constructing a multi-time-scale collaborative distribution model: constructing the multi-time-scale collaborative distribution model, and determining a target power distribution scheme and an energy storage life loss evaluation index of the energy storage equipment; s4, performing dynamic multi-objective optimization solution: constructing a multi-objective function, and performing solution by adopting an improved multi-objective African eagle optimization algorithm; and S5, real-time feedback and dynamic correction: monitoring a system state through real-time acquisition of data, dynamically correcting the multi-time scale collaborative distribution model based on a prediction error, and adjusting an energy storage equipment power distribution strategy to form closed-loop optimization. By adopting the coordinated optimization method for the wind power and photovoltaic energy storage ratio, the renewable energy consumption capability and the system economy are remarkably improved, and the method has great engineering application value.

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 a virtual power plant global optimization scheduling method, system and device based on cloud edge collaboration, and a storage medium, and belongs to the technical field of power system scheduling. The method comprises the following steps: based on a cloud edge coordinated regulation and control framework comprising a cloud layer, an edge layer and an end side layer, taking minimization of the total operation cost of a system as a target, comprehensively considering a power balance constraint, a main network interaction constraint, a distribution network transmission constraint, a distributed resource operation constraint, an energy storage equipment constraint and a renewable energy consumption constraint; establishing a global optimization scheduling model; edge collaborative optimization is realized by adopting an alternating direction multiplier method, a global coupling problem is decomposed into local optimization sub-problems and a cloud coordination problem of each region, and aggregation power information is sent to the cloud after the local optimization sub-problems are solved in parallel in each region; and the cloud performs global coordination optimization to generate an optimal scheduling strategy, and issues a scheduling instruction to the edge layer to control the actual operation of the distributed power supply, the energy storage equipment and the controllable load, thereby realizing the collaborative optimization scheduling of the virtual power plant. The problems that in virtual power plant large-scale distributed resource coordination optimization, calculation complexity is high, communication burden is heavy, and real-time performance and global optimality are difficult to consider at the same time are effectively solved.

The invention discloses a virtual power plant intelligent regulation and control method and system based on artificial intelligence, and belongs to the technical field of electric power system intelligent regulation and control, and the virtual power plant intelligent regulation and control method based on artificial intelligence comprises the following steps: S1, aggregating equipment side data, desensitizing to generate topological codes, and constructing time scale matrix synchronization; s2, constructing a dynamic model by equipment parameters, and mapping real-time data to output a difference map; s3, adding equipment constraints, building a multi-objective function, optimizing a strategy and performing correlation analysis; s4, a wind and light fluctuation overrun trigger RL strategy and an abnormal switching base line generate a mixed instruction; s5, locally verifying the instruction, and correcting and feeding back parameters if the prediction is out of limit; s6, generating a three-dimensional thermodynamic diagram, and displaying an association report and a historical record by AR; s7, aggregating the data to reconstruct the training set, locally fine-tuning the strategy network and performing incremental updating; the method has the beneficial effects that the regulation and control pain point of the virtual power plant is systematically solved, the operation and maintenance cost is reduced, the new energy consumption capability is improved, and the equipment out-of-limit risk is reduced.

The invention is suitable for the technical field of resource scheduling management, and provides an optimal scheduling management method and system for multi-type energy storage resources and related equipment, and the method comprises the steps: obtaining the operation state data of a power grid, the power generation data of renewable energy sources, and the state data of each type of energy storage resources; analyzing power grid stability, renewable energy consumption and economic cost benefits according to the power grid operation state data, the renewable energy power generation data and the state data of various types of energy storage resources; constructing a comprehensive evaluation model, and inputting the analysis results of the power grid stability, the renewable energy consumption and the economic cost benefit into the comprehensive evaluation model to obtain the scheduling priority of each type of energy storage resources; and the optimal scheduling scheme of the energy storage resources is adjusted based on the scheduling priorities of the various types of energy storage resources, so that the scheduling based on the various types of energy storage resources is more comprehensive and accurate.

The invention relates to a photovoltaic power grid energy storage optimization regulation and control method based on multi-scale prediction. The method comprises the following steps: A1, obtaining historical power generation data, real-time meteorological data and numerical weather forecast of a photovoltaic power station; a2, generating a multi-time-scale photovoltaic output prediction sequence; a3, establishing an energy storage dynamic model of charge and discharge efficiency, capacity attenuation and operation constraint; a4, generating an energy storage charging and discharging demand curve under different time scales; a5, constructing a multi-time scale coupled optimization model by taking power grid operation cost minimization and renewable energy consumption maximization as targets; a6, updating an energy storage scheduling instruction in a rolling manner based on latest prediction data by adopting a model prediction control framework; a7, monitoring the deviation between the actual photovoltaic output and the power grid load, and dynamically adjusting the energy storage charging and discharging power; and A8, correcting a prediction error through Kalman filtering and a closed-loop feedback mechanism. According to the invention, high-efficiency operation can be realized, and power grid cost minimization and renewable energy consumption maximization can be realized.

The invention belongs to the technical field of comprehensive energy system optimization, and discloses a comprehensive energy system optimization method and system based on power-to-gas and carbon capture equipment. The comprehensive energy system comprising two-stage power-to-gas equipment, the carbon capture equipment, electric energy storage equipment and the like is constructed, and the coupling relation and adjusting characteristics between load energy conversion are combined; a low-carbon economic dispatching model is constructed, objective functions are established by considering power purchase cost, gas purchase cost and the like, a constraint set is established by considering thermoelectric power balance, system capacity, carbon capture and the like in a comprehensive energy system, and a multi-objective evolutionary algorithm is adopted to carry out optimization solution on a plurality of objective functions. According to the technical scheme, the total operation cost and the carbon emission of the system are comprehensively considered, the low-carbon and environment-friendly characteristics of hydrogen energy are fully played, the renewable energy consumption of the system is improved, the multi-target intelligent algorithm is adopted for solving, economic benefits are considered, the method is environmentally friendly, and the total operation cost and the CO2 emission of the system are reduced.

The invention relates to the technical field of electric power control, in particular to a multi-virtual power plant collaborative scheduling method and system based on mixed games and carbon transactions. The method comprises the following steps: acquiring parameters of each virtual power plant, reading a carbon quota allocation scheme and a carbon transaction market price released by a power grid, and establishing a virtual power plant operation cost expression containing a carbon transaction cost; constructing a non-cooperative game model, and obtaining a preliminary optimal output strategy of each virtual power plant by taking minimization of the operation cost of the virtual power plant as a target; the operation state of the virtual power plant is detected, if the risk that renewable energy consumption is insufficient or the total carbon emission exceeds the standard exists, a cooperative optimization mechanism is triggered, a plurality of virtual power plants are selected to form a joint optimization group, and the joint optimization group is regarded as an independent participant to obtain an optimal output strategy again; and issuing the optimized scheduling instruction to each virtual power plant for execution according to the optimal output strategy, and participating in carbon market transaction according to the actual carbon emission and the quota difference in the settlement period.

The invention relates to a regional power distribution network multi-time scale cooperative control method based on heterogeneous resource coupling, and the method comprises the steps: constructing a cloud-edge-end hierarchical autonomous operation framework which comprises a cloud layer, an edge layer and a terminal layer, the edge layer is used for rolling correction of a day-ahead scheduling plan and generation of a control instruction, and the terminal layer is used for real-time response control; and based on a source-network-load-storage interaction model, executing a multi-time scale collaborative optimization strategy in a cloud-edge-end hierarchical autonomous operation framework, and correspondingly controlling the working state of each device in the regional power distribution network. Compared with the prior art, the method can achieve the dynamic optimization matching of distributed energy, flexible load and energy storage, improves the flexibility and intelligent level of a regional power distribution network, improves the consumption rate of renewable energy sources, reduces the operation cost, and guarantees the power supply reliability and electric energy quality.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention discloses an electric energy quality optimal compensation control method and device based on a photovoltaic converter, and the method and device achieve the optimal compensation control of the electric energy quality of the photovoltaic converter by exploring the self-regulation potential of the photovoltaic converter and carrying out the autonomous adjustment of the electric energy quality while having the renewable energy consumption capability. According to the basic principle of the control method, reactive, negative sequence and harmonic current components are detected from a compensation object, and a compensation current reference value of the photovoltaic converter is generated by combining the output condition of the current photovoltaic converter and through instruction optimization of electric energy quality treatment current; therefore, the purpose of maximally utilizing the capacity of the photovoltaic converter to treat the electric energy quality is achieved.

The invention provides a consumption reduction method and device for a wind and light energy storage complementary thermal power plant system, and the method comprises the steps: obtaining the wind speed, illumination intensity, energy storage charge state, auxiliary power load curve, power grid dynamic electricity price signal and thermal power generating unit auxiliary machine operation parameters of the thermal power plant system, and taking the parameters as a data set; based on a weather prediction model, a load prediction model and an electricity price fluctuation model, generating a wind power generation output prediction value, a photovoltaic power generation output prediction value, a load demand prediction value and an electricity price interval prediction value in a future preset time period according to the data set, and taking the prediction values as prediction results; by taking minimization of plant power cost and maximization of renewable energy consumption as targets, generating an energy storage charging and discharging instruction, a wind power generation and photovoltaic power generation grid-connected priority sequence and a thermal power auxiliary engine regulation and control strategy according to the data set and the prediction result to serve as control instructions; control instructions are executed through control equipment in the wind-solar energy storage complementary thermal power plant system, and energy conservation and consumption reduction are achieved by integrating wind energy, solar energy and an energy storage system.

The invention provides a power distribution network regulation and control method based on a multi-objective optimization dynamic aggregation strategy, and relates to the technical field of power system automation and control, and the method comprises the steps: collecting the voltage, current, power, active and reactive power, and harmonic operation data of each key node of a power distribution network in real time, and carrying out the preprocessing of the operation data, so as to obtain the processed data; according to the processed data, constructing an initial optimization model including a plurality of optimization targets; and according to the running state of the current power distribution network and each optimization target, adjusting and determining the weight and priority of each optimization target, so as to obtain a multi-target optimization model subjected to weight optimization. According to the invention, by dynamically constructing the weight optimized multi-target model, generating and executing the accurate regulation and control instruction, and performing real-time evaluation and closed-loop adjustment, the voltage quality of the power distribution network is comprehensively improved, the network loss is reduced, the consumption of renewable energy sources is promoted, the power supply reliability is enhanced, and the carbon emission is effectively reduced.

The invention relates to the technical field of energy planning, and particularly discloses a source-storage-transmission combined planning method considering carbon emission responsibility allocation, and the method comprises the steps: building a carbon emission responsibility double-layer allocation strategy; based on a carbon emission responsibility double-layer allocation strategy, establishing a source-storage-transmission combined planning model considering carbon emission responsibility allocation; and solving the source-storage-transmission combined planning model considering the carbon responsibility allocation to obtain a system source-storage-network optimal combined planning scheme. According to the method, carbon emission responsibilities are scientifically shared according to the principle of'who causes, who undertakes, who governs and who benefits', the effect of the carbon emission responsibilities in the planning process is effectively considered, an energy storage system is additionally arranged, the system flexibility is improved, and renewable energy consumption is promoted.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant resource aggregation method for a dynamic peak regulation demand of a power grid, which comprises the following steps of: acquiring multi-source data, preprocessing the multi-source data, and then verifying the data quality; aiming at different resource types including temperature control load, energy storage and charging piles, respectively constructing refined models, setting constraint conditions of the refined models, and solving a resource operation feasible region by applying multi-dimensional space mapping and linear programming; establishing a target function and a constraint condition by taking the lowest cost and the minimum energy abandoning as targets; solving a target function by using a dung beetle optimization algorithm, and screening an optimal resource aggregation scheme by using an entropy weight method; and based on the optimal resource aggregation scheme, dividing peak, valley and normal periods, constructing a four-dimensional peak regulation index, determining a weight by using an analytic hierarchy process, and screening an optimal resource combination in each period to execute scheduling. The method can guarantee the accuracy and high efficiency of the peak regulation demand response of the power grid, and assists in improving the stability of the power system and the renewable energy consumption level.

A green evidence-carbon-electricity market coupling modeling analysis method based on system dynamics relates to the field of electricity markets, and comprises the following steps: determining a system boundary, dividing a green evidence-carbon-electricity three-market coupling system into a plurality of sub-modules, and providing a reasonable hypothesis; key elements in each sub-module are extracted, the causal relationship between the key elements is analyzed, and a causal loop diagram of the coupling system is constructed; constructing a stock flow diagram according to the causal loop diagram, establishing an equation, and describing a function relationship between variables; modeling is carried out by utilizing system dynamics simulation software Vensim, parameters are adjusted, and different scenes are set for simulation analysis. According to the method, the interaction among the green certificate market, the carbon market and the electricity market is comprehensively considered, the renewable energy consumption responsibility weight index is concerned, and the influence of the carbon quota tightening coefficient, the carbon quota auction proportion and the renewable energy consumption responsibility weight change on the whole system dynamic state is deeply discussed by establishing the simulation case.

The invention provides a wind power hydrogen production energy scheduling method and a wind power hydrogen production energy scheduling system. The wind power hydrogen production energy scheduling method comprises the following steps: predicting the wind power, the hydrogen load and the electricity price of a wind power plant in a wind power hydrogen production system in a future time period; and based on the predicted wind power, the predicted hydrogen consumption load and the predicted electricity price, determining energy scheduling among a plurality of components in the wind power hydrogen production system, so that at least one of the net income and the renewable energy consumption rate of the wind power hydrogen production system within the predetermined time period is maximum.

The invention discloses a source-grid-load-storage integrated coordinated management and control system for an intelligent micro-grid, and relates to the technical field of intelligent micro-grids and comprehensive energy regulation and control. The system comprises the following components: a multi-energy-flow data acquisition unit, a multi-energy-flow coupling modeling and optimizing unit, a multi-energy-flow gradient utilization execution unit, a mode self-adaptive switching unit and a main control unit, according to the invention, through a multi-energy flow coupling modeling and optimization unit, an electric-thermal-gas multi-energy flow coupling model comprising a heat supply network transmission loss calculation sub-model is constructed, a renewable energy consumption constraint sub-model is additionally arranged, and an improved hybrid particle swarm optimization algorithm is adopted to carry out dynamic optimization solution, so that an optimal scheduling strategy is generated; according to the strategy, the consumption rate of renewable energy sources is increased, electricity-heat gradient utilization and efficient configuration are achieved through a heat energy distribution priority regulation and control mechanism and efficiency optimization control of the waste heat recovery module, and dependence of heat loads on electric energy is remarkably reduced.

The invention provides an energy coordination control system and scheduling method for multi-energy complementary new energy storage, and belongs to the technical field of new energy storage and energy technology management. The data acquisition and processing module is connected with a current and voltage sensor, a power transmitter, a meteorological station sensor and a battery management system sensor, and is used for acquiring voltage, current, power, state of charge and environmental data of photovoltaic power, wind power, energy storage and load through the sensors; and sliding window filtering and wavelet de-noising processing are carried out through the embedded processor. The method has the beneficial effects that the uncertainty of wind and light output is effectively processed by establishing a layered distributed optimization architecture and adopting a method of combining multi-objective optimization and robust optimization, and the consumption rate of renewable energy sources can be remarkably improved, the net load fluctuation can be reduced and the energy consumption can be reduced on the premise of meeting various system operation constraints. Therefore, dependence on conventional fossil energy is reduced, and the economic operation level and the environmental protection benefit of the whole energy system are improved.

The invention relates to the technical field of hydrogen load capacity configuration, in particular to a multi-scene hydrogen load comprehensive energy system double-layer capacity optimal configuration method, which comprises the following steps of: firstly, constructing a multi-scene model containing strong, weak, discontinuous and non-hydrogen loads based on the time sequence characteristics of hydrogen loads in an industrial park; then a comprehensive energy system mathematical model covering wind and light power generation, hydrogen energy conversion, multi-type energy storage and cogeneration units is established; then, constructing a double-layer capacity optimization configuration model of which the upper layer takes the whole life cycle cost minimization and the lower layer takes the operation cost minimization and the system stability optimization as targets; and finally, solving a Pareto leading edge by adopting a non-dominated sorting genetic algorithm with an elitist strategy, making a decision by utilizing an approximate ideal solution sorting method, and screening out a capacity configuration scheme with optimal comprehensive performance. The problem that a traditional configuration scheme is difficult to adapt to multi-scene hydrogen load fluctuation is effectively solved, and the economical efficiency, the stability and the renewable energy consumption capacity of the system are remarkably improved.

The invention provides a renewable energy driven iron-based zero-carbon-emission energy storage power generation system based on iron-oxygen circulation, and belongs to the technical field of renewable energy utilization and power generation, and the renewable energy driven iron-based zero-carbon-emission energy storage power generation system comprises a renewable energy consumption module, an alkaline water electrolysis hydrogen production module, a hydrogen reduction reaction module, a combustion energy supply module and a steam Rankine cycle module; the renewable energy consumption module is used for acquiring and converting renewable energy into electric energy; the alkaline water electrolysis hydrogen production module is used for producing hydrogen by using electric energy; the hydrogen reduction reaction module utilizes hydrogen to carry out reduction reaction to store energy; and the steam Rankine cycle module is used for converting energy released by combustion into electric energy. Based on iron-oxygen circulation and through cooperative operation of the renewable energy consumption module, the alkaline water electrolysis hydrogen production module, the hydrogen reduction reaction module, the combustion energy supply module and the steam Rankine cycle module, efficient consumption of renewable energy, effective storage and conversion of energy and zero-carbon-emission green electricity production are achieved.

The invention provides a wind, light and water storage double-layer robust optimization scheduling strategy construction method and system, and relates to the technical field of energy management, and the method comprises the steps: firstly obtaining multi-source data of a wind power plant, a photovoltaic power station and a cascade hydropower station; performing feature extraction and integrated prediction based on the multi-source data to generate a wind-solar power prediction result; combining hydrodynamic coupling modeling analysis to obtain a hydropower station power generation capacity and adjustable dynamic water volume sequence; the method comprises the following steps: constructing a multi-layer energy coupling graph and performing spectral domain structure optimization to form a power coupling network representing system space-time correlation and ecological sensitivity; and finally, adopting double-layer distributed robust optimization, synchronously optimizing the economic target and the ecological risk constraint, and generating a collaborative scheduling strategy. According to the method, the time sequence matching problem between the wind and light volatility and the hydroelectric time lag is effectively solved, the renewable energy consumption capability and the water resource utilization efficiency are improved, and the ecological operation risk is reduced.

The invention discloses a multi-energy load prediction method based on feature screening and multi-model fusion, and belongs to the field of electric power and comprehensive energy load prediction. The invention provides a three-stage hybrid learning prediction framework. In the first stage, dynamic feature screening is achieved through a recursive feature elimination cross validation method based on expert knowledge constraints, key meteorological elements and multi-energy load time sequence features are reserved, and redundant feature interference is reduced. In the second stage, a multi-task long-short-term memory network is constructed, and coupling relation modeling and collaborative prediction of cold, heat and electricity multi-energy loads are achieved through sharing time sequence characteristic representation and a task exclusive output structure. And in the third stage, a random forest is adopted to carry out nonlinear correction on the residual error of the sub-model, so that the precision and robustness of prediction in sudden disturbance and local non-stationary scenes are improved, the prediction error is effectively reduced, and the stability of multi-energy load prediction is improved. And reliable support is provided for optimized operation, scheduling decision and renewable energy consumption of the park integrated energy system.

The invention discloses a generator set multi-target maintenance optimization scheduling method considering renewable energy access. The method comprises the following steps: establishing an uncertain model of renewable energy output and load; considering an economy target, a reliability target and a renewable energy consumption target, selecting a minimum total cost, a minimum standby capacity sum of squares and a minimum wind curtailment and light curtailment amount for comprehensive evaluation, and constructing a multi-target unit maintenance optimization model; converting a system power balance constraint and a reserve capacity constraint into a deterministic constraint with clear equivalence; selecting a membership function of each target based on a fuzzy theory, and converting the multi-target unit maintenance optimization model into a single-target unit maintenance optimization model; and solving the single-target unit maintenance optimization model converted based on the fuzzy theory by using an improved ant colony algorithm, and obtaining the maintenance and operation conditions of each unit while obtaining the maximum satisfaction degree. The method is applied to a power grid to arrange maintenance scheduling of the generator set, and economic benefits, reliability and renewable energy consumption are improved.

The invention relates to a multi-time scale scene analysis-based micro-grid day-ahead and intra-day optimal scheduling method, which belongs to the field of micro-grid scheduling, and is characterized in that a variational mode decomposition (VMD)-long short-term memory network (LSTM) multi-scale prediction framework is constructed, ultra-short-term precision is improved through variable mode decomposition and frequency division prediction, a Canopy-spectral clustering-K-means hybrid algorithm is designed, and the optimal scheduling of a micro-grid is realized. A typical scene is generated based on Latin hypercube sampling (LHS), the scene coverage capability is enhanced, a day-ahead and intra-day two-stage optimization model is finally constructed, a high-dimensional problem is rapidly solved by adopting a mixed integer programming algorithm, and theoretical support is provided for high-proportion renewable energy consumption and micro-grid refined scheduling.

The invention relates to the technical field of electric energy management, in particular to a high-energy-consumption load side resource aggregation pricing calculation method for source-load collaborative optimization clearing. Comprising the following steps: proposing a cooperative operation model of renewable energy sources and industrial loads in a county power grid; and proposing an industrial load day-ahead dynamic time-sharing pricing model oriented to renewable energy consumption. The invention provides a renewable energy source and industrial load coordinated operation method, a local green electricity market clearing model is established, and green electricity transaction between renewable energy sources and industrial loads is realized. By constructing a clearing model of a local green electricity market, social welfare maximization can be realized through green electricity transaction between renewable energy sources and industrial loads; the designed dynamic green power pricing method can effectively maintain the balance of supply and demand of green power and guide the industrial load to optimize the green power demand.

The invention provides an off-grid synthetic ammonia park electrolytic bath column level-energy storage-hydrogen storage collaborative optimization method based on rolling optimization, and relates to the technical field of energy storage and hydrogen storage collaboration. The method comprises the following steps: constructing an off-grid system architecture comprising wind-solar power generation, a plurality of rows of electrolytic cells, energy and hydrogen storage equipment and a synthesis ammonia device; a multi-device collaborative optimization strategy based on an eight-hour rolling time domain is designed, the system operation state is divided into high, medium and low working conditions through a working condition recognition mechanism, a current device operation scheme is actively adjusted according to wind and light power information of a next scheduling period, and collaborative regulation and control of energy storage and hydrogen storage are achieved. Compared with a traditional scheduling strategy, the collaborative optimization strategy provided by the invention improves the consumption rate of renewable energy sources of the system, reduces abandoned wind and light, improves the yield of synthetic ammonia, and verifies the effectiveness of the strategy.

The invention discloses a dynamic load adjustment-liquid nitrogen cold storage integrated high-temperature superconducting power grid capacity expansion system and method. The system comprises a data preprocessing system, a dynamic load adjustment device, a liquid nitrogen cold storage device, a state monitoring device, a high-temperature superconducting cable capacity expansion device and a communication coordination system. The method comprises the steps of generating a standardized feature matrix through normalization processing; multi-time-scale load prediction is realized through an LSTM (Long Short-Term Memory) network, a collaborative instruction set is generated by adopting an optimal scheduling engine of mixed integer linear programming, and adaptive closed-loop control based on a fuzzy PID (Proportion Integration Differentiation) algorithm is realized; a multi-physics field coupling optimization model of a real-time cold energy distribution diagram is adopted to realize power grid capacity improvement; and the time sequence consistency of the whole system is coordinated and circulation is formed. Zero-resistance power transmission is realized in a liquid nitrogen temperature region by adopting the high-temperature superconducting cable, the loss is reduced to 0.5% or below, and the transmission capacity is improved by 3-5 times; the load demand is predicted in combination with the AI algorithm, and the renewable energy consumption rate is increased to 95%.