328 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 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 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 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 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 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.

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 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 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 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%.

The invention discloses an island micro-grid multi-objective optimization scheduling method based on a pond goose optimization algorithm, and the method comprises the steps: dividing a power supply into a basic load power supply and a frequency modulation power supply through building a micro-grid model which comprises a renewable energy output model, a demand response model and a constraint condition, a basic load power supply scheduling scheme is optimized by adopting a gooseneck optimization algorithm to minimize the average operation cost of a sample, and a frequency response strategy of a frequency modulation power supply is optimized by utilizing a long-short term memory network to predict power distribution based on historical power shortage data, so that multi-target optimization scheduling is realized. And the operation efficiency, the stability and the renewable energy consumption capability of the island micro-grid are improved.

The invention designs a wind power prediction method based on improved time sequence fusion Transform (ITFT). According to the method, a Mama module is adopted to replace a traditional LSTM encoder-decoder structure, so that the long sequence modeling capability is remarkably improved; designing a wind speed prediction network (WFN) to generate future wind speed prediction as auxiliary input of the decoder; the improved EHO algorithm is applied to carry out hyper-parameter optimization, and chaos initialization, a fitness-distance balance strategy and a hybrid variation mechanism are fused. According to the method, the technical bottlenecks of an existing wind power prediction method in the aspects of precision, efficiency and interpretability are solved. Characteristic importance quantitative analysis is realized through a variable selection network, and a credible decision basis is provided for power grid dispatching. The method is suitable for wind power plant short-term power prediction, and the renewable energy consumption capability can be remarkably improved.

The invention relates to an IES low-carbon scheduling method and device considering refined utilization of hydrogen and ammonia and a medium, and the method comprises the following steps: constructing a comprehensive energy system model considering refined utilization of hydrogen and ammonia, which comprises a hydrogen energy refined utilization sub-model and an ammonia energy refined utilization sub-model; introducing a green hydrogen energy certificate based on a renewable energy source forestation system, and establishing a green hydrogen certificate transaction model; based on the integrated energy system model and the green hydrogen certificate transaction model, considering the uncertainty of renewable energy sources, and constructing a two-stage robust low-carbon optimization scheduling model of the integrated energy system by taking the minimum total operation cost as a target; and solving the two-stage robust low-carbon optimal scheduling model of the integrated energy system, outputting a low-carbon scheduling scheme, and completing a low-carbon scheduling process. Compared with the prior art, the system has the advantages that the renewable energy consumption level is improved, and low-carbon, economical and flexible operation of the system is realized.

The invention provides an energy complex flexible dynamic aggregation method and system based on reinforcement learning, and the method comprises the steps: collecting the real-time operation parameters of electric, gas, heat and multi-energy coupling equipment in an energy complex through an industrial Ethernet protocol, and constructing a continuous time dynamic system model through employing a Shenchang differential equation process modeling technology; and the nonlinear dynamic characteristics of various energy resources are accurately described. Modeling an aggregation process into a Markov decision process, and training and optimizing by adopting an improved twin delay depth deterministic strategy gradient algorithm to generate a reinforcement learning aggregation strategy model. The system realizes self-adaptive dynamic optimization through an online learning updating mechanism, and the renewable energy consumption rate and the system operation efficiency are remarkably improved.

The invention discloses a comprehensive optimization method of a power grid, which relates to the technical field of power grid optimization, and comprises the following steps of: constructing a multi-dimensional data fusion layer, integrating data such as power grid real-time measurement, new energy prediction, load response and electric power science and technology information, establishing a'source-grid-load-storage 'collaborative optimization model, and introducing multi-target constraint. An improved interpretable reinforcement learning algorithm is adopted for solving, and a dynamic checking and iterative optimization mechanism is established. The comprehensive optimization method of the power supply power grid is suitable for real-time scheduling and medium and long term planning scenes of a complex smart power grid, can reduce the line loss rate of the power grid, improve the consumption rate of renewable energy sources and improve the decision interpretation degree, is obviously superior to an existing optimization method, provides powerful technical support for construction of a novel power system, and has good application prospects. The technical problems of insufficient collaboration, low decision transparency, weak anti-interference capability and the like of power grid optimization in a high-proportion renewable energy access scene are effectively solved.

The invention discloses a park energy double-layer optimization scheduling method considering electric vehicles and demand response, and the method comprises the following steps: constructing a park integrated energy system which comprises an energy supply side, an energy conversion side and a demand side; a double-layer optimization model is established, the upper layer is a multi-target optimization model with the target of minimizing the total cost of the system, minimizing the carbon emission and maximizing the renewable energy consumption rate, and the lower layer is a single-target optimization model with the target of minimizing the charging cost of the electric vehicle user and the load fluctuation of the power grid; based on the Pareto theory, designing a multi-objective litsea coreana optimization algorithm to solve an upper-layer multi-objective model; introducing chaos initialization, adaptive step length adjustment and a Gaussian disturbance strategy improved litsea coreana optimization algorithm to solve a lower-layer single-target model; and through a vehicle network interaction and demand response cooperation mechanism, real-time scheduling is executed, a day-ahead plan is fed back and corrected, and finally an optimal park energy optimization scheduling strategy is solved.

The invention relates to the technical field of electric power, and discloses an electric power dispatching management system, and the system comprises a data collection module which is used for obtaining real-time data, and the real-time data comprises one or more of wind-solar power prediction data, power generation source information, load demand information and power grid power generation equipment operation parameters; the preliminary scheduling module is used for selecting a matched scheduling model from a model library according to the real-time data and determining a target scheduling strategy corresponding to an optimal solution of the scheduling model; the control module is used for issuing a scheduling strategy to power equipment, and the power equipment comprises one or more of distributed photovoltaic, an electric vehicle charging pile and a user energy storage end; and the real-time optimization module is used for increasing and adjusting constraint conditions in the scheduling model in real time according to the influence factors, and adjusting a target scheduling strategy according to the constraint conditions. According to the system, the renewable energy consumption capability of the power grid is remarkably improved, and the adaptability of the power grid to the fluctuation and complex load requirements is enhanced.

The invention discloses a virtual power plant source load interaction optimization scheduling model based on low-carbon response and a solving algorithm, and belongs to the technical field of power system optimization scheduling. A low-carbon scheduling framework containing a distributed power supply, energy storage, a flexible load and a carbon transaction mechanism is constructed, the carbon emission intensity of each link is quantified to form a carbon flow scheduling signal, and a dynamic carbon emission factor and energy cost are coupled. A multi-objective optimization model is established, a complex function is processed by piecewise linearization, and a hybrid algorithm of an improved genetic algorithm and a commercial solver is designed to improve the solving efficiency. The prediction error is dynamically corrected through a'prediction-optimization-feedback 'closed loop, and the strategy is adjusted. According to the scheme, low-carbon and economic collaborative optimization is realized, renewable energy consumption and system stability are enhanced, user satisfaction and real-time scheduling are considered, and a solution is provided for low-carbon intelligent operation of the power distribution network.

The invention relates to the technical field of optimal dispatching of a power system, in particular to a day-ahead power generation plan compilation method for wind-solar-thermal-storage collaborative optimization, which comprises the following steps of: establishing a wind-solar-thermal-storage collaborative optimization model on the basis of minimum start-stop time of a unit, start-stop cost constraint, wind-solar unit maintenance time period and output coupling constraint and spinning reserve capacity constraint; constructing a unit commitment optimization model taking the minimization of the total operation cost of the system as a target function; based on a unit combination optimization model, a direct current power flow model constraint, an energy storage dynamic characteristic model constraint, a transformer load rate safety threshold constraint and a segmented energy abandoning punishment mechanism constraint, an economic dispatching and safety verification model with wind and light absorption rate maximization as a priority target is constructed; reducing the constraint dimension of the direct current power flow model based on a hierarchical solving strategy and a key transmission section recognition algorithm; and iteratively solving a unit commitment problem and an economic dispatching problem, and analyzing and correcting an out-of-limit risk based on sensitivity. According to the invention, the economical efficiency, the safety and the renewable energy consumption capability of the power system are improved.

The invention relates to the technical field of power system frequency modulation, and particularly provides a super-capacity combined hydrogen fuel cell coupling thermal power frequency modulation system and method. The system comprises a multi-energy-flow collaborative core assembly which comprises a super capacitor energy storage unit, a hydrogen fuel cell RSOC unit and a thermal power generating unit collaborative module; the three-dimensional coupling collaborative link comprises electrical coupling, thermal coupling and control coupling; the system solves the problems of delayed response of traditional thermal power frequency modulation and insufficient adaptability of a single energy storage technology, and realizes collaborative optimization of stable control of power grid frequency and efficient consumption of renewable energy sources.

The invention discloses a flexible interconnection configuration method and system for a power distribution network frame, a medium and equipment, and belongs to the technical field of power distribution network planning of an electric power system, and the method comprises the steps: obtaining an equipment parameter, a source load parameter and a network topology parameter of a current power distribution network; based on the equipment parameters, the source load parameters and the network topology parameters, taking the joint planning of SOP, interconnection switches and lines as an optimization object, taking the lowest comprehensive equiannual value cost of an investment scheme as a target, constructing a target function, and combining a physical structure constraint, an equipment operation constraint, an electrical performance constraint and a network reconstruction constraint to construct a planning model; and iteratively solving the planning model, and outputting a configuration scheme for power distribution network reconstruction. Therefore, by implementing the method, the problems of high investment cost, low operation efficiency and difficulty in realizing efficient consumption of new energy and economic operation of the system under resource constraints caused by lack of cooperation of equipment and line planning in a power distribution network planning configuration method in the prior art can be solved.