1222 results about "Light storage" patented technology

The invention relates to the technical field of data processing, in particular to a building light storage and charging integrated energy management system, which is used for solving the problems of data processing delay and resource coordination mismatch caused by insufficient multi-source heterogeneous data fusion in photovoltaic power generation, energy storage and grid connection links. The system comprises a data acquisition module, a protocol conversion module, a data fusion module, a scheduling optimization module, an instruction generation module and a feedback module. The protocol conversion module carries out Modbus, CAN bus and IEC 61850 protocol analysis on the photovoltaic output time sequence data, the energy storage charging and discharging state data and the power grid dispatching instruction to generate standardized data streams. And the safety verification module intercepts the overrun instruction and triggers an amplitude limiting strategy. Through protocol unification, dynamic fusion and closed-loop control, the system realizes improvement of photovoltaic consumption rate, reduction of energy storage circulation loss and optimization of power grid frequency modulation deviation, and significantly improves the efficiency and economy of light storage and charging cooperative operation.

The invention provides an optical energy storage regulation and control method and system based on multi-time scale rolling optimization in the technical field of energy storage regulation and control, and the method comprises the steps: S1, inputting a 24-hour electricity price prediction curve, a 24-hour photovoltaic output prediction curve, a 24-hour load prediction curve and an initial SOC into a day-ahead prediction model, a 24-hour SOC reference trajectory and a standard charging and discharging plan are obtained; s2, collecting feedback data, and inputting the 24-hour SOC reference trajectory, the standard charging and discharging plan and the feedback data into the intra-day prediction model to obtain a corrected charging and discharging plan; and S3, setting an SOC dynamic limit value, collecting a real-time electricity price and real-time photovoltaic output, and inputting the corrected charging and discharging plan, the SOC dynamic limit value, the real-time electricity price, the real-time photovoltaic output and the actual SOC into the real-time prediction model to obtain a charging and discharging instruction and frequency modulation output power. The method has the advantages that the precision, economical efficiency and power grid adaptability of energy storage regulation and control are greatly improved, and the attenuation speed of the service life of the battery is greatly delayed.

The invention discloses a light storage and charging energy allocation system based on data driving and a control method, and relates to the technical field of light storage and charging energy allocation, and the method comprises the following steps: carrying out the real-time collection of the real-time power generation amount of a photovoltaic power generation system, the current power state of an energy storage battery, and the power consumption data of a load; and cleaning, normalization processing and time serialization are carried out on the collected data, so that the accuracy and continuity of the input data are ensured. According to the invention, by monitoring the power consumption data of the load in real time, combining with the long-term energy prediction model and dynamically adjusting the energy storage and discharge strategy, excessive discharge in a non-peak period is avoided, sufficient energy storage in a peak period is ensured, the power grid dependence is reduced, and the power consumption cost is reduced. Through load sudden increase detection and an adaptive scheduling algorithm, the system can quickly cope with sudden load changes, preferentially guarantee power supply of key loads, flexibly adjust a discharge mode, improve endurance time and system stability, and maximize green energy utilization efficiency.

The invention discloses an optical storage direct-flexible system power scheduling method based on market model prediction response, which comprises the following steps: determining the composition of an optical storage direct-flexible system, and collecting operation state data and external environment data; constructing a hybrid prediction model, and predicting the market price and the uncertainty interval of the market price in the scheduling period; establishing a power scheduling optimization model, and solving to obtain an optimal power scheduling strategy; according to the optimal power scheduling strategy, a control instruction is issued to each control unit of the optical storage direct flexible system, and cooperative adjustment is carried out through a bidirectional coupling feedback mechanism of voltage and power; the market price and the system operation state are monitored in real time, and when the market price deviation exceeds a price deviation threshold value, scheduling strategy online correction is triggered. According to the robust optimization architecture, traditional power scheduling is converted into a confrontation game structure between the system and the market, and the resistance of the system to extreme price situations is improved while economic benefits are guaranteed.

The invention discloses an operation collaborative optimization method for an optical storage direct current flexible interaction system. Comprising the steps of collecting operation data such as photovoltaic output, an energy storage state, household load power and direct current bus transmission power, fusing power market price information, and constructing a multi-dimensional time series data set; then, predicting an adjustable load capacity interval of the system based on a coupled physical constraint neural network model embedded with DC bus power balance, voltage constraint and equipment operation limitation; further constructing a state-action space, solving a Pareto frontier by adopting a multi-objective optimization algorithm, and generating a light storage and home load collaborative scheduling strategy set; then combining the real-time operation state and the prediction deviation information, applying a voltage-power droop control mechanism to carry out strategy decoupling, and generating an energy storage power correction amount and a flexible load priority control instruction; and finally, a control instruction is issued to the optical storage direct flexible system, so that collaborative optimization operation with consideration of economical efficiency, safety and comfort of the system is realized.

The invention belongs to the field of microgrid resource capacity optimization. The invention provides a power distribution network wind and light storage capacity optimization method considering multi-microgrid energy storage cooperation. The method comprises the following steps: step 1, establishing a wind and light combined operation power information data set considering spatial correlation during multi-microgrid source load fluctuation; and step 2, multi-microgrid wind and light storage capacity configuration optimization is realized by using a reinforcement learning algorithm. And step 3, complementing the wind-light fluctuation scene with few samples by using a transfer learning algorithm. Based on deep fusion space-time correlation modeling, multi-agent reinforcement learning and cross-domain transfer learning, a multi-microgrid energy storage collaborative optimization framework with dynamic adaptive capacity is provided. According to the method, the deep association rule of the multi-dimensional operation data of the micro-grid group can be analyzed, global optimal capacity configuration is realized through a coevolution mechanism of an intelligent algorithm, and a brand new solution is provided for solving the problem of power distribution network optimization under high-proportion new energy access.

The invention discloses a wind and light storage adjustment inertia and frequency coordination control method, and belongs to the technical field of new energy grid-connected power generation. The method is used for inertia support and frequency cooperative control of a new energy power station containing wind power generation, photovoltaic power generation, energy storage and a phase modifier, and comprises the steps of system minimum inertia demand evaluation, wind and light storage and adjustment inertia time constant evaluation, calculation of minimum inertia support time needed by the system and a wind and light storage and adjustment inertia distribution strategy. According to the wind and light storage and adjustment inertia and frequency coordination control method, the inertia supporting capacity of a new energy grid-connected system is effectively improved, wind, storage, photovoltaic and phase modifier inertia supporting tasks are effectively distributed, and the system frequency safety setting requirement is flexibly met. By calculating the minimum inertial supporting time, inertial supporting and exiting are carried out in time, power back-filling in the frequency recovery stage is avoided, and the frequency modulation burden of the system is relieved.

The invention relates to a micro-grid multi-objective optimization scheduling method considering the service life of an energy storage battery, and belongs to the field of active power distribution network operation analysis. The invention relates to a micro-grid multi-objective optimization scheduling method considering the service life of an energy storage battery. The method comprises the following steps: establishing an energy storage system refined operation model in a wind-light storage micro-grid; on the premise of maximum income and maximum new energy consumption, the method has the beneficial effects that the new energy power output fluctuation is responded in time, so that the charge and discharge control of the energy storage system is efficient and accurate, and the purposes of improving the energy storage utilization efficiency, the charge and discharge control accuracy and the power output stability are achieved; according to the improved multi-objective grey wolf optimization algorithm, an initial population strategy, a population individual iteration updating strategy and a multi-objective optimal solution solving mechanism are improved, and the convergence speed, the solution set diversity and the calculation efficiency of the multi-objective grey wolf optimization algorithm are improved through multi-strategy cooperation; the problems of excessive uncertainty and single target of the existing wind and light storage micro-grid are effectively solved.

The invention relates to the technical field of micro-grid dispatching, and particularly provides an intelligent dispatching method and system for a photovoltaic-storage-diesel micro-grid, and the method comprises the steps: obtaining the operation data of an energy storage battery, the operation data of a diesel generator, and the global operation data of the micro-grid; evaluating the power response capability of the energy storage battery according to the operation data of the energy storage battery, and evaluating the quick response capability of the diesel generator according to the operation data of the diesel generator; analyzing the stability risk level of the micro-grid according to the global operation data, the power response capability and the quick response capability of the micro-grid; adjusting scheduling optimization target emphasis according to the stability risk level; emphatically acquiring an energy storage battery charging and discharging instruction, a diesel generator starting and stopping instruction and an output instruction according to the adjusted scheduling optimization target, and executing all the acquired instructions; according to the method, the problem that the system stability is sacrificed due to blind pursuit of cost minimization in a traditional strategy can be avoided.

The invention provides a photovoltaic output prediction method and a distributed optical storage transformer area active / reactive support capability optimization method, and belongs to the technical field of power system automation. Comprising the following steps: constructing a prediction model based on meteorological information, extracting multi-scale features of meteorological data, reducing dimensions through kernel principal component analysis, predicting photovoltaic power through a long-short-term memory network, quantifying the uncertainty of the photovoltaic power, and forming a prediction interval. Based on a photovoltaic power prediction result, a robust nonlinear programming model is established, the sum of absolute values of active / reactive power transmitted to a main power grid by a light storage area is maximized as a target, and linear and nonlinear constraints such as energy storage operation, power balance, equipment capacity and power factors are considered at the same time. The robustness under the photovoltaic output uncertainty is ensured through a budget uncertainty set; the model is solved by adopting a sequential quadratic programming algorithm. According to the method, the photovoltaic power prediction precision is effectively improved, and organic combination of high-precision prediction and robust nonlinear optimization is realized.

The invention relates to the technical field of electric power operation and maintenance, in particular to a distributed optical storage unit cooperative scheduling control method and system, the method is applied to a distributed optical storage system comprising a plurality of optical storage units, each optical storage unit comprises a photovoltaic unit, an energy storage unit and a load unit, and is configured with a local unit controller. Comprising the following steps: collecting real-time operation data of an optical storage unit, and extracting local collaborative scheduling characteristics and a local DC bus voltage instantaneous fluctuation ratio from the real-time operation data; each unit controller inputs the corresponding local cooperative scheduling feature into a preset local scheduling strategy generation model, and outputs an initial scheduling action instruction set; and performing global voltage stability evaluation on the distributed optical storage system based on the local direct current bus voltage instantaneous fluctuation rates corresponding to all the optical storage units, and calculating to obtain a system voltage fluctuation index. The distributed local control capability and the global collaborative demand are organically fused, and the global voltage stability and the collaborative scheduling consistency are ensured.

The invention discloses a wind and light storage coordination control method and system for a virtual synchronous machine, and the method comprises the steps: building a wind power plant-energy storage power station double-layer response mechanism at a first stage, and employing a multi-time scale coordination control strategy for a second-stage energy storage system; and in combination with the virtual synchronous control characteristics of the optical storage power station, establishing a multi-scene optimization model of a wind and light storage multi-source collaborative double-layer coupling mechanism, and realizing dynamic optimization of output of each unit among inertia support, frequency modulation capacity and energy storage health state. According to the method, wind power plant rotor kinetic energy release combined overspeed load shedding control, a fuzzy control means based on an energy storage charge state and a VSG control means based on a virtual inertia and damping coefficient self-adaption photovoltaic system are considered, then wind and light energy storage frequency modulation coordination is optimized, and the system inertia safety is effectively improved on various working conditions and different time scales.

The invention discloses an optical storage layered collaborative optimization control method coupling photovoltaic priority absorption and time-of-use electricity price, and relates to the technical field of new energy power system optimization control. The method comprises the following steps: taking a photovoltaic generating capacity prediction value, a load electricity consumption prediction value and peak and valley electricity price information as input data; carrying out rolling optimization by utilizing a model prediction control framework, and generating a light storage plan table; and calculating a photovoltaic output regulation value and energy storage charging and discharging power based on the generated light storage plan table in combination with the photovoltaic power generation power and the load power consumption power which are acquired in real time, executing corresponding energy storage charging and discharging and photovoltaic output processing by applying a decision tree control mechanism, and optimizing light storage control according to a processing result. According to the method, the photovoltaic preferential consumption strategy is executed, the light abandoning amount is reduced, the photovoltaic consumption rate is improved, the time-of-use electricity price dynamic response mechanism and the energy storage efficiency compensation and light abandoning punishment mechanism are combined, peak-valley arbitrage is maximized, meanwhile, the comprehensive electricity utilization cost is reduced, and the power purchase demand of a power grid is reduced.

The invention, which relates to the technical field of optical storage and charging, discloses an optical storage and charging cooperative control method for a transformer area, and the method comprises the steps: carrying out the intelligent and multivariate collection and prediction through intelligent equipment, constructing a function through collected data, solving an optimal solution, selecting an optimal optical storage and charging cooperative control strategy from the optimal solution, generating a corresponding control instruction, and carrying out the transmission of the corresponding control instruction. Cooperative regulation and control of a photovoltaic power generation system, an energy storage system and a charging pile system are achieved, a feedback and correction mechanism is further established, and it is ensured that the photovoltaic storage and charging system stably operates according to a preset strategy. According to the transformer area light storage and charging cooperative control method, a light storage and charging cooperative control mechanism is established by comprehensively considering photovoltaic power generation, an energy storage system and a charging pile system, power resource distribution in a transformer area is optimized, maximum consumption of photovoltaic power generation is achieved, the light abandoning phenomenon is reduced, meanwhile, unnecessary charging and discharging loss of the energy storage system is reduced, and the power utilization rate of the transformer area is improved. And the impact of disordered charging of the charging piles on a power grid system is relieved, so that the overall operation performance of a transformer area power system is improved.

The invention discloses a protocol conversion and protocol self-learning method and system for transformer area optical storage and charging cooperation, and the method comprises the steps: constructing a digital twinborn body of a transformer area optical storage and charging system, carrying out the parallel operation of a protocol agent and a protocol agent in a virtual environment, and achieving the cooperative training of the protocol agent and the protocol agent through a hierarchical reinforcement learning architecture, the protocol agent is responsible for learning a dynamic priority scheduling and compression strategy for heterogeneous protocol messages such as Modbus, CAN and IEC 104, the protocol agent is responsible for learning a power balance and voltage stability control strategy based on photovoltaic output, energy storage SOC and charging load, and the two agents realize cross-domain collaborative optimization through a reward function mutual coupling mechanism. And finally, safely deploying the collaborative strategy obtained by training to the edge control equipment of the physical transformer area. According to the method, the problems of disjunction of protocol conversion and cooperative control, protocol strategy solidification, insufficient cross-domain cooperation and the like in the prior art are solved, and the operation efficiency and the self-adaptive capability of the transformer area optical storage and charging system are improved.

The invention discloses a wind and light storage and charging micro-grid distribution optimization method and system. The method comprises the following steps: acquiring parameter data of each node in a micro-grid in real time; constructing a load demand prediction model, and automatically adjusting the scheduling weight coefficient of the wind and light storage and charging equipment according to the weather type; based on a model output result, establishing a matching relationship between the line impedance and the load emergency degree, generating a power supply path set, and creating a power path selection mechanism based on a dynamic planning algorithm; calculating real-time charge-discharge efficiency based on the equivalent resistance parameter of the power supply path, and dynamically adjusting a charge-discharge rate threshold according to a prediction error; acquiring a real-time monitoring load curve, calculating the similarity between the load curve and historical data, and matching and identifying a potential sudden change risk; and according to a distributed negotiation protocol, each node achieves a scheduling consensus through a priority broadcasting mechanism. The method has the advantages that through load prediction and dynamic scheduling, optimal scheduling, power fluctuation stabilization and sudden change risk identification of the wind and light storage and charging equipment are realized.

The invention belongs to the technical field of new energy charging, and particularly relates to a light storage and charging integrated new energy charging station which comprises a photovoltaic power generation module, an energy storage module, a charging module and an intelligent control unit. The photovoltaic power generation module predicts power generation power through an LSTM neural network in combination with meteorological data, and dynamically adjusts a prediction time window based on a weather type; the energy storage module is directly connected with the photovoltaic direct current side to improve energy efficiency; the charging module obtains the SOC / SOH state of the vehicle and the user demand through the data acquisition unit, and generates a priority coefficient by using a scoring model. The system dynamically distributes photovoltaic power, energy storage power and power grid power through a demand grading module, and when it is detected that the photovoltaic output deviation exceeds a threshold value or the energy storage SOC is insufficient, a power redistribution and compensation mechanism is automatically triggered. According to the invention, efficient utilization of renewable energy sources, intelligent scheduling of an energy storage system and differentiated supply of charging services are realized, and the problems of low photovoltaic consumption rate and response lag of a traditional charging station are solved.

The invention discloses a photovoltaic power generation intelligent control system of photovoltaic panels in an optical storage direct flexible system, and relates to the technical field of photovoltaic power generation, the intelligent control system comprises a plurality of photovoltaic panel assemblies, the plurality of photovoltaic panel assemblies comprise main node assemblies, one main node assembly is arranged every 5-10 groups of photovoltaic panel assemblies, and the main node assemblies are connected with the photovoltaic panel assemblies; the master node assembly is configured with a high-precision double-axis sun sensor and an edge calculation module and is responsible for collecting a solar azimuth angle, a current inclination angle of the assembly and environment temperature and humidity data in real time, and the slave node assembly is configured with a single-axis inclination angle sensor and adjusts a tracking angle by receiving a master node instruction. Specifically, a master-slave node heterogeneous design is adopted, the slave node only needs a single-axis tilt angle sensor and a wireless communication module, the hardware configuration number is reduced, and cost control is achieved. According to the method for photovoltaic panel tracking optimization, the control instruction quantity of the system is reduced, the central control pressure is remarkably reduced, and the problems of cost and complexity during large-scale application of photovoltaic panel tracking are solved.

The invention discloses an analysis method, system, medium and equipment adaptive to faults of a water-optical storage network source system, and relates to the technical field of power system fault analysis. The system comprises a multi-source data acquisition and preprocessing module, a fault feature extraction and classification module, a fault positioning module, a fault influence evaluation module and a dynamic updating and self-adaption module; the method comprises the following steps of multi-source data acquisition and preprocessing, fault feature extraction and classification, fault positioning and influence evaluation, and dynamic updating and adaptive adjustment. The invention provides a fault analysis method adaptive to a water-light storage network source power system, various faults in the complex power system can be comprehensively and accurately analyzed, and rapid and accurate fault type identification, fault position positioning and fault influence range evaluation are realized by comprehensively considering the characteristics of each component of the system. Powerful support is provided for timely and effective fault processing, and stable and reliable operation of a power system is guaranteed.

The invention discloses an agricultural and pastoral area water network system carbon reduction and efficiency improvement optimization operation method and system. The method comprises the following steps: collecting agricultural and pastoral area data, constructing an agricultural and pastoral area database, constructing a dynamic coupling model, and optimizing model parameters; constructing a crop planting structure optimization model, solving the model to obtain a quarterly planting planning scheme, and inputting the dynamic coupling model to obtain a corresponding collaborative scheduling scheme; constructing a daily scale scheduling model, solving the model to obtain a daily scheduling scheme set, and screening an optimal daily scheduling scheme; formulating a water and electricity storage strategy, and adjusting operation of a water pump and wind and light storage equipment; and constructing an agricultural and pastoral area water network system real-time monitoring platform based on a digital twinborn technology, generating a control strategy and a carbon reduction and efficiency improvement evaluation report, and optimizing and adjusting parameters in the dynamic coupling model. Cooperative scheduling of water resources, power resources and agricultural loads is realized, the utilization rate of renewable energy sources such as wind and light is improved, and the overall operation benefit of the system is improved.

The invention discloses a photovoltaic-wind power-energy storage integrated power generation system based on intelligent optimization in the technical field of intelligent scheduling of energy storage systems. The photovoltaic-wind power-energy storage integrated power generation system comprises a power generation module; the hybrid energy prediction module is used for predicting the load of the power generation module by using a hybrid prediction model according to the meteorological data and the equipment operation state data acquired from the communication module; the optimal scheduling module is used for performing real-time power generation power compensation, short-term charging and discharging scheduling and long-term energy storage capacity proportion planning through multi-time scale cooperation; the energy storage collaboration module is used for constructing a full-life-cycle virtual mirror image of the wind and light storage system by using a digital twinning technology and establishing a time-sharing hierarchical energy storage architecture; a communication module and a feedback module. According to the invention, through power generation prediction, energy supply scheduling and energy storage ratio planning on multiple time scales, the overall operation efficiency and stability of the system are improved, the service life of equipment is optimized, the equipment, manual and leveling cost of kilowatt-hour electricity is reduced, and the shutdown loss of abandoning light and electricity is reduced.

The invention discloses a power system random scheduling method based on wind-solar joint probability distribution and double-layer dynamic optimization. The method comprises the following steps: firstly, constructing a wind and light output joint probability distribution model by adopting nonparametric kernel density estimation and a Frank Copula function so as to accurately describe space-time correlation; secondly, generating a wind-solar combined output scene through Monte Carlo simulation, and performing reduction by adopting a k-means + + clustering algorithm to obtain a typical scene set; secondly, establishing a double-layer optimization model, wherein the upper layer takes the minimum net load variance as a target to coordinate wind and light storage output stabilizing fluctuation; and the lower layer optimizes the output of the traditional unit by taking the minimum operation cost of the system as a target. Finally, a dynamic climbing constraint mechanism is introduced, the net load fluctuation standard deviation output by the upper layer is converted into a dynamic constraint threshold value of the climbing rate of the lower layer unit, collaborative optimization of economical efficiency and stability is achieved, and the dispatching robustness of the high-proportion renewable energy power system is remarkably improved.

The invention relates to the technical field of light storage and charging integration, in particular to an intelligent micro-grid power supply and load balance control method based on light storage and charging integration, which comprises the following steps of: synchronously acquiring a photovoltaic string output current frequency spectrum, a charging pile switching frequency set and an energy storage converter impedance curve, and outputting a mismatch characteristic frequency spectrum; the mismatch characteristic frequency spectrum marks a resonance risk frequency band; and based on the mismatched characteristic frequency spectrum, generating a light-storage-charging power coordination instruction set through a harmonic suppression collaborator, including instructing a photovoltaic string to start an active damping mode in a resonant frequency band and dynamically distributing differential switching frequency of a charging pile. According to the method, a mismatch characteristic frequency spectrum model fusing a photovoltaic current frequency spectrum, a charging pile switching frequency set and an energy storage converter impedance characteristic is constructed for the first time, a resonance risk frequency band is analyzed and judged through frequency domain coupling, and feedforward identification of inter-harmonic excitation conditions of multi-source high-frequency conversion equipment is realized; the method has higher system level adaptability and harmonic risk positioning precision.

The invention relates to the field of energy management, and discloses a light storage and charging integrated energy management system, which comprises a central energy management platform used for monitoring and scheduling energy flow among photovoltaic power generation, energy storage equipment and a charging pile in real time, and dynamically adjusting the energy flow according to the change of the power grid load, the state of the energy storage equipment and the photovoltaic power generation amount; the photovoltaic power generation monitoring and scheduling module communicates with the central platform and is used for acquiring the power generation amount data of the photovoltaic module and adjusting the use priority of photovoltaic power generation according to the load condition of the power grid; and an energy storage management and scheduling module. Through cooperative work of the central energy management platform, photovoltaic power generation is preferentially supplied to the energy storage equipment and the charging pile, electric power waste is avoided, maximum utilization is achieved through combined action of the photovoltaic power generation and the energy storage equipment when a power grid is in a low ebb, the energy storage equipment discharges to relieve the pressure of the power grid when the power grid is in a peak period, and therefore the energy utilization efficiency of the system is remarkably improved.

The invention discloses a multi-source data fusion-based optical storage and charging micro-grid optimal configuration method. The method comprises the steps of S1, deploying a photovoltaic monitoring module, an energy storage BMS and a charging pile controller; s2, inputting historical irradiance, temperature and charging record data based on an LSTM algorithm; s3, establishing a multi-target optimization model, and taking the lowest system operation cost and the highest photovoltaic consumption rate as targets; s4, dynamically executing the optimization strategy; the invention relates to the technical field of new energy micro-grids. According to the multi-source data fusion optical storage and charging micro-grid optimal configuration method, the problem of power supply guarantee under different power demands is solved. According to actual requirements, an electric energy flowing path in an electrical architecture is designed, a path with the highest transmission efficiency is planned, disordered power utilization of a charging pile is met through the quick response capability of energy storage, and then an energy management strategy under a light storage and charging multi-energy complementary platform is determined. And finally, under the condition that the charging requirement is met, the photovoltaic and energy storage effects are fully exerted, and the economic maximization of the whole set of system is realized.

The invention relates to the technical field of intelligent power grids, in particular to an optical storage agricultural micro-grid operation optimization method and system, and the method comprises the steps: constructing an optical storage agricultural greenhouse micro-grid, and constructing a robust optimization scheduling model under the photovoltaic output uncertainty to carry out robust optimization scheduling; based on the scheduling scheme of robust optimization scheduling, constructing a micro-grid load model for the light storage agricultural greenhouse; on the basis of the load demand in the micro-grid load model, a cost-benefit optimization model under two operation modes of autonomous investment construction and energy performance contract is constructed, and the cost-benefit is calculated on the basis of the cost-benefit optimization model under the actual operation mode, so that the energy storage capacity configuration and the operation strategy are subjected to collaborative optimization. And constructing an energy storage system capacity configuration and operation optimization model, and finally determining an energy storage configuration scheme and a multi-time scale scheduling strategy. According to the invention, the problems of photovoltaic output uncertainty, single investment operation mode and insufficient economical efficiency optimization of the existing system are effectively solved.

The invention discloses a distributed light storage and charging resource cooperative regulation and control method and system, and the method comprises the steps: collecting historical operation data and equipment parameters, and carrying out the preprocessing, and obtaining a standardized data set; establishing a tree embedding model, respectively constructing characteristic / dynamic characteristic / response models of a photovoltaic device, an energy storage device, a charging facility and an adjustable load, and forming a distributed resource characteristic model set; identifying various resource energy storage behavior modes, and mapping each model into a generalized energy storage equivalent model to obtain a comprehensive generalized energy storage model; a resource schedulable potential matrix is constructed, potential is calculated after dimension reduction processing, and an evaluation report is generated; a multi-target regulation and control optimization function is constructed, distributed resource aggregation is modeled, a collaborative regulation and control scheme is obtained by combining hierarchical optimization of the optimization function and an aggregation characteristic model, then the collaborative regulation and control scheme is decomposed into specific control parameters of all resources, and a distributed resource regulation and control instruction set is formed. According to the invention, efficient cooperative regulation and control of distributed resources are realized.

The invention relates to the field of new energy and oil gas fusion development, and discloses a wind and light storage capacity optimal configuration method and system based on rod-pumped well group load, and the method comprises the steps: obtaining annual meteorological data on a well site and energy consumption data of a pumping unit, substituting the data into a fan and photovoltaic panel output model, and carrying out the processing to obtain a first set of output data and energy consumption data; increasing random fluctuation within a set range for the meteorological data and the energy consumption data, and processing the changed meteorological data to obtain a second set of output data and energy consumption data; an artificial bee colony solving algorithm is designed based on the established energy storage charging and discharging state model and the wind and light storage capacity optimization configuration model, two sets of output data and pumping unit energy consumption data are substituted into the artificial bee colony solving algorithm for solving to obtain two configuration schemes, and a scheme with high total investment cost is selected as an optimal scheme; and carrying out secondary capacity optimization configuration solving by adopting a genetic algorithm, and comparing with an optimal scheme solved by an artificial bee colony algorithm to obtain an optimal capacity optimization configuration scheme.

The invention relates to a multi-data center elastic scheduling method, which comprises the following steps of: establishing a data center polymer (DCA) to integrate the space-time regulation potential of a dispersed data center, establishing a comprehensive model covering dynamic migration of a working load, power consumption regulation and control of a DVFS (Distributed Valve File System) and collaboration of an optical storage system, and adopting a double-layer elastic scheduling architecture, energy storage charging and discharging, photovoltaic output and other hyperopia actions are optimized to improve long-term decision robustness; and the lower layer solves short view actions such as server scheduling and load distribution in real time based on mixed integer linear programming to realize instant cost optimization, so that power-computing power collaborative scheduling optimization of the data center is realized. According to the method, flexible scheduling of time delay sensitive type and time delay tolerant type workloads is combined, the cooperative effect of an energy storage system and photovoltaic power generation is utilized, the power use efficiency and economical efficiency of the data center are optimized, and dynamic participation of multiple data centers in the power market is achieved.

The invention belongs to the technical field of power grid systems, and particularly relates to a cooperative scheduling method for a water-electricity-heat combined supply negative carbon building light-storage direct-flexible energy microgrid group. Comprising the following steps: constructing a negative carbon building light storage direct-flexible energy microgrid group mathematical model; wherein the mathematical model comprises a generator constraint model, an external power grid and micro-grid group exchange power constraint model, a power balance constraint model, a peak-valley difference constraint model, a storage battery constraint model, a phase change heat storage system operation constraint model and a water supply system operation constraint model; constructing a target function of the scheduling model by taking the minimum total operation cost of the negative carbon building light storage direct-flexible energy microgrid group in a scheduling period as a target; wherein the total operation cost comprises electric power procurement cost, carbon emission cost, energy storage cost, fuel cost and peak-valley difference punishment cost. Through linkage of the electricity storage system, the heat storage system and the water storage system, the utilization rate of renewable energy sources and the flexibility of the system are maximized.