296 results about "Economic dispatch" patented technology

The invention relates to the technical field of energy storage systems, in particular to a source network load storage coordination control method for multi-configuration network type energy storage coordination operation. According to the technical scheme, the source-network-load-storage coordinated control method for multi-construction-network-type energy storage coordinated operation comprises the following steps: generating a new energy output and load change trend through a dynamic prediction model based on historical power data, real-time load requirements and meteorological information; optimization is carried out to obtain a power generation plan containing a clean energy consumption target and economical efficiency constraints; decomposing the power generation plan into a photovoltaic power adjusting instruction, an energy storage charging and discharging instruction and a load control instruction according to the power grid safety priority, wherein the emergency control instruction is executed prior to the economic dispatching instruction; and controlling parallel operation of a plurality of pieces of networking type energy storage equipment, and realizing multi-machine power distribution and circulating current suppression through a virtual synchronous control technology. And through layered optimization scheduling and multi-machine cooperative control, the new energy consumption capability is remarkably improved, and the phenomenon of abandoning light and wind is reduced.

The invention relates to the field of power systems, in particular to a multi-energy complementary power supply network cooperative control method and system. The method comprises the following steps: collecting dynamic characteristic data of wind power, photovoltaic power, energy storage and load through a sensor group, and generating a multi-energy state characteristic matrix with a timestamp; and matching the feature matrix with a dynamic response feature library, and outputting a dynamic feature parameter set. Based on the set, a multi-objective optimization model is constructed, second-level frequency deviation suppression, minute-level tie line power stabilization and hour-level economic dispatching are covered, and a cooperative control parameter set is generated. And generating a thermal power generating unit compensation amount, an energy storage charging and discharging sequence and an interruptible load priority list. After execution, power grid deviation data are collected, a dynamic deviation value is calculated, and when a threshold value is exceeded, feature library parameters are corrected and loop optimization is carried out. According to the invention, the problem of insufficient cooperative control of the multi-energy system in the prior art is solved, and the stability and economy of the power grid are improved.

The invention relates to a hydrogen-containing comprehensive energy system low-carbon economic dispatching method based on a near-end strategy optimization algorithm. The method comprises the following steps: constructing an equipment model, a carbon transaction mechanism model and a comprehensive demand response model of an electricity-heat-gas-hydrogen comprehensive energy system; based on the equipment model, the carbon transaction mechanism model and the comprehensive demand response model, determining an objective function and constraint conditions of low-carbon economic dispatching of the hydrogen-containing comprehensive energy system; a Markov decision process is used for describing the uncertainty problem of the hydrogen-containing integrated energy system, a state space, an action space and a reward function are determined, a near-end strategy optimization algorithm is adopted for solving, and output of each device is optimized and dispatched. Compared with the prior art, the new energy consumption can be effectively promoted, the economical efficiency and the low-carbon property of the system are improved, real-time scheduling is carried out according to random fluctuation of energy output, and there is no need to depend on accurate prediction or uncertainty modeling of source and load output.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention discloses an energy storage-photovoltaic-electrolytic aluminum system dynamic optimization method suitable for multiple scenes, and relates to the technical field of intelligent power grids, and the method comprises the steps: collecting and preprocessing energy storage-photovoltaic-electrolytic aluminum system data, obtaining photovoltaic, load and electricity price characteristics, constructing an equipment mathematical model, and carrying out the optimization of the equipment mathematical model; the method comprises the steps of predicting a state of charge, battery cycle aging cost, photovoltaic output power, load baseline power and an adjustable flexible interval, constructing a system state prediction model, an economic dispatching optimization model and a self-adaptive control model, outputting a power prediction report, a day-ahead optimal economic dispatching plan and an energy storage power control instruction, and carrying out energy storage control. The energy storage power control instruction is subjected to multi-scene logic judgment and then is transmitted to the energy storage converter to be executed, feedback is obtained, and visualization is carried out through the integrated digital board. Through intelligent prediction and adaptive control, collaborative optimization of economy, reliability and green power consumption efficiency is realized, and the comprehensive performance of the energy storage-photovoltaic-electrolytic aluminum system under variable working conditions is improved.

The invention provides an economic dispatching method, device and equipment based on thermal power generating unit working conditions and frequency safety constraints and a storage medium, and relates to the technical field of power system operation and optimal dispatching. The method comprises the following steps: acquiring operation data of a power system and actual operation condition data of a thermal power generating unit; based on the permeability change data and the actual operation condition data of the thermal power generating unit, quantifying the frequency modulation relation between the primary frequency modulation up-regulation capability of the thermal power generating unit and the actual operation condition of the thermal power generating unit; based on the frequency response model and the frequency modulation relation of the power system, constructing a dynamic frequency security constraint model of the power system; and based on the traditional constraint condition and the dynamic frequency security constraint model, constructing an economic dispatching model which takes economic optimization as a target and is embedded with frequency security constraint, so as to generate an economic dispatching operation strategy according to the economic dispatching model. The stability and economical efficiency of the power system are remarkably improved by comprehensively considering the frequency safety constraint and the working condition of the thermal power generating unit.

The invention relates to the technical field of power system optimization scheduling, and discloses a distribution robust opportunity constraint low-carbon economic scheduling method considering an N-1 safety criterion, and the method comprises the steps: building a wind-solar-energy-storage combined power generation system carbon transaction model which considers the bidirectional flowing of a carbon emission right, and taking the minimization of the total operation cost of the wind-solar-energy-storage combined power generation system as a target; establishing a low-carbon economic dispatching model considering the safe operation constraint of the power grid; aggregating wind and light output to establish a fuzzy set based on uncertainty moment information, establishing a distribution robust opportunity constraint economic dispatching model considering wind power uncertainty, and adopting a conditional value-at-risk theory to measure a system operation risk; on the basis, a power transmission line N-1 safety constraint rapid dynamic checking and adding method is provided based on branch circuit breaking distribution factors, and an economic dispatching model considering an N-1 safety criterion is established and solved. The power supply reliability of the system can be ensured under the condition of key line faults, and the solving efficiency and the practicability of the scheduling scheme are improved.

The invention discloses a hydropower line economic dispatching and load collaborative optimization system and method, and relates to the technical field of hydropower dispatching, and the system comprises a data collection module which is used for collecting hydropower station unit water consumption characteristic curve data, power grid real-time load demand data, reservoir water level dynamic data and unit vibration area constraint data; the model construction module is used for preprocessing the data acquired by the data acquisition module and constructing a multi-target optimization model with the minimum total water consumption and the minimum line transmission loss as targets in combination with constraint conditions; the improved optimization module is used for receiving the target function and the constraint condition output by the multi-target optimization model module and dynamically calculating the optimal load distribution scheme of each unit; and the result output module is used for receiving the optimal load distribution scheme output by the improvement and optimization module and realizing visual output and decision assistance of the scheme. The problem that a traditional scheduling scheme is low in traditional scheduling decision-making efficiency is solved, and the scheduling decision-making efficiency and engineering practicability are improved.

The invention relates to a multi-time-scale toughness scheduling strategy of a multi-energy complementary system under an extreme high temperature condition in optimal scheduling of a power system. In order to solve the problems of load rising caused by high temperature, derating of a source network and difficulty in guaranteeing power supply safety by traditional economic dispatching, a source-network-load-storage temperature effect model of photovoltaic, thermal power, a power transmission transformer and a load is constructed, and a day-ahead, day-intraday and real-time three-layer collaborative optimization framework is embedded; weighted unsupplied electric quantity is used as a toughness index, node vulnerability and load grade weight are combined, dictionary order optimization is adopted before the day, prediction deviation is corrected in a rolling mode within the day, toughness-oriented model prediction control and rolling supply stop window constraint are adopted in real time, and conventional unit, energy storage and layered demand response are cooperatively scheduled. Therefore, load loss is reduced and new energy is abandoned in an extreme high-temperature scene, and the power supply guarantee capability of key nodes and important loads and the overall toughness and economical efficiency of the system are improved.

The invention discloses an electric power economic dispatching model solving method and device, a storage medium, a program product and computer equipment, and the method comprises the steps: constructing a safety constraint mathematical model based on an overload generator tripping type electric power system safety and stability control device, and determining an original feasible region of an overload generator tripping type condition section according to the safety constraint mathematical model; reconstructing the original feasible region through the introduced cut plane to obtain a reconstructed feasible region; solving is carried out based on power monitoring and data acquisition SCADA real-time acquisition data, market clearing data and a reconstructed feasible region to generate a new cut plane through iteration, so that on the premise of guaranteeing precision, power market intra-day real-time rolling clearing can be rapidly solved, and the efficiency is improved. The method effectively guarantees the speed and precision requirements of power market construction for safety constraint economic dispatching, ensures that the market clearing result has the actual performability of a power grid, and especially solves the problem that the real-time clearing of the power market is difficult due to overload generator tripping type condition sections.

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 data processing, and discloses a shared energy storage cloud platform-based resource scheduling method, which comprises the following steps that: a cloud platform issues a resource commitment request containing task parameters, an energy storage node sets a local state parameter dead zone after autonomously accepting the request and enters a silent execution state; event reporting is triggered only when the dead zone boundary is touched or abnormal. According to the method, a data interaction mode is reconstructed through a commitment-dead zone mechanism, so that an energy storage node automatically maintains a charge state interval locally to absorb power grid fluctuation, meanwhile, emergency response without communication dependence is realized based on power grid frequency monitoring, and a dual-guarantee system of economic dispatching and safety response is formed.

The application belongs to the field of smart grid, and particularly relates to a smart grid economic dispatching control method based on a fixed time event triggering consistency algorithm; the method comprises the following steps: obtaining interaction information of a generator in a power grid system, determining a topological structure and a convergence parameter of the system according to the interaction information; calculating a fixed time upper bound according to the convergence parameter; sending self state information of the generator to neighbor generators; judging whether an update event triggering condition is met according to the self state information of the generator, the state information of the neighbor generators and an update event triggering protocol; if the triggering condition is met, the generator updates the self state information according to a control protocol; judging whether the system converges according to the self state information, and if the system converges, recording a convergence time; judging whether the convergence time is less than the fixed time upper bound, and if the convergence time is less than the fixed time upper bound, the generator saves the current state information to realize the economic dispatching control of the power grid; the application can reduce the cost of the smart grid and the continuous communication probability of the system.

The invention provides a low-carbon optimal scheduling method for an integrated energy system based on a green certificate-carbon transaction mechanism, so as to reduce the carbon emission of the integrated energy system and improve the renewable energy consumption capability. In order to improve the running flexibility of the IES containing the electro-hydrogen coupling unit, a proton exchange membrane electrolytic cell is introduced, and refined modeling is carried out on the hydrogen energy using process and equipment. A transaction model of a common stepped carbon transaction is improved and combined with a green certificate transaction mechanism, and a combined heat and power energy system model with an adjustable heat and power ratio is constructed; secondly, taking the minimum total cost of the system as an objective function, coupling a carbon transaction mechanism and a green certificate strategy, and performing optimization solution by using a Cplex solver; analyzing the influence of different carbon transaction mechanism parameters and green evidence parameters on the system; finally, four typical scenes are established, the proposed strategy is obtained through comparative analysis, the carbon emission can be remarkably reduced, the consumption amount of renewable energy sources is increased, meanwhile, good economic benefits are achieved, and reference can be provided for low-carbon economic dispatching of the comprehensive energy system.

The invention relates to an integrated energy system optimal scheduling method and system based on a carbon emission factor and wind power prediction, and relates to the technical field of energy system scheduling, and the method comprises the steps: building a carbon emission metering model based on a dynamic carbon emission factor through metering the direct carbon emission and indirect carbon emission of an integrated energy system; meteorological features are extracted based on the improved wind power hybrid prediction model, and a wind power prediction interval is generated in combination with kernel density estimation; taking the sum of the energy purchase cost, the carbon transaction cost and the wind curtailment cost to be minimized as a target function, and constructing an economic dispatching model; and on the basis of the economic dispatching model and the target function, solving an optimal dispatching scheme through the dynamic carbon emission metering interval and the wind power prediction interval under the condition that a set constraint condition set is met. According to the method, the technical problems of large carbon emission accounting error and low wind power prediction precision in the optimal scheduling process of the existing integrated energy system are solved.

Disclosed is an integrated energy system optimized dispatching method based on a variable time constant gradient algorithm. A Markov decision making process model is established based on an economic dispatching characteristic of an integrated energy system first, and a target optimization function is established. Then, a neural network is established and trained by applying a double-delay depth deterministic strategy gradient algorithm, effective experience is determined before updating a target network, and a variable time constant is set according to a reward value of a current round and a reward value of the last round of soft update. Finally, a trained intelligent agent is used for intra-day dispatching of the integrated energy system, so as to realize optimal economic cost operation of the integrated energy system.

The invention relates to the technical field of AI large language models, provides an AI large language model-based power spot transaction dynamic decision generation method and system, and solves the problem of low collaborative decision efficiency. The method comprises the following steps: acquiring reactive power distribution, carbon emission intensity, new energy output time sequence and equivalent impedance characteristic data of a target power grid region, and performing space-time fusion on the first three types of data to generate a coupling matrix; calculating sensitivity parameters of each electrical node to carbon emission change by using a graph convolutional network in combination with equivalent impedance characteristics of the power grid; performing dynamic weight distribution and fusion on the sensitivity parameters through an attention mechanism to form a weighted embedded vector; and inputting the vector into an AI large language model, and outputting a joint transaction decision containing a reactive power regulation instruction sequence and a carbon tax avoidance strategy at the same time. According to the invention, the collaborative decision-making efficiency of carbon emission control and economic dispatching in electric power spot transaction is improved.

The invention discloses a virtual power plant scheduling method for evolutionary deep reinforcement learning, and belongs to the technical field of power control. According to the method, a hydrogen-containing virtual power plant low-carbon economic dispatching model is constructed, hydrogen energy gradient utilization is realized through electric hydrogen production, electric-to-gas and hydrogen-to-ammonia technologies, a stepped carbon trading model is improved, a hydrogen energy green certificate trading model is introduced, and an evolutionary soft action device-evaluator (ESAC) algorithm is adopted to carry out optimized dispatching. According to the algorithm, population optimization and deep reinforcement learning of an evolutionary strategy are fused, and the mutation rate is dynamically adjusted through an automatic mutation adjustment mechanism. According to the method, the optimization precision and robustness of virtual power plant scheduling are remarkably improved, the operation cost is effectively reduced, and low-carbon operation of a multi-energy system is promoted.

The invention discloses a multi-scene-oriented distributed photovoltaic cluster regulation and control method and system, and relates to the field of photovoltaic power generation, and the method comprises the steps: determining a cluster division index and a fitness value function, and constructing a cluster offline division method based on an improved genetic algorithm; determining an optimal node connection matrix of each time point by using an offline division method, and forming a mapping sample with the node voltage and the solar radiation intensity; preprocessing each mapping sample to obtain training samples in an economic dispatching scene and an emergency voltage regulation scene, and training to obtain corresponding LSTM models in the economic dispatching scene and the emergency voltage regulation scene; identifying a scheduling scene of the power grid in the next time period, and dividing the distributed photovoltaic cluster by using the corresponding LSTM model; and outputting a distributed photovoltaic cluster scheduling result by using a scheduling model based on a division result. According to the invention, intelligent distributed photovoltaic cluster division and regulation can be realized in a complex and changeable scene, and efficient and stable operation of a power grid is ensured.

The invention discloses a double-layer collaborative energy management and control method and system for a cold plate type liquid cooling data center, and belongs to the technical field of data center energy management. The method comprises the steps that upper-layer day-ahead economic dispatching is based on time-of-use electricity price and outdoor environment temperature prediction, and a server load scheme and a cooling system reference flow track are generated with the minimum total operation cost as the target; in lower-layer intraday dynamic regulation and control, model prediction control is adopted to track the trajectory, the load rate of a server and the rotating speed of a secondary pump are adjusted in real time, and it is ensured that the temperature of a chip is within the safety range of 45-70 DEG C; and the lower layer feeds back the actual chip temperature and energy consumption to the upper layer for online correction of dynamic thermodynamic model parameters to form closed-loop optimization. The system comprises a data acquisition module, a dynamic thermodynamic modeling module, an upper and lower layer optimization control module and an information interaction and feedback module. According to the invention, the overall operation cost and the PUE value of the data center are reduced on the premise of ensuring the temperature safety of the chip.

The invention belongs to the technical field of electric power engineering. The DC interconnected power distribution network optimal scheduling method based on converter control switching is used for solving the problems of power distribution network voltage out-of-limit, low scheduling efficiency and the like under the high permeability of new energy, VSC network following and VSC network construction dual-mode cooperative control is adopted, a photovoltaic-energy storage-VSC multi-level voltage stability support system is constructed, and the optimal scheduling of the DC interconnected power distribution network is realized. Dual-mode undisturbed switching is realized through virtual quantity calculation and proportion superposition; a cross-regional power mutual aid mechanism is established based on a direct current channel, and an electricity purchasing cooperative control system is established in combination with time-of-use electricity price. According to the method, collaborative optimization of voltage stabilization and economic dispatching is realized, the new energy consumption capability is improved, the power purchase cost of a superior power grid is reduced, and the method is suitable for a'source-grid-load-storage 'collaborative dispatching scene.

The invention discloses a method, a system, equipment and a medium for energy management and economic dispatching optimization of an optical storage and charging system, and relates to the technical field of optical storage and charging. The comprehensive operation state model represents an operation rule and an economic index of the optical storage and charging system in a future scheduling period; based on the comprehensive operation state model, solving a multi-objective optimization problem to obtain a Pareto optimal solution set considering economy, environmental protection property and service quality; and selecting an optimal scheduling strategy from the Pareto optimal solution set by adopting a multi-agent equilibrium game mechanism so as to determine a final energy management and economic scheduling scheme. According to the method, the problems of difficulty in multi-target coordination, insufficient electricity price response capability and user behavior modeling simplification in the existing energy management technology of the optical storage and charging system are solved, so that the overall benefit of the system is better, the method can adapt to various market mechanisms and different meteorological conditions, and the application scene is wide.

The invention discloses an intelligent micro-grid multi-mode collaborative optimization method and system, and belongs to the field of intelligent power grids. The method comprises the following steps: collecting electrical data of a common connection point and a distributed unit, and quantifying a harmonic responsibility coefficient by using an improved complex independent component analysis and partial coherence method; constructing a dynamic electricity price game model, and solving an optimal excitation coefficient through a non-cooperative game; and building a multi-objective optimization model, correcting model prediction control parameters by means of a digital twinborn training reinforcement learning agent, and outputting a scheduling instruction in a rolling optimization manner to realize multi-mode switching. The system comprises a data acquisition layer, a regional collaboration layer, a cloud computing and digital twinning layer and an intelligent optimization core layer. According to the method, the electric energy quality and economic dispatching are fused, and the optimization intelligence and safety of the micro-grid are improved.

The invention discloses a multi-energy power distribution network low-carbon cooperative scheduling optimization method based on edge calculation, relates to the technical field of power grid scheduling control, and aims to provide a multi-energy power distribution network low-carbon cooperative scheduling optimization method by constructing a multi-dimensional feature coupled power distribution network partition model based on regional energy supply and demand characteristics and comprehensively considering key features such as energy types and network topology. A power distribution network is divided into a plurality of sub-regions with energy complementation characteristics. Intelligent agent nodes are deployed on the edge side, wind and light output fluctuation, hydropower regulation margin and load demand change data are collected in real time, a multi-time-scale operation boundary is generated in combination with a time sequence prediction model, and a controllable power supply operation interval meeting regional stability is rapidly calculated based on an interval optimization algorithm. And the cloud center receives the operation schemes of different sub-regions, carries out global coordination based on a multi-objective optimization algorithm, comprehensively considers carbon emission constraints, economic dispatching cost and new energy consumption priorities, and generates a cross-region power mutual aid and energy storage coordination regulation strategy.

The invention provides a planning method and system for an electric power traffic coupled light storage and charging integrated station, and the method comprises the steps: carrying out the prediction of the time-space distribution of a charging load through employing an electric power traffic coupled EV load time-space distribution prediction model, carrying out the distribution of traffic flow according to historical data and an actual road network structure, and simulating the driving and charging characteristic parameters of an EV; carrying out path planning according to the O-D solution of the EV and according to the real-time road resistance matrix and the congestion degree, and judging whether to carry out V2G mode power exchange or not to obtain the spatial and temporal distribution condition of the EV load; and determining an objective function and a day-ahead economic dispatching constraint condition, and solving to obtain planning result data of the optical storage and charging integrated station under the electric power traffic coupling condition. According to the method, the deep coupling characteristic of the power distribution network and the traffic network is considered, and the planning and operation of the optical storage and charging integrated station and the power distribution network are coordinated, so that the optical storage and charging integrated station obtains more considerable efficiency and economic benefits, and the method has better applicability.

The invention relates to a low-carbon economic dispatching method and system for an integrated energy system, and the method comprises the steps: accurately describing the variable efficiency characteristics of energy conversion equipment through constructing a dynamic energy concentrator model based on a BP neural network, and processing a nonlinear problem through employing a linearization method; a typical source load prediction scene is generated in combination with a scene generation and reduction technology, and an energy flow-carbon flow double-layer optimization model is established. Wherein the energy flow layer takes the minimum operation cost as a target and considers equipment operation, multi-energy flow balance and transaction constraints; a stepped carbon transaction mechanism and demand response guided by carbon potential are introduced into a carbon flow layer, and the purpose is to minimize the cost of carbon transaction and demand response. And through iterative solution of the double-layer model, collaborative optimization of a system energy purchase and sale plan, equipment output and load change is realized until an optimal scheduling scheme is converged. According to the method, collaborative optimization of the energy flow and the carbon flow is realized, and double improvement of the scheduling of the comprehensive energy system in economy and low carbon is promoted.

The invention discloses a low-carbon economic dispatching method and system for a hybrid energy storage-containing virtual power plant considering multiple uncertainties, and relates to the field of power system optimization dispatching, and the method comprises the steps: designing a pumped storage-flywheel-electrochemical hybrid energy storage layered cooperative regulation and control mechanism; a carbon capture-power-to-gas equipment dynamic collaborative operation model and a green certificate-carbon transaction dynamic coupling model are established, a day-ahead-day two-stage rolling optimization framework is constructed, and a hybrid energy storage capacity optimization configuration model is developed. According to the method, the complementary characteristic of multi-time scale energy storage and the cooperative potential of a low-carbon technology can be fully excavated, and double improvement of economy and environmental protection of the virtual power plant is realized.

The invention provides a transformer area two-stage distribution robust line loss optimization method considering wind and light uncertainty, and belongs to the technical field of new energy. Firstly, a day-ahead-real-time two-stage line loss optimization method is constructed, and in the day-ahead stage, a K-means clustering algorithm and a conditional generative adversarial network are combined to generate a multi-scene sample set capable of reflecting the uncertainty of wind and light output; in a real-time stage, based on a day-ahead optimization result and updated output information, a multi-scene distribution robust optimization model is utilized to realize joint correction of line loss and economic dispatching; in the optimization solution process, a forward-backward substitution method is adopted to embed the load flow calculation model so as to accurately reflect the line transmission loss of the transformer area power distribution network; secondly, a distributed robust optimization method based on scene generation is constructed, historical wind and light data are utilized, a diversified scene set is generated through K-means clustering and a conditional generative adversarial network, and distributed robust optimization planning and correction are carried out in different scenes.

The invention discloses a multi-zone interconnected electric heating combined system low-carbon economic dispatching method and system considering carbon emission flow and demand response, and the method comprises the steps: analyzing the power flow of a tie line, decomposing a multi-zone interconnected electric heating combined system into a plurality of independent regional subsystems, and analyzing the coordinated coupling relation of each regional subsystem; according to the regional decomposition result, based on a target cascade analysis method, a multi-region interconnected electric heating combined system low-carbon economic dispatching model considering carbon emission flow and demand response is established, and the model comprises an upper-level main problem and lower-level sub-problems corresponding to all regional subsystems; linearizing the model and then solving the model; and calculating a carbon emission allocation result of each regional subsystem according to an optimization result. According to the method, the thermodynamic system heat supply network pipeline and the carbon emission flow of the thermodynamic system heat supply network pipeline are subjected to refined modeling, on the basis, electricity and heat load sides are guided to participate in demand response according to the carbon emission allocation condition while multi-region independent optimization is conducted, data secrecy of subsystems in all regions is facilitated, and low-carbon economic dispatching of the multi-region interconnection electric heating combined system is achieved.

The invention discloses a building light storage energy economic scheduling and power compensation management system and method, and relates to the technical field of building energy management, and the method comprises the following steps: obtaining scheduling data in a future preset scheduling period, carrying out the prediction analysis based on the scheduling data, and generating a macroscopic charging and discharging scheduling strategy; the method comprises the following steps: acquiring a sky image sequence by using a visual sensor, analyzing the movement of a cloud cluster based on the sky image sequence to predict the descending trend of photovoltaic power, and sending a pre-compensation instruction to an energy storage converter and controlling the energy storage converter to charge and discharge before the actual descending of the photovoltaic power. According to the method, the descending trend of the photovoltaic power can be accurately predicted in advance, the pre-compensation instruction is sent to the energy storage converter before the actual falling of the power, the peak-valley electricity price arbitrage income is maximized through the combination of high-precision prediction and accurate pre-compensation, and the optimization and improvement of the overall performance of the building optical storage system are further realized.