558 results about "Power system scheduling" patented technology

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

The invention relates to the technical field of power system dispatching, in particular to an emerging load power supply potential improvement method based on dynamic prediction, which comprises the following steps of: acquiring emerging load power consumption, meteorological environment and power grid schedulable resource data in a target area through an Internet of Things sensing terminal, and performing two-channel modeling to obtain a new load power supply potential improvement model; a deep space-time network is used to predict a load curve, a model is established to quantify resource regulation potential, a scheduling priority list and a capacity allocation strategy are generated by means of a matching rule base according to load fluctuation and resource evaluation results, an actual scheduling effect is fed back to the prediction model, parameters are corrected through error back propagation, a closed-loop optimization link is formed, and the scheduling efficiency is improved. The method improves load prediction accuracy and resource scheduling adaptability, is suitable for emerging load power supply optimization in a novel power system, and guarantees stable and efficient operation of a power grid.

The invention relates to the technical field of power system scheduling, and discloses a power distribution network simulation scheduling optimization method and system based on artificial intelligence, and the system comprises a data fusion module, a digital twin modeling module, an intelligent prediction module, a strategy optimization module, and a visual scheduling module. The whole scene of the power distribution network is simulated through the digital twin model, the operation state and fault influence of equipment are accurately simulated, a scientific basis is provided for making a maintenance plan, blind maintenance is avoided, and the maintenance and repair cost of the equipment is reduced; meanwhile, by optimizing a load transfer path and distributed power supply output, the network loss rate is reduced, and the utilization efficiency of electric power resources is improved; in addition, the knowledge graph and the LSTM deep learning algorithm are fused, the distribution network topology entity relation network is constructed, and multi-source data are trained, so that the fault prediction accuracy is improved, the power failure risk can be early warned in advance, the conversion from passive first-aid repair to active prevention is realized, and the power failure frequency outside a plan is reduced.

The invention relates to the technical field of power system dispatching, and discloses a multi-agent reinforcement learning active power distribution network area coordination method and system, and the method comprises the steps: dividing a power distribution network into a plurality of areas, and each area is managed by an agent; collecting observation information; inputting the observation information into an upper reinforcement learning strategy network, and outputting control parameters; inputting the control parameters into a target function of the lower-layer local physical optimization model, and solving an output setting point of the equipment under the condition of meeting the safety operation constraint; constructing a De-POMDP problem, and obtaining a reward signal of each agent; a sequential updating mechanism is introduced, global network parameters are optimized, and corresponding decisions are obtained; and inputting the multi-agent decision into the global active power distribution network model to obtain the total operation cost, feeding back the total operation cost as an award to the reinforcement learning strategy network, updating global network parameters, and converging to obtain an optimal decision. According to the invention, regional wind-solar-storage multi-energy scheduling can be effectively optimized, and energy balance in the region is realized.

The invention relates to the field of power system dispatching control, in particular to a virtual power plant collaborative optimization dispatching method, system and device based on multiple spatial-temporal scales and a storage medium. The method comprises the following steps: acquiring real-time supply and demand data of a multi-energy data source, constructing a dynamic operation data set by adopting distributed data acquisition, and performing time sequence analysis on the data set to extract a multi-energy fluctuation feature set; the fluctuation feature set constructs a network topology model in a spatial dimension, and a resource allocation weight of each energy node is determined through graph calculation to generate a resource allocation optimization scheme; when the real-time demand fluctuation exceeds a threshold value, a reinforcement learning algorithm is adopted to carry out optimization adjustment to obtain a real-time scheduling instruction set; in combination with real-time data of the electricity market, an optimized economic signal set is obtained through multi-objective optimization, and an equipment control instruction set is generated by adopting distributed control; and performing real-time monitoring by utilizing edge calculation according to the equipment control instruction set, and dynamically updating the scheduling instruction set through adaptive adjustment based on the system operation deviation to obtain a final resource optimization configuration scheme.

The present invention relates to the technical field of power system dispatching automation. Disclosed are a rapid identification and response method and system for a weak point-associated power flow based on a sensitivity factor. The method comprises: acquiring parameter data, and establishing a transfer factor matrix; assessing renewable energy integration of a distribution network and assessing a power flow over-limit line; and dispatching flexible loads in the distribution network on the basis of a sensitivity factor, and carrying out safety verification on a dispatching result. According to the rapid identification and response method for a weak point-associated power flow based on a sensitivity factor provided by the present invention, a weak point in new energy integration of the distribution network is identified, to obtain a blocking line that hinders new energy integration, providing a foundation for subsequent precise dispatching control for flexible loads. Sensitivity analysis is carried out on a weak point-associated power flow and flexible load user nodes on the basis of the sensitivity factor, and the line over-limit condition is eliminated by dispatching flexible loads of corresponding nodes, so that the power grid safety is ensured and new energy integration is implemented.

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 relates to a power system scheduling method and device based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a power system, wherein the multi-modal data comprises power grid topology data, meter information, wind and light energy output prediction data, maintenance plan text data and energy comprehensive data; performing feature extraction, space-time alignment processing and hierarchical attention learning on each modal data to obtain a fusion feature vector; training the scheduling model by using the fusion feature vector until a total loss function meets a preset condition, the total loss function comprising a power flow equation constraint term; the method comprises the following steps: acquiring real-time multi-modal data of a power system, and obtaining a real-time fusion feature vector through feature extraction, space-time alignment processing and hierarchical attention learning; and inputting the real-time fusion feature vector into the trained scheduling model to obtain a real-time scheduling decision, and scheduling the power system according to the real-time scheduling decision. The method can improve the dispatching precision of the power system.

The invention discloses a multi-energy-storage thermal power generating unit optimal scheduling method, which belongs to the technical field of power system scheduling, and specifically comprises the following steps: acquiring operation data and power grid load data of a multi-energy-storage thermal power generating unit; constructing a load prediction model based on the collected data to obtain a load prediction result; taking the minimum comprehensive cost as a target, and combining a thermal power generating unit output constraint, an energy storage equipment charging and discharging constraint and a power grid security constraint to construct an optimal scheduling model; solving the optimal scheduling model by adopting an improved intelligent optimization algorithm to obtain an optimal scheduling scheme; performing real-time scheduling on the multi-energy-storage thermal power generating unit according to the optimal scheduling scheme; the method can improve the scheduling flexibility of the multi-energy-storage thermal power generating unit, effectively deals with the load fluctuation of the power grid, and is suitable for the optimal scheduling task of the multi-energy-storage thermal power generating unit in a complex power grid environment.

The invention discloses a source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

The invention discloses a new energy user side multi-energy efficiency collaborative optimization method and system, and belongs to the technical field of power system dispatching, and the method comprises the steps: collecting the real-time operation parameters of photovoltaic equipment, energy storage equipment and electric equipment, carrying out the health degree evaluation of the equipment, generating an operation risk evaluation value, dividing the equipment into a quick response group and a slow compensation group, generating a hierarchical optimization instruction; and adjusting the hierarchical optimization instruction through a preset multi-objective optimization function, generating a control strategy, converting the control strategy into an equipment executable adjustment signal, and respectively sending the adjustment signal to the quick response group and the slow compensation group. According to the method, the real-time parameter acquisition and dynamic equipment grouping technology is adopted, the graded adjustment instruction is generated in combination with the multi-objective optimization function, collaborative optimization of economic cost, carbon emission and equipment loss can be achieved in a user side new energy scene, and the system regulation and control precision and comprehensive energy efficiency are improved.

The invention discloses a power grid photovoltaic output and load sequence modeling method, system and device and a storage medium, and the method comprises the steps: comprehensively utilizing the multi-scale feature extraction capability of a time-frequency decomposition technology, the time sequence dependence modeling capability of a long and short-term memory network, and the global hyper-parameter optimization capability of a Bayesian optimization algorithm; and carrying out collaborative modeling and prediction on the photovoltaic output and the power load under a unified framework. By introducing a source load time-delay correlation analysis and probability interval construction mechanism, point prediction results and uncertainty intervals of photovoltaic, load and net load can be output at the same time, and a set of source load integrated prediction system with high prediction precision, strong robustness and reliable interval characterization capability is constructed. The method can improve the precision and reliability of photovoltaic power and load prediction, also can reduce the risk in power system scheduling, optimizes the energy storage configuration strategy, and especially has wide popularization potential and application prospects in the scenes of new energy grid-connected operation, intelligent micro-grid and virtual power plant management and the like.

The invention provides a distribution network scheduling pre-order ticket issuing method and device, a storage medium and computer equipment, and relates to the technical field of power system scheduling, and the method comprises the steps: analyzing distribution network scheduling information, matching a corresponding target template in a scheduling pre-order ticket template library, filling the target template, and obtaining a scheduling pre-order ticket draft; a structured grammar rule base is used for conducting grammar verification on a scheduling pre-order ticket draft, and the structured grammar rule base is constructed on the basis of the natural language processing technology by extracting scheduling terms from distribution network scheduling standard text terms, analyzing a grammar structure and converting an operation logic relation; when the grammar verification of the scheduling pre-order ticket draft is passed, calculating the text similarity between the scheduling pre-order ticket draft and the historical scheduling pre-order ticket; and when the scheduling pre-order ticket draft similarity verification is passed, obtaining the scheduling pre-order ticket, and issuing the scheduling pre-order ticket after confirming that the scheduling pre-order ticket passes the verification. Therefore, the method effectively improves the timeliness and accuracy of the scheduling instruction.

The invention relates to the technical field of electric power dispatching, in particular to an electric power optimization dispatching method and system based on reinforcement learning, and the method comprises the steps: obtaining the operation data, load prediction data and renewable energy output data of an electric power system; based on the operation data, the load prediction data and the renewable energy output data, constructing a power system scheduling optimization model; generating an optimization scheduling strategy based on the power system scheduling optimization model by using a deep reinforcement learning algorithm; calculating the scheduling cost, the environmental influence and the system reliability index of the power system according to the optimal scheduling strategy; and outputting the optimal scheduling strategy and the corresponding scheduling cost, environmental influence and system reliability indexes, so that the operation cost of the power system can be reduced, the utilization rate of renewable energy sources can be improved, and safe and stable operation of a power grid is ensured.

The invention discloses an electric power system dispatching man-machine cooperation model construction method based on a large language model, and relates to the technical field of new energy electric power system dynamic analysis. According to the method, multi-modal data fusion and natural language interaction capability of ChatGPT are utilized, and a prioritized experience replay (PER) technology is combined, so that bidirectional collaborative decision optimization of the dispatcher and the AI can be realized, and theoretical analysis is verified through an experimental result. A research result shows that after the LLM is introduced, the performance of the model on an active unit scheduling strategy is remarkably improved.

The invention discloses a wind power-containing electric power system source load collaborative scheduling method considering the participation of a smelting type high-energy load, and the method comprises the steps: analyzing the scheduling demands of a wind power-containing electric power system, and building a model considering the load of an electric arc furnace and the wind power output uncertainty based on the schedulable characteristics of the load of the electric arc furnace; a data driving method is adopted to generate a large-scale scene, the uncertainty superposition influence of the electric arc furnace load and the wind power output is analyzed, and the reserve capacity needed by the electric power system for coping with uncertainty fluctuation is determined; a smelting type high-energy load self-adaptive electricity price mechanism considering wind power output change and source load fluctuation is provided, and iron and steel enterprises are stimulated to participate in demand response; and establishing an optimal scheduling model giving consideration to interests of multiple parties, and solving an optimal scheduling scheme by adopting an artificial bee colony algorithm. According to the method, economic benefits and social responsibilities of smelting type high-energy load enterprises are considered, support is provided for the enterprises to participate in consumption of blocked wind power, and new energy consumption and economic and stable operation of a power system are facilitated.

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

The invention discloses an electric energy and inertia combined clearing method and system considering new energy virtual inertia, and relates to the technical field of electric power system dispatching automation, and the method comprises the steps: obtaining data, forming a calculation scene, evaluating a system inertia demand and a droop factor demand based on the calculation scene, and obtaining a clearing result according to an evaluation result. The method comprises the following steps: constructing an all-time electric energy and inertia combined clearing model, solving electric energy bid-winning output and inertia bid-winning quantity of each time period, dynamically adjusting a demand value, establishing a single-time electric energy and inertia combined pricing model based on a solving result of the all-time combined clearing model, and solving node prices of electric energy and inertia of each time period. And outputting the unit output, the on-off state, the inertia standard, the line blocking condition and the node price of each time period. According to the invention, by acquiring multi-source data to form a calculation scene, evaluating inertia and droop factor requirements, constructing a full-time joint clearing model and establishing a single-time pricing model, collaborative optimization configuration and pricing of electric energy and inertia resources are realized.

The invention provides a global adaptive power supply adjustment method and system under the condition of local load increase, and relates to the technical field of power system dispatching. The method comprises the following steps: acquiring regional power grid topology, equipment parameters and real-time operation data; historical power consumption data of classified users in recent three years are collected and preprocessed; generating a reference load curve based on real-time data and a historical rule, calculating a deviation ratio, predicting a load in future 2 hours, and outputting a risk level; the regional energy storage equipment is called to dynamically adjust the discharge power when the risk is medium or low, and the adjacent regional resources are evaluated and cross-regional line switching and power supply support are executed when the risk is high or the energy storage is insufficient. Through layered adjustment and a dynamic response mechanism, rapid identification and accurate control of sudden load increase are realized, power supply stability and resource utilization efficiency are improved, and the method is suitable for local load fluctuation response of scenes such as residential areas and industrial parks.

The invention provides an electric power system source-load coordinated optimization scheduling method considering the flexibility of a steel production process, and relates to the technical field of electric power system scheduling, comprising the following steps: carrying out operation characteristic analysis on the production load of a steel enterprise, and respectively establishing an electric arc furnace model, an air separation system model and a steel rolling line model; constructing an adjustable capacity model under the coupling of the power supply system and the production process; based on the operation cost of the iron and steel enterprise power system, constructing an iron and steel enterprise flexibility day-ahead scheduling model; the electric arc furnace model, the air separation system model, the steel rolling line model, the adjustable capacity model and the iron and steel enterprise flexibility day-ahead scheduling model are integrated into a mixed integer linear programming scheduling model, the mixed integer linear programming scheduling model is solved through a solving tool, and an optimal scheduling scheme of system source-load coordination is obtained. The flexibility of a power system can be remarkably improved, and the problem of supply and demand imbalance caused by wind power output fluctuation is effectively relieved.

The invention relates to an improved TimesNet-based power data time sequence interpolation method and system. The method comprises the following steps of performing missing value identification, anomaly detection and standardized preprocessing on power data, and extracting time sequence features; according to the method, an improved TimesNet model is constructed, a 1D time sequence is converted into a 2D tensor, and features are extracted through a Transformer-CNN hybrid model: an axial attention branch establishes periodic internal and external dependencies in row and column directions, a multi-scale convolution branch captures local features, and a gating mechanism fuses output of the axial attention branch and the multi-scale convolution branch. A deformable convolution kernel is adopted to dynamically adjust a receptive field to adapt to a multi-scale time mode. And in training, time sequence enhancement and GAN are combined to generate data, so that the generalization of the model is improved. The method solves the problem that the traditional interpolation technology cannot effectively model the complex time sequence dependence of the electric power data, remarkably improves the missing value interpolation precision, and provides reliable data support for dispatching and prediction of an electric power system.

The invention belongs to the field of power system dispatching, and particularly relates to a day-ahead-intra-day cooperative dispatching method for multi-time scale coordinated optimization of a photovoltaic new energy access-containing power grid. Firstly, typical power grid source storage load characteristics of a region are analyzed, and a mathematical model is established; secondly, in the day-ahead stage, a large number of typical scenes are generated through a Latin hypercube sampling scene generation method, an initial scheduling scheme is obtained at the same time, a conditional quantile regression technology is introduced according to load prediction and new energy power generation prediction, and a wind power prediction error confidence interval based on prediction duration is established; therefore, the uncertainty of the new energy output is described more accurately. In the intra-day stage, the scheduling scheme is dynamically adjusted in combination with real-time data, and it is ensured that the system runs in the optimal economical and safe state. And finally, an IEEE 33 node standard test circuit is selected for analysis.

The invention relates to the technical field of power system dispatching automation control, and discloses an AGC load distribution control method and system based on a multi-region greedy hydropower station, and the method comprises the steps: generating dynamic boundary thresholds of a high-efficiency region, a medium-efficiency region and a low-efficiency region based on a real-time performance curve and a dispatching set value of a hydroelectric generating set; judging whether to start a cross-interval distribution strategy or not by utilizing the generated dynamic boundary threshold value; when the number of the units participating in distribution is lower than a set scale, an enumeration algorithm is automatically switched to carry out global optimization, and generation of an optimal solution under a small-scale unit combination is ensured; calculating a deviation value between the actual total load and a target value based on the obtained cross-interval distribution strategy; and preferentially adjusting the output of the high-efficiency area unit through a dynamic compensation mechanism. The system comprises an operation interval dynamic calibration module, a region selection and load pre-distribution module and a deviation compensation and adaptive adjustment module. The power grid frequency adjusting precision and the overall operation efficiency of the hydroelectric system are remarkably improved.

The invention discloses an offshore wind power scheduling optimization method and system based on multi-source data fusion, and relates to the related field of power system scheduling, and the method comprises the steps: collecting and obtaining a multi-source offshore wind power data flow; performing mapping fusion and twinborn simulation on the operation distribution characteristic data set of the target offshore wind plant to generate digital twinborn bodies of the offshore wind plant; constructing a multi-scale prediction time window according to an offshore wind power scheduling demand, performing generation power prediction, and outputting multi-scale window prediction wind power; performing scheduling evaluation on the multi-scale window prediction wind power according to a power grid load demand to obtain offshore wind power demand scheduling power; and performing energy scheduling path optimization on the digital twin of the offshore wind plant, determining an offshore wind power energy scheduling path, and performing wind power scheduling optimization control. The technical problem that existing offshore wind power dispatching is insufficient in dispatching accuracy is solved, and the technical effect of efficiently and accurately dispatching offshore wind power is achieved.

The invention relates to the technical field of novel power system optimization operation, in particular to a power system dispatching operation method and device based on participation of gravity energy storage and a medium, and the method comprises the steps: obtaining structure data and operation data of gravity energy storage, and obtaining structure data and operation data of the gravity energy storage based on the structure data and the operation data; determining the quantitative relationship between the charging and discharging power of the gravity energy storage and the gravitational potential energy, and constructing an operation model of the gravity energy storage according to the quantitative relationship between the charging and discharging power and the gravitational potential energy; and establishing a time sequence operation simulation model of the power system in which gravity energy storage participates according to the operation model, and controlling the scheduling operation of the power system based on the operation parameters of the power system at the current moment and the time sequence operation simulation model. Therefore, the problems that the characteristics of tower crane type gravity energy storage and frame type gravity energy storage participating in power grid dispatching operation are not clear, and a gravity energy storage modeling method participating in power system optimization operation is lacked in the related technology are solved.

The invention relates to the technical field of power system dispatching control, and discloses a virtual power plant energy storage dispatching method and system and a storage medium, and the method comprises the steps: obtaining the historical load prediction data and the historical real load data of a virtual power plant; load errors are calculated based on the load historical prediction data and historical real load data, and distribution fitting is carried out; generating probability multi-scene load data based on a fitting result; and carrying out optimization solution on the probability multi-scene load data by adopting an optimization algorithm to obtain an energy storage scheduling scheme. The optimization algorithm adopts a two-stage optimization algorithm, and an energy storage scheduling scheme is generated by drawing a box diagram according to a scheduling time step length and performing statistical analysis on an optimization solving result. According to the method, the probability multiple scenes are constructed by analyzing historical prediction errors, the two-stage optimization algorithm is combined, the adaptability and stability of an energy storage scheduling scheme are improved, the operation cost is reduced, and effective technical support is provided for long-term stable operation of the virtual power plant under the peak-valley time-of-use electricity price policy.

The invention belongs to the technical field of power systems, and more specifically relates to a main and distribution cooperative scheduling method based on deep reinforcement learning. Firstly, a power system model in the active power distribution network is established, various resources contained in the power system model are determined, a mathematical model is constructed, and a power system scheduling agent optimization model is established; then interacting with an active power distribution network environment through an intelligent agent to obtain historical interaction information, and storing the historical interaction information into an experience pool for updating a strategy network and a value network; and finally, regulating and controlling various resources in the power distribution network by utilizing the strategy network. According to the method, the autonomy of the power distribution network is fully considered, an auxiliary decision is provided for scheduling of an upper-layer main network, the peak regulation pressure brought to the main network is reduced, the power deviation of a tie line is reduced, meanwhile, end-to-end input and output control can be achieved through the deep reinforcement learning algorithm based on data driving, and the efficiency in a main and distribution cooperative scheduling task is effectively improved.

The invention relates to the technical field of electric power system dispatching, in particular to a multi-microgrid collaborative optimization dispatching method based on an improved ADMM, and the method comprises the steps: constructing a microgrid operation cost minimization objective function based on a microgrid mathematical model, and carrying out the operation constraint of each microgrid; iteratively updating a local variable, a global variable and a Lagrange multiplier by using an ADMM model to solve the operation cost of the micro-grid; and income distribution is carried out based on the asymmetric Nash negotiation model. According to the method, the problem that the convergence speed and the calculation complexity need to be further improved when an existing model is used for multi-microgrid dispatching optimization is solved.

The invention discloses a hybrid sequential network power consumption prediction method for reinforcement learning dynamic calibration, and the method comprises the steps: collecting multivariable power consumption time sequence data, completing the data preprocessing through resampling, feature engineering, normalization and sliding window technologies, generating a supervised learning sample set, and dividing the supervised learning sample set into a training set, a verification set and a test set; constructing a hybrid prediction model comprising a dynamic capture module, a long-term dependence modeling module, a regression prediction module and a reinforcement learning dynamic fine tuning module; training and optimizing by adopting a staged training strategy to obtain a hybrid prediction model; multivariable power consumption time sequence data are collected in real time and preprocessed, the preprocessed data serve as input, real-time prediction of future total consumption is conducted through the mixed prediction model, and a final prediction result after dynamic fine adjustment is output. According to the method, accurate and efficient prediction of the power grid load can be realized, and a reliable technical solution can be provided for power system scheduling optimization, demand side management, market transaction and other scenes.

The invention belongs to the related technical field of power system dispatching, and discloses a dispatching optimization method and system for a clean energy-containing power system considering delivery, and the optimization method comprises the steps: building a clean energy-containing power system model Mod1 considering the operation constraint of a unit and the network security constraint, and the unit comprises the model Mod1; the system comprises a wind turbine generator, a photovoltaic power station, a thermal power generating unit, a cascade hydropower station unit and a pumped storage unit. Establishing a direct-current delivery system model Mod2 for outputting the electric energy of the electric power system containing the clean energy to each receiving end area; integrating the model Mod1 and the model Mod2 after the upper limit and the lower limit of the safe receiving value of the active power of each receiving end area are substituted into the upper limit constraint and the lower limit constraint of the safe receiving value of the active power in the model Mod2 to obtain a clean energy-containing electric power system model Mod3 considering delivery; and solving the model Mod3 by taking the minimum operation cost as a target to obtain a scheduling scheme in the scheduling time sequence. Through the method, the scheduling strategy can be optimized, and waste of clean energy can be reduced.