360 results about "Economic dispatch" patented technology

The invention discloses a day-ahead and intra-day economic dispatching method for a wind and light storage system, which relates to the technical field of wind and light storage and comprises the following steps of: establishing a dynamic aging model based on historical operation data of a battery, fitting actual attenuation characteristics of the battery through machine learning, acquiring the health state of the battery in different operation states and the change rule of the battery along with time, and calculating the economic dispatching result. Therefore, the basis of the dynamic adjustment strategy is formed. Energy storage charging and discharging are optimized through health factors, the system monitors parameters such as SOH and SOC in real time, tasks are dynamically adjusted, battery aging is reduced, stability is improved, the SOC strategy is optimized in combination with deep reinforcement learning, the service life is prolonged, and the maintenance cost is reduced. Rolling optimization scheduling and experience playback are adopted, a system can dynamically adjust a strategy, the wind and light absorption rate is improved, power grid balance is optimized, thermal power dependence is reduced, scheduling cost is reduced, extreme weather adaptability is enhanced, and green and low-carbon development of electric power is promoted.

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 discloses a thermal power plant multi-target economic dispatch optimization method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: constructing an initial data set based on power grid operation parameters, generator set state parameters and environment monitoring data of a thermal power plant; determining characteristic parameters of the current operation scheduling demand according to the initial data set; based on the characteristic parameters, real-time target weight coefficients of the economic index, the environmental protection index and the scheduling flexibility index are determined through a preset dynamic weight distribution model; according to the real-time target weight coefficient, generating an initial candidate scheduling scheme set meeting the safety constraint of the generator set; performing main target directional optimization on the initial candidate scheduling scheme set to obtain a Pareto optimal solution set meeting a preset convergence condition; and generating a scheduling recommendation scheme based on the Pareto optimal solution set. According to the method, the problem of multi-target conflict caused by multi-target optimization imbalance in economic dispatching of the thermal power plant can be relieved.

The present invention relates to the technical field of electricity markets. Disclosed are a method for coordinated transmission and distribution dispatching of power grids in an electricity market environment, and a system. The method comprises: collecting transmission and distribution data of power grids, and performing economic dispatch modeling for a hybrid system incorporating hydropower and thermal power; performing linear processing on nonlinear terms in a model; and using Benders decomposition to accelerate the solution of the model, and optimizing coordinated transmission and distribution dispatching of the power grids. In the method of the present invention, by means of performing economic dispatch modeling for the hybrid system incorporating hydropower and thermal power, the power system's ability to integrate renewable energy is enhanced, improving the resource utilization efficiency; by means of converting complex nonlinear terms in an original model into linear expressions, the model is easier to solve, providing rapid response capabilities for power grid dispatching; and by using a Benders decomposition method to accelerate the solution, a problem is decomposed into a master problem and a sub-problem, which are solved independently, thus improving the overall solving efficiency, scalability and flexibility.

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.

Disclosed is a power grid transmission and distribution cooperative dispatching method and system in a power market environment, relating to the technical filed of power market. The method includes: power grid transmission and distribution data are collected, and economic dispatching modeling is carried out on a hybrid system containing hydro-thermal power; linearization processing is carried out on nonlinear terms in the model; and accelerated solving is carried out on the model by adopting Benders decomposition, so that power gird transmission and distribution cooperative dispatching is optimized. The integration capacity of the power system containing hydro-thermal power to the renewable energy is enhanced, and the utilization efficiency of resources is improved, and the solving process is simplified in the present invention.

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-saving economic dispatching method for a data center energy system based on a prediction model. The method comprises the following steps: establishing a digital twinborn model of the data center energy system; dividing the data center into a plurality of energy consumption partitions, and installing a temperature acquisition device in each energy consumption partition; after a cooling capacity demand prediction model of the data center under each time period partition is established, target combination parameters and target energy efficiency indexes PUE of the chilled water temperature and the air supply temperature of a refrigeration subsystem under each time period are obtained through calculation; establishing a data center energy efficiency index PUE prediction model, and establishing an upper-layer refrigeration subsystem optimization scheduling model by taking the minimum deviation between an energy efficiency index PUE prediction value and a target energy efficiency index PUE, the minimum deviation between actual combined parameters of chilled water temperature and air supply temperature and target combined parameters and the minimum cold capacity supply and demand deviation of each partition as targets; and establishing a lower-layer data center optimization scheduling model by taking the minimum power utilization operation cost of the system and the minimum load migration cost of the IT equipment as a target.

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 a power system economic dispatching method considering dynamic capacity increase of a line and a related device. The method comprises the following steps: establishing a target function taking the minimum unit operation cost of a power system as a target; determining constraint conditions of the target function according to the parameters of the power system, wherein the constraint conditions comprise unit constraint, network variable constraint, linearized alternating current power flow constraint and linearized line thermal stability constraint; the economic dispatching model of the power system is solved, an economic dispatching scheme of the power system is obtained, and the economic dispatching model of the power system comprises a target function and constraint conditions. According to the method, the temperature change of the line is tracked by using the linearized line thermal stability constraint, so that the transmission capacity of the line can be improved on the basis of meeting the thermal stability constraint, an economic dispatching strategy is optimized, and the economic dispatching efficiency is improved by linearizing the alternating current power flow constraint and the line thermal stability constraint. The power system economic dispatching model is constructed into a mixed integer linear programming model, so that the solving speed and efficiency of the model can be improved.

The invention discloses a continuous time aggregation method for a building micro-grid containing distributed photovoltaics, which relates to the field of micro-grids and comprises the following steps of: modeling heating and ventilation loads and distributed photovoltaics in the building micro-grid to obtain all decision variables Psi D and corresponding constraints A Psi D < = B, and delimiting each scheduling time period as 1 hour; modeling the Psi D into a curve Psi D (t) based on Bernstein interpolation in each time period, taking # imgabs0 # as a vector formed by interpolation of the curve Psi D (t), obtaining a continuous time model # imgabs1 #, obtaining an aggregated power relational expression # imgabs2 # and a constraint # imgabs4 # of an initial feasible region # imgabs3 #, and enabling FiB to be a boundary parameter obtained by ith iteration; and solving a min-max problem with an objective function of 0, obtaining a point # imgabs5 # farthest from the optimal feasible region omega opt on the current initial feasible region, iterating FiB according to the point # imgabs6 #, updating the constraint # imgabs7 #, repeating the steps until a solving result is 0, stopping iteration, and outputting an mth iteration result # imgabs8 #. The method has the characteristics of high accuracy and suitability for economic dispatching.

The invention relates to the technical field of intelligent building energy management, in particular to a power management optimization method based on an intelligent building green energy system, which is used for solving the problem of energy supply and demand imbalance caused by high time-varying characteristics of photovoltaic output and load demand in an intelligent building. The method comprises the following steps: firstly, establishing models of an intelligent building energy system, including a photovoltaic system, an energy storage system, a controllable load and a power grid interaction model; performing time-frequency domain analysis on photovoltaic output and load demand by using short-time Fourier transform, and decomposing a time sequence into a trend term, a periodic term and a random fluctuation term; a multi-time scale prediction model is constructed based on the decomposition result, and different prediction methods are adopted for different frequency domain features; and a hierarchical model prediction control framework including a day-ahead planning layer, an hour-level economic dispatching layer and a minute-level real-time control layer is further established, and multi-time-scale coordination control is realized.

The invention discloses a redundancy constraint identification and elimination method for large-scale power market clearing simulation, and the method comprises the steps: building a safety constraint unit combination and safety constraint economic dispatching optimization model in which a tracking unit, a new energy unit and an energy storage power station jointly participate in market clearing; factors such as unit self-constraint, new energy randomness and energy storage charge-discharge balance are comprehensively considered; according to a large number of line power flow constraints involved in the clearing process, the maximum possible transmission power of a power transmission line is calculated, the conversion relation between unit power and load requirements on the line is combined, and redundancy of safety constraints is rapidly recognized and screened through an analysis method, so that efficient elimination is achieved; and solving the node marginal electricity price based on the solved unit combination start-stop scheme result and the declared unit output plan. The method is more suitable for a large-scale market environment, can effectively reduce the consumption of computing resources, improves the real-time performance of market clearing, and optimizes the operation management of a power transmission network.

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 discloses a mining area comprehensive energy system low-carbon economic dispatching method considering a liquid storage type carbon capture power plant, and relates to the field of comprehensive energy system optimization dispatching, and the method specifically comprises the following steps: S1) building a liquid storage type carbon capture power plant model according to the operation characteristics of the liquid storage type carbon capture power plant; s2) establishing a two-section type electricity-to-gas conversion model; s3) establishing an associated energy utilization model; and S4) introducing a stepped carbon transaction mechanism, establishing a low-carbon optimization model of the comprehensive energy system in the mining area by taking the minimum total cost of the system as an optimization scheduling target, and determining a scheduling strategy. The low-carbon property and the economical efficiency can be considered, and the problems that in the production process of the mining area comprehensive energy system, a large amount of associated energy is wasted, environment pollution is serious, and wind power integration absorption is difficult are solved.

The invention discloses an event-triggered micro-grid distributed economic dispatching method considering a main power grid, and belongs to the field of energy-saving and environment-friendly economic dispatching of intelligent power grids. According to the method, the situation that whether the micro-grid is connected into the main power grid or not is fully considered, the more general incremental cost model is established, the finite time consistency economic dispatching algorithm triggered by the distributed event is designed, and the incremental cost of the generator is finally consistent under the condition that the topological structure is kept switched through the algorithm. Meanwhile, an auxiliary variable is introduced, the auxiliary variable is updated by applying a finite time consistency economic dispatching algorithm, and then the increment cost is updated by applying the auxiliary variable, so that the optimal increment cost and the output power of the generator can be obtained under the power limitation of the generator. In addition, when the micro-grid is connected into the main grid, the algorithm can enable the micro-grid to output power required by the main grid. According to the invention, economic dispatching can be carried out on the micro-grid under the condition of switching the topological structure within finite time, and meanwhile, power required by a main power grid can be output by being connected into the main power grid.

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.