180 results about "Renewable energy consumption" patented technology

The invention relates to the technical field of electric power control, in particular to a multi-virtual power plant collaborative scheduling method and system based on mixed games and carbon transactions. The method comprises the following steps: acquiring parameters of each virtual power plant, reading a carbon quota allocation scheme and a carbon transaction market price released by a power grid, and establishing a virtual power plant operation cost expression containing a carbon transaction cost; constructing a non-cooperative game model, and obtaining a preliminary optimal output strategy of each virtual power plant by taking minimization of the operation cost of the virtual power plant as a target; the operation state of the virtual power plant is detected, if the risk that renewable energy consumption is insufficient or the total carbon emission exceeds the standard exists, a cooperative optimization mechanism is triggered, a plurality of virtual power plants are selected to form a joint optimization group, and the joint optimization group is regarded as an independent participant to obtain an optimal output strategy again; and issuing the optimized scheduling instruction to each virtual power plant for execution according to the optimal output strategy, and participating in carbon market transaction according to the actual carbon emission and the quota difference in the settlement period.

The invention relates to a regional power distribution network multi-time scale cooperative control method based on heterogeneous resource coupling, and the method comprises the steps: constructing a cloud-edge-end hierarchical autonomous operation framework which comprises a cloud layer, an edge layer and a terminal layer, the edge layer is used for rolling correction of a day-ahead scheduling plan and generation of a control instruction, and the terminal layer is used for real-time response control; and based on a source-network-load-storage interaction model, executing a multi-time scale collaborative optimization strategy in a cloud-edge-end hierarchical autonomous operation framework, and correspondingly controlling the working state of each device in the regional power distribution network. Compared with the prior art, the method can achieve the dynamic optimization matching of distributed energy, flexible load and energy storage, improves the flexibility and intelligent level of a regional power distribution network, improves the consumption rate of renewable energy sources, reduces the operation cost, and guarantees the power supply reliability and electric energy quality.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention relates to the technical field of hydrogen load capacity configuration, in particular to a multi-scene hydrogen load comprehensive energy system double-layer capacity optimal configuration method, which comprises the following steps of: firstly, constructing a multi-scene model containing strong, weak, discontinuous and non-hydrogen loads based on the time sequence characteristics of hydrogen loads in an industrial park; then a comprehensive energy system mathematical model covering wind and light power generation, hydrogen energy conversion, multi-type energy storage and cogeneration units is established; then, constructing a double-layer capacity optimization configuration model of which the upper layer takes the whole life cycle cost minimization and the lower layer takes the operation cost minimization and the system stability optimization as targets; and finally, solving a Pareto leading edge by adopting a non-dominated sorting genetic algorithm with an elitist strategy, making a decision by utilizing an approximate ideal solution sorting method, and screening out a capacity configuration scheme with optimal comprehensive performance. The problem that a traditional configuration scheme is difficult to adapt to multi-scene hydrogen load fluctuation is effectively solved, and the economical efficiency, the stability and the renewable energy consumption capacity of the system are remarkably improved.

The invention discloses a multi-energy load prediction method based on feature screening and multi-model fusion, and belongs to the field of electric power and comprehensive energy load prediction. The invention provides a three-stage hybrid learning prediction framework. In the first stage, dynamic feature screening is achieved through a recursive feature elimination cross validation method based on expert knowledge constraints, key meteorological elements and multi-energy load time sequence features are reserved, and redundant feature interference is reduced. In the second stage, a multi-task long-short-term memory network is constructed, and coupling relation modeling and collaborative prediction of cold, heat and electricity multi-energy loads are achieved through sharing time sequence characteristic representation and a task exclusive output structure. And in the third stage, a random forest is adopted to carry out nonlinear correction on the residual error of the sub-model, so that the precision and robustness of prediction in sudden disturbance and local non-stationary scenes are improved, the prediction error is effectively reduced, and the stability of multi-energy load prediction is improved. And reliable support is provided for optimized operation, scheduling decision and renewable energy consumption of the park integrated energy system.

The invention provides an off-grid synthetic ammonia park electrolytic bath column level-energy storage-hydrogen storage collaborative optimization method based on rolling optimization, and relates to the technical field of energy storage and hydrogen storage collaboration. The method comprises the following steps: constructing an off-grid system architecture comprising wind-solar power generation, a plurality of rows of electrolytic cells, energy and hydrogen storage equipment and a synthesis ammonia device; a multi-device collaborative optimization strategy based on an eight-hour rolling time domain is designed, the system operation state is divided into high, medium and low working conditions through a working condition recognition mechanism, a current device operation scheme is actively adjusted according to wind and light power information of a next scheduling period, and collaborative regulation and control of energy storage and hydrogen storage are achieved. Compared with a traditional scheduling strategy, the collaborative optimization strategy provided by the invention improves the consumption rate of renewable energy sources of the system, reduces abandoned wind and light, improves the yield of synthetic ammonia, and verifies the effectiveness of the strategy.

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

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

The invention discloses a new energy low-carbon operation scheduling method and system for an island microgrid, and the method comprises the steps: generating a day-ahead scheduling plan with the minimization of the weighted average carbon emission intensity of a system as a core target through an upper-layer day-ahead optimization model based on load prediction data and renewable energy power prediction data; according to the day-ahead scheduling plan, generating an adjustment instruction of output of each unit through a lower-layer intra-day rolling optimization model; based on the adjustment instruction, calculating the wind and light abandoning power of the renewable energy source, and optimizing the renewable energy source consumption through a cost punishment mechanism; according to the optimized unit output and renewable energy consumption scheme, coordinating a charging and discharging strategy of an energy storage system to obtain an optimized scheduling instruction; and executing the scheduling instruction, and updating the system state through a closed-loop feedback mechanism. According to the embodiment of the invention, power balance and low-carbon operation of the island microgrid under a complex fluctuation working condition can be realized, and the utilization rate of renewable energy sources and the overall environmental protection property of the system are improved.

The invention discloses a charging module energy efficiency optimization scheduling method based on big data prediction, and the method comprises the steps: collecting multi-source heterogeneous data related to a charging load and a power grid state, carrying out the preprocessing and feature fusion, and generating a fusion data set in time-space correlation; based on the fused data set, training and applying a big data prediction model, and outputting a regional charging demand prediction result and a power grid load prediction result in a future scheduling period; inputting the regional charging demand prediction result, the power grid load prediction result and the renewable energy output prediction data into a collaborative scheduling optimization model established for a plurality of charging modules; and solving the collaborative scheduling optimization model to obtain an optimal scheduling instruction sequence of the start-stop state and the output power of each charging module in a future scheduling period so as to execute start-stop control and power distribution on each charging module. According to the invention, the charging energy efficiency can be improved, the service life of equipment is prolonged, and the renewable energy consumption rate is improved.

The invention relates to the technical field of renewable energy consumption, in particular to a vehicle-hydrogen-steel collaborative green hydrogen fluctuation stabilizing method and system, a physical layer is provided with a wind-solar power generation device, an electrolytic hydrogen production device, a hydrogen storage buffer tank, a steel heat supply device, an electric vehicle and a V2G charging pile which are connected with one another through a hydrogen conveying pipeline and a power cable; the information layer takes the central regulation and control platform as a core, collects wind-solar power, hydrogen yield, steel hydrogen consumption and electric vehicle SOC by using sensors, and transmits the collected power, hydrogen yield, steel hydrogen consumption and electric vehicle SOC to the central regulation and control platform; the central regulation and control platform executes a hierarchical dynamic fluctuation regulation and control mechanism; when the fluctuation range of the hydrogen yield does not exceed a set value, the hydrogen is stabilized by controlling the hydrogen storage buffer tank to charge and discharge hydrogen; and when the fluctuation amplitude exceeds a set value, a V2G system is started, and stabilizing is carried out by controlling charging and discharging of the electric vehicle. On the premise that the special energy storage cost is not greatly increased, stable and low-cost supply of green hydrogen is achieved, and energy conservation and emission reduction of steel and other high-energy-consumption industries are achieved.

The invention discloses a power grid flexibility potential prediction and evaluation system based on deep learning, and belongs to the technical field of power system dispatching and artificial intelligence crossing. The system comprises a hardware layer, a software layer and an application layer, wherein the hardware layer realizes data acquisition and calculation support through a multi-source terminal, an edge node and a GPU cluster; in the software layer, the data preprocessing subsystem processes multi-source heterogeneous data by adopting an adaptive feature selection mechanism, the space-time fusion prediction subsystem improves the prediction precision based on GNN-Attention-LSTM dual-mode architecture in combination with transfer learning, and the multi-dimensional intelligent evaluation subsystem dynamically optimizes the index weight through an MLP model. And the reinforcement learning decision subsystem generates a scheduling strategy by using a PPO algorithm. According to the method, the problems of high prediction error, subjective evaluation and disjoint decision in the traditional technology are solved, the short-term prediction MAE is less than or equal to 7.8%, the renewable energy consumption rate is greatly improved, the adjustment cost is reduced, and the method is suitable for power grid dispatching optimization under high-proportion new energy access.

The invention discloses a flexible interconnection and energy storage collaborative planning method for a low-voltage distribution transformer area under high-proportion photovoltaic access, and the method comprises the steps: building a structure-function two-dimensional cluster division model, dividing a distribution transformer area cluster through an electrical distance, a voltage balance degree and an active power balance degree, and reducing the dimensionality of coordinated planning; carrying out connectivity analysis on the transformer areas in the cluster, judging interconnection requirements by taking the maximum net load rate of the transformer and the minimum net load of the transformer areas as feasibility indexes, and generating a candidate scheme set; establishing a power distribution area interconnection device-energy storage double-layer planning model, minimizing equipment capacity configuration at an upper layer according to annual comprehensive investment cost, and formulating a collaborative operation strategy at a lower layer according to minimization of operation cost and maximization of new energy consumption; a hierarchical solution strategy and an improved genetic algorithm for cluster division are adopted to improve the convergence speed and precision, it is ensured that an optimal planning scheme meeting economical efficiency and operation adaptability is obtained, the problem that the influence of factors cannot be comprehensively considered in a single planning dimension is solved, and the actual operation requirement is met.

A distributed photovoltaic energy storage system configuration method based on depth feature clustering and robust double-layer optimization comprises the steps of performing normalization processing on multi-source historical data such as photovoltaic output, load, electricity price and weather, and generating a typical operation scene capable of representing uncertainty by adopting a depth embedded feature extraction and clustering algorithm; constructing a double-layer optimization configuration model: in the upper-layer planning optimization model, taking annual comprehensive cost, carbon emission cost, node voltage deviation and renewable energy consumption rate as multi-objective functions, and determining the access position, capacity and rated power of photovoltaic and energy storage; in the lower-layer operation optimization model, the charging and discharging strategy and reactive power output of the energy storage system are optimized with the daily operation cost, the voltage deviation, the electric energy quality index and the auxiliary service income as optimization targets; and an improved NSGA-II algorithm based on an elitist strategy is adopted to realize closed-loop optimization of planning and operation. According to the method, economy, low-carbon property and electric energy quality can be considered while uncertainty is considered, and the comprehensive performance of operation of the power distribution network is improved.

The invention discloses a dynamic anti-disturbance coordinated scheduling method for a hybrid energy micro-grid, and aims to solve the problems of poor stability, high cost, low consumption rate and the like of the hybrid energy micro-grid caused by disturbance such as renewable energy fluctuation and load abrupt change. The method comprises the following steps: firstly, constructing a system model containing multiple units, and collecting and preprocessing real-time operation data; establishing a multi-target scheduling objective function, and solving an initial scheduling instruction by adopting an improved predictive control algorithm; and finally, generating a coordinated scheduling strategy by combining the disturbance information dynamic correction instruction. Meanwhile, a security constraint and scheduling effect evaluation link is set, and mechanisms such as multi-time scale scheduling and layered response are adopted. The method can significantly improve the anti-disturbance capability and stability of the system, reduces the operation cost by 15%, improves the renewable energy consumption rate by more than 8%, is adaptive to multiple scenes, guarantees the safe operation, and is suitable for various hybrid energy microgrids.

The invention relates to the technical field of comprehensive energy system optimization scheduling, and discloses a multi-energy complementary collaborative optimization scheduling method, system, medium and equipment, and the method comprises the steps: constructing a light-storage-heat integrated system model, and collecting system operation data; based on high-precision weather forecast data and system operation data, outputting a photovoltaic output predicted value and a load predicted value in a future scheduling period in a rolling manner by using a prediction model; establishing a collaborative optimization scheduling model by taking the lowest total operation cost of the system and / or the highest renewable energy consumption rate and / or the optimal comprehensive energy efficiency as an optimization target; based on the system operation data, the photovoltaic output prediction value and the load prediction value, an optimization algorithm is adopted to solve the scheduling model, and an optimal scheduling instruction is generated; and controlling power output of the photovoltaic inverter, the storage battery converter and the electro-thermal conversion equipment in real time according to the instruction to realize collaborative optimization operation. According to the invention, deep coupling and cooperative scheduling of electric heat energy are realized, and the system can operate economically, efficiently and stably.

The invention discloses a vehicle network interaction flow optimization method based on information and physical fusion, and belongs to the technical field of intelligent traffic and energy internet crossing. Comprising the steps that a vehicle network is divided into a plurality of grid areas, and each grid area comprises physical layer operation data and information layer operation data; constructing a vehicle network diagram structure, and calculating the schedulable potential of the EV in the stay duration of the charging station; according to the schedulable potential and the information layer operation data of the EV in the stay time of the charging station, the method aims at minimizing the peak-valley difference, maximizing the renewable energy consumption, the vehicle network interaction income and the operation income. Constructing an objective function of the multi-objective flow optimization model, an EV charging and discharging power constraint, an EV battery charge state constraint, a charging pile capacity constraint and a power grid safety constraint; and solving the multi-target traffic optimization model to obtain a vehicle network interaction traffic optimization result. According to the method, the prediction error of the EV arrival time is reduced, and the V2G flow regulation and control are refined.

The application discloses a kind of integrated energy system low carbon operation method, belong to power grid technical field.The method includes based on baseline method allocation system free carbon emission quota, calculate actual net carbon emissions;Set integrated energy system objective function;Adopt two-stage robust optimization method, handle renewable energy and load uncertainty, first based on predicted worst-case scenario information obtains pre-dispatch optimization result, subsequently based on pre-dispatch information seeks worst-case scenario, ensure stable operation of system by iterative solution;Build and include integrated energy system, integrated energy system and photovoltaic production and sales between and photovoltaic production and sales between multilayer transaction framework, adopt Nash negotiation model and multistage robust method, realize the energy distribution between multiple stakeholders;By solving integrated optimization model, obtain the optimal strategy of integrated energy system low carbon operation.Thereby realized the coordination optimization of carbon emission right and renewable energy consumption, further improve the low carbon operation effect of system.

The invention relates to the technical field of integrated energy system dispatching, in particular to an integrated energy system low-carbon dispatching method considering fuel gas hydrogen mixing and carbon transaction, which comprises the following steps of: constructing an integrated energy system model containing electric power, thermal power, natural gas and coupling equipment, and defining an electric hydrogen production process and an electric methane production process of power-to-gas equipment by the coupling equipment model; a fuel gas mixed hydrogen energy flow calculation model is constructed, a decomposition solution method is adopted for iterative convergence and dynamic calculation of a mixed fuel gas real-time heat value, and energy balance constraints are improved; constructing an optimal scheduling model considering the stepped carbon transaction, and taking the minimization of the total operation cost as a target; and applying hydrogen mixing proportion upper limit constraint, and cooperatively deciding power distribution and a hydrogen storage tank charging and discharging strategy through an optimization algorithm to solve a target function. According to the method, the energy flow calculation precision and the renewable energy consumption capability are improved, and collaborative optimization of system economy and environmental protection benefits is realized.

This invention discloses a joint clearing method and system for the electricity spot market that considers the generalized energy storage characteristics of electric vehicle aggregators. Based on the operational characteristics and physical constraints of large-scale electric vehicle access to the electricity spot market, and combined with the theory of generalized energy storage efficiency, an embedded constraint model is constructed, including charging and discharging power boundaries, energy state temporal coupling, and terminal electricity demand. This enables quantitative modeling and unified scheduling of the flexibility resources of electric vehicle aggregators. Data cleaning and parameter normalization methods are used to map the characteristics of dispersed electric vehicle clusters to standardized virtual power plant parameters. State equation constraints are introduced into the traditional DC optimal power flow and unit combination model. Combining mixed-integer linear programming algorithms and dual pricing theory, the optimal coordination strategy between conventional unit output and electric vehicle charging and discharging is solved, and the low-cost clearing and nodal marginal price of the spot market are calculated. The method of this invention can improve the level of renewable energy consumption and the economy and security of grid operation.

The application provides a kind of wind light water storage double-layer robust optimization scheduling strategy construction method and system, relating to energy management technical field, including first obtaining the multi-source data of wind farm, photovoltaic power station and cascade hydropower station;Characteristic extraction and integrated prediction are carried out based on the multi-source data, and wind light power prediction result is generated;Further, water power coupling modeling analysis is combined to obtain the power generation capacity of hydropower station and the sequence of adjustable water volume;By constructing multi-layer energy coupling graph and carrying out spectral domain structure optimization, power coupling network representing space-time correlation and ecological sensitivity is formed;Finally, double-layer distributed robust optimization is used to optimize economic target and ecological risk constraint simultaneously, and generate collaborative scheduling strategy.The application effectively solves the time sequence matching problem between wind light fluctuation and water power time lag, improves the renewable energy consumption capacity and water resource utilization efficiency, and reduces the ecological operation risk.

This application relates to the field of power grid optimization dispatching technology, specifically to a multi-objective optimization dispatching method and system for active distribution networks with source-grid-load-storage coordination. The method includes: collecting the energy storage capacity of each renewable energy power station in the active distribution network, and the energy dispatched by each transformer from each renewable energy power station; calculating the priority dispatching characteristics of each renewable energy power station; predicting the future energy demand of each transformer and the future energy storage capacity of the renewable energy power stations supplying them; coordinating the transmission distances between each transformer and the renewable energy power stations supplying them; determining the dispatch matching coefficient between the renewable energy power stations and transformers; based on the dispatch matching coefficient, constructing an objective function with the goal of minimizing the source-grid-load-storage coordinated dispatching cost and maximizing renewable energy consumption; and using a multi-objective optimization algorithm to solve for the optimal solution, which is then used for power dispatching. This improves the rationality of power resource dispatching in the distribution network.

The invention relates to an optimal configuration method for calculating the energy storage capacity of a multi-objective integrated energy system. The invention relates to the technical field of planning and optimization of an integrated energy system, and aims to solve the technical problem that a configuration result is difficult to consider economy, reliability and environmental protection due to single target or high subjectivity of weight setting. The method comprises the following steps: constructing a multi-objective optimization model which takes the capacity of the energy storage equipment as a decision variable and simultaneously comprises an economic index, a reliability index and a renewable energy consumption index; and solving the model by adopting a non-dominated sorting genetic algorithm to generate a group of Pareto optimal solution sets representing the trade-off relationship among different targets. The defect of subjective empowerment is overcome. And performing comprehensive scoring and sorting on all the schemes in the Pareto solution set by using a good-bad distance sorting method based on an ideal solution, and finally screening out an optimal solution with the highest score as a final capacity configuration scheme of the energy storage system.

The invention relates to the technical field of renewable energy utilization and heat supply systems, in particular to a heat pump and energy storage combined heat supply system with renewable energy consumption, which comprises a renewable energy power generation unit, an energy storage unit, a low-temperature efficient heat pump unit, a heat supply pipe network unit and an intelligent management and control unit, the energy storage unit and a power grid are connected to the main power supply switching cabinet through an electrical link, and the main power supply switching cabinet supplies power to each power utilization unit through the power distribution unit; the energy storage unit is connected with the low-temperature efficient heat pump unit and the heat supply pipe network unit through heat energy links. The intelligent management and control unit establishes data interaction and control links with the renewable energy power generation unit, the energy storage unit, the low-temperature efficient heat pump unit and the heat supply pipe network unit. The technical problems that in the prior art, the renewable energy consumption rate is low, the heat pump heating efficiency in the cold region is poor, and the system operation stability is insufficient are mainly solved.

The invention discloses a cooperative control method for a hydrogen-electricity energy storage unit cluster in a hydrogen energy supported micro-energy network, and belongs to the technical field of new energy power system control. According to the cooperative control method, the SOH of PEMFC / EC can be considered to be directly related to the health state of the whole hydrogen-electricity energy storage unit; the purpose of the invention is to ensure efficient, long-life and economical operation of a hydrogen-electricity energy storage unit. PEMFC / EC is taken as a main body, SOH of PEMFC / EC equipment individuals, SOH consistency (HSH) of PEMFC / EC similar equipment clusters, overall energy cost (COE) of hydrogen-electricity equipment and complementary adjustment between the hydrogen-electricity equipment are considered, high-proportion renewable energy consumption can be better promoted, and interconnection and intercommunication of hydrogen-electricity heterogeneous energy flows are achieved.

The invention relates to the technical field of electrified railway collaborative operation, and discloses a power grid-traction network resource collaborative evaluation method and system, and electronic equipment, and the method comprises the steps: collecting real-time data of a power grid side, a traction network side and a resource side, and carrying out the preprocessing of the real-time data, and obtaining a standardized data flow; based on the standardized data flow, adjustable potential models are established for traction load, regenerative braking energy, traction station energy storage and road area photovoltaic / wind power respectively, and the adjustable potential range of each resource is obtained; based on the adjustable potential model, performing dynamic optimization evaluation through an optimization algorithm to obtain an adjustable potential evaluation result and a scheduling strategy; outputting an adjustable potential evaluation result and a scheduling strategy; based on the scheduling policy, a peak load shifting, frequency response, or renewable energy consumption application is performed. According to the method, real-time operation information can be fused, multi-resource characteristics are planned as a whole, intraday dynamic evaluation and collaboration are realized, and the flexibility resource potential of a power grid-traction network system is fully excavated.

The application discloses a power distribution network planning method, device and equipment and a storage medium. The method comprises the following steps: acquiring at least one planning data in renewable energy characteristic data, power distribution network topology and element parameters, load time sequence data and economic parameters; then inputting the planning data into an initial model to construct a target model comprising a target function, system constraints and polyhedral uncertainty set constraints; then obtaining an adaptive uncertainty set based on the polyhedral uncertainty set constraints to accurately adapt to long-term and short-term output uncertainty of the renewable energy; and finally inputting the adaptive uncertainty set into the target model to obtain an optimal site selection and fixed capacity scheme of the power distribution network. The application can effectively depict the uncertainty characteristics of the renewable energy, improve the scientificity and economy of the planning scheme, guarantee the safe and stable operation of the power distribution network and improve the renewable energy consumption capacity.

This invention provides a method for optimizing energy storage configuration in distribution networks based on renewable energy consumption, belonging to the field of energy storage optimization configuration. The method involves: a synchronization feature module for time-domain synchronization processing to generate feature vectors; a frequency mapping modeling module to construct an equivalent feature matrix including inter-axis cross terms; an admittance reconstruction module to inject this into the static topology to generate a real-state dynamic admittance model and solve for the impedance matrix; a sensitivity evaluation module to map and generate real-state voltage sensitivity and determine the impedance offset index; a configuration module to perform nonlinear hedging to generate configuration data; and a calibration module to perform recursive calibration. The system also generates source-grid attribution weights based on normalized cross-correlation coefficients, enabling accurate determination and differentiated correction of causes on the equipment side and grid side, and introduces a self-evolving evolutionary flow for closed-loop optimization. This invention effectively reduces asynchronous interference from heterogeneous data, improves the accuracy of risk attribution and control, and ensures the stability of the distribution network.

The application provides a renewable energy supply-distribution-demand-storage system space-time coordination configuration method, relates to the technical field of intelligent energy, and comprises the following steps: acquiring data of multiple regions; calculating time mismatch degrees, space mismatch degrees and space-time coupling mismatch degrees of the regions; decomposing an optimization problem into a long-term planning layer, a medium-term scheduling layer and a short-term real-time layer in a time dimension; constructing multi-objective optimization functions of the time layers; decomposing the optimization problems of the time layers in a space dimension; alternately solving local optimization sub-problems and global coordination problems of the regions by using a distributed iteration method; outputting a configuration result of a current time layer when the algorithm converges; executing a decision of a first time period by each time layer and taking the decision as a constraint condition of a next time layer to obtain power generation installed capacities, power transmission line capacities, energy storage capacities and operation strategies of the regions. The application can improve a renewable energy consumption rate and reduce system operation costs.