916 results about "Electricity cost" patented technology

Provided in embodiments of the present disclosure are a method for scheduling workloads of a data center in an electricity market environment, and a device. In the embodiments of the present disclosure, a multi-stage random optimization solution that allows a data center to participate in a multi-level electricity market is provided. The types and data dependency relationship of workloads are taken into consideration, and a load scheduling model, in a time dimension, for the workloads is constructed; on the basis of electricity source information of the data center, a multi-stage electricity cost optimization model is constructed, so as to comprehensively consider the exogenic uncertainty of an electricity price and carbon emissions and the endogenic uncertainty of the workloads; on the basis of the multi-stage electricity cost optimization model, multi-stage optimization solving is performed on the load scheduling model, so as to obtain a load scheduling scheme and an electricity purchase strategy. Therefore, a more accurate and more reliable estimation is made concerning the electricity flexibility of the data center, such that the electricity cost of the data center can be reduced, support is provided for the reduction of carbon emissions, and the consumption of renewable energy sources is encouraged, thereby achieving the energy use flexibility of the data center in the multi-level electricity market environment.

The invention belongs to the technical field of power file rule extraction and change comparison, and particularly relates to a policy document intelligent rule extraction and change comparison method for electricity charge checking, which comprises the following steps: analyzing, reconstructing and optimizing an electricity charge related policy document, and retaining original context and logic level information; designing a core statement recognition Prompt template and a rule extraction and conversion Prompt template, constructing a policy and rule extraction agent, guiding a large language model to recognize core statements, extracting rule element information and forming standardized policy and rule description; according to the method, similar policy rules are retrieved from a historical policy rule description knowledge base, a policy rule comparison agent is constructed, and deep semantic comparison and change analysis are performed on current standardized policy rule description and historical similar policy rule description, so that automatic identification of newly added and modified contents of current rules and historical rules is realized; and a key technical support is provided for automation and intelligentization of electricity charge checking business rule generation.

The invention provides an electric energy metering detection data quality evaluation method and system, and relates to the field of data processing. Data abnormity generated by a current transformer aiming at gradual change characteristics caused by contact oxidation is firstly eliminated through improved wavelet transform and feature extraction; the abnormal rate of the processed data is calculated according to a support vector machine model, the conditions of noise interference and abnormal fluctuation in actual current data are considered when a support vector machine is designed, and the contact resistance is slowly increased due to contact oxidation of the secondary winding of the current transformer; therefore, the problems that the gradual change abnormity is difficult to find in time and the electric charge metering deviation is easily caused by the existing fixed threshold value evaluation method are solved.

The invention relates to an electric charge recovery dynamic risk assessment method and system based on dual-channel ensemble learning and dynamic PID regulation and control. The prediction method comprises the following steps: selecting power data of a certain number of enterprises for analysis, and screening Top-15 high contribution features; the XGBoost and the LightGBM are fused by adopting a Stacking integration method, and the arrearage probability of the user is predicted; proposing a tail risk capture mechanism of quantile random forest regression, and predicting the arrearage amount of the arrearage user based on the arrearage probability output by the classification model; and a two-channel joint framework of a classification model and a regression model is constructed, and two-dimensional early warning of arrearage probability prediction and arrearage amount quantitative analysis is realized. A dynamic risk scoring mechanism is constructed according to the arrearage probability of classification prediction and the large-amount arrearage risk of regression quantification, upgrading from a single index to a comprehensive score is completed, a dynamic risk regulation and control mechanism based on PID is provided, and intelligent grading and dynamic early warning of arrearage users are achieved.

The invention relates to the technical field of energy storage control, and discloses a demand control method and system based on industrial and commercial energy storage, and the method comprises the steps: obtaining and carrying out the standardized processing of a real-time load demand, an energy storage state and power grid dispatching data; establishing a load prediction model to generate demand baseline parameters, and constructing a dynamic adjustment rule base; the energy storage output is verified in real time, and the dynamic response trend of the power difference value is analyzed; and generating a demand control index to determine a charging and discharging strategy. The system comprises a load monitoring module, a strategy generation module, a dynamic execution module and a control judgment module which are respectively used for realizing data acquisition and processing, model construction, output verification analysis and strategy judgment functions. According to the scheme, through multi-dimensional data integration, dynamic rule base optimization and multi-constraint coordination technologies, the precision and economical efficiency of industrial and commercial energy storage demand control are improved, the demand electric charge can be effectively reduced, the electric energy utilization rate is improved, and the method is suitable for energy storage management and power grid collaborative optimization of industrial and commercial users.

The invention discloses an intelligent regulation and control method and system for a dust removal system of an iron and steel enterprise, and the method comprises the following steps: collecting real-time data according to a set frequency, including the operation parameters of each dust production point, the opening degree of a valve, and the operation parameters and exhaust condition parameters of a dust remover, dust conveying equipment and a fan, and dividing the data into operation variables and regulation variables; a hybrid neural network model is constructed, the hybrid neural network model comprises an LSTM-GPT-Neo backbone network, a DenseNet branch network and a meta learning feature fusion correction layer, and the LSTM-GPT-Neo backbone network fuses an LSTM architecture and a GPT-Neo architecture; inputting time sequence data to carry out model training and optimization; and inputting the data into the trained hybrid neural network model for calculation and analysis, and outputting the opening degree of the valve and the optimal operation parameters of the fan. Under the condition that the dust removal efficiency and stability are guaranteed, the electricity utilization cost of the steel plant can be remarkably reduced, and the problem of equipment loss caused by excessive dust emission or excessive air volume due to insufficient air volume is avoided.

The invention discloses a multi-scale electric energy flow control method and system based on deep reinforcement learning, and the method comprises the steps: S1, constructing prediction models of different time scales, and integrating the trained prediction models into a calculation platform; s2, the state space of the energy storage system optimization control algorithm comprises the charge state of the current energy storage system, real-time load data, predicted load and photovoltaic power generation power, real-time electricity price information and time characteristics; s3, the action of the energy storage system optimization control algorithm is the charging and discharging power of the energy storage system, and the action space is a continuous action space; s4, a reward function of the energy storage system optimization control algorithm considers electric power purchase cost, demand electric charge, energy storage loss, green energy consumption and real-time load fluctuation; and S5, training the energy storage system optimization control algorithm in the simulation environment to output an optimal energy storage control strategy. According to the invention, changes of photovoltaic power generation and real-time load are accurately predicted, and scheduling and optimization requirements of a power system are met.

The invention provides a regional intelligent power utilization method and system based on source-grid-load-storage cooperation and dynamic electricity price. The method comprises the following steps: collecting photovoltaic output, energy storage SOC, load demand and time-of-use electricity price data in real time; determining a corresponding dynamic control mode in combination with the economic target model, the environmental protection target model and the constraint conditions; when the running dynamic control mode is a light storage and charging cooperative arbitrage mode, dynamically determining an energy storage charging and discharging strategy; when the running dynamic control mode is a virtual power plant aggregation mode, forming scheduling capacity by aggregating distributed resources, and responding to a power grid peak regulation instruction; when the running dynamic control mode is a carbon-electricity joint optimization mode, a carbon trading platform is connected, green certificate binding sales is carried out, scheduling capacity is formed through dynamic integration of energy storage charging and discharging strategies and aggregation of distributed resources, the carbon trading platform is connected, green certificate binding sales is carried out, economic benefits are effectively improved, carbon emission is reduced, and the service life of the system is prolonged. And the double-target optimization of the electric cost and the carbon emission is realized.

The invention provides a power marketing archive intelligent management method, which comprises the steps of receiving archive data through a power data acquisition interface, matching an electricity price feature code in an archive based on a real-time electricity price policy identifier, and injecting a version calibration mark into unmatched data; constructing an electricity consumption abnormity prediction model based on the user historical load mode and the environmental factors; when a query request is responded, dynamic penalty parameters are generated based on the user behavior portrait and the rule base; a quantum key distribution path is generated based on a power grid topological structure, and cascade encryption is implemented along physical connection nodes; dynamically associating user operation authority with a power grid operation risk signal, and automatically disabling core operation in a high-risk state; and analyzing archive data in a security isolation environment to drive line loss optimization, electric charge tracing and fault pre-judgment service flow in real time, and outputting feedback archive update based on the service. According to the invention, the safety, reliability and operation efficiency of power marketing archive management are improved.

The invention discloses a multi-microgrid and 5G base station collaborative optimization control method for improving the power supply quality of a photovoltaic power distribution network, and relates to the technical field of electric power, and the method comprises the steps: analyzing the load characteristics of a 5G base station, and building a 5G base station cluster backup energy storage, variable frequency air conditioner and communication load transfer power scheduling model; aiming at a multi-microgrid scene under the same power distribution network, establishing a master-slave game model of the power distribution network and a multi-microgrid-5G base station cluster alliance; and solving a master-slave game model of the power distribution network and the multi-microgrid-5G base station cluster alliance, and carrying out collaborative optimization control according to a model solving result. According to the invention, the demand response potential of various loads of the 5G base station can be explored, and the power consumption cost is saved. And benefit balance between the power distribution network and the multi-microgrid 5G base station cluster system can be realized. Through power mutual aid among multiple micro-grids, the local consumption capability of new energy and the operation benefit of each micro-grid are improved.

The invention relates to the technical field of community new energy control, and discloses an orderly and rapid charging new energy control method, which comprises the following steps: collecting photovoltaic output data, power grid voltage / current data, user charging behavior data and battery state parameters in real time; generating a dynamic charging strategy based on the power grid load data, the time-of-use electricity price information, the photovoltaic output fluctuation range and the battery health state evaluation result; generating a corresponding local optimization strategy based on the operation state data of each charging pile; an economic index and a technical index are calculated, and when any index exceeds a preset threshold value, a strategy adjustment instruction is triggered, and a charging mode switching threshold value is updated; an equipment health index is generated by combining inverter temperature, coil coupling efficiency and insulation impedance parameters with historical fault data, and maintenance early warning is triggered when the health index is lower than a safety threshold value. According to the invention, the electricity cost of the user can be reduced on the basis of improving the multi-energy collaborative optimization capability of the charging facility in the residential area.

The invention relates to a system for a user-side energy storage sharing framework. A power energy scheduling optimization method comprises: establishing a user load model, an energy storage battery model and a user-to-user capacity sharing model; with a minimum electricity cost of a whole community as an objective optimization function, resolving optimal scheduling based on the function using a MILP algorithm as an optimization technique for power energy scheduling of a user-side energy storage sharing framework; and performing power energy scheduling for users according to a resolved result. The invention determines a user cost model under the user-side energy storage sharing framework, establishes an electricity model of non-transferable loads and transferable loads and corresponding constraints, and obtains optimal values of adjustable variables to control electricity consumed by electric loads by resolving an optimal value of an objective function under the precondition of reducing user costs, thus further reducing electricity costs.

The invention relates to the technical field of energy storage load scheduling, in particular to an energy storage load scheduling method and system based on multi-objective optimization, and the method comprises the steps: obtaining historical electricity price, load power and renewable energy power generation power data of each time period before the current time as input variables, and carrying out the normalization processing to obtain input data; inputting the input data into an LSTM-Transformer prediction model, and predicting to obtain the power grid electricity price, the load demand and the renewable energy power generation amount in a future time period; establishing a multi-objective optimization model including electric charge cost, operating profit and energy storage life; substituting the prediction data into the multi-objective optimization model, and solving a Pareto frontier based on a multi-objective optimization algorithm to obtain a non-inferior solution set satisfying the multi-objective optimization model; and taking each solution in the non-inferior solution set as input, and screening the solution with the maximum Sharpley value as an optimal energy storage charging and discharging adjustment strategy. By accurately controlling the working state of the energy storage system, the interactive operation mode of the energy storage system and the power grid is optimized.

The invention discloses a home electricity charge optimization method and device based on an improved ant lion algorithm, and relates to the technical field of home electricity utilization optimization, and the method comprises the steps: S1, constructing a home electricity charge model used for calculating the electricity charge of home electricity utilization equipment and a multi-objective optimization function combining a home electricity charge objective function and user dissatisfaction; s2, taking a multi-objective optimization function as a fitness function of an improved ant lion algorithm, and using the improved ant lion algorithm to solve the household electricity charge model; the improved ant lion algorithm uses chaotic mapping to initialize ants and ant lion populations; cauchy variation disturbance is introduced when local optimum is trapped by using Cauchy distribution. According to the invention, by constructing the household electricity charge model, double-target integration of electricity charge expenditure and user experience is formed, and collaborative optimization of electricity charge expenditure and user comfort is realized; the improved ant lion algorithm is used for solving the household electricity charge model, and the defect that a traditional ant lion algorithm is likely to sink into local optimum is overcome.

The invention provides a multi-stage desensitization electricity charge sensitive data protection system, belongs to the technical field of information security in the power industry, and relates to a full-life-cycle protection scheme of sensitive data such as an identity label, account information and contact information in an electricity charge service system. The problems that in the prior art, only simple masking or full-table encryption is carried out on a database layer, the desensitization dimension is single, multiple links are prone to leakage, no role fine-grained desensitization strategy exists, and exported files are difficult to trace are solved. The system comprises a data acquisition source and acquisition layer desensitization, a transmission layer encryption, a storage layer transparent encryption, an API gateway, a desensitization strategy center, a front-end dynamic mask, a watermark export, a log desensitization plug-in and an auditing log module, covers the whole process from data acquisition to log auditing, dynamically regulates and controls data display according to roles, and exports a file embedded identity watermark. And desensitizing the log and reserving operation marks. Full-link leakage prevention, fine-grained permission control and leakage traceability can be realized, and the transformation cost is low.

The invention discloses an energy storage operation optimization method and system based on hierarchical multi-agent reinforcement learning. The method comprises the following steps: firstly, initializing a hierarchical multi-agent reinforcement learning decision framework, and defining an interlayer communication synchronization mechanism; then, designing an upper layer reward function for balancing the electric charge income and the battery life loss cost, and optimizing an upper layer planning strategy; a lower layer reward function integrating current smoothness and budget tracking precision is designed, and a lower layer execution strategy is optimized; and finally, deploying the optimized upper-layer planning strategy and the optimized lower-layer execution strategy to an energy storage scheduling task, generating a charging and discharging power budget by an upper-layer planning agent in a day-ahead scheduling stage, and receiving a budget instruction and performing power adjustment by a lower-layer execution agent. According to the invention, by constructing the hierarchical decision-making architecture, coordinated optimization of short-term economic benefit and long-term life protection of the energy storage equipment is realized.

The invention discloses an electricity customer credit rating management method and system based on a differentiated service strategy, particularly relates to the field of energy service, and solves the problem of cold start user credit evaluation deviation and power service resource dynamic adaptation optimization. Seamless migration of credit scoring parameters and smooth transition of user portraits are realized by introducing multi-dimensional feature similarity analysis of virtual data and real data and a time-driven weight attenuation strategy, and continuity and adaptive optimization capability of an evaluation system are ensured; meanwhile, an intelligent service matching network is constructed based on the credit rating updated in real time, and a closed-loop service management and control system driven by data feedback is formed by dynamically adjusting a prepayment mode threshold value and an electric charge overdraft elastic interval strategy, so that the credit value of a user is deeply associated with the power resource configuration efficiency, and the user experience is improved. The operation risk and the service cost of a power grid enterprise are reduced, and finally collaborative optimization ecology considering energy safety management and user behavior guidance is established.

The invention discloses a household micro-grid energy optimization scheduling method considering demand response based on time-of-use electricity price. The method comprises the following steps: constructing a household micro-grid model considering various demand loads; considering a discharge depth constraint and an energy storage cycle number constraint, and constructing a full-cycle battery life model; for uncertainty of new energy output of the household micro-grid, a Latin hypercube sampling method is combined with a Kantorovich scene reduction method to generate a photovoltaic output typical scene; and constructing a micro-grid energy optimization scheduling model and solving by taking the minimum family operation cost, energy purchase cost and daily loss cost of the service life of an energy storage battery as a target and combining constraint conditions. According to the method, the power consumption cost of the household micro-grid can be effectively reduced, and the consumption level of new energy is improved. The technical support is provided for the safe operation between the distributed photovoltaic system and the power grid while the home user actively participates in the demand response scheduling.

The present invention relates to the technical field of steel industry load participation in power grid interaction, and provides a double-layer optimization scheduling method and system for steel industry load, the method comprising: constructing a continuous time interactive resource task network model based on double-layer optimization data information; constructing a resource task network model with production process constraints; constructing a complex order characteristic analysis model for steel industry load; setting dynamic maximum demand analysis model operation constraints for the complex order characteristic analysis model; constructing an upper-layer power grid scheduling optimization model that achieves optimal power grid economic cost, and the lower-layer steel industry load side full system model and the upper-layer power grid scheduling optimization model jointly form a double-layer optimization system; proposing a double-layer optimization algorithm for the double-layer optimization system, solving the double-layer optimization scheduling; and outputting double-layer optimization scheduling result information. The present invention significantly reduces the electricity cost of short-process steel industry loads, and allocates production orders in combination with steel order characteristics to achieve better peak shaving and valley filling effects.

The invention discloses a method for improving indoor heating. A circular heating electric stove wire electric stove, a stepless temperature and pressure regulator, a circular electric ceramic stove microcrystalline panel, an upper-layer stainless steel round barrel, a lower-layer stainless steel round barrel and water are matched for use, the water added into the lower-layer stainless steel round barrel is heated and boiled, and low-cost, high-efficiency and energy-saving effects are achieved by efficiently utilizing water vapor generated by heating. The invention discloses a noiseless indoor heating method. A simple and convenient mode is adopted, the comfortable and ideal temperature requirement is met, and the indoor heating method which is low in cost, environmentally friendly and free of noise and reaches about 24 DEG C is achieved. Under different environment conditions, the heating temperature and the heating time are set according to respective requirements; the heating device is simple in structure and convenient to use, continuous heating is conducted for 60 minutes at the power of 200 W-300 W in a room with the area of 15 square meters, and electricity consumption displayed by an electricity meter is 0.10 yuan to 0.15 yuan and is far lower than the electricity consumption cost of air conditioner heating and is only equivalent to about 50% of the concentrated heating cost.

The invention provides a user electricity larceny suspicion coefficient determination method based on multi-dimensional abnormal feature analysis, and belongs to the technical field of electricity larceny prevention. According to the method, a user electricity stealing identification algorithm applied to an electricity utilization inspection system is provided, and on this basis, multi-dimensional parameters such as user longitudinal electricity utilization behavior characteristics and transverse environment correlation characteristics are creatively fused to construct a comprehensive electricity stealing characteristic index system. And after a significant deviation of an electricity consumption prediction value is found preliminarily, according to multiple characteristic quantities extracted by an index system, an abnormal weight coefficient of each characteristic quantity is dynamically determined and calculated in a combined manner, and finally an accurate electricity stealing suspicion coefficient is generated. According to the invention, potential electricity stealing behaviors can be identified more reliably, and the probability of single-index misinformation is reduced at the same time. The method emphasizes the mining of the abnormal power consumption mode of the user and the relationship between the abnormal power consumption mode and multiple factors, fully utilizes the alarm event and the electric charge settlement data of the local metering device, and significantly reduces the dependence on massive complex modeling.

The present application relates to a method, apparatus, computer device, and storage medium for determining a smart meter fault. The method includes: obtaining a data curve measured by the smart meter within a preset time period, where the data curve includes first voltage curves of multiple cycles and current curves corresponding to each first voltage curve. Extracting a preset number of first voltage curves from the data curve as target voltage curves, where the target voltage curves are the first voltage curves corresponding to the current curves with current amplitudes less than a preset value. Determining a target value of the voltage measurement error of the smart meter according to the target voltage curves. Thus, the error value of the voltage measurement caused by the fault of the smart meter is determined, and through this error value, the fault condition of the voltage is quantified, thereby realizing the determination of the meter fault. This enables the staff to repair or replace the smart meter in a timely manner, avoiding situations such as incorrect user electricity bill measurement or power outage caused by meter faults.

The invention provides a cross-day two-stage random scheduling method for an industrial park integrated energy system, and the method comprises the steps: fitting the output fluctuation characteristics of new energy power generation equipment in a cross-day time scale based on historical data, and constructing an uncertainty scene set containing a new energy prediction error; establishing a cross-day two-stage stochastic programming model; according to the two-stage model, an optimal scheduling scheme is solved so as to minimize the overall operation cost, and the operation cost comprises the demand electric charge, the electricity purchase electric charge, the new energy power abandoning cost, the unit start-stop cost and the standby penalty cost; and on the basis of tie line power constraint and dynamic response characteristics of the multi-energy coupling equipment, feasibility verification and rolling optimization adjustment are performed on the scheduling scheme. According to the method, the new energy consumption capability of the industrial park integrated energy system under the cross-day time scale can be effectively improved, the total operation cost is reduced, and collaborative scheduling of demand cost optimization and spot market participation is realized.

The invention discloses a glass factory load low-carbon regulation and control method considering capacity compensation and carbon quota, and aims to realize refined carbon emission control and flexible scheduling in the production process of a glass factory. By fusing multi-dimensional input of historical load power, load change rate, environment temperature, humidity, wind speed, electricity price, time and the like and utilizing an STFormer architecture to model a complex time dependency relationship, a kilowatt-hour electricity carbon emission factor in a future time period is predicted, and prior information is provided for low-carbon scheduling; with minimization of carbon emission and electric charge cost and maximization of capacity income as targets, a future load scheduling strategy is optimized under constraints of carbon quota, an electric power regulation range and equipment operation; and globally searching an optimal load control scheme through a quantum particle swarm algorithm, and generating a multi-scene-oriented dynamic regulation and control strategy under the constraints of a multi-target fitness function and a penalty mechanism. The method not only improves the precision and robustness of carbon factor prediction, but also realizes flexible scheduling of the glass factory load driven by carbon economy.

The invention discloses a method, a system, a medium and equipment for detecting abnormal data of electric charge, and belongs to the field of machine learning, and the method comprises the steps: collecting a historical power utilization data set of a user; based on a preset Affinity Propagation clustering algorithm, clustering division is carried out on the historical electricity consumption data set, and clustering clusters representing different electricity consumption behavior modes and clustering center parameters of the clustering clusters are obtained; recursively constructing an isolated tree for each cluster through a preset isolated forest algorithm, and obtaining a segmentation rule parameter of each isolated tree; integrating each clustering center parameter and a segmentation rule parameter of the isolated tree, and constructing an anomaly detection model; and detecting the current power consumption data according to the anomaly detection model, and outputting a detection result. Therefore, by implementing the method and the device, the problem of low abnormal electricity charge detection precision in a complex electricity consumption scene in the prior art can be solved.

The invention relates to an electric charge calculation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring electricity charge metering data based on a message queue Kafka to obtain an electricity charge metering message; wherein the electricity charge metering message comprises a user identifier, electricity consumption and electricity consumption time; the electricity charge metering message is consumed based on a streaming computation engine Flink, electricity charge calculation is carried out, and an electricity charge calculation result is obtained; and the electric charge calculation result is stored in a Redis cluster through a stream-oriented calculation engine Flink. According to the electric charge calculation method provided by the invention, an efficient and extensible real-time electric charge calculation system can be formed by fusing the message queue Kafka, the streaming calculation engine Flink and the Redis cluster and using the multi-thread concurrent processing and partition characteristics, so that the requirements of high timeliness and complex calculation of electric charge calculation are met.

The invention belongs to the technical field of intelligent electric charge questions and answers, and particularly relates to a hierarchical knowledge network construction and retrieval method for intelligent electric charge questions and answers. The invention provides an iterative adaptive enhanced RAG method, based on a hierarchical knowledge network, information retrieved by a first round of problems is used as a knowledge carrier, newly retrieved information is continuously updated into initialized information in subsequent multi-round iterations, information is adaptively collected from the perspective of knowledge increase, and the information retrieval efficiency is improved. New knowledge and collected knowledge are flexibly integrated, and the problem that information interaction among different retrieval steps is insufficient is solved; in addition, a self-adaptive exploration stopping strategy is formulated, uncertain active retrieval is replaced, time deviation of active retrieval prediction is effectively avoided, and continuous knowledge increase is ensured.

The invention discloses a photovoltaic-heat pump cooperative energy storage system defrosting control method, device and system, electronic equipment and a storage medium, the photovoltaic-heat pump cooperative energy storage system comprises a heat storage module, a heat circulation pipeline and a plurality of electric heating modules, and the heat storage module outputs heat energy to an evaporator through the heat circulation pipeline for defrosting. On one hand, the photovoltaic unit supplies power to the electric heating module for defrosting, and the heat storage module outputs heat energy for defrosting, so that the dependence of defrosting on a power grid can be reduced, the supply proportion of the photovoltaic unit in defrosting energy consumption is increased, the electric charge expenditure is saved for a user, the carbon emission is reduced, the peak load of the power grid is relieved, and the energy utilization rate of the photovoltaic unit is increased; partial defrosting can be achieved by supplying power to part of the electric heating modules through the photovoltaic unit, global defrosting is achieved by outputting heat energy through the heat storage module, different defrosting modes are adopted in different frosting states, the energy efficiency of the system is improved, and defrosting can be achieved through the heat storage module when the generating capacity of the photovoltaic unit is low in extreme weather.

The invention relates to the technical field of control and adjustment, in particular to an energy storage configuration point-based power consumption cost calculation method, which comprises the following steps of: acquiring power supply data, renewable energy output fluctuation data and computing power load fluctuation data, and generating a coupling influence feature vector; splicing the electricity price features and the coupling features to form configuration optimization input features; the input features are processed through a dual-objective optimization model, and robustness configuration parameters and economical efficiency operation parameters are output; calculating the reserve cost compensation amount of the extreme scene based on the robustness parameter, and calculating the power purchase cost and maintenance cost of the power grid based on the economical efficiency parameter; and aggregating the cost component and outputting a power consumption cost calculation result. According to the method, the interactive influence of wind-light fluctuation and load mutation is quantified through coupling features, the target conflict between robustness and economy is coordinated by adopting the Pareto algorithm, the risk compensation amount is generated in combination with a pressure test, a feature updating mechanism is fed back in a closed loop mode, and the problem that the operation cost deviates from expectation due to quantization deviation of an existing model is solved.

The invention discloses a customer credit evaluation method and system based on multi-view features, particularly relates to the field of credit evaluation, is used for solving the problem of customer default risk dynamic identification and accurate management, and is characterized in that a time sequence feature matrix is generated through preprocessing on the basis of historical electricity fee payment records and electricity consumption cycle data; extracting a multi-period overdue increasing factor and a time lag correlation factor, and depicting an accumulative effect and a time sequence characteristic of an overdue behavior; in combination with a time sequence analysis model, modeling an interaction relationship between an overdue progressive increase effect and power consumption fluctuation to realize customer default risk dynamic prediction; recursively deducing superposition influence of overdue behaviors on subsequent risks through a dynamic risk accumulation mechanism, and updating credit scores and marking high-risk customers based on a real-time monitoring window and a dynamic threshold triggering mechanism; in combination with actual payment performance and prediction result deviation quantification, the weight of the feature matrix is dynamically corrected, model rolling optimization and adaptive adjustment are completed, and the dynamic accuracy and management efficiency of credit risk identification are improved.