1309 results about "Electricity cost" patented technology

The invention provides an energy-saving control method and device for a central air-conditioning system and a medium, and the method comprises the steps: constructing a cooling load prediction model of the central air-conditioning system, and predicting the cooling load per hour of the next day; under the conditions that the hourly cooling load of the next day is met and the power utilization cost is the lowest, the cooling capacity in a fixed time period is distributed; and an energy efficiency model and a function model of the central air-conditioning system are constructed, and the operation strategy of the central air-conditioning system corresponding to the given cooling capacity is optimized with the purpose of minimizing the power consumption cost, so that the comprehensive energy consumption of the central air-conditioning system is minimized. According to the method, the cooling load prediction result is improved by comprehensively considering various cooling load influence factors and effectively capturing the long-term dependency relationship in historical cooling load data, the comprehensive energy consumption minimization and the power consumption cost minimization of the central air-conditioning system serve as targets, the operation strategy is optimized through simulation combination and energy efficiency comparison, and the energy efficiency of the central air-conditioning system is improved. Therefore, the requirement of a target environment for an efficient and energy-saving central air conditioning system is met.

An energy storage control method and apparatus for a micro-grid system and a micro-grid system are provided. The method includes: for a target period within a first operation period, based on first predicted operation parameters of the micro-grid system in the target period, obtaining, via a first control model, a first control parameter of the energy storage unit in the target period; and for a target period within a second operation period, based on first historical state parameters of the micro-grid system before the target period, obtaining, via a second control model, a second control parameter of the energy storage unit in the target period. In this way, the energy storage regulation of the entire life cycle of the micro-grid system can be realized, effectively reducing the user electricity cost.

The invention discloses an electricity-hydrogen combined scheduling method and system for large-scale renewable energy source hydrogen production. The method comprises the steps of defining projects, equipment, units and systems, configuring topological relations, setting working conditions, grading loads and accessing data. Adjusting, optimizing and correcting ultra-short time power generation prediction, predicting hydrogen production allowance and identifying a current working condition; pre-judging future working conditions, power supply quantity and hydrogen production direct-current power; simulating balance, and solving an optimal scheduling strategy by multiple targets; and decomposing the scheduling strategy and issuing an asynchronous control instruction. According to the invention, the thought of mutual assistance of source and load is adopted, energy balance and hydrogen production balance are jointly dispatched, the purposes of power-hydrogen coupling as a main part, energy storage stabilization as an auxiliary part and off-grid or grid-connected non-grid are achieved, off-grid stability and 100% green power consumption of a renewable energy hydrogen production system are achieved, the policy requirements of green hydrogen production are met, the dependence on a large power grid is fundamentally reduced, and the green hydrogen production efficiency is improved. The electricity cost is reduced; and meanwhile, the support dependence on energy storage is reduced, and the use cost of energy storage is reduced.

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 power grid interaction, in particular to a two-way energy control method and intelligent equipment, and aims to solve the problem of how to perform charging and discharging control on two-way energy equipment according to power grid load information and peak and valley electricity prices on the basis of an optimal cost principle. The method comprises the steps that the initial electric quantity of an energy storage module is acquired; acquiring historical electrical load information of a set time period; and predicting a power control curve of a set time period according to the initial electric quantity, the historical electric load information and the expected electric quantity based on a cost optimal principle. On the basis of the predicted power control curve, charging and discharging control is performed on the energy storage module through the bidirectional energy equipment, so that the stability of the power grid can be enhanced, the power supply condition of the power grid can be optimized, the electric charge expenditure of the user can be reduced, the user experience can be improved, income can be brought to the user, and the return on investment of the user can be improved. And by adopting a dynamic programming algorithm, the operation speed is improved, and engineering realization is more convenient.

The invention discloses a photovoltaic energy storage scheduling method and system considering source load uncertainty, and relates to the technical field of energy scheduling. A building load prediction model and a photovoltaic power generation prediction model are established in advance; based on the building load prediction model and the photovoltaic power generation prediction model, generating a plurality of typical source load scenes through a Latin hypercube sampling method; and determining an energy scheduling strategy of the photovoltaic energy storage system according to each source load scene. According to the method, photovoltaic and load prediction is performed by using a machine learning model, a plurality of typical scenes are generated by using Latin hypercube sampling based on a prediction result, and the scene uncertainty is fully considered in the generation process of the typical scenes, so that the energy scheduling strategy of the photovoltaic energy storage system is generated by comprehensively considering the typical scenes; the method can effectively cope with the uncertainty of source load output, especially extreme working conditions, so that the robustness of an energy scheduling strategy is effectively enhanced, the energy utilization rate is further improved, and the power utilization cost of a user is reduced.

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 cloud energy storage operation mechanism based on thermal inertia of an urban building air conditioning system, which relates to the field of energy storage and comprises the following steps: (1) establishing a fixed-frequency air conditioner virtual energy storage monomer model and an aggregation model based on the thermal inertia of the urban building air conditioning system; (2) designing a cloud energy storage operation mechanism taking air conditioner aggregation virtual energy storage as a main leasing resource, and establishing a cloud energy storage operator model, a cloud energy storage user model and a transaction pricing strategy; and (3) solving the cloud energy storage operation model by taking the minimum power consumption cost of the user as a target, and distributing and evaluating the social total welfare brought by the cloud energy storage mechanism. According to the energy storage operation mechanism provided by the invention, the electricity consumption cost of the subscribing user can be effectively reduced, and the early-stage investment and the later-stage operation and maintenance cost of an operator are reduced; and under the dual-carbon background, the strategy is also beneficial to the real-time consumption of new energy on the user side.

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 provides a control method and equipment of a cold storage system and the cold storage system. The control method comprises the steps that the accumulated cold storage capacity of the cold storage tank in the previous day is obtained; acquiring 24-hour hourly electricity price per day; obtaining a cooling load demand hour-by-hour value in the next 24 hours in the previous day through a prediction technology; and according to the accumulated cold storage capacity of the previous day, the 24-hour hourly electricity price and the cooling load demand hourly value of the next 24 hours, cold release control is carried out on the cold storage tank with the goal of the lowest all-day operation electricity price. The dynamic cold release potential based on different electricity prices can be brought into full play, and the operation cost is saved to the maximum extent.

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 relates to the technical field of air conditioner dispatching, in particular to a mass air conditioner multi-target day-ahead optimal dispatching method and system considering carbon emission flow. The method comprises the steps that a multi-target optimization model of air conditioner load day-ahead scheduling is constructed with the minimum temperature comfort deviation and the minimum comprehensive cost as targets; wherein the optimal solution of the multi-objective optimization model comprises the minimum temperature comfort deviation and the minimum comprehensive cost, and the comprehensive cost comprises the electricity cost and the carbon emission cost; determining a constraint condition in the multi-objective optimization model, and converting the constraint condition into a penalty function; the constraint conditions comprise a temperature constraint, a power constraint, a load balance constraint, a switch state constraint, a carbon emission constraint and an electric power cost constraint; solving an optimal solution of the multi-objective optimization model by adopting a parrot optimization algorithm, and controlling the operation of each air conditioner according to the optimal solution of the multi-objective optimization model; according to the method, dual-objective rapid optimization scheduling of the power consumption cost and the carbon emission cost of the mass air conditioners is realized.

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 invention discloses an integrated energy system low-carbon economic optimization method and system considering fused magnesium load. The method comprises the following steps: determining a load matching index based on an actual load value and a wind-light-storage combined output value; based on the wind and light storage installed capacity and the operation and maintenance cost coefficient, combining a pre-constructed average electricity cost model to obtain an average electricity cost; with the load matching degree and the average cost per kilowatt-hour as optimization objectives, an NSGA-II algorithm is adopted for optimization to obtain the optimal wind and light storage installed capacity; establishing a master-slave game energy transaction model based on the optimal wind and light storage installed capacity and demand response, and solving the master-slave game energy transaction model based on an improved grey wolf algorithm to obtain a comprehensive energy system low-carbon economic optimization scheme; wherein the pre-constructed average cost-per-kilowatt-hour model is constructed by taking the minimum cost-per-kilowatt-hour as an optimization target and combining constraint conditions. According to the invention, optimization solution is carried out based on the improved grey wolf algorithm to obtain the optimal scheme, and the carbon emission and carbon transaction cost of the system are reduced.