1705 results about "Electricity price" patented technology

A power management apparatus includes a communication unit to receive power rate information from a power provider and receive power consumption information and reservation information from an electric device, and a controller to decide an operation order on the basis of power consumption information and reservation information of the electric device, perform scheduling to decide an operation start time on the basis of the power rate information and reservation information in the operation order, and control an operation time of the electric device on the basis of the scheduling result. As a result, the power management apparatus performs optimum scheduling of an electric device using power rate information provided from the power provider, and power consumption information and reservation information of the electric device, resulting in an increase in user satisfaction.

The present invention provides methods and systems for managing a bulk electric power market. A set of inter-related automated process streams relating to the bulk electric power market are implemented. A plurality of automated processes for each process stream may also be implemented. At least one process of each process stream serves as an input to, or output from, at least one other process stream. Each process may comprise: (i) automated inputs; (ii) automated activities; (iii) and automated outputs. The process streams may be used to manage a bulk electric power market and to control an electric power grid. For example, the process streams may be used for determining market-based wholesale electric rates; scheduling transmission outages; creating a day-ahead market schedule; and calculating monetary positions of customers involved in settlement of the day-ahead market.

A strategic response control for an air conditioning system for use in an environment with increased power rates during a designated peak period. The air conditioning system includes a power board for operating the air conditioning system in one or more operating status having a controller with a processor and a real time clock, an indoor temperature sensor for generating an input signal representing the indoor temperature, an outdoor temperature sensor for generating an input signal representing the outdoor temperature, and an operating program that sets a maximum comfort level temperature, a minimum comfort level temperature and a warmup rate for the environment in which the air conditioner operates, and has a procedure for precooling the environment before the peak period, wherein on activation, the processor signals the power board to enter an operating state for precooling and an “off” or lower level operating state during the peak period.

A system and method are provided for remotely capturing, processing and displaying spot electricity pricing information that enable a consumer to conveniently monitor market prices and use such information to determine when and how to consume electricity. A terminal is conveniently located within the residence of the consumer, and is preferably designed to be ergonomic in its human interface, as well as aesthetically pleasing. The terminal provides a convenient, intelligent and thus useful device so as to enable the consumer to take advantage of favourable spot pricing offered by an electricity supplier using data that has already been transmitted to a smart meter, or similar data provided by an independent entity.

A method of price forecasting in an electrical energy supply network and / or load (energy demand) forecasting of a given consumer of electrical energy, in the context of an electrical energy supply network that is adapted to supply electrical energy to a number consumers connected to the network. The method includes developing a multi-regime, regime switching stochastic model for determining day ahead / spot market energy prices using at least one historical profile and subjective opinion from at least one expert; and the multiple regimes correspond to a number of combinations of physical factors. A regime is identifiable by at least three factors. The method thus facilitates identifying the optimal mix of energy hedge and exposure to day ahead / spot market prices for deriving economic benefits in overall energy expenditure.

The invention discloses an energy monitoring management system and method based on electricity waveform analysis. The system comprises an electric power information collection terminal, an intelligent energy management application platform server and a control terminal. The electric power information collection terminal comprises an electricity waveform collection module used for electric appliances, and a communication module, wherein the electricity waveform collection module used for the electric appliances collects electricity waveforms of the electric appliances and reports electric power information to the application platform server through the communication module. The intelligent energy management application platform server comprises an electric appliance waveform analysis module, an electricity quantity calculation module, an information issuing module and a database module. On the basis of judging the types of the electric appliances through the waveform features, the electricity consumption of subordinate equipment is obtained, an energy efficiency analysis database is formed, users can search for historical electricity consumption information, the electricity consumption ranking, the electricity price policy, demand response, energy efficiency analysis and other information, the energy consumption situations of household electric appliances can be issued to the users through mobile phones, PADs, PCs and other terminals, electric charges are saved for the users, and guidance is provided when the users purchase electric appliances.

The invention provides an electric vehicle charge-discharge optimized dispatching method based on virtual electricity price. The method comprises the following steps: an electric energy public service platform predicts and samples the basic daily load information of a target area within an optimization time interval; when a new EV is connected to a charging pile within the target area, the network connection information of the new EV is read; a user input the charging information of the vehicle; an EV charge-discharge power model is constructed; virtual electricity price is calculated to indirectly reflect the load level of the target area; a dispatching model with the charge-discharge power as an optimization variable is constructed; dynamic time-of-use electricity price for user cost calculation is determined by combining wavelet analysis preprocessing and fuzzy clustering methods; the user makes an automatic response decision; a charge-discharge operation is performed on the EV according to the decision of the user and a plan is uploaded. The electric vehicle charge-discharge optimized dispatching method based on virtual electricity price is capable of realizing peak clipping and valley filling of EV cluster load and reducing the charge-discharge cost of the user on the basis of meeting the charging requirement of the user and the capacity limitation of a power distribution transformer. In case of a great EV cluster scale, the electric vehicle charge-discharge optimized dispatching method based on virtual electricity price is still capable of meeting grid side expectations.

The invention provides an intelligent control method and system for interactive home appliances on the basis of time-of-use electricity price response. The method is characterized by comprising the steps that the home appliances are automatically classified according to types of home appliance loads; use habits of the home appliances of users are determined according to historical home appliance running status data collected in 30 days; according to time-of-use electricity price policy information of a receiving power grid, basic load demand ranges corresponding to all time periods are predicted and determined for the users; according to obtained user home appliance use habit characteristics and load settings, controllable degree indexes of the home appliances are calculated, and running status models of the home appliances are determined; according to the controllable degree indexes of the home appliances, dynamic control priorities and a control algorithm of the home appliances are determined, and intelligent control over the home appliances is achieved. The method and system have the advantages that interactivity is good, the receiving degree of the users is high, the users can be guided to reasonably avoid the peak hours for electricity utilization, and electricity utilization cost is remarkably reduced.

The invention provides a power demand response intelligent decision method based on price and excitation. The method includes the steps of (a) collecting a sample, (b) obtaining an industrial load, (c) obtaining a moment load, (d) building safe operation constraint conditions, (e) building tolerance constraint conditions, (f) obtaining controllable load gross, (g) solving payback load gross, (h) building a basic demand relation, (i) building controllable time constraint conditions, (j) building a user benefit constraint function, (k) building a comfort degree optimal function, (l) building an electricity cost constraint function, (m) building a participation cost constraint function, (n) building a fairness constrain function, and (o) solving an optimal solution. By means of the intelligent decision method, an electric use time interval is adjusted to be a low-electricity-cost time interval, electricity consumption is reduced in a high-electricity-cost time interval, and electric charge is reduced; a user reduces power demands when a system needs or during power shortage, so that a peak load shifting function is achieved, and normal power supply is guaranteed.

The invention discloses a charge and discharge optimizing method considering customer satisfaction and distribution network safety for an electric automobile, and particular relates to a multi-objective bilevel charge and discharge optimizing method considering the customer satisfaction and network distribution safety constraint for the electric automobile. The multi-objective bilevel charge and discharge optimizing method is suitable for scheduling and controlling of network entry of large-scale electric automobiles. Based on the consideration of distribution network purchase and selling electricity price and electricity purchase cost, distribution network load fluctuation, customer charge demand, satisfaction participation of charge scheduling and the like, the invention provides a charge bilevel scheduling model for the electric automobile in the distribution network by using minimum electricity purchase cost and minimum load fluctuation of the distribution network as well as maximum satisfaction that the customer participates in charge scheduling as objects, and an NSGA-II algorithm and a Yalmip / Cplex tool are adopted for solving; electricity purchase cost and net load fluctuation of the distribution network is minimized by a distribution network layer, so operation economy of the distribution network is improved; a charging station layer is matched with the distribution network layer for scheduling; the satisfaction that the customer participates in charge and discharge scheduling is improved by adopting a two-stage optimizing method.

The invention relates to an electric automobile optimal peak-valley time-of-use pricing method giving consideration to the owner satisfaction degree. The electric automobile optimal peak-valley time-of-use pricing method specifically comprises the following steps of (1) establishing probability models of a last journey ending moment and a daily running mileage; (2) establishing demand response models for charging and discharging of electric automobiles, including (2a) a demand response model of an A-type electric automobile, (2b) a demand response model of a B-type electric automobile and (2c) a demand response model of a C-type electric automobile; (3) establishing an optimal peak-valley time-of-use electrovalence solving model. Charging and discharging power models of the electric automobiles are established according to two factors influencing the power demands of the electric automobiles, namely charging beginning moment and the daily running mileage, then an optimal peak-valley time-of-use electrovalence solving scheme applicable to the charging and discharging of the electric automobiles is established based on the electric automobile owner satisfaction degree and power grid benefits, and investment on peak-load units and lines is reduced to some extent.

A power management apparatus includes an electric device including a plurality of operation algorithm information and power information for each operation level corresponding to each operation algorithm information, and a power management unit to receive power rate information from a power provider, determine an operation level of the electric device on the basis of the received power rate information and power information for each operation level of the electric device, and control an operation of the electric device at the determined operation level. As a result, the power management apparatus performs different operation algorithms according to power rate information, and controls power consumption of the electric device, such that energy efficiency at the user side can be maximized. In response to the changed power rate information, the currently-driven electric device and the electric device to be driven can be driven at the optimum operation level appropriate for their power consumption characteristics.

Provided are a charging and discharging system for a solar light power generator in a smart grid environment, a method of operating the same, and a duplex converter for charging and discharging electricity into / from a battery. The system includes: an electricity price monitoring part which collects electricity price information from an electric grid; an electricity charge amount monitoring part which collects electricity charge amount information of a battery; a charge and discharge determination part which determines whether electricity supplied from the electric grid is to be charged into the battery or whether electricity stored in the battery via the electric grid or the solar light power generator is to be discharged to the electric grid, based on the price information and the charge amount information; and a charging and discharging part including the battery, so that electricity is charged into or discharged from the battery based on the determination.

A strategic response control for an air conditioning system for use in an environment with increased power rates during a designated peak period. The air conditioning system includes a power board for operating the air conditioning system in one or more operating status having a controller with a processor and a real time clock, an indoor temperature sensor for generating an input signal representing the indoor temperature, an outdoor temperature sensor for generating an input signal representing the outdoor temperature, and an operating program that sets a maximum comfort level temperature, a minimum comfort level temperature and a warmup rate for the environment in which the air conditioner operates, and has a procedure for precooling the environment before the peak period, wherein on activation, the processor signals the power board to enter an operating state for precooling and an “off” or lower level operating state during the peak period.

The invention discloses a virtual power plant optimization trading strategy model based on two-stage stochastic programming. The virtual power plant optimization trading strategy model based on two-stage stochastic programming comprises the following steps of S1, according to a power market rule, summarizing a VPP bidding process and the theoretical basis such as a multi-scenario technique; S2, establishing the objective function of the virtual power plant optimization trading strategy based on two-stage stochastic programming; S3, establishing the constraint condition of the virtual power plant optimization trading strategy based on two-stage stochastic programming. In consideration of the influence of clean energy output and market electricity price uncertainty on the participation of avirtual power plant in the current market and balanced market bidding strategies, the multi-scenario technique is used to simulate a uncertainty factor and construct the virtual power plant optimization trading strategy model based on two-stage stochastic programming to maximize the operation efficiency of the virtual power plant.

The invention discloses a control method for ordered charging of an electromobile. The control method comprises the following steps of firstly, establishing a charging load characteristic model of the electromobile by integrating factors of battery characteristics, a charging mode and behaviors and habits of users; secondly, with the goal of minimizing the difference between the peak and the valley of a load, establishing a centralized optimization model according to the minimal variance of load demands at all time intervals and the average load level, enabling the load fluctuation to be minimized, and realizing the stabilization of the load to obtain reference optimized power; thirdly, establishing a distributed optimization model, and optimizing an individual charging scheme according to the reference optimized power obtained by a power grid and a middle manager in the optimizing process of the second step and dynamic electricity price in 24 hours. A cooperative control model is established by adopting a centralized and distributed combined optimized control idea, so that the negative effect of large-scale disordered charging of the electromobile on planning and operation of the power grid is reduced.

The invention provides a household electricity intelligent management system. A sensor is used for monitoring the indoor personnel situation, the temperature, the humidity, light brightness and the like in a real-time mode. According to the acquisition of data like the temperature, whether relevant electric appliances are turned on or not is determined, and the temperature value when the relevant electric appliances run is determined. A working period of each electric appliance can also be set, before each relevant electric appliance is turned on, whether the current time is located in the working period of each electric appliance or not is preferentially judged, and if the current time is not in the working period, the electric appliances can not be turned on. According to the corresponding relation between the set electricity prices and periods, and the corresponding relation between the electricity consumption and the electricity prices, after the electricity consumption of each electric appliance is obtained, the total electricity consumption and the electricity consumption in each period are calculated, whether the total electricity consumption exceeds the preset value or not is judged, if the total electricity consumption exceeds the preset value, a prompt is sent to a user through an alarm module, the user is informed of the current electric consumption and the electric charge situation, and some electric appliances are turned off according to the preset electricity consumption weight of the electric appliances. The household electricity intelligent management system can effectively manage the household electric appliances and save electric energy when used.

The invention relates to a microgrid energy optimization method based hybrid energy storage dispatching under different time scales. Microgrid optimized dispatching is divided into day-ahead dispatching and real-time dispatching according to different time scales; for the day-ahead dispatching, the operation plan of future 24 hours is provided one day in advance, the time granularity is 1 hour, the connecting line interaction power and the fuel cell (FC) output are optimized according to the difference between the electricity prices in the peak and valley periods, and a storage battery (SB) is dispatched to achieve low-storage high-output interest arbitrage; the real-time dispatching coordinates with the day-ahead dispatching, the microsource output is allocated according to the day-ahead plan, the time granularity is 1 minute, the power fluctuation in the microgrid is smoothened by use of a first-order low-pass filtering algorithm, the filtered fluctuating power is distributed reasonably between storage battery energy accumulation and a super capacitor by use of a moving average filtering algorithm and then reference is provided for the optimized dispatching of hybrid energy storage.

The invention discloses power utilization mode classification and control method based on user behavior characteristics. With an improved secondary clustering model built by use of a secondary clustering method, the load point of each day of the same user in one year in an industrial park is taken as a characteristic vector, the daily power utilization characteristics of the user can be concluded from a clustering result, and a plurality of typical power utilization modes of the enterprise user can be provided, and therefore, basis can be provided for load prediction, fault diagnosis, electricity pricing and the like in the industrial park; furthermore, the optimal plane power utilization mode in demand side management can be selected by virtue of optimization function modeling on load data; the model is advantageous for a power supply company to reduce the loss of electricity selling profit as much as possible under the premise of guaranteeing power supply; at last, a user power utilization behavior mode library in the industrial park built on the basis is capable of comparing a new settling enterprise inconvenient to model with the modelled typical user mode and obtaining the load characteristics of the new settling enterprise by virtue of analogizing, and therefore, the planning efficiency of the park can be improved.

The invention discloses a cool-heat-electricity cogeneration type microgrid optimal configuration method. The method includes the steps of system level optimization and equipment level optimization. Safe and stable operation of electricity, heat and cool generation systems of a system is used as the constraint condition of system level optimization, load data, electrovalence policies and fuel cost in a microgrid region to be planned are used as input, energy supply equipment categories are selected on the basis of load data analysis, the lowest full life circle cost of a microgrid is used as an objective function, the objective function is solved by the adoption of a mixed integer programming approach, and then equipment capacity in the microgrid is acquired. Equipment level optimization is based on a cool-heat-electricity cogeneration equipment scheme library, the capacity value ranges of equipment are set on the basis of the result of system level optimization, solution is calculated in a weighted mode, and then the optimal configuration scheme of the system is obtained. According to the method, capacity of the generation systems can be balanced, utilization efficiency of primary energy and the utilization rate of the equipment are improved, unnecessary investment is avoided, meanwhile, overall economy of the system is improved, and annual operation cost is reduced.

The invention provides a user electricity consumption relevant factor identification and electricity consumption quantity prediction method under the environment of big data. Multiple electricity consumption modes of users are mined and existing electricity consumption behavior analysis methods are expanded by applying a mass user electricity consumption characteristic subspace clustering analysis method based on the research of the user electricity consumption characteristic evaluation index by aiming at the characteristics that the big data relevant to electricity consumption quantity prediction are various, large in size, high in dimension and high in generation speed. Meanwhile, group division is performed on the users according to different electricity consumption modes, factors relevant to user group electricity consumption quantity are identified from the aspects of regional and industry economic data, weather conditions and electricity price by utilizing mutual information matrixes, and an electricity consumption quantity big data prediction model based on a random forest algorithm is constructed so that data driving of the whole process of electricity consumption prediction is realized, adverse influence on electricity consumption quantity prediction caused by difference of the electricity consumption modes can be avoided, and thus the method has relatively high prediction precision and is suitable for big data analysis and processing.

The invention relates to an intelligent microgrid power-supply system which is formed by a power distribution cabinet special for a microgrid, an energy management system, a distributed power source loop, a load loop, an energy storage loop and an active filtering loop. The intelligent microgrid power-supply system achieves diversified application of a novel power source and a novel load in an intelligent building power-supply system, the problem of negative influence of complexity of the building power-supply system on a power distribution system is solved, and novel intelligent building power supply can be accepted by a large power distribution system. By control of the energy management system, multifunctional innovation of the power distribution cabinet special for the microgrid, reasonable coordinating operation of the distributed power source loop and the energy storage loop and peak valley electrovalence adjustment, the intelligent microgrid power-supply system achieves the aims of the demand side response load adjustment and peak clipping and valley filling of a distributed power source, and the running of the microgrid is safe and reliable through conversion of grid connection and an islanding mode.

The invention relates to a regional energy internet multi-source coordinated optimization operation strategy considering time-of-use power price. The strategy is technically characterized by comprising the following steps that step 1, the initial data parameter of a regional energy internet system is inputted; step 2, cold / heat / electric flexible load response under the time-of-use power price is analyzed, and two classes of response models of cold / heat / electric flexible load are constructed; step 3, a regional energy internet system optimization scheduling model of the minimal economic cost and the operation constraint conditions thereof are constructed; and step 4, the regional energy internet system optimization scheduling model of the minimal economic cost is solved by using a Hessian interior point method. The regional energy internet renewable energy consumption level can be enhanced, and the strategy is also significant for realizing economic and safe operation of the system.

The invention discloses a day-ahead market clearing method, system and device and a computer readable storage medium. The day-ahead market clearing method comprises the steps of obtaining a day-aheadmarket clearing model comprising a target function and a constraint condition by building a target function taking minimum system running cost as a target and the constraint condition comprising machine set running constraint, calculating a initial machine set starting and stopping plan by the day-ahead market clearing model, calculating locational marginal price by the initial machine set starting and stopping plan, a safety constraint economic dispatching target function and the constraint condition, obtaining a correction declaration load more approaching to actual user load demand by the locational marginal price, and substituting the correction declaration load back to the day-ahead market clearing model so as to correct the constraint condition in the day-ahead market clearing modelto obtain a correction constraint condition. By the day-ahead market clearing method, an elastic machine set contribution plan and an elastic machine set starting and stopping plan which are more approach to the actual load demand can be obtained, so that the problems that the start-up capacity is not enough and circuit power flow exceeds a limit during system running are prevented.

The invention discloses an electric vehicle orderly charging control method, which mainly comprises the following steps: firstly obtaining the probability distribution of the electric vehicle daily traveling mileage, the last trip end time and the charging time length according to the use situation of the electric vehicle; simulating the distribution of electric vehicle charging load in differentperiods of 24 hours a day through the Monte Carlo method; accessing the load of the electric vehicle into the IEEE 33-bus distribution system randomly, and calculating the voltage amplitude and network loss of each node of the distribution system through the forward-backward substitution power flow calculation method; constructing an electric vehicle orderly charging optimal dispatching model based on an electricity price guidance scheme according to the 24-hour charging power demand model of the electric vehicle and the negative influence of disordered charging on the distribution network. The optimal dispatching model optimizes the dispatching of the electricity price and grid peak-valley difference to reduce daily load peak-valley difference and electric vehicle user charging cost, andalleviates the influence of electric vehicle access on the grid.

The invention relates to a management optimization method in the field of alternating-current micro grids, and specifically to a multi-time-scale micro grid energy management optimization scheduling method. Aiming at the problems that the existing studies are incomprehensive on consideration of the running status of a micro grid, not detailed enough on the control strategy in each running status of the micro grid, insufficient on calculation accuracy of the running cost and the like, the method comprises a day-ahead economic optimization scheduling phase and an intraday economic optimization scheduling phase; the former considers the electricity price at peak, valley and flat time, and distributed unit power optimization allocation in the micro grid is performed according to day-ahead photovoltaic and load prediction by using the total running cost including the running and maintenance cost of a lithium battery and a fuel battery, interrupted compensation of an interruptible load, price of electricity sold by a large grid and the like as a target function; and the latter considers the outputs of electricity purchased from the large grid by the micro grid, a super-capacitor and the fuel battery, and an independent control strategy is respectively established for the peak, valley and flat time, so that the control strategies are more specific and detailed.

The invention discloses an output optimal operation method of a thermal power unit in the environment of ensuring electricity price bidding marketization, and the method comprises the following steps: establishing a manual input cost parameter table and a unit production real-time system database; establishing a generating fuel cost curve of the unit; calculating the start-stop cost and the fixed cost of the unit; calculating the environment cost of the unit, internalizing the environment cost and calculating the environment cost directly according to harmful gas emission and a discount standard; analyzing bidding risk; establishing an objective function of a mathematical model of economic operation of a single unit based on profit maximization; guaranteeing constraint conditions of optimal operation of the unit; and establishing the best output model table of the unit at various periods. The method considers the factors of environmental protection cost and bidding risk comprehensively, thus reducing harmful gas emission of the power plant when a generation company does not add environmental protection equipment, and moreover, reasonable analysis of the bidding risk can realize price bidding of low risk and high profit.

The invention discloses an electric vehicle ordered charging coordination control method suitable for a plurality of charging stations. Firstly, each charging station calculates a set charging requirement boundary curve of all electric vehicles in the charging station according to charging requirement information and battery information of all the electric vehicles in the station and sends the set charging requirement boundary curve to a control center; then the control center calculates the optimal charging load guide curve of each charging station according to the set charging requirement boundary curve of all the electric vehicles in the station, which is reported by the charging station, electricity price of a power grid and electric vehicle charging load constraint conditions of a system and issues the optimal charging load guide curves to the corresponding charging stations; and each charging station adopts a coordinative optimization charging algorithm to calculate the optimal charging strategy of each electric vehicle in the station, so that ordered charging coordination control among a plurality of charging stations is implemented. The control method depends on an optimization model simple to calculate, has high calculating efficiency and has low requirements on the hardware environment of a control system; and communication resources between each charging station and the control center are effectively saved.

The invention discloses a joint optimized scheduling method for multiple types of generating sets of a self-supply power plant of an iron and steel enterprise, and belongs to the technical field of energy optimized scheduling of the iron and steel enterprise. Influence of fuel types and gas mixed burning amount on energy consumption of the sets is taken into consideration in construction of a set energy consumption characteristic model, fitting is performed under different gas mixed burning, and the accuracy and representativeness of the model are improved; and influence of the fuel cost, time-of-use power price and surplus gas dynamic change on the generating cost is considered comprehensively in construction of an optimized scheduling model, meanwhile, various constraint conditions including power balance constraint, generating set self-running constraint, purchased power quantity constraint, gas supply constraint, variable load rate limit and the like are considered, and the performability of a generation schedule is guaranteed. Optimization solution is performed on the models by adopting the adaptive particle swarm optimization algorithm, the problems of high dimensionality, nonconvexity, nonlinearity and multiple constraints of the power generation scheduling of the self-supply power plant can be well solved, power production optimization and purchasing rationalization are realized, surplus gas is sufficiently used, and the power supply cost is reduced to the greatest extent.

The invention provides a block chain electricity transaction peak shaving and frequency modulating system based on distributed electric storage facilities. The block chain electricity transaction peakshaving and frequency modulating system comprises a household block chain electricity meter (1), distributed electric storage facilities (2) and a block chain control cloud platform (3) with a charge-discharge peak shaving and frequency modulating control function. The charge wall and storage battery charge-discharge system is arranged inside a regional power grid (4), the block chain control cloud platform (3) is used, intelligent control is carried out on the charge-discharge system in combination with an intelligent bidirectional electricity meter of a belt block chain module, a great dealof electricity is stored when the price for electricity is at a low ebb at night, and electricity is sold at high price at peaks demand for electricity in daytime, so that the design capacity of a transmission and distribution network can be greatly reduced, and the difference between peak and valley of the power grid can be optimally and flexibly balanced; and moreover, auxiliary service such aspower grid peak shaving and frequency modulation based on storage batteries can be carried out by using an intelligent contract of block chains.