39results about How to "Smooth load fluctuation" patented technology

The invention discloses an electricity-gas interconnected comprehensive energy system peak clipping and valley filling method based on electricity-to-gas conversion. The method comprises the steps of firstly respectively establishing a power network model, a natural gas network model, an electricity-to-gas conversion model and a gas turbine model, wherein the power network and the natural gas network are coupled to form an electricity-gas interconnected comprehensive energy system via the electricity-to-gas conversion model and the gas turbine model; then introducing an economic conversion coefficient to project a net load peak clipping and valley filling target to an economic dimension, and forming a target with the lowest comprehensive cost together with the target with the lowest system operation cost, and establishing an electricity-gas interconnected comprehensive energy system peak clipping and valley filling model with consideration on various constraints of the power network and the natural gas network; and solving by using optimization software The General Algebraic Modeling System (GAMS), wherein an example analysis result shows that the model provided by the invention has good peak clipping and valley filling effect.

The present invention provides a multi-population genetic particle swarm optimization method containing the micro-grid capacity configuration of electric automobiles. The method realizes the energy storage function of an electric automobile on the premise that the electricity demand of the electric automobile can be met. According to the technical scheme of the invention, a multi-target model, with the annual cost, the annual loss of load probability and the peak-valley difference of a load curve as targets, is proposed. Based on the multi-population genetic particle swarm algorithm, a target function is solved out. In this way, the optimal capacity of each unit in a micro-grid system can be obtained precisely. On the premise that the system reliability is ensured and the load fluctuation is stabilized and inhibited, a higher economic benefit is obtained. Through optimizing the micro-grid system containing the electric automobile, the mobile energy-storage device of the electric automobile is utilized to realize the peak-load shifting purpose on the basis that the reliability and the economy of the system are guaranteed. Meanwhile, the peak-valley difference of the system curve is reduced. Not only is the stability of the power system improved, but also the economic benefit is higher. Therefore, the popularization and the utilization of a cleaning device of the electric automobile are facilitated.

The invention relates to an orderly charging system and method for an electric taxi charging station, and belongs to the field of intelligent power grid control. The system is composed of a battery management system, a batter charger, a charging point, an SCADA (supervisor control and data acquisition) system / a PQ (performance quality) monitoring system, an orderly charging control center and a batter charging information database. According to the background load of a to-be-predicted power grid and the demand on the charging power of an electric taxi, the optimal charging start time and the optimal charging phase plan are formulated for each electric taxi, and the electric power of the power grid at a valley period is fully utilized for charging a power battery of the electric taxi, so that the load of a superior transformer substation can be adjusted, the difference between the peak and the valley can be relieved, the load fluctuation of the power grid can be restrained, and the economy and the efficiency of the power grid are improved. Meanwhile, according to the single phase charging manner of the taxi, the optimal charging phase can be selected via the system, so the unbalance of the three-phase load can be reduced.

The invention discloses an ultra-short-term power load forecasting and early warning method based on a Kalman filter and wavelet echo state network. In order to solve the problem that noise and the like are contained in power load data, a Kalman filtering method is adopted to conduct real-time estimating on 'collected data', with the help of a forgetting factor, the weight of old-fashioned data is weakened, and prediction accuracy is improved. Before ultra-short-term load forecasting is conducted, firstly, a principal component is used for analyzing and determining main working procedures for influencing the change of a power load, the main working procedures are used as the input of a power load capacity prediction model, afterwards, wavelets are used for decomposing the loads of different spectral characteristics (high frequency, follow-up and stability) of the power load, echo state network singe power loads are respectively established for predicting and modeling, various forecasting components are integrated to obtain a total load variation trend, and ultimately an early warning test is conducted on the prediction model specified by a user.

The invention discloses an energy router facing orderly charging of electric vehicles, and a charging management system. The energy router comprises a main control unit, a man-machine interaction module, a communication unit and a function unit; the man-machine interaction module is connected with the main control unit; the communication unit comprises a remote communication module and a local communication module; the remote communication module and the local communication module are connected with the main control unit; the function unit comprises a control module, an alternating current collecting module and an orderly charging module; the control module is matched with the man-machine interaction module to control a terminal; the alternating current collecting module collects the alternating current analog quantity and metering electric energy; and the orderly charging module is used for conducting orderly charging strategy generating and caching, interaction with an orderly charging management platform, orderly charging strategy forwarding, zone area load line crossing control, zone area information and charging information collecting, and zone area identification. Timely andaccurate electricity utilization information can be provided for users, autonomous selection of the users is achieved, diversified customer needs are met, and meanwhile influences of large-scale random disorderly charging of the electric vehicles on a power grid are lowered.

The invention discloses a double-layer bidding optimization method for a pumped storage unit in a day-ahead electric power spot market. The method comprises the steps of firstly randomly generating Wgroups of new quoted prices in a quoted price range; then calling a gurobi optimizer to solve the market clearing model, and optimizing related parameters of the pumped storage unit under the target of minimum total power generation cost of the whole system; and with the goal of obtaining the maximum income of the pumped storage unit in the electric power spot market, optimizing the iterative process through a genetic algorithm until the optimal quotation and capacity optimization result of the pumped storage unit is output. According to the method, reasonable arrangement of the capacity of the pumped storage unit and optimization of resource allocation can be realized, and meanwhile, higher income is obtained, so that the competitiveness of the pumped storage power station in the market is improved.

The invention discloses a charging and discharging automatic demand response optimization method for an electric car, and the method comprises the following steps: S1, obtaining the state information of the electric car connected in a power distribution network; S2, constructing an SDCO-ADR optimization configuration; S3, obtaining an initial charging and discharging plan (shown in the description), which can bring the biggest benefit to a user, based on the demand side optimization of PSDR; S4, obtaining an optimal charging and discharging plan (shown in the description), which enables the load fluctuation variance of the power distribution network to be minimum, based on supply side optimization of IBDR; S5, constructing an SDCO-ADR optimization configuration, and building a CVaR model of a client response intention decision. The method automatically carries out fine resource configuration according to the demand signal of a power company side and the condition of a user side, achieves the coordinative optimization of the supply and demand sides, and irons out the defects that manual response is low in efficiency, poor in reliability, strong in nondeterminacy and easy to cause response fatigue.

The invention relates to an electric vehicle charging load random connecting control system and method of charging pile self-decision, and belongs to the field of electric vehicle intelligent charging. The system comprises a control decision generator and a plurality of intelligent charging piles. The control decision generator obtains the electricity price and load information of a current control area, calculates the probability distribution of corresponding charging start moments of users of different charging time durations according to a load curve by means of the designed random connecting method, and issues control instructions to all the intelligent charging piles, and the charging piles determine the final charging start time according to the probabilities of the connecting users.The method has a good peak-load shifting effect, can restrain the power grid load fluctuation and reduce the charging cost of the users of electric vehicles; the whole control process is independently completed by the device in the control area, concentrated type communication and control are not needed, implementation is simple and convenient, and the system is low in cost and suitable for orderly charging of residential areas with large-scale private electric vehicles.

The invention provides a gas-electric hybrid reversing control system applicable to an engineering ship, and belongs to the technical field of ship power. The hybrid reversing control system comprises three ship reversing modes including a pure electric reversing mode, a diesel-power charging mode and a diesel-electric hybrid mode, and is composed of a bridge reversing control unit, a main propulsion control unit, an electric power reversing control unit, an engine, a clutch, a gear box, a propeller, an alternating-current and direct-current bus, an AC / DC rectifier, an inverter, a lithium battery energy storage unit and a reversing electromotor. According to the gas-electric hybrid reversing system, the ship reversing mode can be determined according to different reversing speeds and different degrees of ship reversing resistance under windy and wavy sea conditions, so that load fluctuations and fuel consumption increase caused by changeable working conditions in the reversing process are restrained effectively; and meanwhile, the reversing maneuverability and reversing capability of the engineering ship can be improved effectively through the application of different reversing modes.

The invention discloses a coal gasification solid oxide fuel cell hybrid energy storage power generation system, and belongs to the technical field of energy storage power generation. The system comprises a coal gasification subsystem, a gas storage subsystem, a heat storage subsystem, a solid oxide fuel cell power generation subsystem and a gas turbine power generation subsystem. In the energy storage stage, the coal-to-gas process is completed by utilizing off-peak electric energy of a power grid load or electric energy generated by intermittent renewable energy sources, and air and synthesis gas are compressed and then stored in a compressed air storage tank and a synthesis gas storage tank. In the energy release stage, compressed air in the compressed air storage tank and compressed synthesis gas in the synthesis gas storage tank are released and supplied to the solid oxide fuel cell power generation subsystem and the gas turbine power generation subsystem. And the heat storage subsystem can carry out gradient storage and gradient utilization on heat energy of different tastes. The system can flexibly operate in different operation modes, is a novel energy storage power generation system, can perform clean utilization on coal, and has the characteristics of high fuel utilization rate, low pollutant discharge and the like.

The invention discloses an active power distribution network multi-level redundant coordinated control method considering an electric vehicle and energy storage and belongs to the new energy coordinated control technological field. The method is characterized by including the following steps that: 1, the operation data of an active distribution network are obtained; 2, the load characteristic model of the electric vehicle is established; 3, the energy storage and release characteristic model of the energy storage is established; 4, the multi-level redundant coordinated control strategy of theactive distribution network containing the electric vehicle and energy storage is constructed; and 5, the multi-level redundant coordinated control of a daily scheduling model, a hourly scheduling model and a real-time scheduling model is solved. According to the method of the invention, starting from the active distribution network, the active power distribution network multi-level redundant coordinated control method considering the electric vehicle and energy storage is researched on the basis of the idea of redundant interval division, and a promotion foundation and technical support can be provided for the further development of new energy sources such as electric vehicles.

The invention belongs to the field of steam explosion and relates to a gas explosion processor. The gas explosion processor comprises a frame (1), an outer cylinder body (2), an inner cylinder body (3), a steam pipe (4), a lower sealing end cover (5), an upper sealing end cover (6), a material receiving bin (7), a material receiving bin support plate (8) and a feeding bin (9). The gas explosion processor is characterized by further comprising a support airbag (10), an airbag fixing plate (11), a synchronous cylinder body (12), a synchronous cylinder piston (13), a pulse cylinder body (14), a pulse cylinder piston (15), a pulse cylinder piston connecting plate (16), a guiding rod (17), an elastic sealing component (18) and a control mechanism. According to the invention, the airbag support is adopted for promoting the sealing effect; the opening force of the lower sealing end cover is increased in the manner of double cylinder stacking; the lower sealing end cover can be opened within a shorter period of time; the materials in the storage tank can be instantly exploded within 60-75 milliseconds; the steam explosion effect and efficiency are greatly promoted; the structure is compact; the gas explosion processor is suitable for popularization.

The invention discloses a steam heat supply network scheduling optimization method based on a demand response. The method comprises the following steps: S1, enabling a steam heat user to get access toa cloud platform dominated and constructed by a heat supply company; S2, acquiring historical load change conditions of each heat user before the demand response, and performing situation evaluationand classification on the historical load change conditions; S3, negotiating with different types of heat users anout a mechanism, a response time period and a load capacity participating in the demand response, and signing an agreement with the heat users; S4, establishing a steam heat supply network optimal scheduling model and an objective function and constraint conditions thereof; S5, solvingthe steam heat supply network optimal scheduling model by using an optimization algorithm; and S6, publishing the final scheduling and pricing scheme based on the excitation scheduling mechanism andthe price scheduling mechanism on a scheduling platform, and adjusting the used amount of steam by the heat user. By adopting the method, the load fluctuation of the steam heat supply network can be effectively slowed down, so that the overall heat energy supply cost and operation and maintenance cost are reduced.

The invention relates to an electric automobile charging power regulating method and system. The method includes firstly determining scheduling plan power of electric automobile charging power, and then solving actual charging power of various electric automobiles under demand response management according to the scheduling plan power, wherein the scheduling plan power is related to electric load,the higher the electric load is, the lower the scheduling plan power is, the actual charging power is positively correlated to the scheduling plan power, the lower the scheduling plan power is, the lower the actual charging power of the electric automobile is. The charging power is regulated, particularly the charging power of the electric automobile is reduced at the electricity utilization peak, and the charging power of the electric automobile is not limited in the valley of electricity utilization. The system can achieve orderly guide and real-time power control in charging of the electric automobile, reduces charging load to a certain degree at the electricity utilization peak on the basis of the full use of the existing distribution network resources, realizes peak shaving of the power grid, stabilizes the difference between the peak and the valley of the power grid, reduces load fluctuation of the power grid, and improve stability of the power grid.

The invention discloses an ultra-short-term power load forecasting and early warning method based on a Kalman filter and wavelet echo state network. In order to solve the problem that noise and the like are contained in power load data, a Kalman filtering method is adopted to conduct real-time estimating on 'collected data', with the help of a forgetting factor, the weight of old-fashioned data is weakened, and prediction accuracy is improved. Before ultra-short-term load forecasting is conducted, firstly, a principal component is used for analyzing and determining main working procedures for influencing the change of a power load, the main working procedures are used as the input of a power load capacity prediction model, afterwards, wavelets are used for decomposing the loads of different spectral characteristics (high frequency, follow-up and stability) of the power load, echo state network singe power loads are respectively established for predicting and modeling, various forecasting components are integrated to obtain a total load variation trend, and ultimately an early warning test is conducted on the prediction model specified by a user.

The invention relates to a cooperative interaction method and system for an electric vehicle charging station and a photovoltaic power station. The method comprises the following steps: acquiring nodepower information of each photovoltaic power station, a user power utilization terminal and an electric vehicle accessed to the electric vehicle charging station in an area; establishing an electricvehicle-photovoltaic power generation collaborative interaction dynamic planning model according to the acquired information, wherein the electric vehicle-photovoltaic power generation collaborative interaction dynamic planning model comprises an objective function for realizing minimum energy system load variance in the region and a corresponding constraint condition; solving the electric vehicle-photovoltaic power generation collaborative interaction dynamic planning model through a particle swarm optimization algorithm based on hybridization; and formulating an energy collaborative interaction strategy in the regional energy system according to the obtained optimal solution. Compared with the prior art, energy cooperative interaction between the photovoltaic power station and the electric vehicle charging station is achieved, stable operation of a regional energy system is ensured, and meanwhile environmental protection benefits are considered.

The invention provides a method and a system for controlling virtual inertia of a mobile energy storage system. According to the method and the system, a DC virtual inertia is added in the DC side control of the mobile energy storage system, the control of a virtual synchronous machine is added to the AC control of the mobile energy storage system and inertia and damping are added to the DC side mobile energy storage system so as to better stabilize the DC side fluctuation. The fluctuation includes random fluctuation of the distributed power source and load fluctuation generated by the DC loadand the virtual synchronous machine so that plug-and-play of the DC side load is realized and stable voltage support is provided for the DC side small inertia system. Meanwhile, inertia and damping are provided for the AC side through the virtual synchronous machine control method so that the AC side load fluctuation is stabilized and the power quality of the power supply area of the mobile energy storage system is improved.

The invention discloses an urban distributed source network load storage scheduling optimization method based on an LSTM (Long Short Term Memory) algorithm in the field of load regulation. The method comprises the following steps: 1, acquiring historical load data of a medium-voltage line through a power system; 2, an available historical load data sequence is cleaned and reserved, and a line load prediction model is established based on an LSTM algorithm; 3, inputting the real-time load of the line at the current moment into the line load prediction model, and performing real-time prediction to obtain a line load value at the next moment; step 4, obtaining an adjustable load interval of the charging station corresponding to the current line; 5, according to the line load value obtained through prediction in the step 3, the optimal load adjustment method of the charging station in the adjustable load interval at the next moment is obtained based on the particle swarm optimization; and 6, the charging station executes the load adjustment method in the step 5, and the steps 3-6 are repeated. According to the invention, the operation efficiency of the distribution network line after large-scale access of various distributed source network load storage can be effectively improved, the network loss is reduced, and the load fluctuation is stabilized.

The invention relates to a comprehensive energy utilization system for desert and an operation method thereof. The comprehensive energy utilization system comprises a wind driven generator, a solar photovoltaic module, a solar heat collector, a heat storage tank, a fermentation tank, a gas storage tank, a biogas generator, a lithium bromide unit, a storage battery energy storage device, a fresh water preparation device, a fresh water reservoir, a psammophyte plantation, a desert animal breeding garden, a water pump and a bitter alkaline water tank. According to the invention, the renewable energy sources of solar energy and wind energy enriched in the desert area are fully utilized, and the generated clean electric energy meets the power consumption of the area. The storage battery energy storage device stores generated surplus electric energy when wind and light resources are rich, the gas storage tank is used for the biogas power generation system, the waste heat utilization system conducts gradient utilization on waste heat of a biogas power generator, and the solar heat collector and the heat storage tank supply energy to the system to achieve multiple guarantees of heat sources.

The invention discloses a new energy real-time electricity price pricing method, which comprises the following steps of: on a supply side, mining related factors which influence the new energy real-time electricity price by using a standardization-Euclidean distance, including a system electricity price, a new energy predicted internet surfing quantity and a load demand predicted quantity, and proposing a fitting function relationship between each related factor and a new energy real-time power supply price; on the demand side, the function relation between the electricity selling price and the load size is expressed through a price demand response elastic matrix; the function relation on the two sides is included in a cost-benefit target, and a min-max model is established and serves as a real-time power supply and sale pricing model of source-load real-time supply and demand; under the condition of considering different scenes, a min-max multi-scene objective function is established, and the model is converted and solved by using a Lagrange optimization duality theory. The method has the advantages of being simple in principle, easy to implement, capable of stabilizing load fluctuation, capable of improving new energy consumption and the like.

The invention discloses an electric vehicle charging and discharging scheduling method based on consideration of an average discharging rate, and the method comprises the following steps: S1, collecting EV information of network access through an intelligent charging device, and carrying out the processing and screening of the EV information, and obtaining schedulable vehicle information; s2, establishing a first-stage optimization model; s3, obtaining optimized payment cost and average discharge rate indexes according to the first-stage optimization model and the constraint conditions of thefirst-stage optimization model; s4, taking the optimized payment cost and the average discharge rate index as constraint conditions, and establishing a second-stage optimization model; s5, collectingpower grid information and schedulable vehicle information through a cluster agent, and inputting the power grid information and schedulable vehicle information into the second-stage optimization model for scheduling optimization to achieve EV vehicle scheduling. The method solves the problems that in the prior art, charging and discharging scheduling is only carried out for power grid or user side requirements, and battery loss is not deeply considered when charging and discharging arrangement is carried out on the electric vehicle.

The invention relates to a schedulable capacity prediction method and device based on a mobile energy storage electric vehicle. The method comprises the following steps: determining response power of each electric vehicle accessed to a power grid at a scheduling moment according to a charge state of each electric vehicle accessed to the power grid at the scheduling moment and a preset driving distance interval to which a preset driving distance belongs; and determining schedulable capacities of all the electric vehicles accessed to the power grid at the scheduling moment according to the response power of each electric vehicle accessed to the power grid at the scheduling moment. According to the technical scheme provided by the invention, the scheduling time and the SOC margin are comprehensively considered, the economy of power grid operation is improved, orderly charging and discharging of an electric vehicle access power grid are ensured, the peak-valley difference of a microgrid system is reduced, an approach is provided for scattered and small-capacity electric vehicles to participate in power grid optimization scheduling, and the device and the method are of great significance to engineering practice.

The invention discloses a microgrid new energy hybrid energy storage system. The system comprises a new energy power generation subsystem, an energy storage subsystem, an energy conversion subsystem and a power distribution subsystem, wherein the new energy power generation subsystem comprises tie lines connected with each power supply loop. The system also comprises a disturbance detection moduleand a reactive power compensation module. The power output end of the new energy power generation subsystem is connected with the input ends of the tie lines through the disturbance detection module;front-end network nodes are arranged at the output ends of the tie lines; middle-end network nodes are arranged at the input end and the output end of the reactive compensation module respectively. The energy storage subsystem comprises capacitors with a number equal to the number of the power supply loops, wherein each capacitor is connected with a switching device, each switching device is provided with a rear-end network node, and the new energy power generation subsystem selects a corresponding reactive power compensation module to establish connection with the energy storage subsystem based on a detection result of the disturbance detection module. According to the system, power generation and load fluctuation can be stabilized on the basis of low cost, and the stable operation of amicrogrid is kept.

The invention discloses heat supply systems and a heat supply method based on coupling phase change, and belongs to the field of civil heat supply. The heat supply systems comprise the civil heat supply system and the solar heating system. The heat supply systems comprise a first water circulation loop composed of a heat source, a thermal station and a heat user which are connected in sequence, the heat storage device comprises a box body, a partition plate is arranged in the middle of the box body and divides the interior of the box body into a first heat preservation area and a second heat preservation area, and the first heat preservation area and the second heat preservation area are each provided with an inlet end and an outlet end; The two ends of the first heat preservation area areconnected with a heating station and a heat source correspondingly. The two ends of the second heat preservation area are connected with the heating station and the heat source correspondingly. and the first heat preservation area, the heater and the second heat preservation area form a second water circulation loop. The high temperature of the second heat preservation area is kept through the second water circulation loop, and therefore it is guaranteed that water entering the thermal station is within the high temperature range. Phase-change heat storage and a solar heating system are combined, the characteristics of intermittency and instability of solar energy can be stabilized, the phase-change material can reach a phase-change area more quickly, and energy consumption is reduced.

The invention provides a park-type comprehensive energy system optimization operation strategy and load characteristic modeling method, and the method comprises the steps: building a system income model of each time section according to the electricity selling income of a park-type comprehensive energy system and the total cost of power generation and by taking the maximum operation benefit as anobjective function; establishing constraint conditions of the park-type comprehensive energy system; and under the constraint conditions of the park-type comprehensive energy system, with the maximumoperation benefit of the park-type comprehensive energy system being taken as the objective function, making a power output strategy of the park-type comprehensive energy system. According to the invention, multiple market factors such as time-of-use electricity price, energy storage and charging and discharging cost and photovoltaic on-grid benchmarking electricity price are comprehensively considered, the maximum operation income of the commercial park comprehensive energy system is taken as a target, the market factors and operation economy are comprehensively considered, a grid-connected load characteristic modeling method is proposed, multiple optimized operation strategies are obtained, and investment and return appreciation of the park-type comprehensive energy system can be realized.

The invention discloses a two-stage full-cycle rolling scheduling method, and belongs to the field of power system automation. Comprising the following steps: step 1, establishing a day-ahead scheduling model, and executing day-ahead scheduling calculation; step 2, calculating each segment parameter of the intra-day scheduling model; and step 3, establishing an intra-day scheduling model, and performing intra-day scheduling calculation according to calculation results of the step 1 and the step 2. According to the method, a rolling scheduling method is adopted, a day-ahead scheduling result and an intra-day scheduling result are updated according to recent prediction results of loads and renewable energy sources, the scheduling accuracy and the calculation efficiency are improved by setting the length of a scheduling range, optimal configuration solution is carried out on different types of resources in all stages, and social welfare optimization is achieved.