18 results about "Capacity factor" patented technology

A renewable power system with a twin-configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., AI training / datacenter). The system can serve as a baseload or semi-baseload plant for CL(s) and / or as a peaker or semi-peaker plant for an electric grid, or vice-versa, and optionally in parallel, can also provide ancillary services to the electric grid and / or to the CL(s). In certain embodiments, e.g. solar PV RES(es), the system can have capacity factors of at least about 60% and up to 100%, higher asset utilization, better economics for the RES-ESS, improved system performance, and lower energy costs as compared with known systems without a CL(s). By making load a variable, and integral part of the system, sophisticated resource allocation strategies, including AI algorithms, can be developed not previously possible with known systems lacking a CL(s).

The invention relates to the technical field of grid-connected evaluation, in particular to a power grid photovoltaic bearing capacity evaluation method and system, and the method comprises the following steps: screening sensitive samples based on user node load fluctuation and abrupt change characteristics, recognizing a coupling boundary in combination with voltage and power flow change characteristics, and judging equipment overload. And calculating a matching relationship between the capacity factor and the sorting, adjusting capacity distribution, and determining a photovoltaic access upper limit. According to the method, the influence trend of load fluctuation on the power grid structure can be effectively identified by constructing a dynamic sensitivity screening mechanism with the load abrupt change rate and the period difference as the core, and the dynamic judgment capability of the node operation state is enhanced by establishing a coupling analysis model between the voltage state and the power flow response; an association rule of equipment operation state grading and capacity sorting factors is introduced, a multi-factor evaluation path covering load characteristics, electrical states and equipment pressure bearing capacity is established, and synchronous matching of node response capacity and voltage safety boundary in the capacity distribution process is achieved.

A renewable power system with a twin-configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., AI training / datacenter). The system can serve as a baseload or semi-baseload plant for CL(s) and / or as a peaker or semi-peaker plant for an electric grid, or vice-versa, and optionally in parallel, can also provide ancillary services to the electric grid and / or to the CL(s). In certain embodiments, e.g. solar PV RES(es), the system can have capacity factors of at least about 60% and up to 100%, higher asset utilization, better economics for the RES-ESS, improved system performance, and lower energy costs as compared with known systems without a CL(s). By making load a variable, and integral part of the system, sophisticated resource allocation strategies, including AI algorithms, can be developed not previously possible with known systems lacking a CL(s).

A fully-submerged multi-stage Horizontally-oriented Conical-helical Hydrokinetic Turbine that captures water stream flows (river, tidal, deep ocean currents) and funnels this kinetic-rich concentrated flow into a conical turbine system consisting of a rotating arrangement of helical hydrofoil blades wherein the internal stream is forced to exit out through the frustum turbine blade assembly. With a higher Capacity Factor and Power Coefficient than conventional renewables, this turbine is designed to be an environmentally friendly baseload electricity generator to meet the ever-increasing electricity demands.

The invention discloses a new energy equipment management method based on big data, and relates to the technical field of new energy equipment, and the method comprises the following steps: data collection, power generation efficiency prediction, prediction of the power generation efficiency of photovoltaic equipment through employing a power generation efficiency prediction algorithm, and electric quantity storage optimization. The method comprises the following steps of: obtaining optimized storage electric quantity through an electricity consumption storage optimization algorithm, detecting a battery state, detecting the battery state by utilizing a battery health evaluation algorithm, obtaining a battery health value and performing data backup, and has the characteristics of quantifying a result by considering the influence of environmental factors such as real-time temperature and irradiance on the power generation efficiency, and improving the power generation efficiency. According to the battery health assessment algorithm, the battery state is assessed by combining the battery charge-discharge cycle index factor, the aging factor and the storage capacity factor, the battery problem is found in time, manual experience is not needed, regular detection is not needed, and the equipment maintenance cost is reduced.

The invention discloses a wind power plant partition and regional wind power output evaluation method and system considering low wind speed, and the method comprises the steps: obtaining the wind speed data of each cell in each time period in a target region range, and converting the wind speed data into the wind speed data of each cell at a specified fan hub height; based on the wind speed data of each cell, wind speed characteristics of each cell are extracted; for each time period, based on the wind speed data and the wind speed characteristics of each cell, clustering analysis is carried out on each cell, so that each cell is divided into a wind area of a corresponding grade, and the output weight of each wind area is adjusted; and based on the output weight of each wind area in each time period, and in combination with the power data of the cells in each corresponding wind area, respectively calculating a regional wind power capacity coefficient in each time period, so as to evaluate the wind power output capability of the target area in each time period. According to the method, the flexibility and adaptability of region division are enhanced, and the accuracy of wind power output evaluation under the low-wind-speed condition is improved.

The present application relates to the field of grid-connected evaluation technology, in particular to a kind of power grid photovoltaic bearing capacity evaluation method and system, comprising the following steps, based on user node load fluctuation and mutation characteristics, sensitive sample is screened, coupling boundary is identified in combination with voltage and power flow variation characteristics, equipment overload is judged, capacity factor and sorting matching relationship are calculated and capacity allocation is adjusted, determine photovoltaic access upper limit.The present application can effectively identify the influence trend of load fluctuation on power grid structure by constructing dynamic sensitivity screening mechanism with load mutation rate and cycle difference as core, enhance the dynamic discrimination ability of node operating state by establishing the coupling analysis model between voltage state and power flow response, introduce the correlation rule of equipment operating state classification and capacity sorting factor, establish the multi-factor evaluation path covering load characteristics, electrical state and equipment pressure capacity, realize the synchronous matching of node response capability and voltage safety boundary in capacity allocation process.

The invention provides a proportioning method, system and equipment for multi-energy complementary generating capacity and a medium, and belongs to the field of new energy, and the method comprises the steps: obtaining energy data of a target sea area; respectively calculating power generation capacity coefficients of wind energy, wave energy, tidal current energy and photovoltaic solar energy in the target area based on the energy data and the parameters of the energy harvesting device; setting a minimum generating capacity threshold value of the target sea area in a preset time period, and constructing a relation function between a total capacity factor and wind, wave, tidal current and a capacity coefficient of the photovoltaic energy harvesting device; and according to the relation function, calculating the weight that each energy meets the minimum generating capacity threshold and the standard deviation of the total capacity factor is minimum, and according to weight parameters, matching wind, wave, tidal current and photovoltaic field generating capacity. According to the proportioning method, the dependence on historical data quality can be reduced, the reliability of a proportioning result is improved, and the cost of transport capacity calculation is reduced.

A renewable power system with a twin-configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., AI training / datacenter). The system can serve as a baseload or semi-baseload plant for CL(s) and / or as a peaker or semi-peaker plant for an electric grid, or vice-versa, and optionally in parallel, can also provide ancillary services to the electric grid and / or to the CL(s). In certain embodiments, e.g. solar photovoltaic (PV) RES(es), the system can have capacity factors of at least about 60% and up to 100%, higher asset utilization, better economics for the RES-ESS, improved system performance, and lower energy costs as compared with known systems without a CL(s). By making load a variable, and integral part of the system, sophisticated resource allocation strategies, including AI algorithms, can be developed not previously possible with known systems lacking a CL(s).

Systems and methods for solar voltaic disaggregation from metered net load is presented. A processor coupled with memory can be configured to identify, using a model trained for a site electrically coupled with an electricity distribution grid, a tilt, azimuth, and capacity factor for a photovoltaic system at the site. The processor can receive, from a data repository, irradiance data for a first time interval. The processor can determine an amount of power generated by the photovoltaic system during the first time interval based on the irradiance data, the tilt, the azimuth, and the capacity factor. The processor can execute an action related to power delivery to the site based on the amount of power generated by the photovoltaic system during the first time interval.

A fully-submerged multi-stage Horizontally-oriented Conical-helical Hydrokinetic Turbine that captures water stream flows (river, tidal, deep ocean currents) and funnels this kinetic-rich concentrated flow into a conical turbine system consisting of a rotating arrangement of helical hydrofoil blades wherein the internal stream is forced to exit out through the frustum turbine blade assembly. With a higher Capacity Factor and Power Coefficient than conventional renewables, this turbine is designed to be an environmentally friendly baseload electricity generator to meet the ever-increasing electricity demands.

A renewable power system with a twin-configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., AI training / datacenter). The system can serve as a baseload or semi-baseload plant for CL(s) and / or as a peaker or semi-peaker plant for an electric grid, or vice-versa, and optionally in parallel, can also provide ancillary services to the electric grid and / or to the CL(s). In certain embodiments, e.g. solar photovoltaic (PV) RES(es), the system can have capacity factors of at least about 60% and up to 100%, higher asset utilization, better economics for the RES-ESS, improved system performance, and lower energy costs as compared with known systems without a CL(s). By making load a variable, and integral part of the system, sophisticated resource allocation strategies, including AI algorithms, can be developed not previously possible with known systems lacking a CL(s).

The present application discloses a method and device for estimating wind power resource potential based on geographical conditions. The method includes: performing wind speed characteristic analysis based on preset GEOS‑FP meteorological data, and calculating the wind speed at the hub height of the wind turbine to obtain the hub wind speed; obtaining the actual generated power of the wind turbine based on the hub wind speed and a preset power wind speed curve, wherein the preset power wind speed curve is a mapping relationship between generated power and wind speed; after calculating the capacity factor based on the actual generated power of the wind turbine, estimating the wind turbine potential based on preset natural geographical condition constraints, the capacity factor, and preset wind power resource constraints; the preset natural geographical condition constraints include land terrain constraints, offshore sea area constraints, and average annual utilization time constraints, and the wind turbine potential includes wind turbine installed capacity potential and wind turbine power generation potential. The present application can solve the technical problem that the existing technical analysis process lacks pertinence, resulting in a lack of reliability and accuracy in resource assessment results.

The application discloses a kind of medium and long-term scale under wind power actual capacity coefficient evaluation method, system and equipment, belong to wind power prediction evaluation technical field, method includes based on correlation selection wind power historical power generation data, and wind power historical power generation data is converted into fan capacity coefficient sample value;Statistical calculation fan capacity coefficient sample value frequency of occurrence;On the basis of fan capacity coefficient sample value frequency of occurrence, respectively statistical calculation left direction probability distribution and right direction probability distribution;Based on given confidence, obtain wind power minimum guaranteed output capacity coefficient and maximum output capacity coefficient by left direction probability distribution and right direction probability distribution;Based on adjustment factor, adjust wind power minimum guaranteed output capacity coefficient and maximum output capacity coefficient, obtain the minimum guaranteed output and maximum output of specific unit under corresponding confidence degree.The application can obtain the capacity coefficient of wind power more real, evaluation result is more targeted, with comprehensiveness and convenience and other advantages.

A renewable energy power system with a dual configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., an AI training / data center). The system can be used as a base-load or half-base-load power plant for CL (s) and / or as a peak-shaving or half-peak-shaving power plant for an electrical grid, or vice versa, and optionally in parallel, and can also provide auxiliary services for the electrical grid and / or for CL (s). In certain embodiments, e.g., solar photovoltaic (PV) RES (s), the system can have a capacity factor of at least about 60% and up to 100% compared to known CL-free systems, higher asset utilization, better economic benefits of RES-ESS, improved system performance, and lower energy costs. By varying the load and making it part of the system, complex resource allocation policies, including the AI algorithm, can be developed, which are previously impossible in known CL-free systems.

The invention discloses a power system capacity planning method, system and device and a storage medium, and relates to the technical field of power system planning, and the method comprises the steps: collecting power generation characteristic parameters through combining with a meteorological condition, and evaluating and analyzing a capacity factor of a new energy generator set according to the power generation characteristic parameters; according to actual operation characteristics of the new energy generator set, operation models of various types of units are constructed, a thermal power unit aggregation model is introduced, and power generation operation constraints of the thermal power unit are reconstructed; and constructing a target function according to the overall operation target of the power system, coupling the target function and the power generation operation constraint of the thermal power generating unit, constructing a power system capacity planning model, inputting the capacity factor and the operation data of each type of unit into the power system capacity planning model, and outputting a power system capacity planning result. The method has better effects in the aspects of model accuracy, calculation efficiency and multi-target collaborative optimization.

The invention provides a high-precision runoff type hydroelectric resource technical potential dynamic measuring and calculating method and a related device, and belongs to the technical field of renewable energy source development and utilization, and the method comprises the following steps: calculating cumulative flow based on ERA5 data and DEM data; relative flow is calculated in combination with ecological flow requirements; water purification head and water head utilization efficiency is obtained through hydrological analysis and hydraulic geometric model correction; comprehensive efficiency is calculated by considering water turbine type characteristics; determining installation potential based on the river parameters and the water turbine layout parameters; and power generation potential and capacity factors are dynamically calculated by fusing multiple factors. According to the method, the installation density, the water head utilization efficiency and the efficiency curve of different water turbine types are comprehensively considered for the first time, hourly high-spatial-resolution data are adopted, the hydroelectric resource potential is quantified from the spatial grid scale, the defects that in the prior art, technical characteristics of water turbines are not considered, and the spatial-temporal resolution is low are overcome, and the measurement and calculation result is more accurate.

The invention provides a method for calculating the full-life-cycle greenhouse gas emission intensity of a wind power plant in consideration of land utilization change, and the method comprises the steps: constructing a full-life-cycle greenhouse gas emission total amount model which comprises wind power turbine part and material emission, vegetation and soil carbon library loss emission and carbon sink loss emission; three types of land utilization indirect emission including vegetation and soil carbon library loss and potential carbon sink loss are taken into accounting, the limitation that only direct emission is concerned is broken through, emission underestimation in scenes such as a forest type wind power plant is avoided, and data support is provided for high-precision carbon footprint evaluation; power generation performance nonlinear degradation caused by equipment aging is accurately captured through staged dynamic attenuation capacity factors, and the power generation capacity and the carbon emission intensity accounting precision are improved; according to a full-life-cycle greenhouse gas emission total amount model and the power generation total amount, the full-life-cycle greenhouse gas emission intensity is obtained, and data support is provided for accounting of the wind power emission reduction potential and sustainable development of wind power.