Solar Power Plant Storage System

The article analyzes changes in the legislation on the rules of electricity generation from renewable sources and the introduction of penalties for imbalances, which encourages producers to improve the forecast of electricity generation and modernization of existing power plants by installing energy storage systems. Possible connection points and charge/discharge device (CDD) converter topologies for an energy storage system are analyzed and the converter that meets technical requirements of the system are selected. As a result, the connection from the direct current side has a number of advantages: simpler CDD structure and control principle, in comparison with alternating current; no galvanic separation between input and output. Converter analysis showed, that due to a high operating voltage, usage of resonant topologies is undesirable and the absence of galvanic separation makes bridge converter usage impractical. Therefore, to solve this problem, a bidirectional converter without galvanic separation with hard switching is proposed. To reduce the level of dynamic and static losses, it is advisable to use a modular topology converter with alternating phases. The operating modes of such a converter at a given error of the weather forecast are analyzed. To improve quality of the generated electricity, it is expedient to use a power stabilization mode. Due to the higher values of charge/discharge currents, as well as higher energy density compared to acid-lead, a lithium-ion battery was chosen. According to the selected operation mode, its minimum capacity was calculated. When using a minimum battery capacity, due to the difference between the maximum discharge and charge current of the battery, a mode of partial power stabilization is possible. This mode is used only when the forecast error is more than 52% in the charging mode. A charge/discharge device were designed for a 50 kW SMA Sunny Tripower CORE1 inverter and 20*315W LP156*156-M-60 solar panels connected in series. The control principle for such CDD is described. Control algorithm can be divided into four stages: obtaining the predicted solar radiation power in the forecast interval; predicted illumination power conversion into electric power; predicted power calculation and the amount of energy that will be generated and transferred by the solar station to the regulator in the forecast interval; power setpoint stabilization on the forecast interval based on the proportional-integral (PI) control law. To verify the obtained theoretical relations, converter parameters with a typical input data were calculated. Model of the converter was created in the MATLAB® Simulink® environment and its operability was checked.

Characterizing Meteorological Forecast Impact on Microgrid Optimization Performance and Design

A microgrid consists of electrical generation sources, energy storage assets, loads, and the ability to function independently, or connect and share power with other electrical grids. Thefocus of this work is on the behavior of a microgrid, with both diesel generator and photovoltaic resources, whose heating or cooling loads are influenced by local meteorological conditions. Themicrogrid's fuel consumption and energy storage requirement were then examined as a function of the atmospheric conditions used by its energy management strategy (EMS). A fuel-optimal EMS, able to exploit meteorological forecasts, was developed and evaluated using a hybrid microgrid simulation. Weather forecast update periods ranged from 15 min to 24 h. Four representative meteorological sky classifications (clear, partly cloudy, overcast, or monsoon) were considered. Forall four sky classifications, fuel consumption and energy storage requirements increased linearly with the increasing weather forecast interval. Larger forecast intervals lead to degraded weather forecasts, requiring more frequent charging/discharging of the energy storage, increasing both the fuel consumption and energy storage design requirements. The significant contributions of this work include the optimal EMS and an approach for quantifying the meteorological forecast effects on fuel consumption and energy storage requirements on microgrid performance. The findings of this study indicate that the forecast interval used by the EMS affected both fuel consumption and energy storage requirements, and that the sensitivity of these effects depended on the 24-hour sky conditions.

Ultra-short-term Load Forecasting Based on Real-Time Response of Classified Flexible Loads

Ultra-short-term load forecasting is an important basis for optimization and adjustment of power generation plans and dispatch plans. Based on the radial basis function neural network, the inert load is predicted, and the flexible load is predicted based on the price elasticity of electricity demand. Then, combined with the range of the flexible load, an ultra-short-term forecast interval for the total load is constructed. This paper studies the total load after considering the flexible load for demand response, and verifies the feasibility of the proposed method with an example.

Ending Storm Version of the 7-day Weighted Analog Intensity Prediction Technique for Western North Pacific Tropical Cyclones

The weighted analog intensity prediction technique for western North Pacific (WAIP) tropical cyclones (TCs) was the first guidance product for 7-day intensity forecasts, which is skillful in the sense that the 7-day errors are about the same as the 5-day errors. Independent tests of this WAIP version revealed an increasingly large intensity overforecast bias as the forecast interval was extended from 5 to 7 days, which was associated with “ending storms” due to landfall, extratropical transition, or to delayed development. Thus, the 7-day WAIP has been modified to separately forecast ending and nonending storms within the 7-day forecast interval. The additional ending storm constraint in the selection of the 10 best historical analogs is that the intensity at the last matching point with the target TC track cannot exceed 50 kt (where 1 kt = 0.51 m s−1). A separate intensity bias correction calculated for the ending storm training set reduces the mean biases to near-zero values and thereby improves the mean absolute errors in the 5–7-day forecast interval for the independent set. A separate calibration of the intensity spreads for the training set to ensure that 68% of the verifying intensities will be within the 12-h WAIP intensity spread values results in smaller spreads (or higher confidence) for ending storms in the 5–7-day forecast intervals. Thus, some extra effort by the forecasters to identify ending storm events within 7 days will allow improved intensity and intensity spread forecast guidance.

Seven-Day Intensity and Intensity Spread Predictions for Atlantic Tropical Cyclones

The extension of the Weighted Analog Intensity Atlantic (WAIA) prediction technique for Atlantic tropical cyclones (TCs) from 5 to 7 days revealed a need for two modifications. The first modification for the 7-day WAIA was to randomly select 70% of the TCs in the entire 2000–15 sample to be the training set and use the remaining 30% as the independent set. The second modification was to ensure that appropriate analogs were selected for ending storm situations such as landfall, postrecurvature, and nondevelopment or delayed intensification within the 7-day forecast interval. By simply constraining the analog selection such that the intensity at the last matching point with the target TC track does not exceed 50 kt (where 1 kt = 0.51 m s−1), an increasing overforecast bias with forecast interval was almost eliminated in both the training set and the independent set. With these two analog selection modifications, the mean absolute errors, and the correlation coefficients of the 7-day WAIA intensities with the verifying intensities, are essentially constant from 5 to 7 days, which establishes this WAIA as a viable technique for 7-day intensity forecasts of Atlantic TCs.

Development and Analysis of a Probabilistic Forecasting Game for Meteorology Students

Calls for moving from a deterministic to a probabilistic view of weather forecasting have become increasingly urgent over recent decades, yet the primary national forecasting competition and many in-class forecasting games are wholly deterministic in nature. To counter these conflicting trends, a long-running forecasting game at Rutgers University has recently been modified to become probabilistic in nature. Students forecast high- and low-temperature intervals and probabilities of precipitation for two locations: one fixed at the Rutgers cooperative observing station, the other chosen for each forecast window to maximize difficulty. Precipitation errors are tabulated with a Brier score, while temperature errors contain a sharpness component dependent on the width of the forecast interval and an interval miss component dependent on the degree to which the verification falls within the interval. The inclusion of a probabilistic forecasting game allows for the creation of a substantial database of forecasts that can be analyzed using standard probabilistic approaches, such as reliability diagrams, relative operating characteristic curves, and histograms. Discussions of probabilistic forecast quality can be quite abstract for undergraduate students, but the use of a forecast database that students themselves help construct motivates these discussions and helps students make connections between their forecast process, their standing in class rankings, and the verification diagrams they use. Student feedback on the probabilistic game is also discussed.