RENEWCONNECT - All about renewables & power sector

 A hypothesis is a statement that can be tested through scientific research, representing a proposed explanation for a phenomenon or a prediction about relationships between variables. There are several types of hypotheses, each with a distinct purpose and use. Below is the outline of the major types of hypotheses in detail: 1. Null Hypothesis (H₀) The Null Hypothesis states that there is no effect or no relationship between the variables being studied. It is used to test whether any observed effects in a study are due to chance. Purpose : The null hypothesis serves as a starting point for testing and provides a benchmark against which the actual outcomes are compared. Example : In a study testing a new drug, the null hypothesis might state that "There is no difference in the effectiveness of the new drug compared to a placebo." 2. Alternative Hypothesis (H₁ or Hₐ) The Alternative Hypothesis states that there is an effect or a relationship between the variables. It is what

P52, P53 and P90 are terms often used in the renewable energy sector, particularly in the context of wind or solar energy production analysis. These refer to statistical probability levels used in energy yield assessments to estimate the expected production of renewable projects over a certain time frame. P50 : Represents the median or "best estimate" production scenario. It means there is a 50% chance that the actual energy production will be higher or lower than this value. It is the expected average production in a typical year. P52 or P53 : These are uncommon notations, but they might represent slight variations from the median estimate, with a slightly higher probability of occurrence than P50. P90 : This represents a conservative estimate, meaning there's a 90% chance that the actual production will be equal to or exceed this value, making it suitable for financial risk assessments. In summary, P-levels like P50, P52, or P90 provide different confidence levels for

State Periphery Charges are included in the DSM, which is introduced by the Himachal Pradesh, Maharashtra, and Telangana State Electricity Regulatory Commissions in India. The term "state periphery" is defined as below: State Periphery  or ‘HP Periphery’ means, except in situations that may warrant any other treatment in specific cases, the interconnection point of the transmission system of the STU with the transmission system of the CTU; or with any other inter-state transmission system not owned by any State Entity, or with the generation bus-bar of the joint sector projects from where power is supplied to the Distribution Licensee through the STU system; [or] The periphery of the electrical power system and its components thereof under operational supervision and control area jurisdiction of SLDC (State Load Dispatch Centre) covering InSTS (Intra-State Transmission System) Source: Himachal Pradesh State Electricity Regulatory Commission (HPERC) Note: Interlinks for the a

Forecasting: The process of calculating and predicting future events, usually based on extrapolation from the past experience and with varying degrees of uncertainty. Forecasting is an integral part of business decision processes. The energy industry relies on forecasters to forecast load, generation and prices, etc. These forecasts are being used by all segments of the energy industry for planning and operations of both power systems and business entities.  Some of the key topics related to renewable energy (RE) forecasting are as below: 1. Machine Learning Techniques for Renewable Energy Forecasting: Exploring how AI and machine learning algorithms improve the accuracy of renewable energy predictions. 2. Weather Radar Technology for Wind Energy Forecasting: Discussing the use of weather radar data to enhance wind energy forecasting models. 3. Blockchain Applications in Renewable Energy Forecasting: Exploring how blockchain technology can improve transparency and efficiency in RE fore

Over the last 20 years, 3 day forecasts are better than, one day forecast delivered 20 years ago. “Numerical forecasting”, mathematical equations to predict weather forecasting require powerful computers and lots of observable data collected from land, sea, and air. Thousands of (more than 01 million) weather stations are located across the globe for data pooling. Instruments like ground-based wind gauges (anemometers), rain collectors, and temperature sensors are used by amateur weather watchers. Complex mathematical models to predict, new based on incoming data, weather conditions might change over time. Large-scale phenomena, each are governed by multiple factors considering external and internal factors of the solar system, observing sun temperature and other radiations, earth temperature, pressure difference, will form clouds and winds, change in phase of ice-water-gas which will affect the flow of energy. The National Centres for Environmental Prediction (NCEP), objective is