Electrical engineering has always been one of the most prominent branches of engineering studies for centuries. With rampant technological advancements and innovations, electrical engineering has built its significance in our lives over time. Renewable energy is another important aspect of electrical engineering and has been gaining momentum for quite some time now. Many top electrical engineering colleges in Nashik offer cutting-edge undergraduate and postgraduate programs in electrical engineering to train future engineers in this field.
Let us understand the futuristic advancements in the field of electrical engineering and renewable energy for coming decades:
Transitional Shift Towards Green Technologies
Modern energy infrastructure is experiencing a transitional shift towards sustainable and clean technologies. Significant development in the field of power electronics has enabled this paradigm shift through the inclusion of renewable powered distributed generation systems, FACTS devices, HVDC systems and microgrids. Through these technical advancements, modern power system operations are expected to be self-reliant, stable, carbon free, flexible and cost effective. However, the modern power system operation becomes more complex from modelling, optimisation and computational perspective.
A generalised power system is a complex network of generation systems, transmission lines and distribution systems. Arguably, the complexity of the power system is techno-economically challenged on integration of new age energy technologies such as DGs, FACTS devices, PMUs and HVDC transmission for achieving better energy security, stability, reliability and operational flexibility. Challenges related to intrinsic/extrinsic uncertainties, optimal power flows, reactive power management, optimal resource allocation and generation/transmission expansion becomes more dominant from an operational, economical, computational and technical perspective. Proper planning is essential in order to facilitate sustainable transition towards a self-reliant modern power system through multi-disciplinary studies.
Over the last two decades, research themes related to power system planning focussed majorly on various optimisation and operation research techniques for power system analysis, operation and decision making. From a historical perspective, Carpentier in 1962 formulated a foundational tool through an economic dispatch problem to perform power flow studies in an inter-connected power system network. To summarise initial contributions in power flow studies, Frank et. All reviewed about various classes of formulation fundamentally required for lower-level decision making in the form of optimal power flows. Additionally, authors provided a comprehensive context on OPF solutions through linear, non-linear, mixed integer linear and non-linear programming frameworks.
Identified as a highly non-linear and non-convex problem, efforts were made by the researchers, power system engineers and operation research experts to simplify power flow studies through deterministic, non-deterministic, meta-heuristics and hybrid optimisation techniques. To leverage the operational flexibility and improve techno-economic capability of the modern power systems, et. al later discussed the necessity of uncertainty aware optimisation framework in high level or low-level decision making through power flow studies. Sun et. al emphasised on recent developments in power system planning through stochastic and robust optimisation frameworks where the uncertainties are fundamentally modelled using random variables characterised by known probability distributions.
Barriers to Accelerated Deployment of Renewable Based Technologies
Energy and access to energy is essential for the socio-techno-economic development of human civilisation and nation. However, demand for electricity in the final consumption mix is increasing at a steady rate and is projected to grow from around 20% today to 40% by 2050 due to increase in digitisation, industrial automation and end use electrification in transportation and heating. Renewable energy and low carbon technologies are the most reliable options to address the current growing energy demand and carbon related emission issues.
Over the past two decades, solar based power generation has become increasingly popular and sufficiently competitive over other renewable options. However, there is a fundamental issue in accelerated deployment of solar based power technologies. The intermittent and variable nature of solar pose technical challenges and may disturb the reliability and stability of the system.
Additionally, extreme weather conditions can also lead to several contingencies such as blackouts impacting the overall system resilience. Thus, a strategic shift is required to facilitate techno-economic development and implementation of low carbon renewable-based technologies such as solar by incorporating forecasting as an integral tool in the decision-making process.
Role of Renewable Energy Forecasting
Low carbon technologies particularly, solar and wind are at the heart of global energy transformation to achieve net zero emissions in the energy sector. However, the stochastic nature and spatio-temporal variability of renewable energy resources pose real time technical challenges in energy analytics, planning and decision-making process. Renewable energy forecasting is an effective tool for reliable integration of renewable energy sources into the overall energy landscape by offering numerous benefits and providing valuable insights for energy planners on grid integration, stability, reliability, resource planning, demand side management, energy market operations, grid planning or expansion, risk assessment, environmental impact. The growing significance of accurate renewable power prediction has made solar forecasting an appealing field of research. Therefore, recent research and studies have focused on improving the prediction accuracy.
Additionally, advances in machine learning have led to progress in a variety of research areas, including time series forecasting. Over the last decade, numerous data driven machine learning algorithms and techniques have been proposed by the researchers to achieve accurate time series forecasting. Fundamentally, solar power forecasting techniques are divided into two groups: indirect forecasting and direct forecasting. Through indirect forecasting techniques, renewable resources quantity is initially predicted and then converted into power output by incorporating the knowledge of their electrical characteristics. Alternatively, renewable power can be directly predicted using historical information of temporal renewable power output and spatial weather conditions through direct forecasting techniques and approaches.
Researchers have classified solar forecasting based on different factors. Broadly, temporal forecasts based on different time horizons and spatial forecasts based on geographical segmentation (local or regional) are the most common among them.
Based on different time horizons, temporal renewable forecasting can be sub-classified into:
- Very short-term forecasts (0 – 30 min)
- Short term forecasts (30 min – 48 hours)
- Medium term forecasts (48 hours – 800 hours)
- Long term forecasts (beyond 800 hours)
Based on geographical segmentation, spatial renewable forecasting can be classified into:
- Local forecasts – preferable for local distributed generation and microgrid operations.
- Regional forecasts – preferable for grid operators and plant operators to maintain supply and demand balance of regional power system
Accurate renewable power forecasting plays an important role in energy dispatch and operation of modern power systems which can lead to technical, operational and economic benefits thereby increasing the overall reliability of the electrical supply system. It is essential for the power system operator to maintain a reasonable balance between power generation and consumption without exceeding the operational and technical limits of the power system. However, the stochastic nature of solar based power generation makes it difficult to achieve desirable power management. Hence, it is essential for power system operators to quantify uncertainties while forecasting future power forecasts. Probabilistic forecast techniques are statistically more convenient than conventional point forecasts and provide a realistic outlook on future renewable generation profile in the form of prediction intervals, quantiles or probability density functions.
Conclusion
Overall, this blog aims to identify opportunities, challenges, and advances in accelerated deployment of renewable energy resources for efficient operation of sector coupled modern power systems. B.Tech in Electrical Engineering is a dynamic and globally-recognised degree program to gain the right foundation for a bright career in this field. Do some research and enrol into a UGC-recognised college to pursue this program and explore top career options in electrical engineering and renewable energy.