Mini S. Thomas’s research while affiliated with Jamia Millia Islamia and other places

Gender equality and female leadership in the energy industry represent crucial dimensions in the broader pursuit of diversity, equity, and inclusivity. Despite initiatives in acknowledging the importance of gender diversity, the energy sector continues to demonstrate a clear gender gap, particularly at the leadership level. This research explores the challenges women encounter in the energy industry, examines the current landscape, and advocates for transformative strategies to promote gender equality and empower female leaders. This study concludes by advocating for ongoing research, education, and advocacy efforts and strategies. By fostering a culture of inclusion and empowerment, the energy industry can tap into a wealth of diverse perspectives, thereby unlocking innovation, creativity, and sustainable solutions to propel the industry forward.

The application of Markowitz and tangency portfolio and Black–Litterman models is extended by the authors to energy portfolio selection in transmission‐distribution environments with high penetration of renewable energy. As Transmission System Operator (TSO) and Distribution System Operator (DSO) contextually take mutualistic or conflicting positions in their portfolio selection process, their risk‐return interactions and behaviours depend on their subjective views on generation and operation. Here, the financial portfolio allocation tool Black–Litterman Model is adapted to incorporate subjective views of the operators to arrive at more intuitive portfolios. The best portfolios are searched within the acceptable risk‐return search space of each operator defined by their Markowitz efficient frontiers (EF), for Pareto‐optimising their profits. The tangency portfolio approach, which is generally used to determine the portfolio of risky and risk‐free assets in finance, is used here to determine the portfolio of renewable (energy‐risky) and fuel‐based sources (energy‐risk‐free). The proposed methodology is adopted in an HV–MV interconnected test system operated by one TSO and two DSOs, having wind, solar, coal, gas and nuclear generation technologies. It is observed that completely customisable portfolios can be constructed for TSO and DSO based on their inherent financial and energy risk‐return behaviours and posterior views.

Renewable energy sources (RES) and electric vehicles (EVs) pose ‘energy‐risk’ in peer energy commitments due to their temporal and spatial uncertainties. Thus, optimistic commitments in the peer‐to‐peer transactive energy market (P2P TEM) are improbable. This paper proposes a two‐stage master–slave portfolio optimization approach for combining energy‐risk of RES and EVs, and welfare‐risk of peers, in building clean energy portfolios. The master portfolio (MP) refers to the shares of renewable and EVs in P2P market settlement, whereas the slave portfolio (SP) gives the wind‐solar mix within renewables. Here, Rachev Ratio (RR), an index used in financial portfolio selection for tail‐risk management, is adapted and combined with Markowitz Efficient Frontier (EF) to find the optimal slave portfolio. Both the extreme tails are optimized, encouraging energy outputs far above forecast and discouraging those far below forecast. The master portfolio is obtained by maximizing the sum of the average welfare of the peers at the best (right) and worst (left) tails of the welfare distribution curve using Stochastic Weight Trade‐off Particle Swarm Optimization (SWT‐PSO). The proposed portfolio selection approach is better in terms of increased expected energy output, improved utilization of RES and EVs, and better collective peer welfare.

Identification of fault localization in microgrid (MG) network using travelling wave (TW) mainly depends on the propagation velocity (ϑ) of the reflected wave and type of mother wavelet chosen for extraction TW using discrete wavelet transform (DWT). Estimation of ϑ in non-homogeneous MG network and selection of the appropriate mother wavelet are a challenging task. This paper proposes a method to overcome the defined challenges in a non-homogeneous MG network. Proposed method majorly works in two parts: i) fault detection, faulty section identification and ii) fault location scheme in a medium voltage MG network. The first part utilizes added decoupled RMS currents (ADRMSC) values to detect and identify faulty section. Second part utilizes the consecutive sample difference approach (CSDA), and first arrival time instants of travelling wave (TW) at section terminals, computed using discrete wavelet transform (DWT) to locate the fault. Proposed scheme depends on the capture of time instants, which makes it least affected by the change in mother wavelets. The proposed scheme also performs well for a non-homogeneous system as it is independent of TW propagation velocity to locate the fault. The performance of the proposed method is validated on the non-homogeneous IEEE-33 bus system in EMTDC/PSCAD for various case studies which includes changes in fault parameters such as fault resistance, fault inception angle and noise level and found effective even with the change in mother wavelets.

The transition of the conventional power grid into the smart grid requires continuous monitoring of integrated grids speared over wide-area through Phasor Measurement Units (PMU). These PMUs additionally perform protection and state estimation functions in the smart grid. This paper discusses implementation of a new phasor estimation method to eliminate the effects of Decaying DC (DDC) component and off-nominal frequencies during the extraction of the phasors from a relaying signal. The practical implementation of the proposed method in a low-cost microcontroller (ESP32-WROOM-32 development board) in compliance with the requirements of IEEE C37.118.1a-2011 standard is also demonstrated. The analysis of various existing algorithms estimating the phasors is carried out. The microcontroller is programmed with the best among the analysed algorithm and its feasibility to function as a proper Phasor Measurement Unit is tested. The newly designed PMU is rigorously tested with different estimation methods compliant with IEEE C37.118a-2011 standard. The comparison of the proposed method with different phasor estimation algorithms is also discussed.

Distributed generation (DG) penetration leads to bi-directional power flow which raised tangible challenges in front of power system protection engineers to maintain the sustainability of power system specially during fault conditions. This article proposes a new approach to identify the fault location in a multiterminal nonhomogenous ac microgrid. Implemented method utilizes the information of traveling waves (TW) with the help of intelligent electronic devices connected at the end terminals of the test system. Proposed approach is independent of the propagation velocity of TW for the estimation of fault. Algorithm identifies the fault section using an approach that utilizes the difference of change in voltage difference (prefault and during fault) at different buses. Then, variational mode decomposition signal processing technique is applied to TW waves captured at the fault section terminals, to estimate the fault location. This scheme is implemented on IEEE 33 bus system, modeled in electromagnetic transients including dc/power systems computer aided design (EMTDC/PSCAD), to validate the methodology. Impacts of variations in fault resistance, input noise level, and inception angle has been considered for the validation of proposed scheme. The outcome of test results indicates that the proposed method gives better performance compared to the conventional TW-based techniques.

The use of electric vehicles (EVs) has recently increased in a smart city environment. With this, the optimal location of the charging station is a great challenge and, hence, the energy efficiency performance (EEP) of an electrical system is important. Ideally, the EEP is realized through passive energy boosters (PEBs) and active energy boosters (AEBs). PEBs require no external resources, and EEP is achieved through altering the network topology and loading patterns, whereas, in AEBs, integrating external energy resources is a must. The EEP has also become dynamic with the integration of an energy storage system (ESS) in a deregulated environment. Customer energy requirement varies daily, weekly, and seasonally. In this scenario, the frequent change in network topology requires modifying the size and location of AEBs. It alters the customers’ voltage profile, loadability margin, and supply reliability when the EV works differently as a load or source. Therefore, a comprehensive EEP analysis with different probabilistic loading patterns, including ESS, must be performed at the planning stage. This work uses a harmony search algorithm to evaluate EEP for AEBs and PEBs, in coordination, when ESS works as a load or source, at four locations, for customers’ and utilities’ benefits.