In the transportation sector, electric battery bus (EBB) deployment is considered to be a potential solution to reduce global warming because no greenhouse gas (GHG) emissions are directly produced by EBBs. In addition to the required charging infrastructure, estimating the energy consumption of buses has become a crucial precondition for the deployment and planning of electric bus fleets. Policy and decision-makers may not have the specific tools needed to estimate the energy consumption of a particular bus network. Therefore, many state-of-the-art studies have proposed models to determine the energy demand of electric buses. However, these studies have not critically reviewed, classified and discussed the challenges of the approaches that are applied to estimate EBBs’ energy demands. Thus, this manuscript provides a detailed review of the forecasting models used to estimate the energy consumption of EBBs. Furthermore, this work fills the gap by classifying the models for estimating EBBs’ energy consumption into small-town depot and big-city depot networks. In brief, this review explains and discusses the models and formulations of networks associated with well-to-wheel (WTW) assessment, which can determine the total energy demand of a bus network. This work also reviews a survey of the most recent optimization methods that could be applied to achieve the optimal pattern parameters of EBB fleet systems, such as the bus battery capacity, charger rated power and the total number of installed chargers in the charging station. This paper highlights the issues and challenges, such as the impact of external factors, replicating real-world data, big data analytics, validity index, and bus routes’ topography, with recommendations on each issue. Also, the paper proposes a generic framework based on optimization algorithms, namely, artificial neural network (ANN) and particle swarm optimization (PSO), which will be significant for future development in implementing new energy consumption estimation approaches. Finally, the main findings of this manuscript further our understanding of the determinants that contribute to managing the energy demand of EBBs networks.

The Phasor Measurement Unit (PMU) is becoming very effective device for power system as it produces synchronized parameters of power system. This measurement data helps us in monitoring, controlling and protecting the system effectively. To design a wide-area measurement system that could observe the complete system, a Binary Cuckoo Search (BCS) method is suggested in this paper. It also considers the system observability under line/PMU outage conditions. After testing the methodology on some benchmark systems, it has been applied on the Indian power grid. The results are then compared with the methods available in literature. This comparison has proven the proposed method as the best and accurate.Keywords: Optimal PMU placement (OPP); Binary Cuckoo Search (BCS); Power system observability

Relay coordination is important for maintaining proper power system operation and control. Relays should be organized in a special way such that every relay should have a backup and coordination time interval (CTI) between primary and back up and different zones of the relays should be maintained. The relays should operate in minimum desirable time satisfying all the co-ordination constraints optimally. Heuristic techniques are frequently used to get optimal solution of this kind of problems. The problem is solved using Teaching Learning Based Optimization(TLBO). The results are compared with the results obtained by Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) on a 6bus system. Selection of intelligent over current relay characteristics are incorporated to get the desired results in this work. The results obtained from TLBO are found better than the same obtained from GA and PSO.

As the increase in demand for electricity is not been supplied by adequate increase in electrical power generation, the pressure on the electric grids is tremendously growing. so, reliability and integrity is becoming more difficult, sometimes, which may leads to large blackout. In addition, the grid experiences sudden changes due to the inability of the relays to adjust to the prevailing network conditions on the occurrence of a disturbance. This can be reduced by continuous monitoring of system transmission capacity margins and increasing stress on the system. The invention of measuring device, called Phasor Measuring Unit (PMU), that measures the phasor data across the system and communicates to dispatch center through a communication channel, made it possible to redesign network protection schemes to make them more adaptive and thus improve the security of the system. This paper presents the applications of the PMU data in making the relays more adaptive and in the monitoring of grids behavior under disturbances.

A second order Taylor-Kalman-Fourier (T²KF) filter based backup protection of teed-transmission network is demonstrated in this paper. The measured current and voltage signals at relaying end will be supplied to the proposed T²KF filter to estimation of the faulty section from relaying point. This algorithm has considered the out-feeding effect in the presence of practical load for estimating the fault impedance accurately. The proposed method is tested on a teed-transmission network. The results show that this method has identified the faults in backup zones successfully.

In this paper, a back-up protection philosophy based on second order Taylor-Kalman-Fourier (T²KF) filter is suggested for a multi-terminal transmission network. The measured current and voltage signals at relaying end will be supplied to the proposed T²KF filter to estimate of the fault section from the relaying point. This algorithm will estimate the fault impedance in presence of in-feeding effect. The proposed method is tested on a teed-transmission network. The results show that this method has identified the faults in backup zones successfully.