• Energy Research

India's steadily expanding populace has made it important to create innovative methods of transportation with electric vehicles (EV's) being the most favoured alternative. The major impediment is the negative impact caused by the improper setup of charging stations on the utility distribution system. This paper takes up the issue of optimal planning (siting and sizing) of charging station (CS) infrastructure in the city of Allahabad, India. It has been labelled as a future smart city under a pilot transportation project started by Government of India. A hybrid algorithm comprising of Genetic Algorithm and improved version of conventional Particle Swarm Optimization (GAIPSO) is employed in this study. The aim is to find optimal location of proposed charging stations in the Allahabad distribution system. PSO further re-optimizes the received sub-optimal solution (site and size of station) which leads to an overall improvement in its functionality and solution. Simulation studies on a real time system of Allahabad city clearly exhibits the superior performance of the aforementioned technique with respect to GA and PSO in terms of improvement in voltage profile and quality of solution obtained for the objective function.

Now a days, wind and solar energy are most commonly used as renewable energy sources (RES). These sources highly depend on environment and geographical conditions of a particular location. Main drawback of RES is randomness in power production due to their inherent uncertainty nature. Hence, energy management is difficult task for these sources. Wind, photovoltaic (PV) and sodium nickel chloride (SNC) battery based hybrid power system (HPS) is used as study system in this paper. Two battery models, i.e., internal resistance (IR) and two time constant (TTC) model are modeled. System operation is carried in two stages for these two battery model separately, in first stage voltage of system is maintained at constant value using voltage source converter (VSC) after that in second stage dynamic programming (DP) is applied to system to get maximum revenue from the system.

Abstract Since last decade, the concept of microgrid (MG) is growing rapidly with increasing electricity generation through renewable energy sources (RES) and small dispatchable sources. A stand-alone MG is a better option for growing unserved electricity demand especially in remote areas, where classical power transmission system is not economically and technically feasible. The energy scheduling of RES and small dispatchable sources can be efficiently handled by the inclusion of battery storage system (BSS) along with RES. Further, the BSS in MG includes some degree of complexity in the objective function of optimal scheduling strategy. This paper deals the optimal energy scheduling in stand-alone MG consisting of wind turbine (WT), photovoltaic (PV), diesel engine generators (DEs) and BSS, which is not an easy task because of uncertainty in nature, dynamic market bid, and demand profiles. The BSS is operated in three different strategies based on fluctuation in RES power, load, and market bid to obtain the optimal energy scheduling of MG in continuous and discrete operation modes of DEs. The scheduling strategies maximize the arbitrage of MG system in both modes of DEs operation. The problem is simulated in MATLAB® environment, using artificial bee colony (ABC) and its variant global best (Gbest) guided ABC (GABC) algorithms, and other existing algorithms as particle swarm optimization (PSO) and genetic algorithm (GA). The obtained results depict that the GABC provides better revenue and, exploration, and exploitation capabilities in all operational strategies, than ABC, PSO and GA algorithms. The operational strategy based on variable market bid, load, and RES power gives optimal revenue and reduced or equal green house gases (GHG) emissions than other strategies in the considered modes of DEs operation.

Electrical load forecasting is an important topic within the electrical market which has been done by a machine learning methodology: Support Vector Machines (SVM). Load forecasting with SVM will form the non-linear relations with the parameters that have an effect on the load; additionally to the correct modeling of the load curve on weekends and holidays. The past information is used as a sample for the applying and therefore holidays associated demand as an important factor inprediction. The LibSVM package and Python codeis used for modeling the SVM. Resultsare obtainedand comparison is made for the two methods.

A number of control algorithms have been developed for load compensation using DSTATCOM. Choice of an algorithms according to the system therefore, becomes a topic of interest for the researchers. In this paper, nine control algorithms viz. instantaneous p-q theory, instantaneous modified p-q theory, synchronous reference frame theory, instantaneous p-q-r theory, average unity power factor (AUPF) theory, vectorial theory, instantaneous symmetrical components theory (ISCT), average global control theory (AGCT), and instantaneous global control theory (IGCT) are compared for different operating conditions of distribution system. The cases considered for system operation are nonlinear balanced load with balanced source, nonlinear unbalanced load with balanced source, nonlinear unbalanced load with unbalanced source, and nonlinear unbalanced load with non-sinusoidal balanced source. The performance of the system simulated on MATLAB platform is evaluated considering source current total harmonic distortion (THDI), total distortion content (TDC), and neutral current magnitude. The results show that ISCT, AGCT and IGCT control algorithms are able to compensate the load and improve system performance over other algorithms.

In this paper, a novel technique of voltage sag characterization and detection have been presented. In the proposed work, firstly, two types of data inputs, namely, peak, and fundamental component values of voltage signal, have been derived from IEEE test waveform of instantaneous voltage signal. Then, these two derived and one test waveform data, i.e., three data inputs, have been used to obtain WT coefficients and their energies with the help of Maximal Overlap Discrete Wavelet Transform (MODWT). Finally, WT coefficients and their energies, which are obtained with these three data inputs, have been applied to estimate the magnitude, duration, points on wave (POW) of start and recovery of the voltage sag for its characterization and detection. It is found that the magnitude and duration, obtained with the fundamental voltage component data is closer to the real characteristics than the other two data inputs whereas POW of start and recovery provides accurate result with instantaneous data input. Additionally, for computing WT coefficients and their energies, boundary effects in the one cycle sliding window have also been considered here.

In this paper, a new efficient algorithm is developed for optimal placement of capacitor using Nelder-Mead particle swarm optimisation (NM-PSO). The developed capacitor placement problem (CPP) has been formulated to make the objective function (i.e., planning method) more realistic by incorporating essential economic factors, viz., maintenance cost, interest on investment cost and inflation. This developed CPP is found capable of providing more cost-effective solution than existing approaches. Simulation results are obtained for IEEE 9-bus and 69-bus radial distribution system. Results obtained with NM-PSO, are compared with the PSO approach. For both the IEEE test systems, simulation results reveal more effective and profitable planning with NM-PSO. In addition, sensitivity analysis is carried out to highlight the impact of newly incorporated economic factors, justifying their inclusion in CPP.