In deregulated environment voltage stability has become very important factor for the purpose of analysis. In this paper some important features associated with voltage stability use in power system have discussed. Line Stability index is used for estimation of the maximum loadability and in other words index is used to recognise the weak bus in electrical power system. In this paper Artificial Neural Networks (ANNs) are used for assessment of voltage stability or to confirm secure and insecure mode of the power system. The input data of neural network are yield from the Newton-Raphson (NR) load flow analysis in the platform of MATLAB R2015b. The result obtained from the N-R method also validates through Feed-Forward Back Propagation (FFBP) Layer Recurrent (LR) and Radial Basis Function Network (RBFN) in terms of accuracy to foresee the status of the power system. The effectiveness of the analyzed methods is validated through IEEE 14 test system and IEEE 30 test bus system, using Fast Voltage Stability Index (FVSI).

Lithium based battery gives huge exposure in Battery Electric Vehicle in recent decade. The main dis-appointment in the field of Pure Electric Vehicle is range estimation. Many researcher work on range maximization of Li-ion Battery (LIB), the range of LIB is affected due to following reason: Initial SOC (State of charge), chemistry used in cell, temperature, road condition, driving performances and behavior. Main objective of this paper to use of adaptive algorithm for estimate accurate SOC and terminal voltage (E_t) of battery pack. Here we use online available experiment battery data for Evaluation: Turnigy Graphene for three drive cycle HWFET, LA92 and US06 for 25 ℃. This experimental data used to find estimated SOC, terminal voltage and error estimation for above drive cycle and temperature range. Compare the result using Extended Kalman filter, Support Vector Machine (SVM), RNN-LSTM and Hybrid approach to estimate SOC error and terminal voltage error. The result shows that for error is minimized using data driven model, SOC estimation error is decrease 33.69%, 55.06% and 62.71% Second, Voltage error also decrease with respect to EKF model-based estimation method is 1.88%, 3.12% and 20.04% for the drive cycle of HWFET, LA92 and US06 respectively.

Demand side management initiatives have gained attention recently because of the development of the smart grid and consumer-focused regulations. The demand side management programme has numerous goals. One of the main goals is to control energy demand by altering customer demand. This can be done in several ways, including financial discounts and behaviour changes brought about by providing knowledge to support the grid’s stressed conditions. In this study, demand side management techniques for future smart grids are presented, including load shifting and strategic conservation. There are many controlled devices on the grid. The load shifting and day before strategic conservation approaches mentioned in this study are derived analytically for the minimization problem. For resolving this minimization issue, the RUNge Kutta optimizer (RUN) was developed. On a test smart grid with two service zones, one with residential consumers and the other with commercial consumers, simulations are performed. By contrasting the outcomes with the slime mould algorithm (SMA), Sine Cosine Algorithm (SCA), moth–flame optimization (MFO), and whale optimization algorithm (WOA), RUN demonstrates its effectiveness. The simulation findings demonstrate that the suggested demand side management solutions produce significant cost savings while lowering the smart grid’s peak load demand.