The advancement in electrical load forecasting techniques with new algorithms offers reliable solutions to operators for operational cost reduction, optimum use of available resources, effective power management, and a reliable planning process. The focus is to develop a comprehensive understanding regarding the forecast accuracy generated by employing a state of the art optimal autoregressive neural network (NARX) for multiple, nonlinear, dynamic, and exogenous time varying input vectors. Other classical computational methods such as a bagged regression tree (BRT), an autoregressive and moving average with external inputs (ARMAX), and a conventional feedforward artificial neural network are implemented for comparative error assessment. The training of the applied method is realized in a closed loop by feeding back the predicted results obtained from the open loop model, which made the implemented model more robust when compared with conventional forecasting approaches. The recurrent nature of the applied model reduces its dependency on the external data and a produced mean absolute percentage error (MAPE) below 1%. Subsequently, more precision in handling daily grid operations with an average improvement of 16%–20% in comparison with existing computational techniques is achieved. The network is further improved by proposing a lightning search algorithm (LSA) for optimized NARX network parameters and an exponential weight decay (EWD) technique to control the input error weights.

In grid advancement, energy storage systems are playing an important role in lowering the cost, reducing infrastructural investment, ensuring reliability and increasing operational capability. The storage system can provide stabilization services and is pivotal for backup power for emergencies. With a continuous rise in fuel prices and increasing environmental issues, the energy from renewable resources is gaining more popularity. The main drawbacks of some renewable sources are their intermittent energy generation and uncertain source availability, which has increased interest in energy storage systems (ESSs). This paper investigates the economic feasibility when ESSs are introduced in the electric grid with an expansion of a storage system as well as more percentage of the renewable energy integration and less percentage of fuel consumption by conventional power sources. The Artificial Neural Network is implemented to validate the forecasted load model. The uncertainties associated with the renewable energy system are handled by a chance-constrained model and solved by a genetic algorithm (GA) in MATLAB; selection criteria of GA for optimization process is also discussed in detail. The effectivity of the proposed methodology is verified by applying it to a case that lies in the western region of China.

The penetration of renewable energy sources (RES) into a grid via inverters causes a stability issue due to the absence of an inertia. A virtual synchronous generator (VSG) is designed to provide an artificial inertia and droop control to the grid-connected inverters. The different power ratings of multiple VSGs create complications in the coordination due to unequal droop or damping coefficient ‘ $D$ ’. The dependency of a factor ‘ $D$ ’ on $P-\omega$ droop control under static state and a damping behavior during power oscillation under dynamic state is analyzed by considering three cases on multi-VSGs microgrid system and the equivalent equations of $P-\omega$ droop control are derived for all three cases to see the effect of a load on the overall system’s frequency. A master–slave configuration of a VSG is proposed to deliver maximum power during static state, but provides $P-\omega$ control during the dynamic state. Simulation results verify the improvement introduced by the proposed VSG control.