The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO ${}_2$ ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

Due to the high spectral efficiency (SE) and fast synchronization, the time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM) system has gotten much more attraction of researchers as compared to cyclic-prefix (CP) and zero padding (ZP) OFDM in terrestrial as well as underwater acoustic communication. Inter-block interference (IBI) degrades the TDS-OFDM performance due to its long-delay multiple channels. In TDS-OFDM, dual pseudo-random noise (DPN) sequences utilize two PN sequences as a guard interval (GI) after every data block to cope with interference from the OFDM data block to the next PN sequence resulting in compromising the energy efficiency (EE) and spectral efficiency. We have proposed compressed sensing-based technique compressive sensing matching pursuit (CoSaMP), orthogonal matching pursuit (OMP), and look-ahead and backtracking OMP (LABOMP) for TDS-OFDM over the real-time underwater channel in this paper. Moreover, prior to estimating the channel, the received PN sequence is considered in the time domain to compensate for the Doppler shift of the UWA channel. The real-time data experiment has been initially conducted for testing in a water tank in our laboratory. Furthermore, it has been tested on the sea for long communications under the water at the Wuyuan sea area in Xiamen, China. Simulations and experimental results evident that the compressed sensing techniques have better performance over the conventional TDS-OFDM and DPN-TDS-OFDM, even LABOMP outperform OMP and CoSaMP in terms of bit-error-rate (BER), SE, and EE.