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Conventional power systems are almost at the verge of their existence and are quickly being replaced by modern, especially smarter alternatives. Rapid and wide deployment of Load side management, which is a smarter and modern way to ensure efficient and economical use of power, is a classic example. However, there are several challenges which need to be addressed before it can be realized as a commercially viable solution. Suitable communication method, called "Home Area Networks", is a mandatory requirement which should be cost effective, robust and with ability to simultaneously handle large number of devices. Due to various inherent issues, communication protocols commonly used now-a-days, are not fully capable to address the requirements of HANs. Power Line Carrier Communications (PLCC) can be a suitable and efficient alternative of quick realization of HANs. In this paper, various aspects of PLCC have been analyzed for use in HANs. Modulation methods, materials, physical parameters and effect of noise have been analyzed. The paper evaluates performance and effectiveness of FSK modulation with aluminum transmission medium for PLC within a residential environment, focusing on the tradeoffs between distance, transmission medium diameter, and type of transmission medium, and cable gauge, which can degrade the performance of PLCC. A novel simulation model has been presented incorporating RLCG (Resistance, Inductance, Capacitance, and Conductance) tools and additive white Gaussian noise (AWGN). Performance parameter have been comprehensively analyzed of implementation using aluminum as a communication medium. This paper discusses cost effectiveness of PLCC by replacing copper by aluminum as the transmission medium. The study reveals that aluminum cables must be twice the size of copper cable in order to achieve same communication distances. Higher carrier frequencies in FSK modulation increase noise susceptibility, consequently reducing achievable communication distances. Cables with smaller diameters results in lower transmission ranges. These results highlight the trade-offs involved in implementing FSK for in-home PLCC (iPLCC). The study highlights how crucial it is to take these trade-offs into account while developing and refining FSK-based iPLCC systems for use in smart homes. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for electricity has presented a grave threat to traditional energy sources, which are finite, rapidly depleting, and have a detrimental environmental impact. These shortcomings of conventional energy resources have caused the globe to switch from traditional to renewable energy sources. Wind power significantly contributes to carbon-free energy because it is widely accessible, inexpensive, and produces no harmful emissions. Better and more efficient renewable wind power production relies on accurate wind speed predictions. Accurate short-term wind speed forecasting is essential for effectively handling unsteady wind power generation and ensuring that wind turbines operate safely. The significant stochastic nature of the wind speed and its dynamic unpredictability makes it difficult to forecast. This paper develops a hybrid model, L-LG-S, for precise short-term wind speed forecasting to address problems in wind speed forecasting. In this research, state-of-the-art machine learning and deep learning algorithms employed in wind speed forecasting are compared with the proposed approach. The effectiveness of the proposed hybrid model is tested using real-world wind speed data from a wind turbine located in the city of Karachi, Pakistan. Moreover, the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are used as accuracy evaluation indices. Experimental results show that the proposed model outperforms the state-of-the-art legacy models in terms of accuracy for short-term wind speed in training, validation and test predictions by 98% respectively. [ABSTRACT FROM AUTHOR]

Expected widespread deployment of Peer-to-Peer energy transactions through affective utilization of Renewable Energy Sources require efficient energy transaction mechanism among the microgrids. We propose a scheme to establish peer-to-peer energy trading in multi-microgrid network by considering photovoltaic and wind energy systems. The research objectives are to minimize overall cost of all microgrids in multi-microgrid network and minimize the loading on centralized power network. Various parameters of photovoltaics and wind energy systems are modeled to explore their impact on P2P energy trading. Energy Management Unit establishes the smart contracts among microgrids, manages power transactions and calculates the cost based on dynamic pricing scheme in the multi-microgrid network. Two different cases are considered with respect to the types of power transaction among the microgrids in the multi-microgrid network and main grid. The effectiveness of the proposed scheme is validated by implementing on local small-scale power distribution system. [ABSTRACT FROM AUTHOR]