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Exponential growth in power consumption of wireless communication devices and lack of progress in battery capacity are increasing pressure for more energy efficient (EE) wireless networks. This paper presents an algorithm for optimum EE time allocation for two cooperative relay selection schemes: opportunistic decode-and-forward (ODF) and opportunistic energy efficiency (OEE) with and without rate constraint. By dynamically optimising transmission time between source and relay it is possible to simultaneously improve EE and minimise capacity loss. Simulation in a multiuser scenario with randomly distributed number and location of cooperative nodes demonstrates the algorithm's effectiveness for improving network performance and applicability to both dynamic and static networks. Results imply a unique globally optimum time and power allocation dependent on relay position.

Growing penetration of Electric Vehicles (EV) and Distributed Generation (DG) is driving sharper peaks in demand and supply, which, if poorly managed, manifest as overor undervoltage and disrupt grid service quality. Smart charging schemes reschedule EV charging load according to factors such as grid stability, price signals, etc. It remains unclear how to do this while meeting the diverging needs and expectations of multiple concerned participants. This paper proposes two smart charging schemes for secondary voltage control in the distribution network and analyses performance-cost tradeoffs relating to key players in the Smart Grid. To support these schemes, a distributed communications architecture is designed that jointly minimises traffic burden, computation load and investment in Information and Communications Technology (ICT) hardware. Scheme I (Smart Curtailment), curtails load and DG for peak shaving. Scheme II (Smart Correction) optimises cost-efficiency for subscribing users by maximising power transfer during off-peak hours or when renewable energy is high. Performance of both schemes is consolidated statistically under almost 6 months of practical input profiles. Dramatic improvements in EV & DG capacity are demonstrated and key performance-cost tradeoffs relating to Voltage Control, Peak Shaving, User Inconvenience, CO 2 Emissions and ICT Deployment Cost are identified.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.