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Electricity demand exceeds the supply leading to regional blackout. The power generated is transmitted through a large network and significant power losses in transmission and distribution feeders. Loss reduction through various methods has the potential to yield huge savings in economic growth and environmental impact. One such effective loss minimization method is to inject reactive power by placing shunt capacitors at appropriate places with proper size. A Harmony Search Algorithm methodology is proposed in this work to identify the appropriate size of shunt capacitors by taking the objective as minimizing the cost associated with real power losses along with installation cost of shunt capacitors in an unbalanced radial distribution network. The bus voltage limits, number/size of installed capacitors at each node are taken as constraints. A two stage methodology is adopted in this work to get the solution of the optimization problem. In the first stage, the voltage stability index values at each bus is computed and the node with minimum value of voltage stability index is identified as the most receptive node to voltage slump and such a node is considered as candidate node for installation of shunt capacitors. In the second stage the HSA methodology is used to ascertain the suitable capacitor size. The backward / forward sweep algorithm based Radial distribution load flow algorithm is utilized for power flow simulation. The proposed Harmony Search Algorithm is implemented on the modified IEEE 13 and 37 bus URDN.

Cloud data center’s total operating cost is conquered by electricity cost and carbon tax incurred due to energy consumption from the grid and its associated carbon emission. In this work, we consider geo-distributed sustainable datacenter’s with varying on-site green energy generation, electricity prices, carbon intensity and carbon tax. The objective function is devised to reduce the operating cost including electricity cost and carbon cost incurred on the power consumption of servers and cooling devices. We propose renewable-aware algorithms to schedule the workload to the data centers with an aim to maximize the green energy usage. Due to the uncertainty and time variant nature of renewable energy availability, an investigation is performed to identify the impact of carbon footprint, carbon tax and electricity cost in data center selection on total operating cost reduction. In addition, on-demand dynamic optimal frequency-based load distribution within the cluster nodes is performed to eliminate hot spots due to high processor utilization. The work suggests optimal virtual machine placement decision to maximize green energy usage with reduced operating cost and carbon emission.