Future generation Smart Grids are transforming into networks of small scale microgrids. Further, in developing nations where severe power deficits are set to remain a harsh reality, such microgrids are expected to be equipped with brownout based power management in order to avoid rolling blackouts, which affect complete denial of power to a service area during power shortage. Brownouts allow power provisioning to selective loads while curtailing power supply to others. Given the electricity demands of appliances associated with a set of establishments in a microgrid, this work presents a novel centralized, price-induced brownout based appliance scheduling mechanism. The consumers express their appliance's priority/urgency towards uninterrupted power supply by subscribing to appropriate price tariffs and notify their preferred operation intervals. Two different types of appliances have been considered, i.e., rigid (non-deferrable but curtailable) and elastic (deferrable). We first formulate the scheduling problem as an Integer Linear Programming (ILP) problem with the objective of maximizing total revenue and show that such an optimal strategy incurs high overheads in terms of solution generation times. Therefore, we have proposed a fast yet efficient heuristic algorithm namely, Revenue-aware Appliance Scheduler (RaAS), which is able to produce appreciably good solutions which are only about 2% lower on average, than the optimal ones. However, the solution generation times taken by RaAS are about 28 times faster than the optimal ILP based strategy. The proposed algorithm is extensively evaluated by conducting experiments on various empirically generated microgrid scenarios. RaAS is found to be scalable to microgrids with significantly large number of establishments.

Power shortage is a serious issue especially in developing nations. Such power deficits are traditionally handled through rolling blackouts - a service area is divided into subareas, each of which is denied power during a designated time in the day. Today, smart grids provide the opportunity of avoiding complete blackouts, converting them to brownouts which allow selective provisioning of power supply to support essential loads while curtailing supply to less critical loads. We formulate the brownout based power distribution problem as an integer linear programming (ILP) and show that solution strategies such as conventional dynamic programming (DP) impose substantial overheads. So, we propose the streamlined DP-based priority level allocator (SDPA) which utilizes the discrete nature of power demands of each subarea and generates the overall optimal solution far quicker by focusing on a lower number of non-dominating partial DP-solutions. SDPA is found to be about 9 to 33 times faster than DP and applicable to real-time brown-out based power distribution in moderate sized grids. However, even SDPA may fail to meet the real-time requirements of dynamic power imbalance mitigation in very large grids. So, a fast yet effective power adjustment approach namely, Proportionally Balanced Priority level Allocator (PBPA), has been designed and implemented. Experimental results show that although solutions provided by PBPA could be less effective by upto 12 percent compared to optimal dynamic programming based schemes, being about 4 orders of magnitude faster, it can be deployed for real time allocations of power.

Power shortage is a serious issue especially in third world countries, and is traditionally handled through rolling blackouts. Today, smart grids provide the opportunity of avoiding complete blackouts by converting them to brownouts, which allow selective provisioning of power supply to support essential loads while curtailing supply to less critical loads. In this work, we formulate brownout based power distribution scheduling as an optimization problem and propose a modified Dynamic Programming (DP) based optimal algorithm (suitable for moderate sized grids) that is 9 to 40 times faster than the conventional DP approach.