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Brownout Based Blackout Avoidance Strategies in Smart Grids

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Brownout Based Blackout Avoidance Strategies in Smart Grids Brownout Based Blackout Avoidance Strategies in Smart Grids Basina Deepak Raj Satish Kumar Sambit Padhi IIT Guwahati IIT Guwahati IIT Guwahati Arnab Sarkar Arijit Mondal Krithi Ramamritham IIT Guwahati IIT Patna IIT Bombay ABSTRACT during demand overloads, such that uninterrupted power supply to Power shortage is a serious issue especially in third world countries, essential appliances/establishments may be selectively maintained and is traditionally handled through rolling blackouts. Today, smart while cutting down supply to less critical loads (lower priority). grids provide the opportunity of avoiding complete blackouts by In [1], authors introduced the brownout based approach and pre- converting them to brownouts, which allow selective provisioning sented a customer-end hardware prototype system to support the of power supply to support essential loads while curtailing supply to brownout based energy distribution scheme. less critical loads. In this work, we formulate brownout based power Our Contributions: Informed distribution of available Power. distribution scheduling as an optimization problem and propose In this paper, we develop the algorithmic support to answer the a modified Dynamic Programming (DP) based optimal algorithm following question: Given a system-wide upper-bound on consump- (suitable for moderate sized grids) that is 9 to 40 times faster than tion in times of power scarcity, how do we ensure that the available the conventional DP approach. power is equitably distributed to the consumers, taking into account the criticality of their needs. KEYWORDS First, we devise a comprehensive framework for brownout based electricity scheduling. The framework is guided by the following Smart Grids, Rolling Blackouts, Brownout. soft real-time specification (governed by recommendations of IEEE ACM Reference Format: standards on power quality [2]): Brownout based mitigation of im- Basina Deepak Raj, Satish Kumar, Sambit Padhi, Arnab Sarkar, Arijit Mon- balances in demand-supply must be conducted quickly and within dal, and Krithi Ramamritham. 2018. Brownout Based Blackout Avoidance about ∼0:5 seconds (for a 50Hz power system) in order to maintain Strategies in Smart Grids. In e-Energy ’18: The Ninth International Conference satisfactory power quality. on Future Energy Systems, June 12–15, 2018, Karlsruhe, Germany. ACM, New York, NY, USA, 3 pages. https://doi.org/10.1145/3208903.3212059 Second, we address brownout based power distribution sched- uling as an optimization problem, which essentially boils down 1 INTRODUCTION to the Multiple Choice Knapsack Problem (MCKP). A conventional Dynamic Programming (DP) based solution to the problem proves The Problem: Power deficit in developing countries leads to be prohibitively expensive in terms of computational overheads to blackouts. Many developing countries suffer from significant and violates the real-time requirement stated above. power deficits. Currently, power deficits are typically handled through Third, we propose a modified version of DP named, Streamlined a mechanism known as Rolling Blackout: Intentionally engineered DP-based Priority level Allocator (SDPA). It capitalizes on the discrete electricity shutdown, where electricity distribution is fully stopped nature of the power allocation demands of subareas, to work with for non-overlapping time intervals over different subregions within a far lower number of non-dominating partial DP-solutions and a distribution region. As blackouts cut down supply to even essen- allows the ultimate optimal solution to be generated much faster. tial loads like lights, fans in households, there is a dire need to have SDPA is found to be about 9 to 40 times faster than DP and so, is schemes with more benign side effects. appropriate for real-time brownout based power distribution in The Solution: With Smart Grids, we can convert Blackouts to moderate sized grids. Brownouts. In Smart Grids, Smart Meters installed at households are capable of monitoring and controlling the electricity usage 2 SYSTEM MODEL AND PROBLEM of smart appliances based on scheduling strategies chosen by the FORMULATION consumer and/or the utility provider. In this paper, we consider a practical alternative to rolling black- The system considered in this work is modeled as a set of N subar- outs: a brownout based electricity scheduling strategy. Brownout eas S1; S2; :::; SN , under the purview of a utility/electricity service refers to the controlled distribution of a limited amount of power provider. Each subarea Si in turn consists of a collection of establish- ments, where establishments may represent households, administra- Permission to make digital or hard copies of all or part of this work for personal or tive buildings, business and industrial units, hospitals, schools, etc. classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation Electrical appliances within establishments or the establishments on the first page. Copyrights for components of this work owned by others than ACM themselves are then partitioned into a set of distinct equivalence must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, priority classes, where the priority of an appliance/establishment to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. is determined by its urgency towards uninterrupted power supply. e-Energy ’18, June 12–15, 2018, Karlsruhe, Germany Basic household conveniences like lights/fans and business critical © 2018 Association for Computing Machinery. establishments like hospitals schools etc., may be considered essen- ACM ISBN 978-1-4503-5767-8/18/06...$15.00 https://doi.org/10.1145/3208903.3212059 tial and allocated the highest priority. The remaining loads within e-Energy ’18, June 12–15, 2018, Karlsruhe, Germany B D Raj, S. Kumar, S. Padhi, A. Sarkar, A. Modal, and K. Ramamritham a subarea fall under the broad class of non-essential loads and may Table 1: Comparison of Execution Times and Speedup consist appliances like air conditioners, washing machines etc. The 1 2 Ki Power (in MW) 100 200 300 400 utility further provides Ki priority levels (li ;li ; :::;li ) within each DP 3472 9355 17233 26236 subarea Si at various distinct price rates for the non-essential loads, Time (in ms) such that higher the priority, higher becomes the price of electricity. SDPA 283 468 597 681 Speed UP A consumer may choose any one of these priority levels for each of 12 20 29 39 his non-essential loads. Thus essentially, the loads in Si get divided (DP vs SDPA) into K + 1 disjoint subgroups, one of which is (l0) corresponding i i sub-problem that considers atmost i subareas (S ; S ;:::; S ) and to essential load. 1 2 i atmost ρ power allocation levels among the total P available power Let, p0 and r 0 denote the power demand corresponding to l0 of i i i allocation levels. 0 j Si and reward earned by satisfying pi , respectively. Let pi represent The computational complexity of DP is O(NLP), and our experi- the power demand of all non essential loads contained in priority mental results show that, given 50 subareas with 10 priority levels levels 1 through j. After satisfying this total essential power demand, per subarea and with 200 MW of available power, DP takes ∼10 sec. a revenue aware allocation/distribution of the residual power PRG Clearly, this overhead is significantly high and also violates the soft − PN 0 (= PG i=1 pi , where PG is the total available power the utility real-time requirement as discussed in Section 1. has) among the subareas is done, so that reward to the utility is 3.2 Streamlined DP-based Priority Level Allocator (SDPA) maximized as shown in Equation 1. A closer look at the optimization problem reveals that we do not obtain continuous improvements in rewards of a subarea with N Ki X X j j each quantum of incremental power allocated to it. Rather, improve- maximize ri ∗ xi (1a) i=1 j=1 ments in reward has a step-wise nature with Ki steps corresponding XN XKi to each subarea Si . Now typically, Ki << P and consequently, a ma- j ∗ j ≤ subject to pi xi PRG (1b) jority of the optimal solutions R(i; ρ) do not return distinct values. i= j= 1 1 As a result, the generic dynamic programming algorithm which XKi j j memoizes all partial solutions for each distinct value of i and ρ, xi ≤ 1; 8i 2 [1; N ]; xi 2 f0; 1g (1c) j=1 may suffer from high and unnecessary computational overheads. j This overhead may be reduced (often drastically) through a more In the equation, xi is a binary decision variable which is set j j efficient implementation which memoizes only those partial opti- to 1 (xi = 1) if subarea Si is allocated power at level li . The first mal solutions which provide distinct enhancements in reward with constraint in equation 1b guarantees that the total amount of power increment in the bound on power ρ, for each value of i. Further, it allocated to all subareas do not surpass the total residual power may be observed that this reduced memoization does not lead to (PRG ) available with the utility for non-essential loads. The second loss of optimality. We develop an algorithm SDPA, that uses the constraint as given in equation 1c forces each subarea to select at above idea to generate quicker results. most one priority level. Equation 1 essentially boils down to the Our experimental results show that, given 50 subareas with 10 MCKP problem which is a NP-Hard problem in general. priority levels per subarea and with 200 MW of power (step size 3 PROPOSED SOLUTION STRATEGIES = 1kW ; P = 2 × 105), SDPA takes ∼0:5 sec on average to generate a 3.1 Dynamic Programming Based Priority level Allocator (DP) solution on a 3.2 GHz computing core, and is thus able to satisfy We first propose a Dynamic Programming solution to the sched- the stipulated soft real-time constraint discussed in the Section 1.
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