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Energy Saving: an Algorithmic Approach | |
*** Last update: May 11 2009 18:07:18.
*** Disclaimer: this page is under construction.
*** Comments and questions will be appreciated. Please send them to amotz at sci dot brooklyn dot cuny dot edu.
Energy-related challenges - availability, cost, efficiency, cleanliness - are of paramount importance today. One widespread effort is to reduce energy consumption. Individual users can save energy by practices such as turning off appliances when not in use; companies can save energy by producing and marketing more efficient appliances; and society as a whole can save energy by seeking alternative natural energy sources and by incentivizing the practices mentioned above. In this project, we demonstrate that computer scientists too can contribute to the goal of reducing energy consumption by inventing smarter algorithms for governing energy requests and storage.
In one of our efforts, we are developing algorithms for use in an energy-buffering device called the Gaia Power Tower, which is a computer-controlled battery placed between an energy consumer and an energy provider (i.e., the power company). The device operates by sometimes requesting more energy than the consumer currently requires and sometimes requesting less - and charging or discharging the difference in the battery. The energy conversion for charging and discharging unavoidably involves the loss of a certain fraction of the energy involved, and so use of this device will actually increase energy usage, at least for the particular consumer involved. With intelligent algorithms that spread out energy requests over time, storing it until needed, however, the device will lower the peak energy requests that the provider must support. This result will ease the load on the grid, allowing energy providers to scale down the energy-intensive efforts required to supply energy at peak levels, perhaps reducing the total overall energy consumption. It may even help to reduce the likelihood of black-outs, which inflict tremendous societal costs.
 | "Filling a Gap in the Facility Management Software Market: Predictive Control of Building Energy Use," a New York State Office of Science, Technology and Academic Research (NYSTAR) grant, 09/01/2006-05/31/2009, $281,100.00. |
 | "A Demonstration of a Dispatchable Peak-Shaving Photovoltaic System with Electric-Energy-Storage for Commercial Buildings in New York City.s Area Network." A New York State Energy Research and Development Authority (NYSERDA) grant, 09/01/2009 - 08/31/2011, $792,076.00. |
Abstract: In some energy markets, large clients are charged for both to- tal energy usage and peak energy usage, which is based on the maximum single energy request over the billing period. The problem of minimiz- ing peak charges was recently introduced as an online problem in [4], which gave optimally competitive algorithms. In this problem, a battery (previously assumed to be perfectly effcient) is used to store energy for later use. In this paper, we extend the problem to the more realistic set- ting of lossy batteries, which lose to conversion ineffciency a constant fraction of any amount charged (e.g. 33%). For this setting, we provide e±cient and optimal online algorithms as well as possibly competitive online algorithms. Second, we give factor-revealing LPs, which provide some quasi-empirical evidence for competitiveness. Finally, we evaluate these and other, heuristic algorithms on real and synthetic data.
Amotz Bar-Noy, Matthew P. Johnson and Ou Liu, Peak Shaving Through Resource Buffering. In proceedings of 6th Workshop on Approximation and Online Algorithms (WAOA 2008). [slides]
Abstract: We introduce and solve a new problem inspired by energy pricing schemes in which a client is billed for peak usage. At each timeslot the system meets an energy demand through a combination of a new request, an unreliable amount of free source energy (e.g. solar or wind power), and previously received energy. The added piece of infrastructure is the battery, which can store surplus energy for future use. More generally, the demands could represent required amounts of energy, water, or any other tenable resource which can be obtained in advance and held until needed. In a feasible solution, each demand must be supplied on time, through a combination of newly requested energy, energy withdrawn from the battery, and free source. The goal is to minimize the maximum request. In the online version of this problem, the algorithm must determine each request without knowledge of future demands or free source availability, with the goal of maximizing the amount by which the peak is reduced. We give efficient optimal algorithms for the offline problem, with and without a bounded battery. We also show how to find the optimal offline battery size, given the requirement that the final battery level equal the initial battery level.We give efficient Hn-competitive algorithms assuming the peak effective demand is revealed in advance, and provide matching lower bounds.
Amotz Barnoy, Yi Feng, Matthew P. Johnson, and Ou Liu, Saving Energy For (and From) a Sunny Day: Lowering Peak Demands with Batteries. In posters of the 28th IEEE Conference on Computer Communications (INFOCOM 2009).
Last update: May 11 2009 18:07:18.
*** Disclaimer: this page is under construction.
*** Comments and questions will be appreciated. Please send them to amotz at sci dot brooklyn dot cuny dot edu.