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Efficient Energy Consumption Scheduling: Towards Effective Load Leveling

Yuan Hong (), Shengbin Wang () and Ziyue Huang ()
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Yuan Hong: Department of Information Technology Management, University at Albany, SUNY, 1400 Washington Ave., Albany, NY 12222, USA
Shengbin Wang: Department of Marketing Transportation & Supply Chain, North Carolina A&T State University, 1601 E. Market St., Greensboro, NC 27411, USA
Ziyue Huang: Department of Information & Supply Chain Management, University of North Carolina at Greensboro, 1400 Spring Garden St., Greensboro, NC 27412, USA

Energies, 2017, vol. 10, issue 1, 1-27

Abstract: Different agents in the smart grid infrastructure (e.g., households, buildings, communities) consume energy with their own appliances, which may have adjustable usage schedules over a day, a month, a season or even a year. One of the major objectives of the smart grid is to flatten the demand load of numerous agents (viz. consumers), such that the peak load can be avoided and power supply can feed the demand load at anytime on the grid. To this end, we propose two Energy Consumption Scheduling (ECS) problems for the appliances held by different agents at the demand side to effectively facilitate load leveling. Specifically, we mathematically model the ECS problems as Mixed-Integer Programming (MIP) problems using the data collected from different agents (e.g., their appliances’ energy consumption in every time slot and the total number of required in-use time slots, specific preferences of the in-use time slots for their appliances). Furthermore, we propose a novel algorithm to efficiently and effectively solve the ECS problems with large-scale inputs (which are NP-hard). The experimental results demonstrate that our approach is significantly more efficient than standard benchmarks, such as CPLEX, while guaranteeing near-optimal outputs.

Keywords: smart grid; scheduling; load leveling; demand response; demand side management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
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