Incentive-compatible demand-side management for smart grids based on review strategies

Abstract

Demand-side load management is able to significantly improve the energy efficiency of smart grids. Since the electricity production cost depends on the aggregate energy usage of multiple consumers, an important incentive problem emerges: self-interested consumers want to increase their own utilities by consuming more than the socially optimal amount of energy during peak hours since the increased cost is shared among the entire set of consumers. To incentivize self-interested consumers to take the socially optimal scheduling actions, we design a new class of protocols based on review strategies. These strategies work as follows: first, a review stage takes place in which a statistical test is performed based on the daily prices of the previous billing cycle to determine whether or not the other consumers schedule their electricity loads in a socially optimal way. If the test fails, the consumers trigger a punishment phase in which, for a certain time, they adjust their energy scheduling in such a way that everybody in the consumer set is punished due to an increased price. Using a carefully designed protocol based on such review strategies, consumers then have incentives to take the socially optimal load scheduling to avoid entering this punishment phase. We rigorously characterize the impact of deploying protocols based on review strategies on the system's as well as the users' performance and determine the optimal design (optimal billing cycle, punishment length, etc.) for various smart grid deployment scenarios. Even though this paper considers a simplified smart grid model, our analysis provides important and useful insights for designing incentive-compatible demand-side management schemes based on aggregate energy usage information in a variety of practical scenarios.

Energy Disaggregation Using Elastic Matching Algorithms

“…Moreover, the development of smart grids and energy demand management systems as well as the fluctuation of power generation due to the increasing percentage of power generated by renewable energies units can confine the issue of annually increasing energy demands [7,8]. These changes in energy demand and generation are challenging for network operators and power generation units, since power needs are becoming less stable and less predictable while at the same time energy demand increases [9,10]. To address the above mentioned challenges, precise monitoring of electrical energy consumption in the residential sector is needed [10], as well as proper energy demand prediction and management [9].…”

“…These changes in energy demand and generation are challenging for network operators and power generation units, since power needs are becoming less stable and less predictable while at the same time energy demand increases [9,10]. To address the above mentioned challenges, precise monitoring of electrical energy consumption in the residential sector is needed [10], as well as proper energy demand prediction and management [9]. At the moment energy consumption monitoring is mostly done by measuring the aggregated energy consumption in the form of monthly bills and therefore does not address the above-mentioned issues.…”

Robust energy disaggregation using appliance-specific temporal contextual information

“…These changes in energy demand and generation are challenging for network operators and power generation facilities, since power needs are becoming less stable and unpredictable while rising at the same time [7,8]. To address those challenges, accurate and fine-grained monitoring of electrical energy consumption within residential environments is needed [2,9] as well as proper demand management [10]. However, nowadays, energy monitoring is mostly done via an aggregated measure of energy consumption in the form of monthly bills and therefore does not address the above-mentioned issues.…”