When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers

Demand response (DR) has been known to play an important role in the electricity sector to balance supply and demand. To this end, the DR baseline is a key factor in a successful DR program since it influences the incentive allocation mechanism and customer participation. Previous studies have investigated baseline accuracy and bias for large, industrial and commercial customers. However, the analysis of baseline performance for residential customers has received less attention. In this paper, we analyze DR baselines for residential customers. Our analysis goes beyond accuracy and bias by understanding the impact of baselines on all stakeholders’ profit. Using our customer models, we successfully show how customer participation changes depending on the incentive actually received. We found that, in general, bias is more relevant than accuracy for determining which baseline provides the highest profit to stakeholders. Consequently, this result provides a valuable insight into designing effective DR incentive schemes

Decentralized Planning of Energy Demand for the Management of Robustness and Discomfort

The robustness of smart grids is challenged by unpredictable power peaks or temporal demand oscillations that can cause blackouts and increase supply costs. Planning of demand can mitigate these effects and increase robustness. However, the impact on consumers in regards to the discomfort they experience as a result of improving robustness is usually neglected. This paper introduces a decentralized agent-based approach that quantifies and manages the tradeoff between robustness and discomfort under demand planning. Eight selection functions of plans are experimentally evaluated using real data from two operational smart grids. These functions can provide different quality of service levels for demand-side energy self-management that capture both robustness and discomfort criteria

Cluster-based Aggregate Forecasting for Residential Electricity Demand using Smart Meter Data

While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlation-based Feature Selection to select features relevant to each household. Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households, forecasting the clusters' energy consumption separately, and finally aggregating the forecasts. We found that the improvement provided by CBAF depends not only on the number of clusters, but also more importantly on the size of the customer base

Privacy Enhanced Demand Response with Reputation-based Incentive Distribution

Demand response (DR) is a Smart Grid application that aims at managing consumption of electricity in response to supply-side signals. The exchange of sensitive information (i.e., electricity consumption data) between the consumers and the DR provider is necessary for the functioning of DR, but at the same time it is an obstacle to its wide-spread adoption, due to the related privacy concerns. This paper proposes the use of homomorphic encrypted user aggregation and reputation-based incentive distribution to address the trade-off between enhancing user privacy and correctly assessing the contribution of each user to the demand reduction

Measuring and Controlling Unfairness in Decentralized Planning of Energy Demand

Demand-side energy management improves robustness and efficiency in Smart Grids. Load-adjustment and load-shifting are performed to match demand to available supply. These operations come at a discomfort cost for consumers as their lifestyle is influenced when they adjust or shift in time their demand. Performance of demand-side energy management mainly concerns how robustness is maximized or discomfort is minimized. However, measuring and controlling the distribution of discomfort as perceived between different consumers provides an enriched notion of fairness in demand-side energy management that is missing in current approaches. This paper defines unfairness in demand-side energy management and shows how unfairness is measurable and controllable by software agents that plan energy demand in a decentralized fashion. Experimental evaluation using real demand and survey data from two operational Smart Grid projects confirms these findings. © 2014 IEEE

RoutineSense: A Mobile Sensing Framework for the Reconstruction of User Routines

Modern smartphones are powerful platforms that have become part of the everyday life for most people. Thanks to their sensing and computing capabilities, smartphones can unobtrusively identify simple user states (e.g., location, performed activity, etc.), enabling a plethora of applications that provide insights on the lifestyle of the users. In this paper, we introduce routineSense: a system for the automatic reconstruction of complex daily routines from simple user states, implemented as an incremental processing framework. Such framework combines opportunistic sensing and user feedback to discover frequent and exceptional routines that can be used to segment and aggregate multiple user activities in a timeline. We use a comprehensive dataset containing rich geographic information to assess the feasibility and performance of routineSense, showing a near threefold improvement on the current state-of-the-art

An Economic Analysis of Pervasive, Incentive-Based Demand Response

Demand response (DR) emerges as one of the cheapest and greenest solutions to match supply and demand in the electricity sector. While DR has been focused on large and industrial consumers, pervasive implementation (by including residential consumers) is needed to maximize its potential. This paper presents theoretical analysis of pervasive, incentive-based DR from the economics perspective. Our analysis consider cases whether (1) DR is used to encourage consumers to decrease or increase their demand, and (2) utility companies have access to a single or multiple energy sources. We determine the necessary conditions and derive the optimal incentives to benefit from DR events

SmartD: Smart Meter Data Analytics Dashboard

The ability of smart meters to communicate energy consumption data in (near) real-time enables data analytics for novel applications, such as pervasive demand response, personalized energy feedback, outage management, and theft detection. Smart meter data are characterized by big volume and big velocity, which make processing and analysis very challenging from a computational point of view. In this paper we presented SmartD, a dashboard that enables the data analyst to visualize smart meter data and estimate the typical load profile of new consumers according to different contexts, temporal aggregations and consumer segments