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Efficient integration of smart appliances for demand response programs
Abstract
Power utilities rely on Demand Response (DR) programs in order to shave the peak load at critical times, when there is an excessive demand. In the context of automation, DR programs are categorized as manual or automated. With the emergence of home energy management (HEM) systems that monitor and operate the household appliances, the opportunities for automated DR have emerged. For example, smart appliances with deferrable loads can be scheduled to shift their load without consumers' intervention, given that many consumers might not engage enough to perform the manual DR. However, it has been shown that unjustified load compensation from many HEM-enabled consumers in peak times could result in high off-peak demand. Therefore, it is essential for utilities to identify and target the consumers for participation based on certain criteria. To address this issue, in this paper, we proposed a method for the selection of consumers who are using smart appliances with the highest potential for a DR program. The proposed method measures the (1) frequency, (2) consistency, and (3) peak time usage of deferrable loads across several days. We evaluate our approach on a historical real-world electricity consumption dataset from residential households. The findings demonstrate the efficacy of the proposed method to sort consumers (with the smart appliance) based on their potential to participate in DR.
... The technological advancement of home energy management systems and the potential unwillingness / inability of consumers to follow manual control instructions around the clock, indicate that automated DR is preferable compared to manual DR. Specifically, several projects deploying manual or automated DR for electricity loads have been implemented in the past [10,11], with automated DR implementations presenting better customer engagement [12]. For this reason, automated DR is deemed as the most appropriate mechanism to control the electric load close to real-time. ...
... b) Extreme weather conditions: Users' comfort loss is considered as the most important component of the welfare function when extreme conditions are observed. This is an implicit characteristic of the system since high comfort deviation is penalised by the exponential component of Equation (12) (defined in Section 3.6). c) Unobserved data patterns: Especially during the first periods of the system deployment, the data that have been used during the training phase can be limited. ...
This paper presents a novel, fully automated system for the implementation of explicit gas Demand Response in the Gas Balancing Market, with real-time control instructions provided to domestic gas boilers. The Demand Response is applied in both the upward and downward direction, enabling the respective gas consumer to provide both upward and downward Balancing Services to the Gas Transmission System Operator. The system targets at the welfare maximization of domestic gas consumers, i.e., the revenues attained by providing Balancing Services to the Gas Transmission System Operator minus the cost of gas supply, while maintaining the house’s indoor temperature within the residents’ comfort limits. Real-time data are derived from interconnected commercial thermostats and used by two Recurrent Neural Networks for each house, in order to attain forecasts for the indoor temperature change and the expected boiler’s modulation levels for the next future time intervals. Such forecasted condition changes within each house are then considered in an optimization model that results in the optimal instruction signal that must be provided to the boiler. The signal concerns the boiler operation level for the following 5 min duration. A Genetic Algorithm is employed for the optimization problem solution. The whole system is deployed using a containerized software architecture to ensure scalability and full-service availability. The implemented real-world tests exhibit that domestic consumers can increase their profits from both gas consumption minimization and from the provision of gas DR services in real-time.
... Loads control has been considered by many DR strategies in SGs-adopted buildings [21], [52], [75], [187], [188], [189], [190], [191], [192], [193] focusing on, for example, smart appliances control [194], [195], [196] and plugs loads control [197]. EVs have also been considered by several studies to apply DR strategies with the best performance to enhance SG infrastructure [198]. ...
In the so near future, climate change caused by Carbon dioxide emissions as a result of an increase in energy demand (ED) in the buildings sector becomes really hazard to our green environment. Both climate change and increase in ED are affected by each other. Such increase in ED would increase both climate change and global warming. Fortunately, there is, however, still plenty of time to address this issue. This increase might be significantly and effectively controlled. One of the effective solutions is to apply demand response (DR) strategies exploiting the smart grid (SG). Hence, SG contributes to help reduce both ED and CO emissions. Of importance is establishing SGs-adopted buildings in which DR role could act as an operating and management system aiming to reach energy sustainability. We review researches on DR strategies and technologies applied to SGs-adopted buildings. Unlike previous literature reviews considering certain types of buildings or strategies which therefore narrow the focus of review papers, this review has analyzed numerous types of buildings and a variety of technologies to expand the review’s scope. Concluded remarks, insights, challenges faced by researchers, and suggestions proposed by this review paper have been in detail discussed. Limitations, further gaps in future research on DR strategies, potential implications of DR in SG-adopted buildings have been also discussed. Towards sustainable energy in the buildings sector utilizing DR strategies, points of strength and weakness concluded from the reviewed articles have been highlighted and discussed to open trends that might potentially enhance future proposed DR strategies applied to SG-adopted buildings.
... In community-level local energy exchange, the goal is to cover the demand of local consumers using the surplus from prosumers and small-scale DERs. However, balancing the surplus-deficit could be affected by the inherent temporal mismatch between solar generation and demands that are driven by uncertain and volatile energy consumption and generation patterns observed across different households [7][8][9][10]. The research efforts on community-level energy exchange have investigated different dimensions including energy-flow optimization [11], the impact of network constraints [12], and its financial aspects [13]. ...
With the increased adoption of distributed energy resources (DERs) and renewables, such as solar panels at the building level, consumers turn into prosumers with generation capability to supply their on-site demand. The temporal complementarity between supply and demand at the building level provides opportunities for energy exchange between prosumers and consumers towards community-level self-sufficiency. Investigating different aspects of community-level energy exchange in cyber and physical layers has received attention in recent years with the increase in renewables adoption. In this study, we have presented an in-depth investigation into the impact of energy exchange through the quantification of temporal energy deficit–surplus complementarity and its associated self-sufficiency capacities by considering the impact of variations in community infrastructure configurations, variations in household energy use patterns, and the potential for user adaptation for load flexibility. To this end, we have adopted a data-driven simulation using real-world data from a case-study neighborhood consisting of ~250 residential buildings in Austin, TX with a mix of prosumers and consumers and detailed data on decentralized DERs. By accounting for the uncertainties in energy consumption patterns across households, different levels of PV and energy storage integration, and different modalities of user adaptation, various scenarios of operations were simulated. The analysis showed that with PV integration of more than 75%, energy exchange could result in self-sufficiency for the entire community during peak generation hours from 11 a.m. to 3 p.m. However, there are limited opportunities for energy exchange during later times with PV-standalone systems. As a potential solution, leveraging building-level storage or user adaptation for load shedding/shifting during the 2-h low-generation timeframe (i.e., 5–7 p.m.) was shown to increase community self-sufficiency during generation hours by 17% and 5–10%, respectively, to 83% and 71–76%.
... However, the successful implementation of adaptive operations in the residential sector requires a sound understanding of energy usage patterns such as load shapesi.e., the variation of power demand over the span of a day. Detailed analysis of the usage patterns reveals temporal drivers of demand, which in turn enables efficient targeting of customers for customized energy programs and demand control automation [1][2][3]. Building energy load shapes are commonly characterized through data-driven segmentation methodologies by clustering smart meter data to help engage consumers for DR programs [4,5]. However, understanding the drivers of demand variations through an in-depth analysis of the human-building interactions (HBI) at the appliance (i.e., individual load) level will improve the efficacy of managing loads for demand-supply balance as shown in previous research [1,6]. ...
Load shapes obtained from smart meter data are commonly utilized to understand daily energy use patterns for adaptive operations in applications such as Demand Response (DR). However, they do not provide information on the underlying causes of specific energy use patterns – i.e., inference on appliances’ time-of-use (ToU) as actionable information. In this paper, we investigated a scalable machine learning framework to infer the appliances’ ToU from energy load shapes in a collection of residential buildings. A scalable and generalized inference model obviates the need for model training in each building to facilitate its adoption by relying on training data from a set of previously observed buildings with available appliance-level data. To this end, we demonstrated the feasibility of using load shape segmentation to boost ToU inference in buildings by learning from their nearest matches that share similar energy use patterns. To infer an appliance ToU for a building, classification models are trained for inference on subintervals of load shapes from matched buildings with known ToU. The framework was evaluated using real-world energy data from Pecan Street Dataport. The results for a case study on electric vehicles (EV) and dryers showed promising performance by using 15-min smart meter load shape data with 83% and 71% F-score values, respectively, and without in-situ training.
... electricity consumption data). In order to characterize the user-appliance interaction patterns as S ij n , as illustrated in Fig. 1, we have considered three attributes (i.e., dimensions) [56], namely (i) frequency of use, (ii) consistency of use, and (iii) magnitude of demand during the peak time. All these attributes are measured for a specific load type ( j): ...
Demand response (DR) is considered an effective approach in mitigating the ever-growing concerns for supplying the electricity peak demand. Recent attempts have shown that the contribution from the aggregate impact of flexible individual residential loads can add flexibility to the power grid as ancillary services. However, current DR schemes do not systematically distinguish the varying potential of user contribution due to the highly-varied usage behaviors. Thus, this paper proposes a data-driven approach for quantifying the potential of individual flexible load users for participation in DR. We introduced a metric to capture the predictability of usage in a future DR event using the historical consumption data for different load types. The metric helps to sort the users with flexible loads in a community according to their potential for load shifting scenarios. We then evaluated the applicability of the metric in the DR context to assess the extent of energy reduction for different segments of users. In our analysis, we included electric vehicle, wet appliances (dryer, washing machine, dishwasher), and air conditioning. The analysis of real-world data shows that the approach is effective in identifying suitable user segments with higher predictive potential for demand reduction. We also presented a cross-appliance comparison for assessing the flexibility potential of different user segments. As a case study based on Pecan Street Project, the findings suggest that potentially ~160 MWh reduction might be achieved in Austin, TX through only 20% participation of the selected flexible loads for the residential sector during a 2-h event.
... Their importance stems from the fact that consumers are either unwilling or not concerned enough to manually adjust their appliance use schedule at the DR time [3]. From this perspective, several appliances with deferrable This article is an extended version of the paper "Efficient Integration of Smart Appliances for Demand Response Programs" [1], published in the Proceedings of 5 th ACM International Conference on Systems for Built Environments (BuildSys'18). ...
Power utilities leverage Demand Response (DR) events to effectively reduce the peak load at critical times with excessive power demand. DR programs are generally categorized as manual or automated from the automation perspective. The opportunities for automated DR in the residential sector have emerged with the integration of smart and connected loads. For example, smart appliances with deferrable loads can be scheduled to shift their load without consumers’ direct involvement, given that many consumers might not engage sufficiently to participate in the manual DR. However, it has been shown that unjustified load shifting from many consumers in peak times could result in high off-peak demand. Therefore, it is essential for utilities to identify and target consumers for participation according to efficacy criteria. To address this issue, in this paper, we propose a data-driven method for the selection of consumers according to their potential for demand reduction in a DR program. The proposed method characterizes the frequency, consistency, and the peak time usage of deferrable loads across several subsequent days. By measuring the impact on peak-load shaving, we evaluated our approach on a subset of electricity dataset from the Pecan Street Dataport. The findings demonstrate the efficacy of the proposed method in selecting consumers with deferrable loads based on their potential for demand reduction in future events.
The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.
Electrical power consumption data and load profiles of major household appliances are crucial elements for demand response studies. This paper discusses load profiles of selected major household appliances in the U.S., including two clothes washers, two clothes dryers, two air conditioners, an electric water heater, an electric oven, a dishwasher, and two refrigerators. Their electrical power consumption data measured in one-second intervals, together with one-minute data (averaged over 60 one-second readings), are provided in an online data repository (URL: www.ari.vt.edu/research-data/). The data were gathered from two homes in Virginia and Maryland during July–October 2012. In this paper, demand response opportunities provided by these appliances are also discussed.
A home energy management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. This paper presents an intelligent HEM algorithm for managing high power consumption household appliances with simulation for demand response (DR) analysis. The proposed algorithm manages household loads according to their preset priority and guarantees the total household power consumption below certain levels. A simulation tool is developed to showcase the applicability of the proposed algorithm in performing DR at an appliance level. This paper demonstrates that the tool can be used to analyze DR potentials for residential customers. Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.
Electrical utilities depend on Demand Response programs to manage peak loads by incentivizing consumers to voluntarily curtail a portion of their load during a specified period. Utilities first categorize consumers based on their energy consumption patterns into different clusters and then request consumers of a particular cluster to participate in the demand response program. At a coarse level, clustering approaches do well, but we may not be able to correctly predict which cluster's profile will fit that day's power availability. We address this issue by examining the consistency of consumer's consumption patterns across several consecutive days. We demonstrate that measuring consistency quantitatively helps to understand predictability of consumer's energy consumption.
In the rest of the paper, we provide details of our proposed consistency metric. Further, we propose a methodology to select a few consumers among the consistent ones such that they have a peak at the time specified by the demand response program. We validate our approach using real-world energy consumption data from residential buildings.
This paper presents a well-founded quantified estimation of the demand response flexibility of residential smart appliances. The flexibility from five types of appliances available within residential premises (washing machines, tumble dryers, dishwashers, domestic hot water buffers and electric vehicles), is quantified based on measurements from the LINEAR pilot, a large-scale research and demonstration project focused on the introduction of demand response at residential premises in the Flanders region in Belgium. The flexibility potential of the smart appliances, or the maximal amount of time a certain increase or decrease of power can be realized within the comfort requirements of the user, is calculated. In general, the flexibility potential varies during the day, and the potential for increasing or decreasing the power consumption is in general not equal. Additionally, an extrapolation of the flexibility potential of wet appliances is presented for Belgium. The analysis shows that, using smart wet appliances, an average maximum increase of 430 W per household can be realized at midnight, and a maximum decrease of 65 W per household can be realized in the evening. The resulting flexibility potential can be used as an instrument to determine the impact or economic viability of demand response programs for residential premises.
This report describes the implementation and results of a field demonstration wherein residential electric water heaters and thermostats, commercial building space conditioning, municipal water pump loads, and several distributed generators were coordinated to manage constrained feeder electrical distribution through the two-way communication of load status and electric price signals. The field demonstration took place in Washington and Oregon and was paid for by the U.S. Department of Energy and several northwest utilities. Price is found to be an effective control signal for managing transmission or distribution congestion. Real-time signals at 5-minute intervals are shown to shift controlled load in time. The behaviors of customers and their responses under fixed, time-of-use, and real-time price contracts are compared. Peak loads are effectively reduced on the experimental feeder. A novel application of portfolio theory is applied to the selection of an optimal mix of customer contract types.