Residential Demand Side Management Research Articles

Design and validation of a power modulation system for residential demand-side management

Residential loads have great potential to provide flexibility and other services to the grid, but many legacy or non-networked loads need additional hardware to enable such functionality. Currently available devices (e.g., smart thermostats or load control switches) that equip legacy loads with energy management features, provide narrow functionality to address specific use cases.We propose the Smart Dim Fuse (SDF), a unified system with general purpose hardware, to enable legacy residential loads with versatile grid-interactive functionalities. By combining sensing, power electronics, and load modeling into a single architecture, the SDF offers comprehensive capabilities that would otherwise require a large number of disparate devices that are not inherently compatible. Based on a thoroughly tested prototype, we suggest that such a device can deliver this flexibility at a levelized cost of 0.018-0.052 $/kWh. The prototype power electronics operates at efficiencies between 96.4-98.5% at full load. The system can deliver fast load power modulation with a mean average percentage error below 1.8%.

Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations

Demand Side Management (DSM) is a cost-effective approach to managing electricity networks, aimed at reducing capacity requirements and costs, increasing the penetration of renewable generation, and reducing power system emissions. This review article explores the distinctive characteristics of electricity demand in the industrial, commercial, and residential sectors, and their relationship to successful implementation of DSM. The constraints and considerations for DSM are characterized as technical, economic, and behavioral factors, such as process requirements, business operation constraints, and consumer decisions, respectively. By considering all three types of factors and their impacts in each sector, this review contributes novel insights that can inform the future implementation of DSM. DSM in the industrial and commercial sectors is found to be primarily constrained by technical considerations, while DSM in the commercial sector is also subject to economic constraints. Conversely, residential demand is found to be primarily constrained by human behavior and outcomes, highly variable, and the largest contributor to peak demand. This review identifies sector-specific opportunities to enhance DSM uptake. Industrial DSM uptake will benefit from technological and process improvements; commercial DSM uptake can benefit from enhanced economic incentivization; and residential DSM uptake can benefit from improved understanding of the interactions between human behavior, human outcomes, and energy use. Finally, this review investigates behavioral models and concludes that agent-based models are best suited for integrating these interactions into energy models, thereby driving the uptake of DSM, particularly in the important residential sector.

Efficient privacy-preserving aggregation for demand side management of residential loads

An effective tool used in practice to maintain network balance is residential demand side management (DMS). However, privacy concerns related to the processing of user personal consumption data often result in a slow wide-scale adoption and acceptance. Various general purpose cryptographic techniques are available, such as homomorphic encryption and secure multi-party computation (MPC), to address these privacy-concerns. Unfortunately, these come at a significant computation price which makes them not applicable in many practical scenarios. In this work, a privacy-preserving aggregation algorithm, based on additive random shares and a combination of well-established symmetric and asymmetric key cryptography methods is proposed and compared with additive homomorphic encryption (AHE) techniques and state-of-the-art MPC protocols. The results show that generic techniques such as homomorphic encryption come with significant computational and communication cost, especially at the users side. On the other hand, MPC approaches provide better performance and resilience to dropouts for very large networks but entail a complex communication among users. In this context, the proposed additive random shares algorithm was the most balanced choice for DSM, with a good performance, simpler information flow and the possibility of adding redundant intermediary parties for enhanced resilience.

A new Internet of Things based optimization scheme of residential demand side management system

The steady increase in the energy demand and the growing carbon footprint has forced electricity-based utilities to shift from their use of non-renewable energy sources to renewable energy sources. Furthermore, there has been an increase in the integration of renewable energy sources in the electric grid. Hence, one needs to manage the energy consumption needs of the consumers, more effectively. Consumers can connect all the devices and houses to the internet by using Internet of Things (IoT) technology. In this study, the researchers have developed and proposed a novel 2-stage hybrid method that schedules the power consumption of the houses possessing a distributed energy generation and storage system. Stage 1 modeled the non-identical Home Energy Management Systems (HEMSs) that can contain the DGS like WT and PV. The HEMS organise the controllable appliances after taking into consideration the user preferences, electricity prices and the amount of energy produced /stored. The set of optimal consumption schedules for every HEMS was estimated using a BPSO and BSA. On the other hand, Stage 2 includes a Multi-Agent-System (MAS) based on the IoT. The system comprises two portions: software and hardware. The hardware comprises the Base Station Unit (BSU) and many Terminal Units (TUs).

A User-Centric View of a Demand Side Management Program: From Surveys to Simulation and Analysis

Residential demand side management (DSM) strategies increase the efficiency of the smart grid. However, the efficacy of these strategies relies on the participation of customers in DSM programs, an issue usually neglected in the analysis. To encompass all aspects, we tried to identify what are the drivers for the user engagement, focusing on the social and psychological behavior of the user in order to simulate and analyze a residential DSM program with a centralized approach. In particular, the DSM program minimizes costs taking into account different energy sources and performing load shifting considering and learning users’ acceptance of requests. The results show the advantage of a preferences-aware approach, highlighting the importance of user satisfaction on participation.

Dynamic pricing of electricity: Enabling demand response in domestic households

Fluctuations in retail energy prices may incentivize domestic households to adapt their load pattern in order to minimize the cost of electricity. This paper determines the price volatility necessary for automatic load shifting to cover the cost of the associated smart metering within a household. This study shows that current price volatility does not allow sufficient savings to compensate for additional metering costs. However, results indicate that a change towards an ‘ad-valorem’ electricity taxation dependent on exchange prices could make residential demand-side management profitable. At the same time, the potential price risk for consumers that do not react to pricing signals because of a lack of smart devices, is almost negligible. As consumption patterns suggest, these households consume in times of both high and low prices, thereby almost canceling out higher payments at peak times even without any load shifts. With these results, policymakers can anticipate the effects of dynamic retail prices on electricity and derive the implications of different taxation settings.

Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision

Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (EVs), heating/cooling devices, and energy storage. This review contributes to a more holistic understanding of residential demand-side management when considering various methods, models, and applications. This work also aims to identify future research areas and possible solutions so that PSO can be widely deployed for scheduling and control of distributed energy resources in real-life DR applications.

Analysis of supervised graph signal processing-based load disaggregation for residential demand-side management

With the widespread adoption of advanced metering infrastructure (AMI) smart meters in recent years, interest in using residential buildings as demand-side management resources has increased. To characterize the amount of energy available for demand-side management in residential buildings, non-intrusive load monitoring (NILM) may be applied to the low-resolution data collected by AMI smart meters to provide insight into the energy consumption of individual controllable appliance types within the house. The disaggregation of water heaters and HVAC system components is investigated in this study, as these appliances are the greatest consumers of energy in U.S. homes. A supervised graph signal processing (GSP) algorithm is applied to real power data for appliances sampled at a 15-minute rate, with results demonstrating that disaggregation sequencing, graph scaling factor, and graph classifier threshold values have a significant impact on supervised GSP NILM accuracy. Scaling factor (ρ) was varied from 20 to 200, graph signal classifier threshold value (q) was varied from 0 to 0.5, and six disaggregation sequences were considered. Varying these parameters resulted in a maximum range in accuracy, as measured by F-measure, of 0.0 to 1.0 for a single appliance, demonstrating that choosing a set of optimal parameters is critical for disaggregation accuracy.

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications

• Studies on energy flexibility of residential buildings have been reviewed. • Energy flexibility technologies by types, purposes, and scopes are summarized. • Modeling techniques, tools, and characterization of flexible resources are presented. • Quantification methods and metrics of energy flexibility are analyzed. • Research trends, open questions, and future research opportunities are identified. With building electric demand becoming increasingly dynamic, and a growing percentage of intermittent renewable power generation from solar photovoltaics and wind turbines, the power grid is facing increasing challenge to manage the real time balance between the supply and demand. With advancements in smart sensing and metering, smart appliances, electric vehicles, and energy storage technologies, demand side management of residential buildings can help the grid to improve stability by optimizing flexible loads. This paper reviews recent studies on residential building demand side management, with a focus on characterization and quantification of energy flexibility covering various types of flexible loads, metrics, methods, and applications. The reviewed studies showed four levels of applications: building level (45%), district or community level (29%), system level (19%), and building sector level (7%). Shifting loads is the dominant flexibility type in 60% of applications, followed by shedding (19%), generation (16%), and modulating (6%). Depending on the technology and application scope, flexible operations have a wide range of performance, with peak power reductions of 1%~65%, energy savings up to 60%, operational cost reduction of 1%~48%, and greenhouse gas emission reductions of up to29%. More than half (51%) of the studies employed control strategies to achieve flexibility; among those 72% used optimal controls, while 28% used rule-based controls. About 58% of the studies used mathematical formulation to quantify energy flexibility. Most studies were based on simulation, while less than 15% of the studies had measurements from experiments or field tests. The review reveals research opportunities to address significant gaps in the existing literature: (1) establishing a common definition and performance metrics for energy flexibility of buildings that are technology and application agnostic, (2) developing an ontology to standardize representation of flexibility resources for interoperability, (3) integrating occupant impacts into the quantification and optimization of energy flexibility, and (4) developing requirements and credits of energy flexibility in building energy codes and standards. Findings from the review can inform future research and development of energy flexible buildings which are essential to a reliable and resilient power grid.

Residential demand-side management using integrated solar-powered heat pump and thermal storage

Due to the mismatch between rooftop solar energy generation and residential electrical demand, considerable solar energy is exported into electricity networks causing a range of issues such as voltage instability and connection overload. To address these problems, we propose and analyse a residential hot water, heating and cooling system, which features a heat pump combined with thermal energy storage to align peak thermal loads with output from a rooftop solar system. This work quantifies the impacts of thermal storage on residential space-conditioning peak load reduction. Annual hourly thermal and domestic hot water loads were determined for a representative Australian house, located in Brisbane, using building energy simulation software and verified using measured data. Combined with the measured sub-metered electrical loads of other electrical appliances, this data was used to simulate the solar system export, heat pump demand, and thermal storage system performance. Results show that by combining a 5-kW solar system, the proposed system can reduce annual grid-electricity demand by approximately 76% compared with a conventional non-thermal storage system. Peak electrical load was also observed to undergo a temporal shift and reduce by approximately 45%. Furthermore, the solar fraction for air-conditioning and domestic hot water loads reached 84%, while solar self-consumption increased to about 56%. This study demonstrates that the proposed system is an effective means of managing electricity demand, shifting peak load and improving solar utilization, thus relieving stress on electricity networks from high penetration of solar photovoltaics.

Residential power demand side management optimization based on fine-grained mixed frequency data

Residential power demand side management optimization based on fine-grained mixed frequency data

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium's Niagara Framework for Residential Demand-Side Management.

Electricity is a vital resource for various human activities, supporting customers’ lifestyles in today’s modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium’s Niagara Framework® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable.

Integrating Future Smart Home Operation Platform With Demand Side Management via Deep Reinforcement Learning

Residential demand side management (DSM) is a promising technique in smart grids to improve the power system robustness and to reduce the energy cost. However, the ongoing paradigm shift of computation, such as mobile edge computing for smart home, poses a big challenge to residential DSM. Therefore, it is important to schedule the new smart home computing tasks and traditional DSM in a smart way. In this paper, we investigate an integrated home energy management system (HEMS) that participates in a DSM program and implements smart home computation tasks by offloading tasks with the help of a Smart Home Operation Platform (SHOP). The goal of HEMS is to maximize the user’s expected total reward, defined as the reward from completing computing tasks minus the cost of energy consumption, execution delay, running the SHOP servers, and the penalty of violating the DSM requirements. We solve this task scheduling based DSM problem using a deep reinforcement learning method. The DSM program considered in this paper requires the household to reduce a certain amount of energy consumption within a specified time window, which, in stark contrast to the well-studied real-time pricing, results in a long-term temporal interdependence and thus a high-dimensional state space in our formulated problem. To address this challenge, we use the Deep Deterministic Policy Gradient (DDPG) method to characterize the high-dimensional state space and action space, which uses deep neural networks to estimate the state and to generate the action. Experimental results show that our proposed method achieves better performance gains over reasonable baselines.

Financial and economic feasibility of bio-digesters for rural residential demand-side management and sustainable development

This paper addresses the financial and economic feasibility of biogas production to meet energy demands at a household or village scale. To this effect, the potential cost and benefit of biogas digesters were explored. The biogas digesters were characterised under controlled conditions before being deployed to the KrwaKrwa and Upper Ncera villages in the Eastern Cape, South Africa. At the control site, meteorological weather parameters that influence biogas production were monitored continuously. Based on typical rural household electrical usage profile from 2-years of data observed at the sub-station level, assumptions about the cooking times and electrical demands were made. It was found that the bio-digesters are capable of supplying the energy required for cooking, provided the digesters are continuously fed at least every 5th day. Also, 1.0 m3 of CH4 is equivalent to 4.70 kWh of electricity. With reloading after every 5th day, the total biogas produced per day over 31 days is 2.93 m3. This is equivalent to demand-side avoidance of 13.77 kWh/day and monetary savings of R26,88/day or R833,43/month. A financial cost of approximately R 17 000,00 per digester, Net Present Value of R22 000,00 and a payback period of 22 months was obtained for the feasibility of the project. Based on the findings of the study, it can be said that biogas digesters are financially sound investments and economically feasible at both household and societal levels. These digesters can also ensure that the household remains above the poverty threshold.

Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.

In Which Time Slots Can People Save Power? An Analysis Using a Japanese Survey on Time Use

Residential demand-side management (DSM) of electricity has been gaining attention as a way to reduce energy consumption at home and as a way of maximizing the utilization of fluctuating solar power generation. To promote the smooth introduction of DSM into homes, power usage trends according to the time of the day should be examined for individuals in relation to their lifestyles. The analyses of power usage trends can identify the types of home appliances that should be utilized differently in order to increase energy efficiency. Such analyses can also predict the individual behavioral changes that should result in home appliances being used in the time slots in which solar power is more conveniently available. The purpose of this research was to estimate and observe the amount of power saving potential for each daily time slot with respect to an individual’s particular attributes, and to derive the power saving potential of the whole country by accumulating these data on individuals. This was achieved by using the Survey on Time Use and Leisure Activities (STULA) and Energy-Saving Performance Catalog (ESPC) in Japan. According to the results of our estimation, a meaningful power saving potential is sufficient to address a power supply shortage after a disaster such as an earthquake. It is possible to save power by replacing existing home appliances with more energy efficient ones, by making environmentally conscious choices when using home appliances, and by discontinuing the use of home appliances during electricity shortages within the community as a whole. Using the estimated power saving potentials, we examined the effects of two DSMs: (1) adjusting the time for which home appliances are used; and (2) aggregating the power demand of households with different attributes. The results showed that these DSMs would contribute to a more stable power system operation. Future research might address the rapid penetration of community energy management systems and demand response systems.

Multi-objective home energy management with battery energy storage systems

Home energy management (HEM) is one of the crucial requirements for future smart grid environment. Usually, utility companies motivate residential consumers to participate in household demand side management (DSM) techniques. The residential consumers’ main intention is to reduce their electricity bills whereas utilities’ primary goal is to reduce their system peak load demand. The benefits of DSM can be enhanced with smart energy storage systems. In this paper, an intelligent multi-objective household DSM with battery storage systems is proposed to minimize both the objectives simultaneously. Generally, the residential DSM deals with various controllable appliances that normally have different operating time priorities according to the consumers’ preferences. Because of this, the DSM problem is modelled as a mixed integer optimization problem. In this paper, the multi-objective mixed integer linear programming technique (MOMILP) is used to solve the proposed residential DSM problem. The proposed model is tested on a system having four different residential consumers. The benefits of the battery energy storage systems are analysed by taking six different scenarios. An economic analysis of each residential consumer's battery storage system with load uncertainty modelling is also done in this paper. Simulation results of various scenarios with different objectives demonstrate the effectiveness of the proposed model.

Towards demand side management control using household specific Markovian models

We devise a real-time control mechanism to aid electricity network operators facilitate effective residential Demand Side Management. Contrary to existing algorithms where individual customer behavior patterns are required or estimated, we propose a novel load management scheme for highly stochastic loads where each household possesses individually suited parameters. If the actual demand in a feeder is found to exceed its rating, selected customers are requested by the operator to lower their loads. The benefit of the proposed approach is to maximize the certainty of meeting Demand Response targets whilst minimizing the overall cost associated with the Demand Response control actions. Our contribution is a model that takes customer behavior patterns into consideration while selecting customers. We model load behavior of individual households using Markov chains and by treating the problem as a Markov Decision Process optimal control problem, we deduce an easily implementable strategy for load reduction. This strategy is then incorporated in an aggregate model that utilizes household specific transition probabilities. The practical application is explained based on real data. Our numerical results illustrate the virtue of the strategy, showing potential for the use of household specific Markovian models for Demand Side Management applications.

Automated Distributed Electric Vehicle Controller for Residential Demand Side Management

Electric vehicles (EVs) are recently gaining traction in the power sector due to various challenges and opportunities they provide to utility operators. For electric utilities that incorporate demand side management (DSM) programs, EVs could become either a burden or an advantage depending on their charging control strategy and the signaling of the DSM program. This paper proposes a decentralized fuzzy-based controller to successfully integrate and coordinate the charging of EVs that ensures fair charging of EVs at different point of connections (POCs) in the distribution grid where voltage conditions might not be the same. The controller operates in an autonomous mode that reduces the monetary cost of the communication overhead and preserves bandwidth. The proposed controller takes into consideration the owner requirements in terms of energy needed and time to charge, the voltage at the POC with the grid, and the pricing signal coming from the utility. The controller is tested under different DSM programs that exist in the literature. This paper also proposes a new DSM program that is capable of benefiting from EVs as prosumers that can provide grid services. The controller's performance was validated through MATLAB simulations that showed the controller's ability to successfully coordinate the charging of EVs in a fair manner and achieve flat system peaks without any rebound effect. The controller was also successfully tested in the presence of voltage control units, such as capacitor banks, and in the presence of distributed generations.