Residential Energy Management System Research Articles

Multi-agent-based decentralized residential energy management using Deep Reinforcement Learning

In smart grid, energy consumption has grown exponentially in residential houses, which necessitates the adoption of demand response management. To alleviate and handle the energy management in residential houses, an efficient residential energy management (REM) system can be employed to regulate the energy consumption of appliances for several energy loads such as non-shiftable, shiftable, and controllable loads. Many researchers have focused on the REM using machine learning and deep learning techniques which is not able to provide secure and optimal energy management procedure. Thus, in this paper, a multi-agent-based decentralized REM, i.e., MD-REM approach is proposed using Deep Reinforcement Learning (DRL) with the utilization of blockchain. Furthermore, The combinatorial model DQN, i.e., Q-learning and deep neural network (DNN) is employed, to gain the optimal price based on the reduced energy consumption by appliances associated with different energy loads utilizing the Markov Decision Process (MDP). Here, multiple agents are designed to handle different energy loads and consumption is controlled by the DQN agent, then reduced consumption data is securely shared among all stakeholders using blockchain-based smart contract. The performance evaluation of the proposed MD-REM approach seems to be efficient in terms of reduced energy consumption, optimal energy price, reward, and total profit analysis. Moreover, blockchain-based result is evaluated for the proposed MD-REM approach considering the performance metrics such as transaction efficiency, Interplanetary File System (IPFS) bandwidth utilization, and data storage cost comparison.

A PROTOTYPE DESIGN OF A DEDICATED SMART HOME ENERGY MANAGEMENT SYSTEM

<em>With the continuous rise of electrical energy costs for heating, cooling, and ventilation and the increasing number of energy-consuming home appliances, designing a cost-effective efficient residential electric energy management system has become crucial. This paper presents a prototype simulated design of a Smart Home Energy Management System (SHEMS), dedicated to the management of residential electrical energy according to the user-preassigned comfort requirements and cost criteria. All planted sensors and home appliances are controlled by one master microcontroller. The Proteus Software ISIS was used for all simulation results. The proposed application collects information from sensors to control the required home appliances by switching them ON or OFF based on a specified design algorithm. The sensors' assigned values and home appliance status are stored in a main database. The proposed design also allows home appliances to be monitored and controlled remotely via the Internet. Simulated results are given for a two-room residential home</em>.

Optimizing residential DC microgrid energy management system using artificial intelligence

Optimizing residential DC microgrid energy management system using artificial intelligence

Smart residential distribution energy management system with integration of demand response and Aggregator

Smart residential distribution energy management system with integration of demand response and Aggregator

On the trade-off between profitability, complexity and security of forecasting-based optimization in residential energy management systems

With the emergence of affordable access to data sources, machine learning models and computational resources, sophisticated control concepts for residential energy management systems (EMSs) are on the rise. At the heart of those are production and consumption forecasts. Given the wide spectrum of implementation opportunities, selection of appropriate forecasting strategies is challenging. This work systematically evaluates forecasting-based optimization for residential EMSs in terms of trade-offs between economic profitability, computational complexity and security. The foundation of the study is two real prosumer cases equipped with a photovoltaic-battery system. Results demonstrate that, within the considered scenarios, best trade-offs are achieved based on forecasts of a default gradient-boosted decision trees model, using a short initial training set, weather forecast inputs and regular retraining. Over 90% of the theoretical maximum economic benefit is achieved in this scenario, at significantly lower computational complexity than others with similar savings, while being applicable to new systems without large data history. In terms of security, this scenario exhibits tolerance against weather input manipulation. However, sensitivity to price tampering may require data integrity checking in residential EMSs.

A Systematic Literature Review on Data-Driven Residential and Industrial Energy Management Systems

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, energy management systems are central as they coordinate energy flows and optimize them toward economic, technical, ecological, and social objectives. While numerous scientific publications study the infrastructure, optimization, and implementation of residential energy management systems, only little research exists on industrial energy management systems. However, results are not easily transferable due to differences in complexity, dependency, and load curves. Therefore, we present a systematic literature review on state-of-the-art research for residential and industrial energy management systems to identify trends, challenges, and future research directions. More specifically, we analyze the energy system infrastructure, discuss data-driven monitoring and analysis, and review the decision-making process considering different objectives, scheduling algorithms, and implementations. Thus, based on our insights, we provide numerous recommendations for future research in residential and industrial energy management systems.

A novel energy management model among interdependent sections in the smart grids

AbstractTechnically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes.

Two-stage residential community energy management utilizing EVs and household load flexibility under grid outage event

To relieve the impact of the power grid outages on the residential sector, residential building energy management under grid outage events has been becoming the research hotspot in both the academic and industry. Considering the coordination of electric vehicles (EVs) and household load flexibility, this paper proposes a two-stage energy management approach for residential community under the planned outages. In the optimal scheduling stage, after receiving the information of the planned outage from the grid, the EVs’ charging–discharging power and community load curve reshaping schemes are optimal determined by residential community energy management system (CEMS), aiming at to minimize the total amount of unserved load of the whole community over the planned outage horizon. In the power allocation stage, with the formulated power allocation model, the determined residential community load curve from the above stage is then allocated to each house. The numerical test is finally conducted and the results validated the effectiveness and feasibility of the proposed approach.

Systems of social practice and automation in an energy efficient home

The development of residential automation and energy management systems have aimed to achieve energy reduction within the home. These systems have been developed to compliment human behaviour and potentially impart change in the way people consume energy. The development of energy efficient buildings has been considered a sustainable approach to better manage or optimise energy consumption and generation within a building however, multiple studies have demonstrated some limitations to these buildings and systems. The advancement of the home system of practice using the basis of Social Practice Theory has demonstrated a new focus for the design methodology of these systems. The home system of practice outlines the routines and repetitive nature of individual lifestyles, and these routines result in the temporal characteristic of energy consumption. This study monitored an Australian home that consisted of renewable energy systems, smart technology and automated systems designed to reduce the use of grid electricity. The aim was to understand the performance of the automation systems and interaction with the occupants and their routines. The paper discusses the effectiveness of incorporating social theories and the home system of practice into the design of energy efficient buildings and energy management systems. The methodology utilised machine learning techniques to identify patterns in energy consumption data and related these patterns to the routines and lifestyles of the home occupants. These patterns aimed to describe why the automation system incorporated into the study home did not operate as intended and how design changes can increase effectiveness and performance.

A Novel Generalised Model for Residential Energy Management System

Disaggregated data is often used to model the cost-benefit of residential energy management systems. However, obtaining such data is time-intensive and monetarily expensive. This hinders the depth of analysis that can be done on these systems and negatively influences their large-scale uptake. This study proposes a novel generalised model of these systems that uses smart meter load profile data to model their cost-benefit. Using two years of half-hourly electricity consumption data from 5379 households in London, the model was used to examine how sociodemographic, tariff structures, and the choice of operational objectives of these systems, interact to influence their cost-benefit. The results showed that the proposed model produced reliable cost-benefit results within what is normally obtained in literature. The model demonstrated that applying one set of objectives to different customers leads to an inequitable distribution in benefits; rather, an optimal set of objectives for a given customer under a specific tariff structure can be found to produce a more equitable distribution in benefits across all customers. The proposed model is replicable and uses data that can be obtained easily and cheaply from smart meters, making it versatile for large-scale cost-benefit analysis by any electricity retailer.

Potential Benefits for Residential Building with Photovoltaic Battery System Participation in Peer-to-Peer Energy Trading

The increasing number of residential buildings that are installing distributed energy resources enforces the need for schemes to facilitate a local energy balance. With the continuing evolution of Internet of Things (IoT) technology, Peer-to-Peer (P2P) energy trading is becoming a viable solution to incentivize prosumers and promote efficient energy sharing in a community. This paper develops a model to quantitatively analyze the potential benefits of P2P energy trading for residential buildings that have installed photovoltaic battery systems. The integration of the bidding strategy into a residential energy-management system is feasible to realize cost savings for prosumers. However, the coordination between the bidding strategy and the optimal scheduling of energy has received far too little attention. To better participate in the P2P market, we propose a novel separate bidding energy-management system (SBEMS) that can realize rolling optimal energy scheduling while determining energy bids. The model’s effectiveness is verified via case studies of 75 participants in a community. The results indicate that the prosumers can reduce their costs by up to 24% by employing the proposed SBEMS in the P2P market. In addition, the proposed method is found to offer better performance in terms of economic and technical indices.

Residential energy management system based on integration of fuzzy logic and simulated annealing

With the increase in prosperity level and industrialization, energy need continues to overgrow in many countries. To meet the rapidly increasing energy needs, countries attach great importance to using limited natural resources rationally, diversifying their energy production using novel technologies, improving the efficiency of existing technologies, and implementing policies and strategies toward alternative energy sources. In particular, individual energy prosumers (someone that both produces and consumes energy) head toward smart home energy management systems (SHEMS) that include renewable energy sources in their homes. By integrating PV solar panels into houses, there is a need to optimize home energy production/consumption scenarios by consumer behavior. In this study, an intelligent residential energy management architecture and algorithm to manage residential energy production/consumption are proposed. The algorithm controls the energy flow in the home according to real-time potential solar power estimation, demanded energy estimation, electricity consumption price, and battery state-of-charge (SoC). The fuzzy logic algorithm has been developed to determine the estimated comfort and cost-effectiveness ratios in the near future. The simulated annealing algorithm, a meta-heuristic algorithm, is performed to obtain the best operating point decision of the battery using the comfort and cost-effectiveness ratios. Energy flow direction and battery SoC are optimized using simulated annealing based on the comfort and cost-effectiveness ratio (comparison of alternatives with respect to multiple criteria of different levels of importance for energy usage). The focus is to generate maximum profit from energy sales for monthly profit to be achieved. Prototyped hardware and software are implemented and tested in real-time. The test results show that the 20% reduces energy consumption, and a monthly gain of $89.2 is obtained from energy sales using the proposed method. Therefore, the test results reveal the effectiveness of the proposed architecture and algorithm.

Uncertainty-Aware Model Predictive Control for Residential Buildings Participating in Intraday Markets

Buildings with generation and storage assets have the opportunity to trade in electricity markets. Residential buildings, however, have load patterns that are more difficult to predict and less flexible – introducing uncertainties to their operation. In addition, local intermittent renewable energy sources further increase these uncertainties. In this regard, an uncertainty-aware model predictive control (UA-MPC) is proposed in this paper. The proposed UA-MPC allows residential building energy management systems to trade in intraday markets without violating the operational constraints of their batteries despite the uncertainties in load consumption and solar irradiation. Moreover, the proposed UA-MPC only needs prediction intervals instead of detailed probability distribution functions to describe the uncertainties. Furthermore, in the proposed UA-MPC, the optimization is formulated as a shortest path problem. Thus, the optimization can be solved in polynomial time, which is desirable in intraday settings. Numerical simulations for two active residential buildings in grid-connected and isolated-neighborhood scenarios have been used to evaluate the proposed UA-MPC. Furthermore, the performance of the proposed UA-MPC was compared with the performance of a deterministic model predictive control (MPC) and a robust MPC. The results show that the proposed UA-MPC eliminates constraint violations that would otherwise occur in using deterministic MPC while providing lower costs than those using robust MPC. The results also show that the proposed UA-MPC can generate bidding curves in a few seconds, demonstrating its applicability in intraday market settings.

A Novel Residential Energy Management System Based on Sequential Whale Optimization Algorithm and Fuzzy Logic

Demand side management has become inevitable in today’s smart gridenvironment to balance electricity supply and demand. Many methodolo-gies/algorithms have been developed for realizing and implementing thistechnique at different levels of distribution systems. Advanced meteringinfrastructure and the latest communication technologies have empow-ered residential consumers to participate in the demand side managementschemes. After careful investigations and analyses, the authors of this paperhave made a decisive effort to propose a novel sequential strategy for devel-oping an energy management system for scheduling loads of residentialconsumers. The proposed work aims at a fuzzy logic and an evolutionaryalgorithm-based approach of demand side management that considers theusers’ preference of operating time of the appliances at the residence of theirchoice, which has not been addressed earlier. This approach reduces the peakdemand and cuts the cost of electricity per billing period for a consumer. This study also encourages the consumers to install solar rooftop PV systemsby indicating the cost benefits reaped over a more extended period. Theproposed framework is implemented in MATLAB, and the case studies provethe effectiveness of using this algorithm from the consumers’ perspective

Internet of things‐based smart residential building energy management system for a grid‐connected solar photovoltaic‐powered DC residential building

The intermittent nature of photovoltaic (PV), energy conversion losses, appliance compatibility, and load dynamics pose serious challenges to PV-based DC power distribution system in residential buildings. This article proposes a holistic approach to develop a new smart residential building energy management system using internet of things (IoT)-based multifunction compatible relaying system. The proposed system monitors the loads and provides uninterruptible power supply with minimum energy conversion losses. It achieves best operating load configuration, load prioritization in various modes, and demand-side management (DSM). IoT-based multifunction compatible relaying dystem is embedded with Information and Communication Technology-enabled PZEM module, smart relays, and appliance status monitoring devices. The proposed scheme has been implemented in a DC residential building located at Madurai, Tamil Nadu, India, with 1.5 kW PV system. The results are compared in four scenarios. The experimental results prove that the losses are minimized by 18% ensuring uninterrupted power supply. The implementation of DSM further reduces the losses by 5%. The appliances can be monitored through Grafana based in home touch screen.

RETRACTED ARTICLE: IoT based residential energy management system for demand side response through load transfer with various types of domestic appliances

RETRACTED ARTICLE: IoT based residential energy management system for demand side response through load transfer with various types of domestic appliances

A generalized economic model for optimally selecting forecasted load profiles for measuring demand response in residential energy management system

To forecast the demand response of a residential customer, forecasted load profiles must first be calculated and the demand response produced by residential energy management devices determined. Finding an efficient method to select an optimal forecasted load profile to assess the demand response of these devices is a fundamental issue currently overlooked. Poorly forecasted load profiles can affect customer compensation for demand reduction, estimation of retail profits and allocation and reconciliation processes for electricity markets. This research resolved to develop an economic model for selecting an optimal forecasted load profile from which the demand response of residential energy management devices can be determined. Using 2 years of half-hourly electricity consumption data for 5379 households obtained from the “Low Carbon” London project (UK), a novel economic model was created to assess the economic advantage of nine different forecast methods. Results suggest that when creating forecasted load profiles to measure demand response of these devices the sociodemographic of customers, tariff type and the operational objectives of the device must be taken into account to maximize the economic benefit of demand response initiatives for the customer and retailer.

An Appliance Scheduling System for Residential Energy Management.

In this work, an Appliance Scheduling-based Residential Energy Management System (AS-REMS) for reducing electricity cost and avoiding peak demand while keeping user comfort is presented. In AS-REMS, based on the effects of starting times of appliances on user comfort and the user attendance during their operations, appliances are divided into two classes in terms of controllability: MC-controllable (allowed to be scheduled by the Main Controller) and user-controllable (allowed to be scheduled only by a user). Use of all appliances are monitored in the considered home for a while for recording users’ appliance usage preferences and habits on each day of the week. Then, for each MC-controllable appliance, preferred starting times are determined and prioritized according to the recorded user preferences on similar days. When scheduling, assigned priorities of starting times of these appliances are considered for maintaining user comfort, while the tariff rate is considered for reducing electricity cost. Moreover, expected power consumptions of user-controllable appliances corresponding to the recorded user habits and power consumptions of MC-controllable appliances corresponding to the assigned starting times are considered for avoiding peak demand. The corresponding scheduling problem is solved by Brute-Force Closest Pair method. AS-REMS reduces the peak demand levels by 45% and the electricity costs by 39.6%, while provides the highest level of user comfort by 88%. Thus, users’ appliance usage preferences are sustained at a lower cost while their comfort is kept impressively.

Improved residential energy management system using priority double deep Q-learning

In the current era, electricity demand has skyrocketed. Power grids have to face a lot of uneven power demand daily. During a certain period in a day, the power demand peaks, making it difficult for the grid to meet the demand. To deal with this problem, an intelligent Home Energy Management (HEM) can be beneficial. Smart HEM systems can schedule loads from peak to low peak hours. Thereby reducing peak load on the grid as well as reducing decreasing the costs incurred by a user. In this paper, we proposed a Deep Reinforcement Learning model with prioritized experience sampling (PQDN-DR) for appropriate demand response, and the problem of load shifting is simulated as a game. We also propose a novel reward system for better convergence of the DRL model to near-optimal strategies and a DR adapted Epsilon Greedy Policy to guide the agent in exploration phase for faster convergence. The proposed system minimizes power demand peak and consumers’ bills simultaneously. The proposed method has successfully reduced the peak load and peak costs in smaller DR environment. The agent reduced costs and overall variance of the load profile for all customers for 24 h in the standard DR environment.

A framework for building energy management system with residence mounted photovoltaic

Efficient utilization of a residential photovoltaic (PV) array with grid connection is difficult due to power fluctuation and geographical dispersion. Reliable energy management and control system are required for overcoming these obstacles. This study provides a new residential energy management system (REMS) based on the convolution neural network (CNN) including PV array environment. The CNN is used in the estimation of the nonlinear relationship between the residence PV array power and meteorological datasets. REMS has three main stages for the energy management such as forecasting, scheduling, and real functioning. A short term forecasting strategy has been performed in the forecasting stage based on the PV power and the residential load. A coordinated scheduling has been utilized for minimizing the functioning cost. A real-time predictive strategy has been used in the actual functioning stage to minimize the difference between the actual and scheduled power consumption of the building. The proposed approach has been evaluated based on real-time power and meteorological data sets.