Intelligent Energy Management System Research Articles

IoTivity Cloud-Enabled Platform for Energy Management Applications

In developing countries today, population growth and the penetration of higher standard of living appliances in homes has resulted in a rapidly increasing residential load. In South Africa, the recent rolling blackouts and electricity price increase only highlighted this reality, calling for sustainable measures to reduce overall consumption and peak load. The dawn of the smart grid concept, embedded systems, and ICTs have paved the way for novel Home Energy Management Systems (HEMS) design. In this regard, the Internet of Things (IoT), an enabler for intelligent and efficient energy management systems, is the subject of increasing attention for optimizing HEMS design and mitigating its deployment cost constraints. In this work, we propose an IoT platform for residential energy management applications focusing on interoperability, low cost, technology availability, and scalability. We addressed the backend complexities of IoT Home Area Networks (HAN) using the Open Consortium Foundation (OCF) IoTivity-Lite middleware. To augment the quality, servicing, reduce the cost, and the development complexities, this work leverages open-source cloud technologies from Back4App as Backend-as-a-Service (BaaS) to provide consumers and utilities with a data communication platform within an experimental study illustrating time and space agnostic “mind-changing” energy feedback, Demand Response Management (DRM) under a peak shaving algorithm yielded peak load reduction around 15% of the based load, and appliance operation control using a HEM App via an Android smartphone.

Long Short-Term Memory Network-Based Metaheuristic for Effective Electric Energy Consumption Prediction

The Electric Energy Consumption Prediction (EECP) is a complex and important process in an intelligent energy management system and its importance has been increasing rapidly due to technological developments and human population growth. A reliable and accurate model for EECP is considered a key factor for an appropriate energy management policy. In recent periods, many artificial intelligence-based models have been developed to perform different simulation functions, engineering techniques, and optimal energy forecasting in order to predict future energy demands on the basis of historical data. In this article, a new metaheuristic based on a Long Short-Term Memory (LSTM) network model is proposed for an effective EECP. After collecting data sequences from the Individual Household Electric Power Consumption (IHEPC) dataset and Appliances Load Prediction (AEP) dataset, data refinement is accomplished using min-max and standard transformation methods. Then, the LSTM network with Butterfly Optimization Algorithm (BOA) is developed for EECP. In this article, the BOA is used to select optimal hyperparametric values which precisely describe the EEC patterns and discover the time series dynamics in the energy domain. This extensive experiment conducted on the IHEPC and AEP datasets shows that the proposed model obtains a minimum error rate relative to the existing models.

A fuzzy logic energy management system of on-grid electrical system for residential prosumers

The fast emergent of smart grid technologies in the last few decades has significantly altered the energy profiles of residential buildings by allowing them to resume an active role in generating energy in addition to consuming the energy delivered by the utility. This dual role has transformed residential buildings into prosumers (i.e., joint producer-consumers), imposing elevated energy management challenges as a result of the increased complexity in decision making. Considering the physical and time constraints of human residents, there is a need for automated intelligent energy management systems (EMSs) that achieve efficient decisions concerning power consumption needs and expenses. In the current literature, no widely accepted approach integrates all the power components of a modern prosumer aiming at reducing the electricity bill that is applicable in various climate conditions. To that end, this paper proposes a climate-independent fuzzy logic EMS that integrates solar and wind generators, battery energy system (BES), electric vehicle (EV) load, dynamic electricity pricing, and tariffs and has the goal of reducing the prosumer's electricity bill. To test the performance of the fuzzy management system, a residential prosumer simulation has been implemented with data (weather, prices) taken from three regions with different climate characteristics over a period of three years. Its performance is benchmarked against two different management systems – a simple rule-based system and a linear optimization approach – with results showing that the proposed method attained the lowest consumption cost.

Climatization and luminosity optimization of buildings using genetic algorithm, random forest, and regression models

With the rise in popularity of artificial intelligence, coupled with the growing concern over the environment, there has been a surge in the use of intelligent energy management systems. Additionally, as more buildings transition into the smart grid and, consequently, more energy and environmental data is gathered, there has been a significant increase in the number of data-driven approaches for building management systems. This paper proposes a methodology that aims to optimize the climatization and luminosity inside a building, using a genetic algorithm, a random forest, and two polynomial models. The proposed methodology enables the real-time management of the building taking into account the user needs and preferences. Air conditioner units and light systems are optimized to minimize energy costs, while also improving the air quality and considering the users’ temperature and luminosity preferences. This paper shows the results achieved, by the proposed solution, in an office building case study. The promising results demonstrate the possibility of minimizing energy costs while maximizing the users’ comfort.

Sustainable Intelligent Charging Infrastructure for Electrification of Transportation

For sustainable electrification of surface transportation, a viable charging infrastructure is necessary. Firstly, this paper focuses on emphasizing the viability of a free fuel-based photovoltaics and/or wind turbines and lithium-ion battery-based power network to enable sustainable electric power. The importance of power electronics for a DC-based power network and extremely fast charger based on DC power is presented. Finally, the core design concepts of intelligent charging infrastructure using an intelligent energy management system are discussed. The paper aims to cover all aspects associated with a clean, reliable, efficient, and cost-effective solution to the novel charging infrastructure.

Optimal Energy Management Strategy for a DC Linked Hydro–PV–Wind Renewable Energy System for Hydroelectric Power Generation Optimization

The goal of this article is to create an intelligent energy management system that will control the stand-alone microgrid and power flow of a grid associated that includes Battery Energy Storage System, Fuel Cell, Wind Turbine, Diesel Generator, Photovoltaic, and a Hydro Power Plant. Storage systems are required for high dependability, while control systems are required for the system's optimum and steady functioning. The control, operation, and planning of both energy demand and production are all part of energy management. By controlling unpredictable power and providing an appropriate control algorithm for the entire system, the suggested energy management strategy is designed to handle diverse variations in power demand and supply. Under the TOU Tariff, the problem is presented as a discrete time multi-objective optimization method to minimize grid imported energy costs. It also maximizes earnings from surplus RE sales to the grid at a pre-determined RE feed-in tariff. Simulations were run using SIMULINK/MATLAB to validate and evaluate the suggested energy management approach under various power demand and power supply scenarios. The simulations indicate that the proposed energy management can fulfill demand at all times utilizing unreliable renewables like wind, solar, and hydroelectric power plants, as well as hydrogen fuel cells and batteries, without affecting load supply or power quality.

Intelligent energy optimization system development and validation for German building types

Abstract Cost-effective building energy optimization solutions have a strategic function to assist German and European energy policies for the simple reason that buildings account for 40% of the total energy consumption in Europe. The International Energy Agency considers energy efficiency as the first fuel and a major resource. In this work, a cost-effective building energy optimization system consisting of 14 different wireless network embedded sensors, control units (actuators), wireless communication protocol, multi-dimensional information management system, context sensitive graphical user interfaces and energy optimization software with artificial intelligence-backed control algorithms is examined. The R&D activity is accomplished in the ‘Intelligent Building Energy Management System’ research project, which is funded by the State of Lower Saxony (Germany) in the frame of Innovation Support Program between the years 2014 and 2018. The ‘Energy Optimization System (EOS)’, which is subject to this research, reduces the total energy use and CO2 emissions of buildings through optimization of energy-consuming building systems, such as heating, cooling, lighting systems and home/office appliances with the support of an integrated system solution consisting of artificial intelligence-based software and wireless network embedded hardware. The system is deployed and validated in two appropriately selected test buildings in Germany. It has been recorded that the system provides energy efficiency levels between 29.34% and 38.18% under different seasonal and occupancy conditions in office and residential building types. The developed system can be deployed to all types of historical and new buildings to optimize energy consumption and reduce carbon emissions.

Implementation of Battery Energy Storage System for an Island Microgrid With High PV Penetration

This article presents the innovative integrated control strategies of the battery energy storage system (BESS) to support the system operation of an offshore island microgrid with high penetration of renewable energy. An intelligent energy management system (iEMS) was implemented to perform the supervisory control and data acquisition of diesel generators, distribution feeders, photovoltaic (PV) systems, and the BESS. An expert system was designed and embedded in the iEMS to derive the decision making for fast power discharging of BESS to improve the system transient stability for the severe contingency of large disturbances caused by the tripping of generators and PV farm. To mitigate the impact of PV generation fluctuation and to maintain the stable operation of diesel generators, the adaptive control of power charging and discharging of the BESS is applied to reduce the ramping rate of PV generation. To achieve the economic dispatch of the microgrid, the strategy of peak shaving and valley filling of the system net load curve by the BESS was derived considering the forecasting daily profiles of system loading and PV power generation. The computer simulation of actual diesel generator tripping and the field test of intentional PV system shutdown have been executed to demonstrate the effectiveness of the proposed control strategies of the BESS system to improve the system operation and transient stability of the study microgrid with high penetration of renewable energy.

Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

An energy-efficient smart home generally integrates renewable energy sources (RESs) with intelligent energy devices (IEDs) to make itself cost-efficient. However, the stochastic nature of the RESs and IEDs makes the power network uncertain, and it degrades the economic efficiency of the smart home. Hence a co-simulation based intelligent home energy management system (HEMS) is proposed in this article. It interconnects the rolling horizon-based model predictive energy scheduling mechanism with robust control strategies. It manages the load following energy supply entities (ESEs) optimally and efficiently. A time-ahead scheduler integrates an optimal algorithm to estimate the cost-optimal (e.g., minimizing total energy cost) decision signals for various ESEs by incorporating component based losses and efficiencies. Besides, a real-time distributed robust control strategy is established executing a coordinated energy sharing mechanism by incorporating an auxiliary power source. Time-triggered low-jitter wireless communication architecture is adopted to transfer the optimal decision signals from the central scheduler to the distributed device level local controller. A scenario based comparison of various ac/dc models of a home area power network evaluates the strength of the prospective HEMS.

Intelligent Energy Management System for an all-electric ship based on adaptive neuro-fuzzy inference system

The International Marine Organization (IMO) regulations forcing shipbuilders to use electric ships to reducing pollution emitted from ship engines. Renewable energy resources are the perfect solution to solve this issue. The ship’s hybrid energy power system consists of several non-homogeneous energy resources diesel generator, renewable energy source or more, energy-storing system, and may hydrogen source as fuel cells. The EMS manages and controls the balance between the different types of sources and loads demand to ensure system stability and dependability. In this paper, energy management strategy (EMS) for fuel cell and battery hybrid systems is an essential property of controlling power flow between sources. A crucial ingredient of an intelligent control strategy is to manage the flow of a hybrid system corresponding to the changing of the load demand and battery state of charge (SoC) using the ANFIS/Simulink toolbox implements a case study architecture. This paper proposes a hybrid energy source for use in a naval ship’s silent mode of operation while looking for submarines with a low acoustic signature based on adaptive neuro-fuzzy procedures. The main objective of this analysis is to investigate the efficiency of the proposed system preserving compared with practical results obtained from previous work. These studies provide valuable understandings into hybrid power systems’ flow of EMS on the marine field.

Intelligent energy management: Evolving developments, current challenges, and research directions for sustainable future

In the last decade, there have been significant developments in the field of intelligent energy management systems (IEMSs), with various methods and new solutions proposed for managing the energy resources intelligently. An important issue related to finding the desired outcomes remains unexplored, i.e., how to determine key insights from the sparse academic literature in the age of digital publishing. To mitigate the issue, this study proposes a novel strategy to systematically survey the relevant studies by converting the sparse literature into visual presentations. We first apply a systematic approach called a PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) statement to provide the insights from the published literature of the past decade (2010–2020). Then, VOSviewer experiments are conducted to transform these sparse scholarly data into visual representations. In total, eighty-one papers published in high-impact journals are identified based on their scientific soundness and relevance, and a VOSviewer analysis is applied. The analysis revealed the existence of three research clusters focused on the following main thematic areas: the energy management in smart homes and smart grids (35 journal papers); the emerging concept of context-awareness (26 journal papers); and the role of privacy preservation in IEMSs (20 journal papers). This analysis uncovers the current state of IEMSs and explores existing issues, methods, findings, and gaps. Thus, future research directions have been recommended to fill the existing gaps. This systematic literature review is to assist both researchers and industry practitioners to understand the research gaps of previous studies.

Research on Supply and Demand Forecast of Regional Integrated Energy System Based on Computer Internet of Things Technology

With the development of technology, the demand of integrated energy is increasing in our country. However, the corresponding energy consumption rate and energy utilization rate are too different. Therefore, an intelligent energy management and control system has been proposed, which is based on he Internet of Things technology. This paper also gives the system structure and main functional modules so that energy efficiency can be improved better.

Deep learning-based energy management of a hybrid photovoltaic-reverse osmosis-pressure retarded osmosis system

This paper investigates the energy management of a hybrid grid-connected reverse osmosis (RO) desalination process consisting of photovoltaic (PV), pressure retarded osmosis (PRO), and energy storage system. The developed intelligent energy management system (IEMS) aims to maximize the total water production and contaminant removal efficiency while keeping the grid’s supplied power as low as possible. To promote the performance of the IEMS, the prediction of PV solar power is performed by three deep neural networks based on convolutional neural networks and long short-term memory neural networks. These networks are designed to perform 5-hour-ahead PV power forecasting, and the model with the smallest error is selected. The IEMS employs the particle swarm optimization (PSO) algorithm to find the optimum solutions of the system for each time step. Four performance indices are defined through which the IEMS is evaluated. The results of the proposed technique are compared with two benchmark methods, one of which is similar to the IEMS; however, it does not incorporate the PV power predictions. The superiority of the IEMS over the first benchmark is demonstrated by studying three scenarios: two successive sunny days, two successive cloudy days, and 10 days of operation. Moreover, the simulations are executed for different forecast horizons to investigate the effects of this parameter on the optimization results. The impacts of the best-found forecaster errors are also explored by repeating the simulations with the actual PV power. Finally, the optimization is performed by two other stochastic algorithms: grey wolf optimizer (GWO) and genetic algorithm (GA). It is found that PSO outperforms GWO and GA for solving this optimization problem.

Markov model in home energy management system

An intelligent home energy management system was proposed. Reinforcement learning and a markov prediction model were used to help the system make decisions. The Markov model predicted the future state of users or the weather, and the intelligent decisionmaking support system sent signals to local controllers to control furniture. This work benefits energy management because if the system knows the user’s next state, it can control a specific appliance to save energy. Meanwhile, if the system can predict the weather, the house can use green energy rationally. The proposed energy management system could be applied in an intelligent house, city energy management systems, and building energy management. The state prediction helped the decision-making system make accurate and rational decisions.

An optimized energy management system for vehicle to vehicle power transfer using micro grid charging station integrated Gridable Electric Vehicles

World nations are promoting Gridable Electric Vehicles (GEVs’) in the context of increasing environmental pollution. GEVs’ include Plug-in Hybrid Electric vehicles (PHEVs’) and Battery Electric vehicles (EVs). The increased penetration of GEVs’ into the utility grid for charging, can lead to grid instability and regulation issues. The charging scenarios would be un-coordinated in countries like India, resulting in unstable load demand on the grid. The solution to this is the adoption of an aggregator regulated Micro Grid charging station (MGCS). The MGCS would facilitate energy flow to acceptor GEV clients from donor GEV clients through Vehicle to Vehicle (V2V) power transfer, thereby reducing the load on the utility grid. The MGCS would be supported by a photovoltaic system. This paper proposes the utilization of GEVs’ in supporting the MGCS through the regulation of an on-board Intelligent Energy Management System (IEMS). The IEMS performs communication and price negotiation with the MGCS aggregator; decides the energy level that the donor GEV clients can provide considering its future journey and criticality; and regulate the on board converter to facilitate slow, regular and fast charging of the acceptor GEV clients.

Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems

Intelligent electronic devices (IEDs) along with advanced information and communication technology (ICT)-based networks are emerging in the legacy power grid to obtain real-time system states and provide the energy management system (EMS) with wide-area monitoring and advanced control capabilities. Cyber attackers can inject malicious data into the EMS to mislead the state estimation process and disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented in this paper to detect false data injection attacks (FDIAs) in an IED-based EMS. In addition, stealthy construction of FDIAs and their impact on the detection rate of MLAs are analyzed. Furthermore, the impacts of natural disturbances such as faults on the system are considered, and the research work is extended to distinguish between cyber attacks and faults by using state-of-the-art MLAs. In this paper, state-of-the-art MLAs such as Random Forest, OneR, Naive Bayes, SVM, and AdaBoost are used as detection classifiers, and performance parameters such as detection rate, false positive rate, precision, recall, and f-measure are analyzed for different case scenarios on the IEEE benchmark 14-bus system. The experimental results are validated using real-time load flow data from the New York Independent System Operator (NYISO).

Designing and Implementation of an Intelligent Energy Management System for Electric Ship power system based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Designing and Implementation of an Intelligent Energy Management System for Electric Ship power system based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Smart Energy Management and Monitoring System for Public Lighting System based on Photovoltaic and Storage Systems

The public lighting system was being a vital part all over the world for many years. Indeed, as the number of population and urbanism grow, this system is becoming a primordial part of our life comfort. Therefore, it is used extensively leading to an increased important amount of electrical energy being consumed. This paper proposes an idea of an intelligent energy management system (EMS) for public lightening system consumption based on an autonomous hybrid sources of electrical energy. It is composed of a PV system installed at the top of the lightening system and a battery storage system, where an intelligent energy management system is used to ensure the charging of the battery during the daylight with the option of supporting the PV system production in case of low period irradiation during the day. Whereas, during the night, the battery fully powers the lightening system. In the same for the protection of the battery system and its related lifespan, the proposed (EMS) takes into accounts this issue. Furthermore, this system has the ability of recognizing accurately the basic data on the length of the day, the sunrise time and sunset time automatically based on the location of the used public lightening system which is defined by its longitude and latitude. The proposed EMS is built around a special electronics card including display screen of main information with the insertion of special sensors such as current and voltage sensors, which were built by the authors. The proposed system has been tested in a known location where the obtained results prove the validity of the proposed management system for ensuring the operation of the public lightening system.

Economic, social and environmental impact analysis of an indigenously developed energy optimization system for German office and residential building types

Abstract This work addresses the economic, social and environmental analysis of an indigenously developed building energy optimization system for residential and office buildings in Germany. The developed system consists of 14 different wireless network embedded sensors, wireless communication protocol, multi-dimensional data warehouse, graphical user interfaces and energy optimization software with artificial intelligence-backed control algorithms. The R&D activity is accomplished in the ‘Intelligent Building Energy Management System’ research project, which is funded by the State of Lower Saxony—Germany in the frame of Innovation Support Program between the years 2014 and 2018. The system is tested in two appropriately selected test buildings in Germany. It has been recorded that the system provides energy efficiency levels between 29.34% and 38.18% under different seasonal and occupancy conditions in office and residential building types. The analysis is accomplished within the framework of four main indicators. These are economic indicators including calculations of lifetime cost and payback rate methods, resource use indicators including the calculation of reduced energy use as a result of the energy efficiency provided by the system, social indicator including additional expendable income calculations and environmental indicators including calculations depicting the benefits of reducing harmful emissions. Analysis results illustrate that building energy optimization systems provide a cost-effective method for promoting energy efficiency goals. Results of the indicators prove monetary benefits for the building occupants in terms of resource use, return on investment and social impact, as well as quantifiable benefits for easing the harmful effects of climate change phenomenon.

Day-Ahead Optimal Power Flow for Efficient Energy Management of Urban Microgrid

This study deals with the urban microgrid energy management that is dedicated to individual and collective self-consumption by providing flexibility to the distribution grid (DG). The proposed urban community microgrid is interconnected to a DG and it consists of an association of centralized storage units (called community energy storage system), community intermittent renewable generation, and intelligent energy management system (EMS). One of the main advantages of urban microgrid is that, in case of faults in the DG, it can cut existing interconnections and continue to supply the responsible community in the island mode. In this study, the developed urban microgrid EMS is based on the predictive control management through the day-ahead optimal power flow (DA-OPF) strategy. The main contributions of this work can be defined by two points. The first point is related to a development of the DA-OPF strategy for the urban microgrid based on the intelligent deep learning data forecasting and the mixed-integer nonlinear programming optimization methods. The second point concerns a development of an optimization function integrating the concept of ancillary services of DG flexibility. Experimental results and economical evaluation are presented in this article. By using the proposed strategies, it results in an important electricity price reduction for the considered urban microgrid, compared to a conventional distribution system and basic operation schemes.