Smart Grid Energy Management Research Articles

Energy management of smart grid equipped by an Internet of Things (IoT) configuration: A DDFAT methodology

In this manuscript, an Energy Management System (EMS) with Internet of Things (IoT) framework in distribution system (DS) based on hybrid technique is proposed. The proposed method is the combination of Dynamic Differential Annealed Optimization (DDAO) and Feedback Artificial Tree (FAT) algorithm; therefore called as DDFAT method. The main intention of proposed method is “to optimize the power control and DS resources by constantly tracking the data into communication framework based on IoT”. In this work, the DS is inter connected to the data acquisition method that is the Internet of things uses single IP address resulting in mesh wireless network devices. The internet of things based communication scheme used to facilitate the growth of demand response (DR) for energy management (EM) DS. The transmitting data is carried out by DDFAT method. In this way, the IoT distribution scheme increases the network flexibility and offers optimal utilization of the accessible resources. Moreover, the DDFAT method is reliable to meet global supply along energy demand. The DDFAT method is implemented in MATLAB Simulink platform under three test cases and its performance is analysed with the existing improved artificial bee colony (IABC), squirrel optimization with gravitational search–assisted neural network (SOGSNN), particle swarm optimization (PSO)–assisted artificial neural network (ANN), Fruit fly Optimization algorithm (FOA), and grasshopper optimization algorithm (GOAPSNN) methods.

An efficient energy management in smart grid based on IOT using ROAWFSA technique

An efficient energy management in smart grid based on IOT using ROAWFSA technique

Renewable energy management in smart grids by using big data analytics and machine learning

The application of big data in the energy sector is considered as one of the main elements of Energy Internet. Crucial and promising challenges exist especially with the integration of renewable energy sources and smart grids. The ability to collect data and to properly use it for better decision-making is a key feature; in this work, the benefits and challenges of implementing big data analytics for renewable energy power stations are addressed. A framework was developed for the potential implementation of big data analytics for smart grids and renewable energy power utilities. A five-step approach is proposed for predicting the smart grid stability by using five different machine learning methods. Data from a decentralized smart grid data system consisting of 60,000 instances and 12 attributes was used to predict the stability of the system through three different machine learning methods. The results of fitting the penalized linear regression model show an accuracy of 96% for the model implemented using 70% of the data as a training set. Using the random forest tree model has shown 84% accuracy, and the decision tree model has shown 78% accuracy. Both the convolutional neural network model and the gradient boosted decision tree model yielded 87% for the classification model.The main limitation of this work is that the amount of data available in the dataset is considered relatively small for big data analytics; however the cloud computing and real-time event analysis provided was suitable for big data analytics framework. Future research should include bigger datasets with variety of renewable energy sources and demand across more countries.

Privacy-Preserving Optimal Energy Management for Smart Grid With Cloud-Edge Computing

Optimal energy management of smart grids requires the information exchange between devices, which may disclose private information to the adversaries and further lead to great losses. To this end, this article considers the privacy-preserving optimal energy management problem for smart grids, which integrates both the power allocation of distributed energy resources on the supply side and the demand response of distributed load demands on the demand side. We first propose a cloud-edge computing structure of the smart grid and model the optimal energy management problem as the maximization problem of social welfare including the supply-side net benefit and the demand-side net utility, while maintaining the supply–demand balance and satisfying the operating constraints. A privacy-preserving average consensus algorithm is then developed, where each node sends the projected states to their neighbors to protect the privacy of the initial state. By applying the privacy-preserving average consensus algorithm, we propose a distributed privacy-preserving optimal energy management algorithm based on the generalized alternating direction method of multipliers. Finally, simulation examples are provided to validate the effectiveness of the proposed algorithms.

ESP32 Based Electric Energy Consumption Meter

In recent years, the Smart Energy Meter has attracted a lot of attention from all over the world. In this paper a design and prototyping a low-cost IoT energy monitoring is presented, which may be utilized in a variety of applications such as power billing, smart grid energy management, and home automation. The system is based on a low-cost ESP32 microcontroller that is interfaced non-invasive Current Transformer (CT) sensors, and voltage sensor to get data from sensor nodes and deliver it to a Blynk server over the internet. The studies' findings showed that the system for monitoring energy consumption can precisely record voltage, current, active power, and cumulative power consumption.

Electric Mobility: An Overview of the Main Aspects Related to the Smart Grid

Electric mobility has become increasingly prominent, not only because of the potential to reduce greenhouse gas emissions but also because of the proven implementations in the electric and transport sector. This paper, considering the smart grid perspective, focuses on the financial and economic benefits related to Electric Vehicle (EV) management in Vehicle-to-Building (V2B), Vehicle-to-Home (V2H), and Vehicle-to-Grid (V2G) technologies. Vehicle-to-Everything is also approached. The owners of EVs, through these technologies, can obtain revenue from their participation in the various ancillary and other services. Similarly, providing these services makes it possible to increase the electric grid’s service quality, reliability, and sustainability. This paper also highlights the different technologies mentioned above, giving an explanation and some examples of their application. Likewise, it is presented the most common ancillary services verified today, such as frequency and voltage regulation, valley filling, peak shaving, and renewable energy supporting and balancing. Furthermore, it is highlighted the different opportunities that EVs can bring to energy management in smart grids. Finally, the SWOT analysis is highlighted for V2G technology.

Low power multiplier based long short-term memory hardware architecture for smart grid energy management

Low power multiplier based long short-term memory hardware architecture for smart grid energy management

Solving day-ahead scheduling problem with multi-objective energy optimization for demand side management in smart grid

Demand side management (DSM) strategy implementation plays a vital role in energy management of smart grid (SG) by involving distributed energy resources (DERs) to reduce operational cost, pollution emission and provide end users satisfaction. In this study, day ahead scheduling problem in SG is adopted by using DSM strategy in SG considering different types of consumers to reduce operational cost and pollution emission, load curtailment cost by considering curtailable loads (CLs), and coordination between shiftable loads (SLs) and wind turbines (WTs) output power. The consumers participating in the DSM strategy are responsive consumers, which can shift and curtail loads, and non-responsive consumers who cannot shift or curtail loads. The DERs used in the proposed day-ahead scheduling problem consists of wind energy source (WES), energy storage systems (ESSs), and diesel generators (DGs). Before integrating wind energy sources with SG, its forecasting is necessary; thus, the probability distribution function (PDF) is used to forecast wind speed. The day-ahead scheduling problem with tri objective function is solved using the multi-objective wind driven optimization (MOWDO) technique using the decision-making mechanism (DMM) to obtain the best solution in search space. Simulation results show that the day ahead scheduling multi-objective problem is solved using MOWDO algorithm. To check the effectiveness of the proposed model, it is applied to SG considering different constraints to receive balance power at the user end.

An IOT based efficient energy management in smart grid using DHOCSA technique

• Energy management system for distribution system with IoT with hybrid technique. • Deer hunting optimization (DHO) algorithm and crow search algorithm (CSA). • Optimally control the power and distribution system resources depends on IoT. • Distribution system interconnected with the data acquisition module. • IoT-based communication structure utilized to facilitate the demand response. • IoT distribution system maximizes that flexibility of network. This article proposes an Energy Management System (EMS) for distribution system with Internet of Things (IoT) framework through hybrid technique. The proposed hybrid system is the joint implementation of Deer Hunting Optimization (DHO) and Crow Search algorithm (CSA) and thus it is known as DHOCSA method. The major purpose of the proposed work is optimally control that power and distribution system resources through continuous data monitoring as communication framework depends on IoT. In the proposed system, the distribution system interconnected with the data acquisition module, which is IoT object through single IP address that results at mesh wireless network of devices. The IoT-based communication structure utilized for facilitating the development of demand response (DR) for the EMS distribution system. This framework collects the DR from load and broadcast the data to a centralized server. The transmitted data is processed using the DHOCSA technique. In this manner, the IoT distribution system maximizes that flexibility of network provides optimal utilize of the available resources. Also the proposed system is dependable to satisfy that global supply and energy demand. Finally, the proposed system performance actualized on MATLAB/Simulink platform with three test cases and compared with various approaches.

Demand side energy management for smart homes using a novel learning technique–economic analysis aspects

In this study, a new distributed pattern of demand side energy management in residential smart grids regarding Improved Sparse Bayesian Learning (ISBL) method along different types of domestic electrical appliances have been presented. Considering the overall expense of electricity usage, the restrictions of supply quantity is sustained through distribution set-ups and the obtained power demands for individual appliances, the optimal energy management (OEM), for the demand side users, such as households, became an elaborated optimization issue in relation to the coupled objective performance across spatially and temporally of doubled constraints. Usually, in distributed model, figuring out of this kind of issue is problematic. Hence, in this study, we had changed it into ISBL method, and developed a distributed algorithm in order to obtain an optimal quantity of the unified OEM across the procedure of implementation. The presented scheme does not need any personal information about individual operators, meanwhile each of optimality and convergence are achieved. Additionally, this article demonstrated that the presented scheme has high robustness in unreliable communications. In addition, the suggested scheme is demonstrated and confirmed through simulations.

A multi-timescale smart grid energy management system based on adaptive dynamic programming and Multi-NN Fusion prediction method

The complexity of power grids, the intermittent renewable energy generation and the uncertainty of load consumption bring great challenges to modern energy management systems (EMSs). To solve the energy optimization problem in the time-varying smart grid, this paper proposes a multi-timescale EMS based on the adaptive dynamic programming (ADP) algorithm and multi-neural-network fusion (MNNF) prediction technology. In detail, according to different power consumption characteristics, this paper uses fuzzy C-means (FCM) clustering algorithm to classify power users into industrial users, commercial users and residential users. Based on the classification results, an MNNF prediction method is proposed that can integrate different influencing factors to predict load consumption and renewable energy generation. Then a multi-timescale ADP optimization algorithm is proposed to maximize the utilization of renewable energy on daily, intra-day and real-time (i.e., three timescales) of energy behavior. The convergence of the multi-timescale ADP algorithm is proved mathematically when the initial value is a random semi-positive definite function. Finally, the proposed ADP with MNNF energy management system is verified on a hardware-in-the-loop (HIL) platform.

SMART GRID ENERGY CONSERVATION MANAGEMENT BASED ON THE UNIVERSITY'S ENERGY INNOVATION HUB OF KNOWLEDGE

BACKGROUND AND OBJECTIVES. Due to increasing energy costs, as well as strict environmental regulations, there is a growing need for greater resource efficiency, which makes energy-efficient solutions necessary. Thus, the importance of innovations based on technologies designed to save energy, such as the Smart Grid, is increasing. Smart Grid is not just a compilation of smart meters or other electrical devices, it is a series of technologies, a concept of a fully integrated, self-regulating and self-healing power grid, which has a network topology and includes all sources of generation, transmission and distribution, managed by a single network of information and control devices and systems.METHODS. As the main method used was the calculation of the synthetic balance of savings from the use of different types of energy resources by the university after the implementation of the application Smart Grid-energy conservation management on the basis of the university energy-innovation Hub of knowledge.FINDINGS. A mechanism for the implementation of the Smart Grid energy-saving management application on the basis of the university energy-innovation Knowledge Hub is proposed. Smart Grid is designed to provide real-time data on the almost instantaneous balance of energy supply and demand. To ensure grid reliability by reducing peak demands and improving energy efficiency, Smart Grid data management is an affordable and effective tool for data analysis and decision making.CONCLUSION. The results of calculation of the predicted effect of the Smart Grid application implementation for the 4th building of Kyiv National University of Technologies and Design proved that the reduction of installed capacity as a result of the project was 80.5%, i.e. a 1% reduction in capacity creates an economic effect of 0.58% of the costs associated with modernization. Given the current level of electricity consumption, we can predict a potential reduction of 951 thousand UAH per year or almost 50% of the cost of electricity consumed in 2020.

A Distributed Online Learning Approach for Energy Management With Communication Noises

An online distributed neurodynamic optimization method for energy management in smart grids is proposed to minimize the operational cost of smart grids, considering various constraints, such as output bounds, state of charge of battery storage systems, network congestion and voltage limits. The proposed distributed neurodynamic optimization method adopts a consensus protocol using the signs of the information differences between neighbors for information exchanging and a recurrent neutral network for optimization without dual multipliers. It has the following merits. First, the calculation and information exchange of dual multipliers are avoided to mitigate the computational complexity and reduce communication bandwidth. Second, the proposed optimization method is robust against communication noises from various components. Third, the uncertainties of renewable energy and loads are addressed by the online optimization approach, which takes advantage of the historical data. The convergence, optimality, consensus and robustness of the proposed distributed neurodynamic optimization method are rigorously proved in theory and verified in case studies on the modified IEEE 33-bus and IEEE 123-bus distribution systems.

A DAG-based cloud-fog layer architecture for distributed energy management in smart power grids in the presence of PHEVs

• New framework based on the directed acyclic graph and distributed multi-layer cloud-fog computing. • Finding a solution for the optimal energy management of the smart grids. • Considering a machine learning algorithm to solve augmented Lagrangian function in each agent. In this paper, a new framework based on the directed acyclic graph (DAG) and distributed multi-layer cloud-fog computing to find the optimal energy management of the smart grids, considering high penetration of plug-in hybrid electric vehicles (PHEVs). The presented distributed structure lets neighboring agents make a consensus together. The uncertainties have been modeled according to the Monte Carlo simulations, due to wide usages of diverse renewable energy resources such as photovoltaic panels and wind turbines. Three diverse charging schemes have been considered in the smart grid test system which contains controlled, uncontrolled and smart chargings. The Whale Optimization Algorithm (WOA) has been used to solve the augmented Lagrangian function in each agent. The simulation results are shown that the suggested scheme is effective.

Short-Term Load Forecasting in a microgrid environment: Investigating the series-specific and cross-learning forecasting methods

A reliable and accurate load forecasting method is key to successful energy management of smart grids. Due to the non-linear relations in data generating process and data availability issues, load forecasting remains a challenging task. Here, we investigate the application of feed forward artificial neural networks, recurrent neural networks and crosslearning methods for day-ahead and three days-ahead load forecasting. The effectiveness of the proposed methods is evaluated against a statistical benchmark, using multiple accuracy metrics. The test data sets are high resolution multi-seasonal time series of electricity demand of buildings in Belgium, Canada and the UK from private measurements and open access sources. Both FFNN and RNN methods show competitive results on benchmarking datasets. Best method varies depending on the accuracy metric selected. The use of cross-learning in fitting a global RNN model has an improvement on the final accuracy.

Emerging Trends in Smart Grid Energy Management Systems

The rising penetration of renewable energy sources into the power system has necessitated the transformation of the conventional power system fromgeneration, transmission and distribution' to ‘Localized generation, on-site storage and distribution integrated with multi-site communication and transmission'. The change in load type from incandescent lamps and induction motors to digital (electronic) systems requires robust control algorithms to ensure better power quality, mitigate system cost and manage energy effectively. This paper attempts to overview the evolution of the power system from its inception to date and presents the emerging trends for cost and energy-efficient smart grid systems. The benefits of smart-grids and various approaches by different countries to reduce carbon emissions and promote intelligent power systems have been analyzed. The technical advancements in the conventional power system architecture at the communication level and power level have been discussed in detail, along with the microgrid types and deployed energy management strategies. In the last section, a summary of emerging trends in the nested / multi-microgrid systems collated from various projects going on across the globe and their implications on cost and battery reductions for widespread deployment is presented.

Intelligent approach for optimal sizing in photovoltaic panel-battery system and optimizing smart grid energy

Renewable energy plays a very important role in solving energy problems, and solar energy is one of the most important renewable sources, especially in sunny countries. This paper deals with two problems: The first one is about optimal sizing in a photovoltaic panel (PVP)-battery system, and the second consists in energy management in smart grids. To achieve the first objective, an adaptive neuro-fuzzy inference system (ANFIS) estimation algorithm is developed in order to estimate a database of instantaneous photovoltaic power. The estimated instantaneous photovoltaic power is used in an optimal algorithm to size a PVP-battery power station to supply a 1.5 kW AC load. For the second objective, a deep learning forecasting algorithm is realized to estimate the smart grid parameters so as to optimize the consumption energy. All results are checked carrying out Matlab simulation using real weather data. The simulation results give a good performance of our proposed sizing and management systems.

Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems

Recently, home energy management systems (HEMS) are gaining more popularity enabling customers to minimize their electricity bill under time-varying electricity prices. Although they offer a promising solution for better energy management in smart grids, the uncoordinated and autonomous operation of HEMS may lead to some operational problems at the grid level. This paper aims to develop a coordinated framework for the operation of multiple HEMS in a residential neighborhood based on the optimal and secure operation of the grid. In the proposed framework customers cooperate to optimize energy consumption at the neighborhood level and prevent any grid operational constraints violation. A new price-based global and individualized incentives are proposed for customers to respond and adjust loads. The individual customers are rewarded for their cooperation and the network operator benefits by eliminating rebounding network peaks. The alternating direction method of multipliers (ADMM) technique is used to implement coordinated load scheduling in a distributed manner reducing the computational burden and ensure customer privacy. Simulation results demonstrate the efficacy of the proposed method in maintaining nominal network conditions while ensuring benefits for individual customers as well as grid operators.

A new multiscale tool for simulating smart-grid energy management based on a systemic approach

Electricity needs are growing rapidly in developing countries. Some governments are encouraging the integration of renewable energy into the electrical grid to deal with the environmental issues, depletion issues, and pollution issues from current conventional power generation systems. Therefore, a reliable tool is needed to manage this energy mix. A tool based on two new different strategies is proposed for the management of smart grids. A comparison with the HOMER software was carried out to ensure the reliability of the proposed tool to simulate the management of this energy mix. Furthermore, this tool is capable of simulating the management of energy-mix flows from a building to a country. Users can visualize the simulated environment in a two-dimensional map and graphs of the energy mix management associated with this environment. This approach can serve as a decision-support tool, for instance, in the implementation of a new photovoltaic field or wind turbines.

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.