Smart Microgrid Energy Management Research Articles

Practical prototype for energy management system in smart microgrid considering uncertainties and energy theft

The conventional electrical grid faces significant issues, which this paper aims to address one of most of them using a proposed prototype of a smart microgrid energy management system. In addition to relying too heavily on fossil fuels, electricity theft is another great issue. The proposed energy management system can simultaneously detect electricity theft and implement demand response tactics by employing time-of-use pricing principles and comparing real electricity consumption with grid data. The system uses the Al-Biruni earth radius (BER) optimization algorithm to make smart choices about how to distribute the load, intending to reduce energy consumption and costs without sacrificing comfort. As a bonus, it considers limitations imposed by battery charging/discharging and decentralized power generation. Incorporating sensors and SCADA-based monitoring, the system provides accurate measurement and management of energy usage through load monitoring and control. An intuitive mobile app also helps consumers connect, allowing for more active participation and better control over energy use. Extensive field testing of the prototype shows that by moving loads from peak period to another off-peak period, electricity expenditures can be reduced by up to 48.45%. The energy theft value was calculated to be 1199 W, proving that the system's theft detection model was effective.

Adaptive sliding mode control based on fuzzy logic for hybrid smart microgrid energy management system

ABSTRACT In this paper, an adaptive sliding mode controller (ASMC) based on fuzzy logic is applied to control hybrid smart microgrid power systems under uncertainty. The chattering effects can occur if not adjusted properly due to the sliding mode amplitude control law. The approximation of the ASMC law function is performed by Fuzzy logic, and chattering effects are reduced through a low pass filter. The Lyapunov theory proves the stability of the control system and is also applied to stabilise the system’s control mechanism. The hybrid micro-grid system operation, which consists of distribution generation, loads, PWM IGBT inverter and low pass filter, is simulated using MATLAB 2017b. The reference speed is compared with the hybrid microgrid real speed, which was 1.0 p.u. and gradually controlled through ASMC-fuzzy until the speed desired is achieved for power supply stability. A comparison of results between the simulated ASMC-Fuzzy and PI controller indicated a drastic reduction of THD, which satisfies the required ‘below 5% THD level according to IEEE 1547 standard’ required for operation.

Integration of IoT and edge cloud computing for smart microgrid energy management in VANET using machine learning

A significant subset of ubiquitous computing known as vehicular ad hoc networks (VANETs) serves as a foundational piece of technology for VANET applications. Blockchain technology can be used in microgrids to conduct safe point-to-point transactions between anonymous parties while also addressing needs for security. This research is o proposes novel method in IoT(internet of things) based edge cloud computing architecture with microgrid energy management of VANET. here the VANET communication is carried out based on IoT edge cloud computing module and the smart microgrid architecture is used for energy management in VANET. then each vehicle energy has been analysed using structural reinforcement variational encoder neural networks. experimental analysis is carried out in terms of energy efficiency, network lifetime, training accuracy, QoS, communication overhead. the proposed technique attained energy efficiency of 96%, network lifetime of 85%, training accuracy of 98%, QoS of 95%, communication overhead of 55%.

Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid

Energy consumption schedulers have been widely adopted for energy management in smart microgrids. Energy management aims to alleviate energy expenses and peak-to-average ratio (PAR) without compromising user comfort. This work proposes an energy consumption scheduler using heuristic optimization algorithms: Binary Particle Swarm Optimization (BPSO), Wind Driven Optimization (WDO), Genetic Algorithm (GA), Differential Evolution (DE), and Enhanced DE (EDE). The energy consumption scheduler based on these algorithms under a price-based demand response program creates a schedule of home appliances. Based on the energy consumption behavior, appliances within the home are classified as interruptible, noninterruptible, and hybrid loads, considered as scenario-I, scenario-II, and scenario-III, respectively. The developed model based on optimization algorithms is the more appropriate solution to achieve the desired objectives. Simulation results show that the expense and PAR of schedule power usage in each scenario are less compared to the without-scheduling case.

Field-Validated Communication Systems for Smart Microgrid Energy Management in a Rural Microgrid Cluster

This paper demonstrates a smart energy management scheme for solar photovoltaic-biomass integrated grid-interactive microgrid cluster system. Three interconnected microgrids were chosen as a cluster of microgrids for validation of the proposed community energy management scheme. In this work, a Global System for Mobile (GSM)-based bidirectional communication technique was adopted for real-time coordination among the renewable energy sources and loads. To realize the common phenomenon of local grid outage in rural distribution networks, a practical case study is designed in this work. The optimized scheduling of the energy sources and loadsof different microgrids and the distribution grid were implemented to ensure zero loss of power supply probability (LPSP) for dynamic load profiles. The laboratory-scale prototype of the proposed microgrid clustering was first developed in this work by establishing real-time communication among multiple energy sources and loads through different energymeters located at different places inside the academic campus. The field validation was performed with a microgrid cluster consisting of 45 kWP solar photovoltaic, 50 kVA biogas plant, community loads in a village. The developed smart energy management solution is a generalized one and applicable to satisfy scalable community energy demands as well.

Optimal Multi-Operation Energy Management in Smart Microgrids in the Presence of RESs Based on Multi-Objective Improved DE Algorithm: Cost-Emission Based Optimization

Today, in various leading power utilities in developing countries, achieving optimal operational energy management and planning, taking into account the costs reduction of generation, transmission and distribution of electricity, and also reducing the emission of an environmental pollutant becomes more and more important. Optimal use of renewable energy sources (RESs) is an effective way to achieve these goals. In this regard, in this research article, an improved multi-objective differential evolutionary (IMODE) optimization algorithm is suggested and performed to dispatch electricity generations in a smart microgrid (MG) system, taking into account economy and emission as competitive issues. In this paper, a nonlinear equation of multi-objective optimization issue with various equality and inequality limitations is formulated in order to lower the total operational costs of the MG considering environmental pollution effects simultaneously. In order to address the issue of optimal operation of the MG in single-objective and multi-objective forms, an intelligent method according to the improved differential evolutionary (IDE) optimization is utilized and performed and the proposed algorithm is implemented on different problems. First, it is assumed that there is no limit to the exchange of power overhead, and secondly, the limitation of power exchange with the upstream grid is considered. In multi-objective mode, these two modes are also considered. In order to show the impact of renewable energy on the cost, in the third part of the simulations, the operation is solved with maximum participation of renewable energy sources. In the final section, the sensitivity analysis on the number of populations in this problem is performed. The obtained results of the simulation are compared to differential evolutionary (DE) and particle swarm optimization (PSO) techniques. The effectiveness of the suggested multi-operational energy management method is confirmed by applying a study case system.

A two-step approach to energy management in smart micro-grids aimed at improving social welfare levels and the demand side management effect

A two-step approach to energy management in smart micro-grids aimed at improving social welfare levels and the demand side management effect

Fuzzy expert system for management of smart hybrid energy microgrid

This paper proposes a fuzzy expert system for demand-side management, management of renewable energy sources, and electrical energy storage for smart households and microgrids. The proposed fuzzy expert system is used for automatic decision making regarding energy management in smart microgrids containing renewable sources, storage systems, and controllable loads. The fuzzy expert system optimizes energy consumption and storage in order to utilize renewable energy and maximize the financial gain of a microgrid. In order to enable energy management, the fuzzy expert system uses insolation, price of electrical energy, temperature, wind speed, and power of the controllable and uncontrollable loads as input variables. These input data can be directly measured, imported from grid measurements, or predicted using any data prediction method. This paper presents fuzzification of input variables, defines a set of rules of the expert system, and presents defuzzification of outputs. The outputs of the expert system are decisions, i.e., answers to the question of how to manage energy production and consumption in a microgrid. Three outputs are defined to decide about produced energy, controllable loads, and own consumption. The first output is used to store, sell, or consume produced energy. The second output is used to manage the controllable load. The third output shows how to supply own consumption of the prosumer. The expert system is tested on hourly values of input variables in a single day in Serbia. The proposed approach is compared with other available approaches in order to validate the results.

The effect of price responsive loads uncertainty on the risk-constrained optimal operation of a smart micro-grid

The financial and technical decisions of a smart micro-grid energy management system have always been affected by the existence of uncertainty in various parameters, such as electric load, electricity price, and renewable generation. Especially when the behavior of price and load is influenced by each other in a smart micro-grid. In this paper, we describe the uncertainty issue in the consumption behavior of price responsive loads and we utilize a model based on robust optimization to consider this uncertainty. The effects of considering the uncertainty of price responsive loads with the proposed model on a typical risk-constrained optimal operation of a smart micro-grid are illustrated through two case studies. The first case involves solving the considered optimal operation problem without considering the proposed uncertainty model while the second one shows the effects of proposed uncertainty model on the optimal solutions. The operation optimization problem is formed as a mixed-integer two-stage stochastic framework. Results show that with this uncertainty model the manager of smart micro-grid can increase its bids in day-ahead market at peak and mid-peak time slots while it can decrease them in off-peak time slots. Also, considering the impacts of this uncertainty model on profitability and system reliability, the smart micro-grid manager needs a trade-off between increasing its profit and increasing the system operation risk.

Synthesis of an intelligent rural village microgrid control strategy based on smartgrid multi-agent modelling and transactive energy management principles

Humanitarian and other development organisations are calling for replicable self-sustaining solutions to shared micro-utilities to ensure equitable modern energy delivery to energy-deprived village communities. Distributed smart microgrid technology allows for the efficient integration of sustainable resources to provide localised energy delivery at improved levels of reliability and resilience. Smart energy management in decentralised renewable systems requires computational intelligence to implement pivotal energy-aware/cost-aware procedures in decision-making for a conceptualised next generation Smart Village microgrid platform. Smart Village microgrid control automation aims at offering pragmatic and, intelligent control capabilities to perform price-based demand response energy management. This paper proposes the use of cascaded control abstraction in the implementation of a price-sensitive cyber-physical Smart Grid approach in a rural off-grid microgrid environment. The modular microgrid design integrates renewable energy resources through an adaptive control algorithm developed in a model-based design approach. The solution is based on distributed market-based control, using multi-agent transactive principles to navigate automated demand response. Multi-priority load clusters with the selective parallel control of non-intelligent device groups allow for prioritised supply/demand resource coordination. The proposed Smart Village solution operates as a self-regulating smart microgrid energy management system and by virtue of its market-based transactive reasoning approach is able to meet multiple operating requirements of rural village energy systems. Simulation results for this value-based control technique highlight the value of customer engagement combined with supply-, demand- and economic cost optimisation for hybrid renewable distributed energy microgrids.

Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization

At present, renewable energy sources (RESs) integration using microgrid (MG) technology is of great importance for demand side management. Optimization of MG provides enhanced generation from RES at minimum operation cost. The microgrid optimization problem involves a large number of variables and constraints; therefore, it is complex in nature and various existing algorithms are unable to handle them efficiently. This paper proposed an artificial shark optimization (ASO) method to remove the limitation of existing algorithms for solving the economical operation problem of MG. The ASO algorithm is motivated by the sound sensing capability of sharks, which they use for hunting. Further, the intermittent nature of renewable energy sources is managed by utilizing battery energy storage (BES). BES has several benefits. However, all these benefits are limited to a certain fixed area due to the stationary nature of the BES system. The latest technologies, such as electric vehicle technologies (EVTs), provide all benefits of BES along with mobility to support the variable system demands. Therefore, in this work, EVTs incorporated grid connected smart microgrid (SMG) system is introduced. Additionally, a comparative study is provided, which shows that the ASO performs relatively better than the existing techniques.

Assisted energy management in smart microgrids

Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources. Pricing is accordingly used to reflect energy availability, to allocate such a limited resource to those loads that value it most. However, the strictly competitive mechanism can result in service interruption in presence of competing demand. To solve this issue we investigate on the use of forward contracts, i.e., service-level agreements priced to reflect the expectation of future supply and demand curves. Given the limited resources of microgrids, service availability is an opposite objective to the one of system reactivity. We firstly design policy-based brokers and identify then a learning broker based on artificial neural networks. We show the latter being progressively minimizing the reimbursement costs and maximizing the overall profit.

Intelligent multi-agent system for smart microgrid energy management

In this paper, an intelligent energy management is presented for distributed structure such as a smart microgrid MG that can operate in islanded or connected mode with the main grid. To model this ...

Intelligent multi-agent system for smart microgrid energy management

In this paper, an intelligent energy management is presented for distributed structure such as a smart microgrid MG that can operate in islanded or connected mode with the main grid. To model this MG, a multi-agent is proposed. This system is based on wind power prediction and fuzzy logic control for storage device. The goal of the MAS is to control the amount of power delivered or taken from the main grid in order to reduce the cost and maximise the benefit, to achieve the mentioned goal, the neural network is proposed to predict the amount of electricity that will be produced for the next hour and fuzzy logic control for the battery to taking a reasonable decision about storing or selling electricity, finally and for the simulation, the jade platform has been used to show the impact of using the proposed system.

Efficient Energy Management in Smart Micro-Grids: ZERO Grid Impact Buildings

In a smart micro-grid (MG) each generator or load has to take part in the network management, joining in reactive power supply/voltage control, active power supply/frequency control, fault ride-through capability, and power quality control. This paper includes a new concept for building integration in MGs with zero grid-impact so improving the MG efficiency. These aims are shown to be achievable with an intelligent system, based on a dc/ac converter connected to the building point of coupling with the main grid. This system can provide active and reactive power services also including a dc link where storage, generation, and loads can be installed. The system employed for validation is a prototype available at ENEA Laboratories (Italian National Agency for New Technologies). A complete and versatile model in MATLAB/SIMULINK is also presented. The simulations results and the experimental test validation are included. The trial confirms the model goodness and the system usefulness in MG applications.

Enzymatics Enriches NGS with AMP

Genetic Engineering & Biotechnology NewsVol. 34, No. 14 Corporate ProfileEnzymatics Enriches NGS with AMPTargeted Sequencing Technology Enhances Gene Mutation Identification and DiscoveryGail DuttonGail DuttonSearch for more papers by this authorPublished Online:30 Jul 2014https://doi.org/10.1089/gen.34.14.05AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byA Review of Smart Microgrid Energy Management and Control StrategySmart Grid, Vol. 11, No. 03 Volume 34Issue 14Aug 2014 Information© 2014 by GEN PublishingTo cite this article:Gail Dutton.Enzymatics Enriches NGS with AMP.Genetic Engineering & Biotechnology News.Aug 2014.10-11.http://doi.org/10.1089/gen.34.14.05Published in Volume: 34 Issue 14: July 30, 2014PDF download

Synthesizing interpreted domain-specific models to manage smart microgrids

The increase in prominence of model-driven software development (MDSD) has placed emphasis on the use of domain-specific modeling languages (DSMLs) during the development process. DSMLs allow for domain concepts to be conceptualized and represented at a high level of abstraction. Currently, most DSML models are converted into high-level languages (HLLs) through a series of model-to-model and/or model-to-text transformations before they are executed. An alternative approach for model execution is the interpretation of models directly without converting them into an HLL. These models are created using interpreted DSMLs (i-DSMLs) and realized using a semantic-rich execution engine or domain-specific virtual machine (DSVM).In this article we present an approach for model synthesis, the first stage of model interpretation, that separates the domain-specific knowledge (DSK) from the model of execution (MoE). Previous work on model synthesis tightly couples the DSK and MoE reducing the ability for implementations of the DSVM to be easily reused in other domains. To illustrate how our approach to model synthesis works for i-DSMLs, we have created MGridML, an i-DSML for energy management in smart microgrids, and an MGridVM prototype, the DSVM for MGridML. We evaluated our approach by performing experiments on the model synthesis aspect of MGridVM and comparing the results to a DSVM from the user-centric communication domain.