Smart Microgrid Research Articles

Smart microgrid construction in abandoned mines based on gravity energy storage

The share of new energy in China's energy consumption structure is expanding, posing serious challenges to the national grid's stability and reliability.As a result, it is critical to construct large-scale reliable energy storage infrastructure and smart microgrids. Based on the spatial resource endowment of abandoned mines' upper and lower wells and the principle characteristics of the gravity energy storage system, an intelligent microgrid system model for abandoned mines based on gravity energy storage is proposed, and the system's feasibility and key influencing factors are discussed and analyzed from the standpoint of economic benefits. The gravity energy storage system principle, system structure, subsurface powerhouse, underground storage, and transit system are all examined and analyzed.The viability of establishing intelligent microgrid systems in abandoned mines is proved using the resource conditions, technical conditions, economic advantages, and social benefits of Panyidong Mine in Huainan Coal Mine.The findings indicate that the project concept has good economic and social benefits as well as practical viability.Next, from the perspectives of technology, policy, and the ecological environment, several recommendations for the development of a smart microgrid system based on gravity energy storage power station are made. This study presents a novel concept for the advancement of energy storage technology and the reuse of abandoned mine resources, which is critical to the long-term development of abandoned mine resources and the advancement of energy storage technology.

AI-Assisted Hybrid Approach for Energy Management in IoT-Based Smart Microgrid

AI-Assisted Hybrid Approach for Energy Management in IoT-Based Smart Microgrid

Anomaly Detection in a Smart Microgrid System Using Cyber-Analytics: A Case Study

Smart microgrids are being increasingly deployed within the Department of Defense. The microgrid at Marine Corps Air Station (MCAS) Miramar is one such deployment that has fostered the integration of different technologies, including 5G and Advanced Metering Infrastructure (AMI). The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify and identify specific cyber-attacks against this infrastructure. Both network traffic in the form of packet captures (PCAP) and time series data (from the AMI sensors) are considered. We train the autoencoder model on three traffic data sets: (1) Modbus TCP/IP PCAP data from the hardwired network apparatus of the smart microgrid, (2) experimentally generated 5G PCAP data that mimic traffic on the smart microgrid and (3) AMI smart meter sensor data provided by the Naval Facilities (NAVFAC) Engineering Systems Command. Distributed denial-of-service (DDoS) and false data injection attacks (FDIA) are synthetically generated. We show the effectiveness of the autoencoder on detecting and classifying these types of attacks in terms of accuracy, precision, recall, and F-scores.

Improving Directional Overcurrent Relay Coordination in Distribution Networks for Optimal Operation Using Hybrid Genetic Algorithm with Sequential Quadratic Programming

In recent years, with the growing popularity of smart microgrids in distribution networks, the effective coordination of directional overcurrent relays (DOCRs) has presented a significant challenge for power system operators due to the intricate and nonlinear nature of their optimization model. Hence, this study proposes a hybrid GA-SQP algorithm to enhance the coordination of directional overcurrent relays (DOCRs) in radial and non-radial interconnected distributed power networks. The proposed approach combines the advantages of both the genetic algorithm (GA) and sequential quadratic programming (SQP) methods to optimize the objective function of relay coordination in the best manner. Thus, the proposed hybrid techniques improved the convergence of the problem and increased the likelihood of obtaining a globally optimal solution. Finally, to validate the effectiveness of the proposed algorithm, it was tested through three case studies involving the IEEE 3-Bus, 8-Bus, and modified 30-Bus distribution networks. In addition, the results were compared to those obtained using previous methods. The results obtained from the comparison of the proposed method and recent advanced research indicate that the proposed optimization approach is preeminent in terms of accuracy and total operating time as well as the continuity of the minimum margin time requirements between the primary/backup relay pairs.

Sizing strategies for electrical smart microgrids for rural customers

Micro Smart Grids are configured in the panorama of technological areas related to clean energies, as an alternative for the efficient distribution of electric energy. For this purpose, the present study aimed to carry out a documentary analysis of the sizing strategies of smart microgrids for rural users. The research was carried out from the approach of a documentary-bibliographic research. In this framework, the search was carried out in the Internet database of sites specialized in academic information and reliable sources using the descriptors: electric microgrids, strategies, sizing. For the selection of the material under analysis, the following criteria were taken into consideration: pertinence, relevance, language, place of origin, published in indexed journals during the years 2017 to 2021. Based on this, the content analysis of the information found in the literary sources collected was performed. Of the total number of documents used, seven (07) of them were considered for the analysis as they were considered to be the ones that most accurately matched the interests of this study. The results found show that: The integration of distributed energy resources (distributed generation and energy storage), are resulting in a great revolution in generation, transmission, distribution, operation and energy consumption, among others. It is concluded that microgrids become one of the support strategies for the conventional electrical grid that can help in the purpose of undertaking actions for the development of projects aimed at generating a process of allocation or improvement in the supply of electricity in rural localities.

An optimal multi-objective demand side management of a smart Microgrid consists of various building loads, considering relativity index and uncertainty

This paper addresses microgrid energy management in the presence of distributed generation and active loads while accounting for operational, economic, environmental, and reliability factors. The objective is to minimize the cost of shutdown during N-1 contingencies, projected emission-related expenses, and the overall operational cost of microgrids integrated with distributed generations. A stochastic scheduling technique that optimizes short-term microgrid operation, reducing costs and pollution through renewable resources is introduced. Utilizing demand response programs among residential, commercial, and industrial participants is proposed to counter the uncertainty of renewable resource-generated power. The demand response program implementation suggests considering financial incentives as bid prices, along with energy packages managed by demand response providers. Simulation results demonstrate three strategies to decrease operating costs and pollutants, contrasting scenarios with and without responsive load engagement. Equations governing AC power flow, microgrid operations, reliability, distributed generation, and, demand response programs and batteries, form the analytical core. Stochastic programming addresses uncertainties tied to demand, energy prices, renewable distributed generation production, and microgrid equipment availability. To attain dependable, optimal solutions with dynamic responsiveness, the combined ant lion optimization and crow search algorithm are employed. The proposed method is tested on a sample smart microgrid for validation. Numerical findings unequivocally underscore demand-side management potency in reducing power generation uncertainties from wind turbines and photovoltaics. This paper offers insights into microgrid energy management complexities, paving the way for resilient, cost-effective, and environmentally conscious energy distribution paradigms.

Risk-based Peer-to-peer Energy Trading with Info-Gap Approach in the Presence of Electric Vehicles

Small-scale smart microgrid is prone to economic consideration of adequate transactive energy sharing and reliable certified power pool hub. Moreover, direct integration of efficient with renewable energy providers in presence of storage such as electric vehicle (EV) aggregation improves the performance of energy systems and guarantees secure trade among the consumers. However, unified modeling of the energy community framework faces the challenge of load management. In this paper, an optimal risk management procedure is proposed for renewable-based prosumers such as photovoltaic (PV), and EV to maximize the horizon of uncertainty parameter. The energy demand in peer-to-peer (P2P) household energy sharing is considered as the uncertain parameter. To this end, in order to study the behavior of a risk-averse and a risk-seeker decision-maker, an information-gap decision theory (IGDT) probability model is applied. In order to demonstrate the performance of the proposed approach, the model is implemented in a test microgrid and the simulation results are presented and discussed.

A blockchain‐based smart contract model for secured energy trading management in smart microgrids

AbstractThe extension of emerging renewable energy sources such as wind and water turbines, solar panels, and the increasing usage of electric vehicles requires the supply and distribution of energy in a small device on local scale and it has created new methods of supplying and selling electricity. Middle buyers and end users can obtain the local energy with the peer‐to‐peer trading method in this large and hierarchical market. This method enables market to manage and exchange the electricity between major suppliers and medium and local levels. Blockchain technology is developing in peer‐to‐peer exchange of electricity and acts as a reliable, efficient, and safe technology in the electricity trading market. In this method, while preserving the privacy of electricity users, by using smart contracts and by removing intermediaries in the energy supply and demand market, direct commercial interactions between energy suppliers and consumers are done. The blockchain technology, while creating trust between the parties in the energy market, reduces the cost of electricity trading and increases its scalability with using the intermediate energy aggregators. In this research, the blockchain‐based model, is presented for distribution and peer‐to‐peer transactions in the energy market. The suggested model provides the possibility of registration low‐cost instant transactions at the power grid in any specific period of time. The above method, unlike periodic payments, provides immediate access to bills and small payments. Since the transactions outside the blockchain chain are not recorded, this system guarantees its honest and independent operation without fraud and failure. The smart contract method based on blockchain, reduces the transaction fees and speeds up electricity trading. Also, the experimental investigation in 20 nodes shows the time required to determine the exchange contract in the blockchain method. The average is improved by 49.7% in this method. Also, the negotiation convergence time has become 47% faster.

Battery Swapping Station Service in a Smart Microgrid: A Multi-Method Simulation Performance Analysis

The integration of Battery Swapping Stations (BSSs) into smart microgrids presents an opportunity to optimize energy generation, storage, and consumption. However, there exists a gap in the literature regarding the detailed analysis of the profitability of integrating a BSS within a smart microgrid, particularly utilizing second-life batteries for storage and renewable energy sources for generation. This study aims to address this gap by employing a multi-method simulation approach to thoroughly investigate the economic viability of such integration. The simulation model developed for this study is a digital twin of the microgrid, incorporating components such as the BSS, renewable energy sources (wind and photovoltaic), second-life battery storage, and utilities. By optimizing energy flows within this model, considering the cost-effectiveness of diverse generation sources and prioritizing the utilization of renewable energy, we aim to provide a comprehensive assessment of the economic benefits. Furthermore, the simulation takes into account crucial factors including battery swapping operations, warehouse management, and battery charging scheduling. The profitability analysis undertaken in this study is grounded in the objective of minimizing total costs while effectively meeting the energy demands of residential loads. Ultimately, the integration of the BSS into the smart microgrid not only targets economic efficiency but also strives to maximize the utilization of second-life batteries, contributing to the concept of a circular economy.

Experimental Validation of a Remedial Action via Hardware-in-the-Loop System Against Cyberattacks Targeting a Lab-Scale PV/Wind Microgrid

This paper experimentally validates the effectiveness of a primary/backup framework in preventing/mitigating the impacts of false data injection (FDI) cyberattacks targeting a lab-scale wind/photovoltaic (PV) microgrid. As the primary mecha-nism, an artificial intelligence-based false data detection algorithm is proposed to forecast the upcoming measurements and alert the operator about the accuracy of sensors readings, directly collected from the field devices. Given that some FDI attacks are designed to bypass the utilized detection methods, it is essential to be prepared for such circumstances. Hence, as the backup mechanism, a remedial action scheme (RAS) is also introduced to mitigate the impacts of such malicious cyberattacks and keep the functionality of the microgrid. The proposed framework (i.e., FDI model, detection approach, and remedial action scheme) is developed as a hardware-in-the-loop (HIL) testbed within the cyber-physical structure of the smart microgrid. The experimental results validate 1) the negative impacts of modeled FDI attacks on the lab-scale microgrid, 2) the effectiveness of the developed false data detection technique, and 3) the efficiency of the proposed RAS to keep the normal operation of the targeted microgrid when the detection system fails to recognize the attack.

Optimal Energy Scheduling in Smart Buildings with Electric Vehicle and Demand Response

In a number of countries, smart microgrid (SMG) technology is being exploited in the energy system infrastructure along with other generators such as electric and storage systems. In the framework of SMGs, communication links between generation and demand sides have been established to optimise energy consumption by means of economic signals. The framework of energy management for Smart Home appliances is presented in the present paper, taking account of appliance and electric vehicle participation. The approach to the energy schduling at day-ahead is defined as three strategies, such as optimum use of existing appliances, optimal production of electricity resources and optimal discharge and charging for electrical vehicles. Reducing energy generation costs and minimizing emissions of air pollutants are key objectives. Two case studies with the aim of reducing costs and emissions while maximizing system flexibility are taken into account to demonstrate preference and viability of home energy scheduling.

Integration of renewable energy resources into the water-energy-food nexus–Modeling a demand side management approach and application to a microgrid farm in Morocco

This paper introduces a framework aimed at analyzing the water-energy-food nexus (WEF) within the context of sustainable farming practices utilizing renewable energy sources, specifically Solar PV, to optimize water management efficiency. The focus lies on investigating the interplay between the water-energy-food nexus and both technical and institutional factors. The study particularly delves into the utilization of distributed energy systems and microgrids for electricity distribution. To achieve the objectives outlined, the framework is applied to a case study involving an off-grid farm in Morocco, aligned with the country's “Green Morocco Plan” of 2008. The study employs the AnyMOD open-source modeling framework in combination with the publicly available decision support tool CropWat (Version 8.0). Through this coupling, a linear optimization model is created to assess various irrigation practices, thus evaluating the energy and water supply variations across different crop growth stages. By employing scenario analysis, the study reveals that the integration of a smart microgrid alongside storage technologies proves beneficial in terms of reducing overall system costs. This integration presents cost-effective solutions and enables the establishment of a sustainable energy supply driven by renewable energy resources. Furthermore, the investigation highlights that constraining irrigation to specific hours of the day results in increased storage requirements and higher associated costs. In conclusion, the study underscores that enhancing the water-energy-food nexus through the integration of a renewable-based microgrid is a complex task. However, it significantly contributes to the development of sustainable farming solutions. This research sheds light on the challenges and opportunities associated with aligning renewable energy, water management, and agricultural practices, ultimately facilitating the pursuit of environmentally conscious and efficient farming methodologies.

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.

Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, generation conditions. The adopted MG regulates the bidirectional DC/AC main converter using digital proportional resonant controllers in a synchronous reference frame. A maximum power point tracker-based boost DC/DC converter enables the wind turbine and solar photovoltaic to harvest maximum power. Traditional methods such as perturb and observe and incremental conductance maximum power trackers cannot solve nonlinearity and inaccurate responses. This work provides a hybrid maximum power tracker strategy to modify the responses of standard maximum power point techniques based on particle swarm optimization-trained adaptive neuro-fuzzy inference system (ANFIS-PSO) to achieve quick and maximum solar power with minimal oscillation tracking. Concerning the management system, this paper adopts a recent meta-heuristic algorithms-based DSM program to modify consumers’ electricity use by shifting the load appliances to off-peak demand periods. The adopted algorithms for DSM are sparrow search algorithm (SSA), binary orientation search algorithm (BSOA), and cockroach algorithm (CA). Finally, based on energy Internet technology, ThingSpeak cloud-based MATLAB is adopted to gather and display real-time data streams and generate graphical analyses. The simulation results reveal that the recommended coordinating control produces quick grid frequency responsiveness and zero steady-state errors. The optimal demand management program minimizes peak energy consumption from 5.2 kWh to 4.6 kWh. All DSM methods cost 439.1 $ per month, compared to 484.4 $ for the nonscheduling load profile.

Information Gap Decision Theory-Based Stochastic Optimization for Smart Microgrids with Multiple Transformers

Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park’s peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids.

Multi-Objective Operational Cost Management with Minimum Net Emission of a Smart Microgrid

This research article considers a microgrid (MG) with various distributed generation sources (DGs) such as microturbine (MT), fuel cell (FC), wind turbine (WT), and solar cell (PV) along with the battery storage system. This article aims to simultaneously minimize operational costs and emissions for a daily schedule, while considering uncertainties such as load demand, market prices, and electricity generated by different renewable energy sources (RESs). This article proposes the slime mould algorithm (SMA) to solve this complex optimization problem. Initially, SMA is tested for three scenarios considering the minimization of operational cost and emission, respectively, as two single objectives. In Scenario A, a reduction of 1.95% in operational cost and 4.57% in emissions is observed compared to the Grasshopper Optimization Algorithm (GOA). In scenarios B and C, SMA reduces operational cost by 0.78% and 2.65%, respectively, while also reducing emissions by 6.27% and 0.28%, respectively, when compared to GOA. The results obtained by other algorithms are worse than those obtained by GOA. This study also showed that SMA effectively considers multi-objective functions by using the weighted sum method and the fuzzy decision-maker to approach the Pareto-optimal solution, achieving a good tradeoff between operational cost and emissions in each scenario.

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%.

Study of an Indirect Injection Diesel Engine Using Pure Coconut Oil, Pure Tamanu Oil and B-20 as Fuel for Smart Microgrid Applications. Part I: Laboratory Testing

Smart Microgrid (SMG) is a hybrid system based on renewable energy which can use biofuel, taking advantage of local resources, as one of its energy sources. This study was conducted to determine the effect of using pure tamanu oil and pure coconut oil on engine performance, emissions, as well as their effects on particular components before and after endurance testing. The experiments were done using a diesel engine at speeds of 2200 rpm. In this study, engine performance and emission tests were done before and after the accelerated endurance tests, with loads of 800 W to 4000 W. The dimensions of the fuel injector nozzle needle and plunger pump were also measured. The fuel performance and emissions results showed slight differences between, before, and after endurance testing. The emissions tests also showed that the two biofuels, especially tamanu, are cleaner than B-20 and have better dimensional measurement results, compared to B-20. Therefore, these biofuels are feasible for replacing B-20, as shown in laboratory testing

Study of an Indirect Injection (IDI) Diesel Engine Using Pure Coconut Oil, Pure Tamanu Oil and B-20 for Smart Microgrid Applications Part II: Pilot Testing

The Smart Microgrid (SMG) can be used as a solution to overcome problems with electrical distribution on remote islets. The SMG system allows for a combination of conventional and renewable energy for power generation. Biofuel was chosen as a renewable energy source because of its abundant availability and ease of mobilization. This study examined the performance of B-20, Pure Tamanu Oil (PTO), and Pure Coconut Oil (PCO) in an IDI diesel engine that acts as a backup for SMG systems in the Karimunjawa Islands. The entire SMG system consists of: diesel engine, stabilizer, inverter, PV, batteries, ice maker, and a channel to the electrical grid. The results show that PTO has the highest value of thermal efficiency, that of 17.38%, but with a higher BSFC of 0.54 kg/kWh when compared to B-20 usage (14.69% and 0.51 kg/kWh). According to performance test results, their performance can be compared to the first part of this twin paper, which is laboratory testing, with a range of 2400-3200 W loads. Therefore, it can be said that biofuels are feasible for replacing B-20, as shown in pilot testing.

Grid-Forming Inverter Control for Power Sharing in Microgrids Based on P/f and Q/V Droop Characteristics

Grid-forming inverters are anticipated to be integrated more into future smart microgrids commencing the function of traditional power generators. The grid-forming inverter can generate a reference frequency and voltage itself without assistance from the main grid. This paper comprehensively investigates grid-forming inverter modelling and control methodology. A decentralized method employing an active power versus frequency P − f droop and a reactive power versus voltage Q − V droop is exploited to drive the operation of the grid-forming inverter. This decentralized method ensures balancing the supply and demand beside the power-sharing task between two or more inverters. The performance of the grid-forming inverter is examined by monitoring the frequency and RMS voltage of the inverter bus for three different periods of a varying PQ load. In addition, the performance of the resultant droop is compared with the assumed droop to validate the effectiveness of the proposed method. Finally, two grid-forming inverters equipped with the same droop characteristics are connected to a single load to observe the power-sharing concept among them. All simulations are implemented and executed using Matlab/Simulink version R2014b.