To accomplish feasible large-scale integration of distributed energy resources (DER) into the existing grid system, microgrid implementation has proven to be the most effective. This article reviews the vital aspects of DER based microgrid and presents simulations to investigate the impacts of DER sources, electric vehicles (EV), and energy storage system (ESS) on practicable architectures’ resilient operation. The focus is primarily on the concept and definition of microgrid, comparison of control strategies (primary, secondary, and tertiary strategies), energy management strategies, power quality (PQ) issues associated with DER based microgrid, and state-of-the-art entities such as ESS and EV's applications toward microgrid reliability. Following discussion on the different attributes of DER sources-based microgrid, simulations are performed to verify the results of the past works on the effects of solar, wind energy sources, ESS, and EVs on the microgrid frequency response. Additional simulations are conducted to assess the influences of DERs, ESS, EVs, and their operational strategies on the microgrid reliability aspects.

Distributed Energy Resources Based Microgrid: Review of Architecture, Control, and Reliability

A novel fuzzy cloud stochastic framework for energy management of renewable microgrids based on maximum deployment of electric vehicles

In a deregulated power system, Demand Side Management (DSM) plays a vital role in handling the uncertain renewable power generation and load. The flat load-profile can be obtained using the Demand Response (DR) techniques with the storage elements and proper switching. The increasing penetration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) supports the DR measures which facilitate both the utility and the consumer. The objective of DSM is to minimize the peak demand, electricity cost and emission rate by the effective utilization of storage with RES. This review article mainly focuses on the layers of microgrid, different techniques involved in DSM, mathematical models of DSM, latest optimization techniques and application of storage devices such as battery energy storage system and EVs in DSM. The state of the art of this article lies on the critical analysis related to datascience, advanced metering infrastructure and blockchain technologies which are the uniqueness of this article. The key issues and approaches are examined critically with the existing works to show how DSM implementation can be effectively done in the microgrids to reduce the electricity cost. This article helps the researchers to identify the research gap by gaining knowledge on the implementation of DSM in the microgrid and the factors affecting the DSM implementation. Few recommendations are discussed to provide future directions for researchers who started working in the DSM implementation. 

Demand side management in microgrid: A critical review of key issues and recent trends

Global environmental changes, nuclear power risks, losses in the electricity grid, and rising energy costs are increasing the desire to rely on more renewable energy for electricity generation. Recently, most people prefer to live and work in smart places like smart cities and smart universities which integrating smart grid systems. The large part of these smart grid systems is based on hybrid energy sources which make the energy management a challenging task. Thus, the design of an intelligent energy management controller is required. The present paper proposes an intelligent energy management controller based on combined fuzzy logic and fractional-order proportional-integral-derivative (FO-PID) controller methods for a smart DC-microgrid. The hybrid energy sources integrated into the DC-microgrid are constituted by a battery bank, wind energy, and photovoltaic (PV) energy source. The source-side converters (SSCs) are controller by the new intelligent fractional order PID strategy to extract the maximum power from the renewable energy sources (wind and PV) and improve the power quality supplied to the DC-microgrid. To make the microgrid as cost-effective, the (wind and PV) energy sources are prioritized. The proposed controller ensures smooth output power and service continuity. Simulation results of the proposed control schema under Matlab/Simulink are presented and compared with the super twisting fractional-order controller.

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Hybrid Wind/PV/Battery Energy Management-Based Intelligent Non-Integer Control for Smart DC-Microgrid of Smart University

One of the crucial challenges in the present power distribution system is the conversion loss phenomenon. Modern microgrid integrates various converters for varieties of applications, such as distributed power generation interconnection, energy storage management system, grid integration, demand management, etc. The increased usages of power converters further worsen the existing situation. Any initiatives taken towards energy conservation go in vain due to the excessive conversion loss phenomenon in the present distribution schemes. In this regard, a novel microgrid energy management scheme is proposed and developed to reduce the conversion losses in the residential distribution system. It uses a new control algorithm that finds the strength of power available in the DC side before being transferred. The conversion process is invoked only if the power is adequate, and if found feeble, then the conversion process is withdrawn and stored in an auxiliary battery. Conversion of feeble power would result in high loss across the converters and transformers. In this scheme, the AC loads are supplied by the utility grid, and the DC loads are fed by a solar PV and an auxiliary battery bank. The power conversion is done only during unavoidable circumstances. A prototype hardware setup has been developed, and the objective of the proposed research task has been validated. Further, the proposed scheme would gain importance in reducing the cost of the electricity for a time-of-use tariff system by optimization. A genetic algorithm is proposed to optimize the energy management of the microgrid system.

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A Novel Battery Supported Energy Management System for the Effective Handling of Feeble Power in Hybrid Microgrid Environment

An efficient energy management system for a small-scale hybrid wind-solar-battery based microgrid is proposed in this paper. The wind and solar energy conversion systems and battery storage system have been developed along with power electronic converters, control algorithms and controllers to test the operation of hybrid microgrid. The power balance is maintained by an energy management system for the variations of renewable energy power generation and also for the load demand variations. This microgrid operates in standalone mode and provides a testing platform for different control algorithms, energy management systems and test conditions. A real-time control is performed by rapid control prototyping to test and validate the control algorithms of microgrid system experimentally. The proposed small-scale renewable energy based microgrid can be used as a test bench for research and testing of algorithms in smart grid applications.

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Energy Management System for Small Scale Hybrid Wind Solar Battery Based Microgrid

In this paper, the design and implementation of a wind turbine emulator for a stand-alone system is presented. There is a need for a testing platform for the wind turbine in order to improve the de...

Design and implementation of wind turbine emulator using FPGA for stand-alone applications

Incentive programs and ongoing reduction in costs are driving joint installation of solar PV panels and storage systems in residential households. There is a need for optimal investment decisions to reduce the electricity consumption costs of the households further. In this letter, we first develop analytical expression of storage investment decision and then of solar investment decision for a household which is under net metering billing mechanism with time of use pricing condition. Using real data of a residential household in Austin, TX, USA, we study how the investment decisions would provide benefit for a period of one year. Results show significant profit when using storage devices and solar panels optimally for the system. It is important to note that though our approach can help significantly to take investment decisions, the solution will still be sub-optimal for somebody who needs optimal investment jointly on both storage and solar systems.

Optimal Storage and Solar Capacity of a Residential Household Under Net Metering and Time-of-Use Pricing

—Integration of distributed energy resources (DERs) at a residential level has given way to the development of a self-sufﬁcient energy-resilient system and peer-to-peer (P2P) energy trading at a community level. Several researchers have developed models to implement energy trading in the P2P network with DERs that enables prosumers to avail cost beneﬁts. However, the impact of DERs and P2P energy network on the system’s resilience is not evaluated with a quantiﬁable measure. In this work, we use the percolation threshold as a measure to quantify energy resilience, which can be computed using complex network. We consider a standard IEEE-123 node test feeder integrated with renewable energy sources and partitioned into microgrids. The microgrid with the highest percolation threshold value is considered for P2P energy trading analysis. We use cooperative game theory to model the P2P energy trading so that all prosumers take part rationally. The simulation is carried out using time series data of houses with varying load proﬁles, solar generation, and energy storage capacities. The results show 5.81% of total cost beneﬁt in a year and 10.67% improvement in the resilience of the system due to P2P energy trading.

Evaluation of Energy Resilience and Cost Benefit in Microgrid with Peer-to-Peer Energy Trading

According to the Global Electricity Review 2022, worldwide renewable energy generation has increased by 20\% primarily due to the installation of large renewable energy power plants. However, monitoring renewable energy assets in these large plants remains challenging due to environmental factors that can result in reduced power generation, malfunctioning, and degradation of asset life. Therefore, the detection of surface defects on renewable energy assets is crucial for maintaining the performance and efficiency of these plants. This paper proposes an innovative detection framework to achieve an economical surface monitoring system for renewable energy assets. High-resolution images of the assets are captured regularly and inspected to identify surface or structural damages on solar panels and wind turbine blades. We use the Vision Transformer (ViT), one of the latest attention-based deep learning (DL) models in computer vision, to classify surface defects. The ViT model outperformed other DL models, including MobileNet, VGG16, Xception, EfficientNetB7, and ResNet50, achieving high accuracy scores above 97% for both wind and solar plant assets. From the results, our proposed model demonstrates its potential for monitoring and detecting damages in renewable energy assets for efficient and reliable operation of renewable power plants.

K. Victor Sam Moses Babu

Papers

Energy Management System for Small Scale Hybrid Wind Solar Battery Based Microgrid

Design and implementation of wind turbine emulator using FPGA for stand-alone applications

Optimal Storage and Solar Capacity of a Residential Household Under Net Metering and Time-of-Use Pricing

Evaluation of Energy Resilience and Cost Benefit in Microgrid with Peer-to-Peer Energy Trading

Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model