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Renewable-Based Microgrids: Design, Control and Optimization

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 25273

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Special Issue Editors

Dr. Marcos Tostado-Véliz

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Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain
Interests: energy/battery management; energy communities; electric vehicles; energy storage; energy systems modeling and optimization; renewable energy
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Dr. Paul Arévalo

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Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain
Interests: advancements in energy storage technologies; innovations in electrical protection systems; power quality enhancement strategies; power smoothing techniques in electrical networks; batteries
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Dr. Salah Kamel

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Guest Editor

Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
Interests: power system analysis and optimization; smart grid; renewable energy systems
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Prof. Dr. Ragab A. El-Sehiemy

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Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Interests: power system modeling, computation, and control; renewable energy; microgrids
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Prof. Dr. Tomonobu Senjyu

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Department of Electrical and Electronics Engineering, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan
Interests: high-efficiency energy conversion system; renewable energy in small islands; optimization of power system operation and control
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Special Issue Information

Dear Colleagues,

The electricity sector is evolving toward a decarbonized future. In this context, microgrids are emerging as an unvaluable framework for the integration of renewable generators, storage facilities and, also, for energy supply in remote areas. At present, there are already several types of renewable sources that take advantage of natural resources such as solar, wind, biomass, hydroelectric, etc., where each one contributes different and complementary properties. In some cases, especially in standalone grids, energy storage systems serve as support to increase the reliability of the network. However, microgrid operation, design, and control are still complex tasks because they involve integration of multiple energy vectors and renewable-based generators that are often affected by stochastic, intermittent, and unpredictable behavior of weather parameters. In this context, typical tools that have been used for years in conventional large power systems based on non-renewable sources have to be revisited. This Special Issue aims to cover the most recent advances in the optimization, design, and control of microgrid systems with high penetration of renewable sources and the involved technologies, thus collecting innovative and original works alongside literature reviews and comparative studies. Topics of interest include, but are not limited to, the following:

Optimal design of renewable-based microgrids
Developing and applying recent optimization techniques for renewable-based microgrids
Hybrid power systems based on renewable energies
Energy management in microgrids with renewable energy sources
Energy management tools for renewable-based microgrids
Modeling and control of microgrid
Communication infrastructures for renewable-based microgrids
Hybrid energy storage system for microgrids
Multi-energy microgrids
Weather and demand forecasting methods
Microgrid security assessment
Clean mobility integration in microgrids
Power electronics for renewable-based microgrids
Microgrid clusters and energy markets

Dr. Marcos Tostado-Véliz
Dr. Paul Arévalo
Prof. Dr. Salah Kamel
Prof. Dr. Ragab El Sehiemy
Prof. Dr. Tomonobu Senjyu
Guest Editors

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Keywords

renewable-based microgrids
hybrid power systems
optimization techniques
renewable energy resources
energy management strategies
control of microgrids
communication infrastructures of microgrids
micro hybrid energy storage systems
multi-energy microgrids
weather and demand forecasting
security analysis of microgrids
clean mobility integration in microgrids
home energy management tools
power electronics for microgrids
microgrid energy markets
microgrid clusters

Published Papers (11 papers)

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Editorial

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3 pages, 201 KiB

Open AccessEditorial

Renewable-Based Microgrids: Design, Control and Optimization

by Marcos Tostado-Véliz, Paul Arévalo, Salah Kamel, Ragab A. El-Sehiemy and Tomonobu Senjyu

Appl. Sci. 2023, 13(14), 8235; https://doi.org/10.3390/app13148235 - 15 Jul 2023

Cited by 1 | Viewed by 710

Abstract

To achieve carbon neutrality by 2050, additional measures must be taken, including the extensive incorporation of renewable energy sources (RESs) [...] Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

Research

Jump to: Editorial, Review

19 pages, 14654 KiB

Open AccessArticle

A Novel Model for Enhancing the Resilience of Smart MicroGrids’ Critical Infrastructures with Multi-Criteria Decision Techniques

by Abdulaziz Almaleh, David Tipper, Saad F. Al-Gahtani and Ragab El-Sehiemy

Appl. Sci. 2022, 12(19), 9756; https://doi.org/10.3390/app12199756 - 28 Sep 2022

Cited by 2 | Viewed by 1308

Abstract

Microgrids have the potential to provide reliable electricity to key components of a smart city’s critical infrastructure after a disaster, hence boosting the microgrid power system’s resilience. Policymakers and electrical grid operators are increasingly concerned about the appropriate configuration and location of microgrids to sustain post-disaster critical infrastructure operations in smart cities. In this context, this paper presents a novel method for the microgrid allocation problem that considers several technical and economic infrastructure factors such as critical infrastructure components, geospatial positioning of infrastructures, power requirements, and microgrid cost. In particular, the geographic allocation of a microgrid is presented as an optimization problem to optimize a weighted combination of the relative importance of nodes across all key infrastructures and the associated costs. Furthermore, the simulation results of the formulated optimization problem are compared with a modified version of the heuristic method based on the critical node identification of an interdependent infrastructure for positioning microgrids in terms of the resilience of multiple smart critical infrastructures. Numerical results using infrastructure in the city of Pittsburgh in the USA are given as a practical case study to illustrate the methodology and trade-offs. The proposed method provides an effective method for localizing renewable energy resources based on infrastructural requirements. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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17 pages, 526 KiB

Open AccessArticle

Design and Development of a Management System for Energy Microgrids Using Linear Programming

by Mateo Espitia-Ibarra, Pablo Maya-Duque and Álvaro Jaramillo-Duque

Appl. Sci. 2022, 12(8), 3980; https://doi.org/10.3390/app12083980 - 14 Apr 2022

Cited by 1 | Viewed by 1456

Abstract

Energy is a fundamental tool for human development and this paper presents an approach that seeks to improve its use in Colombian off-grid communities. This approach is based on microgrid concepts where generation, storage, and consumption units interact with each other, and these interactions are presented through a linear programming model. In this approach, specific strategies are implemented according to the Colombian context, where some isolated communities already have diesel-based solutions for energy access, and the type of element that is studied, finding that the proposed optimization model is capable of adequately managing the loads of the microgrid,, thus improving the way in which the generated energy is stored and used through said horizon. Finally, different characteristics of the model are evaluated against multiple indicators and it is concluded that there may be much more specific strategies that improve its operation. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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14 pages, 3283 KiB

Open AccessArticle

Mixed-Integer Linear Programming for Decentralized Multi-Carrier Optimal Energy Management of a Micro-Grid

by Mohammad Faghiri, Shadi Samizadeh, Amirhossein Nikoofard, Mahdi Khosravy and Tomonobu Senjyu

Appl. Sci. 2022, 12(7), 3262; https://doi.org/10.3390/app12073262 - 23 Mar 2022

Cited by 9 | Viewed by 1681

Abstract

Increasing the load demand and penetration of renewable energy sources (RESs) poses real challenges for optimal energy management of distribution networks. Moreover, considering multi-carrier energy systems has increased the efficiency of systems, and provides an opportunity for using the advantages of RESs. In this regard, we adopted a new framework based on the new challenges in the multi-carrier energy micro-grid (MEMG). In the proposed method, a comprehensive MEMG was modeled that benefits from a large assortment of distributed energy resources (DERs), such as micro-turbines, fuel cells, wind turbines, and energy storage. Considering many DERs is necessary, because these resources could cover one another’s disadvantages, which have a great impact on the total cost of the MEMG and decrease the emission impacts of fossil-fuel-based units. Furthermore, waste power plants, inverters, rectifiers, and emission constraints are considered in the proposed method for modeling a practical MEMG. Additionally, for modeling the uncertainty of stochastic parameters, a model based on a multilayer neural network was used in this paper. The results of this study indicate that using a decentralized model, along with stochastic methods for predicting uncertainty, can reduce operational costs in micro-grids and computational complexity compared with optimal centralized programming methods. Finally, the equations and results obtained from the proposed method were evaluated by experiments. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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17 pages, 7280 KiB

Open AccessArticle

Performance Analysis of Modular Multilevel Converter with NPC Sub-Modules in Photovoltaic Grid-Integration

by Suresh Mikkili, Raghu Vamsi Krishna, Praveen Kumar Bonthagorla and Tomonobu Senjyu

Appl. Sci. 2022, 12(3), 1219; https://doi.org/10.3390/app12031219 - 24 Jan 2022

Cited by 2 | Viewed by 2011

Abstract

In this article, a three-phase modular multilevel converter (MMC) with three-level neutral point clamped converter (NPC) sub-modules (SMs) along with the placement of transformers in place of arm inductors is proposed for PV grid integration. Compared to the traditional MMCs, the proposed configuration reduces the voltage and power rating for the switches and the requirement of a high capacitor bank. In order to analyze the performance of the proposed converter arrangement, we have implemented four pulse width modulation schemes, such as Sine PWM with phase-level shifted carrier (SPWMLSC), Sine PWM with a phase-shifted carrier (SPWMPSC), Sine with the third harmonic injected level-shifted carrier (STHILSC), and Sine with the third harmonic injected phase-shifted carrier (STHIPSC). The proposed converter was simulated in the MATLAB/Simulink platform. Under normal and faulty operation, the results were presented with their performance indices of voltage and current harmonic distortion and sub-module capacitor voltage ripples at various modulation indices. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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16 pages, 4126 KiB

Open AccessFeature PaperArticle

Parameter Identification of Photovoltaic Cell Model Using Modified Elephant Herding Optimization-Based Algorithms

by Amer Malki, Abdallah A. Mohamed, Yasser I. Rashwan, Ragab A. El-Sehiemy and Mostafa A. Elhosseini

Appl. Sci. 2021, 11(24), 11929; https://doi.org/10.3390/app112411929 - 15 Dec 2021

Cited by 5 | Viewed by 1479

Abstract

The use of metaheuristics in estimating the exact parameters of solar cell systems contributes greatly to performance improvement. The nonlinear electrical model of the solar cell has some parameters whose values are necessary to design photovoltaic (PV) systems accurately. The metaheuristic algorithms used to determine solar cell parameters have achieved remarkable success; however, most of these algorithms still produce local optimum solutions. In any case, changing to more suitable candidates through elephant herd optimization (EHO) equations is not guaranteed; in addition, instead of making parameter α adaptive throughout the evolution of the EHO, making them adaptive during the evolution of the EHO might be a preferable choice. The EHO technique is used in this work to estimate the optimum values of unknown parameters in single-, double-, and three-diode solar cell models. Models for five, seven, and ten unknown PV cell parameters are presented in these PV cell models. Applications are employed on two types of PV solar cells: the 57 mm diameter RTC Company of France commercial silicon for single- and double-diode models and multi-crystalline PV solar module CS6P-240P for the three-diode model. The total deviations between the actual and estimated result are used in this study as the objective function. The performance measures used in comparisons are the RMSE and relative error. The performance of EHO and the proposed three improved EHO algorithms are evaluated against the well-known optimization algorithms presented in the literature. The experimental results of EHO and the three improved EHO algorithms go as planned and proved to be comparable to recent metaheuristic algorithms. The three EHO-based variants outperform all competitors for the single-diode model, and in particular, the culture-based EHO (CEHO) outperforms others in the double/three-diode model. According the studied cases, the EHO variants have low levels of relative errors and therefore high accuracy compared with other optimization algorithms in the literature. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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28 pages, 8989 KiB

Open AccessFeature PaperArticle

Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt

by Hoda Abd El-Sattar, Salah Kamel, Hamdy Sultan, Marcos Tostado-Véliz, Ali M. Eltamaly and Francisco Jurado

Appl. Sci. 2021, 11(21), 10191; https://doi.org/10.3390/app112110191 - 30 Oct 2021

Cited by 9 | Viewed by 1689

Abstract

This paper presents an analysis and optimization of an isolated hybrid renewable power system to operate in the Alrashda village in the Dakhla Oasis, which is situated in the New Valley Governorate in Egypt. The proposed hybrid system is designed to integrate a biomass system with a photovoltaic (PV), wind turbine (WT) and battery storage system (Bat). Four different cases are proposed and compared for analyzing and optimizing. The first case is a configuration of PV and WT with a biomass system and battery bank. The second case is the integration of PV with a biomass system and battery bank. The third case is WT integrated with biomass and a battery bank, and the fourth case is a conventional PV, WT, and battery bank as the main storage unit. The optimization is designed to reduce component oversizing and ensure the dependable control of power supplies with the objective function of reducing the levelized cost of energy and loss of power supply probability. Four optimization algorithms, namely Heap-based optimizer (HBO), Franklin’s and Coulomb’s algorithm (CFA), the Sooty Tern Optimization Algorithm (STOA), and Grey Wolf Optimizer (GWO) are utilized and compared with each other to ensure that all load demand is met at the lowest energy cost (COE) for the proposed hybrid system. The obtained results revealed that the HBO has achieved the best optimal solution for the suggested hybrid system for case one and two, with the minimum COE 0.121171 and 0.1311804 $/kWh, respectively, and with net present cost (

NPC

) of $3,559,143 and $3,853,160, respectively. Conversely, STOA has achieved the best optimal solution for case three and four, with a COE of 0.105673 and 0.332497 $/kWh, and an NPC of $3,103,938 and $9,766,441, respectively. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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24 pages, 5693 KiB

Open AccessArticle

Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks

by Zhiwen Xu, Changsong Chen, Mingyang Dong, Jingyue Zhang, Dongtong Han and Haowen Chen

Appl. Sci. 2021, 11(19), 8916; https://doi.org/10.3390/app11198916 - 24 Sep 2021

Cited by 7 | Viewed by 1572

Abstract

By constructing a DC multi-microgrid system (MMGS) including renewable energy sources (RESs) and electric vehicles (EVs) to coordinate with the distribution network, the utilization rate of RESs can be effectively improved and carbon emissions can be reduced. To improve the economy of MMGS and reduce the network loss of the distribution network, a cooperative double-loop optimization strategy is proposed. The inner-loop economic dispatching reduces the daily operating cost of MMGS by optimizing the active power output of RESs, EVs, and DC/AC converters in MMGS. The outer-loop reactive power optimization reduces the network loss of the distribution network by optimizing the reactive power of the bidirectional DC/AC converters. The double-loop, which synergistically optimizes the economic cost and carbon emissions of MMGS, not only improves the economy of MMGS and operational effectiveness of the distribution network but also realizes the low-carbon emissions. The Across-time-and-space energy transmission (ATSET) of the EVs is considered, whose impact on economic dispatching is analyzed. Particle Swarm Optimization (PSO) is applied to iterative solutions. Finally, the rationality and feasibility of the cooperative multi-objective optimization model are proved by a revised IEEE 33-node system. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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28 pages, 14317 KiB

Open AccessArticle

Optimal Incorporation of Photovoltaic Energy and Battery Energy Storage Systems in Distribution Networks Considering Uncertainties of Demand and Generation

by Hussein Abdel-Mawgoud, Salah Kamel, Marcos Tostado-Véliz, Ehab E. Elattar and Mahmoud M. Hussein

Appl. Sci. 2021, 11(17), 8231; https://doi.org/10.3390/app11178231 - 05 Sep 2021

Cited by 5 | Viewed by 1829

Abstract

In this paper, the Archimedes optimization algorithm (AOA) is applied as a recent metaheuristic optimization algorithm to reduce energy losses and capture the size of incorporating a battery energy storage system (BESS) and photovoltaics (PV) within a distribution system. AOA is designed with revelation from Archimedes’ principle, an impressive physics law. AOA mimics the attitude of buoyant force applied upward on an object, partially or entirely dipped in liquid, which is relative to the weight of the dislodged liquid. Furthermore, the developed algorithm is evolved for sizing several PVs and BESSs considering the changing demand over time and the probability generation. The studied IEEE 69-bus distribution network system has different types of the load, such as residential, industrial, and commercial loads. The simulation results indicate the robustness of the proposed algorithm for computing the best size of multiple PVs and BESSs with a significant reduction in the power system losses. Additionally, the AOA algorithm has an efficient balancing between the exploration and exploitation phases to avoid the local solutions and go to the best global solutions, compared with other studied algorithms. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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19 pages, 4915 KiB

Open AccessArticle

A Comparison between Particle Swarm and Grey Wolf Optimization Algorithms for Improving the Battery Autonomy in a Photovoltaic System

by Habib Kraiem, Flah Aymen, Lobna Yahya, Alicia Triviño, Mosleh Alharthi and Sherif S. M. Ghoneim

Appl. Sci. 2021, 11(16), 7732; https://doi.org/10.3390/app11167732 - 22 Aug 2021

Cited by 21 | Viewed by 4942

Abstract

This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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Review

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43 pages, 35434 KiB

Open AccessReview

An Insight into the Integration of Distributed Energy Resources and Energy Storage Systems with Smart Distribution Networks Using Demand-Side Management

by Subhasis Panda, Sarthak Mohanty, Pravat Kumar Rout, Binod Kumar Sahu, Shubhranshu Mohan Parida, Hossam Kotb, Aymen Flah, Marcos Tostado-Véliz, Bdereddin Abdul Samad and Mokhtar Shouran

Appl. Sci. 2022, 12(17), 8914; https://doi.org/10.3390/app12178914 - 05 Sep 2022

Cited by 13 | Viewed by 3857

Abstract

Demand-side management (DSM) is a significant component of the smart grid. DSM without sufficient generation capabilities cannot be realized; taking that concern into account, the integration of distributed energy resources (solar, wind, waste-to-energy, EV, or storage systems) has brought effective transformation and challenges to the smart grid. In this review article, it is noted that to overcome these issues, it is crucial to analyze demand-side management from the generation point of view in considering various operational constraints and objectives and identifying multiple factors that affect better planning, scheduling, and management. In this paper, gaps in the research and possible prospects are discussed briefly to provide a proper insight into the current implementation of DSM using distributed energy resources and storage. With the expectation of an increase in the adoption of various types of distributed generation, it is estimated that DSM operations can offer a valuable opportunity for customers and utility aggregators to become active participants in the scheduling, dispatch, and market-oriented trading of energy. This review of DSM will help develop better energy management strategies and reduce system uncertainties, variations, and constraints. Full article

(This article belongs to the Special Issue Renewable-Based Microgrids: Design, Control and Optimization)

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