Sustainability

Research

24 pages, 6345 KiB

Open AccessArticle

Economic Viability of NaS Batteries for Optimal Microgrid Operation and Hosting Capacity Enhancement under Uncertain Conditions

by Mohammed M. Alhaider, Ziad M. Ali, Mostafa H. Mostafa and Shady H. E. Abdel Aleem

Sustainability 2023, 15(20), 15133; https://doi.org/10.3390/su152015133 - 22 Oct 2023

Cited by 1 | Viewed by 1641

Abstract

Recent developments have increased the availability and prevalence of renewable energy sources (RESs) in grid-connected microgrids (MGs). As a result, the operation of an MG with numerous RESs has received considerable attention during the past few years. However, the variability and unpredictability of [...] Read more.

Recent developments have increased the availability and prevalence of renewable energy sources (RESs) in grid-connected microgrids (MGs). As a result, the operation of an MG with numerous RESs has received considerable attention during the past few years. However, the variability and unpredictability of RESs have a substantial adverse effect on the accuracy of MG energy management. In order to obtain accurate outcomes, the analysis of the MG operation must consider the uncertainty parameters of RESs, market pricing, and electrical loads. As a result, our study has focused on load demand variations, intermittent RESs, and market price volatility. In this regard, energy storage is the most crucial facility to strengthen the MG’s reliability, especially in light of the rising generation of RESs. This work provides a two-stage optimization method for creating grid-connected MG operations. The optimal size and location of the energy storage are first provided to support the hosting capacity (HC) and the self-consumption rate (SCR) of the RESs. Second, an optimal constrained operating strategy for the grid-connected MG is proposed to minimize the MG operating cost while taking into account the optimal size and location of the energy storage that was formerly determined. The charge–discharge balance is the primary criterion in determining the most effective operating plan, which also considers the RES and MG limitations on operation. The well-known Harris hawks optimizer (HHO) is used to solve the optimization problem. The results showed that the proper positioning of the battery energy storage enhances the MG’s performance, supports the RESs’ SCR (reached 100% throughout the day), and increases the HC of RESs (rising from 8.863 MW to 10.213 MW). Additionally, when a battery energy storage system is connected to the MG, the operating costs are significantly reduced, with a savings percentage rate of 23.8%. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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17 pages, 3384 KiB

Open AccessArticle

A Distribution Network Planning Method Considering the Distributed Energy Resource Flexibility of Virtual Power Plants

by Zhichun Yang, Gang Han, Fan Yang, Yu Shen, Yu Liu, Huaidong Min, Zhiqiang Zhou, Bin Zhou, Wei Hu and Yang Lei

Sustainability 2023, 15(19), 14399; https://doi.org/10.3390/su151914399 - 30 Sep 2023

Cited by 2 | Viewed by 1619

Abstract

To solve the overload problem caused by the high proportion of renewable energy into the power system, it is particularly important to find a suitable distribution network planning scheme. Existing studies have effectively reduced the planning cost by incorporating virtual power plants into [...] Read more.

To solve the overload problem caused by the high proportion of renewable energy into the power system, it is particularly important to find a suitable distribution network planning scheme. Existing studies have effectively reduced the planning cost by incorporating virtual power plants into the distribution planning process, but there is no quantitative analysis of the flexible resources inside the virtual power plant. At the same time, the traditional planning process does not pay much attention to the acquisition of photovoltaic and load data. Therefore, in this paper, we propose a distribution network planning method considering the flexibility of distributed energy resources in virtual power plants. Firstly, taking the distribution network planning including the virtual power plant as the research object, the flexibility of the distributed energy resource of the virtual power plant was quantified. Then, in order to achieve the goal of minimizing the operating cost of system planning, a distribution network planning model considering the flexibility of distributed energy resources in the virtual power plant is established. In this model, the impact of virtual power plants flexibility on the distribution network planning process is mainly considered. Secondly, this paper uses the improved k-means clustering algorithm to obtain the typical data of PV and load. The algorithm effectively overcomes the impact of PV and load output fluctuations on the planning process. Finally, the simulation results show that the proposed planning model can effectively reduce the operation cost of system planning by using distributed energy storage system and distributed energy resource flexibility. At the same time, the PV absorption rate of the PV power station inside the distribution network is improved. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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28 pages, 2644 KiB

Open AccessArticle

EnergyAuction: IoT-Blockchain Architecture for Local Peer-to-Peer Energy Trading in a Microgrid

by Felipe Condon, Patricia Franco, José M. Martínez, Ali M. Eltamaly, Young-Chon Kim and Mohamed A. Ahmed

Sustainability 2023, 15(17), 13203; https://doi.org/10.3390/su151713203 - 2 Sep 2023

Cited by 18 | Viewed by 3771

Abstract

The widespread adoption of distributed energy resources (DERs) and the progress made in internet of things (IoT) and cloud computing technologies have enabled and facilitated the development of various smart grid applications and services. This study aims to develop and implement a peer-to-peer [...] Read more.

The widespread adoption of distributed energy resources (DERs) and the progress made in internet of things (IoT) and cloud computing technologies have enabled and facilitated the development of various smart grid applications and services. This study aims to develop and implement a peer-to-peer (P2P) energy trading platform that allows local energy trading between consumers and prosumers within a microgrid which combines IoT and blockchain technologies. The proposed platform comprises an IoT-cloud home energy management system (HEMS) responsible for gathering and storing energy consumption data and incorporates a blockchain framework that ensures secure and transparent energy trading. The proposed IoT–blockchain architecture utilizes a Chainlink oracle network and a private Ethereum blockchain. Through the use of smart contracts, consumers and prosumers can participate in an open auction to trade energy, while the settlement process involves acquiring external energy data from an API through the oracle network. The performance of the platform is evaluated through a testbed scenario using real-world energy data from a real house in Valparaiso, Chile, while storing those measurements in AWS cloud, validating the feasibility of the proposed architecture in enabling local energy trading. This work contributes to the development of energy management systems by providing a real-world implementation of an IoT–blockchain architecture for local energy trading. The integration of these technologies will allow for a more efficient and secure energy trading system that can benefit prosumers, consumers, and utilities. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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36 pages, 7074 KiB

Open AccessArticle

An Evaluation of ANN Algorithm Performance for MPPT Energy Harvesting in Solar PV Systems

by Md Tahmid Hussain, Adil Sarwar, Mohd Tariq, Shabana Urooj, Amal BaQais and Md. Alamgir Hossain

Sustainability 2023, 15(14), 11144; https://doi.org/10.3390/su151411144 - 17 Jul 2023

Cited by 27 | Viewed by 4965

Abstract

In this paper, the Levenberg–Marquardt (LM), Bayesian regularization (BR), resilient backpropagation (RP), gradient descent momentum (GDM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and scaled conjugate gradient (SCG) algorithms constructed using artificial neural networks (ANN) are applied to the problem of MPPT energy harvesting in solar photovoltaic (PV) [...] Read more.

In this paper, the Levenberg–Marquardt (LM), Bayesian regularization (BR), resilient backpropagation (RP), gradient descent momentum (GDM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and scaled conjugate gradient (SCG) algorithms constructed using artificial neural networks (ANN) are applied to the problem of MPPT energy harvesting in solar photovoltaic (PV) systems for the purpose of creating a comparative evaluation of the performance of the six distinct algorithms. The goal of this analysis is to determine which of the six algorithms has the best overall performance. In the study, the performance of managing the training dataset is compared across the algorithms. The maximum power point tracking energy harvesting system is created using the environment of MATLAB or Simulink, and the produced model is examined using the artificial neural network toolkit. A total of 1000 datasets of solar irradiance, temperature, and voltage were used to train the suggested model. The data are split into three categories: training, validation, and testing. Eighty percent of the total data is used for training the model, and the remaining twenty percent is divided equally for testing and validation. According to the results, the regression values of LM, RP, BR, and BFGS are 1, whereas the regression values for SCG and GDM are less than 1. The gradient values for LM, RP, BFGS, SCG, BR, and GDM are 7.983 × 10⁻⁶, 0.033415, 1.0211 × 10⁻⁷, 0.14161, 0.00010493, and 11.485, respectively. Similarly, the performance values for these algorithms are 2.0816 × 10⁻¹⁰, 2.8668 × 10⁻⁶, 9.98 × 10⁻¹⁷, 0.052985, 1.583 × 10⁻⁷, and 0.15378. Overall, the results demonstrate that the LM and BFGS algorithms exhibit superior performance in terms of gradient and overall performance. The RP and BR algorithms also perform well across various metrics, while the SCG and GDM algorithms show comparatively less effectiveness in addressing the proposed problem. These findings provide valuable insights into the relative performance of the six evaluated algorithms for MPPT energy harvesting in solar PV systems. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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20 pages, 3557 KiB

Open AccessArticle

Parameters Identification of Photovoltaic Cell and Module Models Using Modified Social Group Optimization Algorithm

by Habib Kraiem, Ezzeddine Touti, Abdulaziz Alanazi, Ahmed M. Agwa, Tarek I. Alanazi, Mohamed Jamli and Lassaad Sbita

Sustainability 2023, 15(13), 10510; https://doi.org/10.3390/su151310510 - 4 Jul 2023

Cited by 8 | Viewed by 1730

Abstract

Photovoltaic systems have become more attractive alternatives to be integrated into electrical power systems. Therefore, PV cells have gained immense interest in studies related to their operation. A photovoltaic module’s performance can be optimized by identifying the parameters of a photovoltaic cell to [...] Read more.

Photovoltaic systems have become more attractive alternatives to be integrated into electrical power systems. Therefore, PV cells have gained immense interest in studies related to their operation. A photovoltaic module’s performance can be optimized by identifying the parameters of a photovoltaic cell to understand its behavior and simulate its characteristics from a given mathematical model. This work aims to extract and identify the parameters of photovoltaic cells using a novel metaheuristic algorithm named Modified Social Group Optimization (MSGO). First, a comparative study was carried out based on various technologies and models of photovoltaic modules. Then, the proposed MSGO algorithm was tested on a monocrystalline type of panel with its single-diode and double-diode models. Then, it was tested on an amorphous type of photovoltaic cell (hydrogenated amorphous silicon (a-Si: H)). Finally, an experimental validation was carried out to test the proposed MSGO algorithm and identify the parameters of the polycrystalline type of panel. All obtained results were compared to previous research findings. The present study showed that the MSGO is highly competitive and demonstrates better efficiency in parameter identification compared to other optimization algorithms. The Individual Absolute Error (IAE) obtained by the MSGO is better than the other errors for most measurement values in the case of single- and double-diode models. Relatedly, the average fitness function obtained by the MSGO algorithm has the fastest convergence rate. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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23 pages, 4835 KiB

Open AccessArticle

A Novel Evolving Framework for Energy Management in Combined Heat and Electricity Systems with Demand Response Programs

by Ting Chen, Lei Gan, Sheeraz Iqbal, Marek Jasiński, Mohammed A. El-Meligy, Mohamed Sharaf and Samia G. Ali

Sustainability 2023, 15(13), 10481; https://doi.org/10.3390/su151310481 - 3 Jul 2023

Cited by 2 | Viewed by 1296

Abstract

In recent years, demand response programs (DRPs) have become an effective method of encouraging users to participate in energy system operations. The problem of optimal energy flow (OEF) is a complex challenge in multiple power systems. Accordingly, this study aims to propose a [...] Read more.

In recent years, demand response programs (DRPs) have become an effective method of encouraging users to participate in energy system operations. The problem of optimal energy flow (OEF) is a complex challenge in multiple power systems. Accordingly, this study aims to propose a novel evolving framework for optimal OEF operation of an electricity, heat, and gas integrating system, taking into account flexible heat and electricity demands. To this end, a switching idea between input energy carriers has been introduced to combine the traditional DRP with demand-side energy supply management. Switching between the feeding energy carriers could change how power is supplied to the end users and thus would affect the total cost of the grid. Operators of integrated systems minimize the operational costs associated with supplying flexible power to users in this study. Considering the high nonlinearity of the problem, a novel optimization algorithm is presented for solving the complex OEF based on the improved teaching–learning-based optimization algorithm (ITLBOA). According to the outcomes, flexible DRP reduces operational prices and smooths power demand curves for power and heating networks. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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20 pages, 6291 KiB

Open AccessArticle

A New Five-Port Energy Router Structure and Common Bus Voltage Stabilization Control Strategy

by Xianyang Cui, Yulong Liu, Ding Yuan, Tao Jin and Mohamed A. Mohamed

Sustainability 2023, 15(4), 2958; https://doi.org/10.3390/su15042958 - 6 Feb 2023

Cited by 1 | Viewed by 2112

Abstract

Multi-port energy routers are a core device that integrates distributed energy sources and enables energy-to-energy interconnections. For the energy routing system, the construction of its topology, the establishment of internal model switching and the control of common bus voltage stability are the key [...] Read more.

Multi-port energy routers are a core device that integrates distributed energy sources and enables energy-to-energy interconnections. For the energy routing system, the construction of its topology, the establishment of internal model switching and the control of common bus voltage stability are the key elements of the research. In this paper, a five-port energy router structure is proposed, including a PV port, an energy storage port, a grid-connected port, a DC load port, and an AC load port. Among them, the energy storage port and the grid-connected port involve bidirectional energy flow, which are the core ports of control. For the system state, a model switching strategy is proposed based on the topology and the port energy flow direction. When the external conditions change, the system can be stabilized by means of a quick response from the energy storage port. When the energy storage is saturated, the state is switched, and the grid-connected port works to achieve system stability. The rapid stabilization of the bus voltage and the free flow of energy are achieved by combining the fast response of the model predictive control with the properties of multiple model switching. Finally, the feasibility of this energy router topology and control strategy is verified by building simulations in MATLAB. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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23 pages, 3337 KiB

Open AccessArticle

A Multi-Objective Planning Strategy for Electric Vehicle Charging Stations towards Low Carbon-Oriented Modern Power Systems

by Hassan Yousif Ahmed, Ziad M. Ali, Mohamed M. Refaat and Shady H. E. Abdel Aleem

Sustainability 2023, 15(3), 2819; https://doi.org/10.3390/su15032819 - 3 Feb 2023

Cited by 18 | Viewed by 3332

Abstract

This paper proposes a multi-objective planning framework for electric vehicle (EV) charging stations in emerging power networks that move towards green transportation electrification. Four cases are investigated to study the impacts of EV integration on environmental and economic requirements. In order to facilitate [...] Read more.

This paper proposes a multi-objective planning framework for electric vehicle (EV) charging stations in emerging power networks that move towards green transportation electrification. Four cases are investigated to study the impacts of EV integration on environmental and economic requirements. In order to facilitate the installation of EV charging stations, the proposed model is formulated to combine the planning models of renewable energy systems, energy storage systems (ESSs), thyristor-controlled series compensators, and transmission lines into the EV-based planning problem. The first objective function aims to maximize EVs’ penetration by increasing the networks’ capacity to supply charging stations throughout the day, whereas the second objective, on the other hand, emphasizes lowering the carbon dioxide emissions from fossil fuel-based generation units in order to benefit the environment. The third objective is to meet the financial requirements by lowering the initial investment and operating costs of the installed devices. The proposed model is written as a multi-objective optimization problem that is solved using the multi-objective version of the Gazelle optimization algorithm (MGOA). The efficiency of the MGOA was tested by solving a set of four benchmark test functions and the proposed problem. The obtained results demonstrated the MGOA’s superiority in solving multi-objective optimization problems when compared to some well-known optimization algorithms in terms of robustness and solution quality. The MGOA’s robustness was between 20% and 30% and outperformed other algorithms by 5%. The MGO was successful in outperforming the other algorithms in providing a better solution. The Egyptian West Delta Network simulations revealed a 250 MWh increase in the energy supplied to EVs when energy storage was not used. However, storage systems were necessary for shifting EV charging periods away from high solar radiation scenarios. The use of ESS increased greenhouse gas emissions. When ESS was installed with a capacity of 1116.4 MWh, the carbon emissions increased by approximately 208.29 million metric tons. ESS’s role in improving the EV’s hosting capacity grows as more renewables are added to the network. ESS’s role in improving the EV’s hosting capacity rises as more renewables are added to the network. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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18 pages, 4144 KiB

Open AccessArticle

Robust Power System State Estimation Method Based on Generalized M-Estimation of Optimized Parameters Based on Sampling

by Yu Shi, Yueting Hou, Yue Yu, Zhaoyang Jin and Mohamed A. Mohamed

Sustainability 2023, 15(3), 2550; https://doi.org/10.3390/su15032550 - 31 Jan 2023

Cited by 10 | Viewed by 2733

Abstract

Robustness is an important performance index of power system state estimation, which is defined as the estimator’s capability to resist the interference. However, improving the robustness of state estimation often reduces the estimation accuracy. To solve this problem, this paper proposes a power [...] Read more.

Robustness is an important performance index of power system state estimation, which is defined as the estimator’s capability to resist the interference. However, improving the robustness of state estimation often reduces the estimation accuracy. To solve this problem, this paper proposes a power system state estimation method for generalized M-estimation of optimized parameters based on sampling. Compared with the traditional robust state estimator, the generalized M-estimator based on projection statistics improves the robustness of state estimation, and the proposed optimized parameter determination method improves the overall accuracy of state estimation by appropriately adjusting its robustness. Considering different degrees of non-Gaussian distributed measurement noises and bad data, the estimation accuracy the proposed method is demonstrated to be up to 23% higher than the traditional generalized M-estimator through MATLAB simulations in IEEE 14, 118 bus test systems, and Polish 2736 bus system. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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24 pages, 6023 KiB

Open AccessArticle

A Novel Control Method for Active Power Sharing in Renewable-Energy-Based Micro Distribution Networks

by Wael J. Abdallah, Khurram Hashmi, Muhammad Talib Faiz, Aymen Flah, Sittiporn Channumsin, Mohamed A. Mohamed and Denis Anatolievich Ustinov

Sustainability 2023, 15(2), 1579; https://doi.org/10.3390/su15021579 - 13 Jan 2023

Cited by 15 | Viewed by 3022

Abstract

The microgrid is an emerging trend in modern power systems. Microgrids consist of controllable power sources, storage, and loads. An elaborate control infrastructure is established to regulate and synchronize the interaction of these components. The control scheme is divided into a hierarchy of [...] Read more.

The microgrid is an emerging trend in modern power systems. Microgrids consist of controllable power sources, storage, and loads. An elaborate control infrastructure is established to regulate and synchronize the interaction of these components. The control scheme is divided into a hierarchy of several layers, where each layer is composed of multi-agents performing their dedicated functions and arriving at a consensus of corrective values. Lateral and horizontal interaction of such multi-agents forms a comprehensive hierarchical control structure that regulates the microgrid operation to achieve a compendium of objectives, including power sharing, voltage, and frequency regulation. The success of a multi-agent-based control scheme is dependent on the health of the communication media that is used to relay measurements and control signals. Delays in the transmission of control signals result in an overall deterioration of the control performance and non-convergence. This paper proposes novel multi-agent moving average estimators to mitigate the effect of latent communication links and establishes a hierarchical control scheme incorporating these average estimators to accurately arrive at system values during communication delays. Mathematical models are established for the complete microgrid system to test the stability of the proposed method against conventional consensus-based methods. Case-wise simulation studies and lab-scale experimental verification further establish the efficacy and superiority of the proposed control scheme in comparison with other conventionally used control methods. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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17 pages, 6162 KiB

Open AccessArticle

A Vector Inspection Technique for Active Distribution Networks Based on Improved Back-to-Back Converters

by Weiming Zhang, Hui Fan, Jiangbo Ren, Xianzhi Wang, Tiecheng Li and Yibo Wang

Sustainability 2023, 15(1), 750; https://doi.org/10.3390/su15010750 - 31 Dec 2022

Cited by 4 | Viewed by 1871

Abstract

In this paper, an improved back-to-back converter is proposed, and the converter is used as a test power source for vector inspection of relay protection in an active distribution network, which effectively solves the problem that the output voltage and current of the [...] Read more.

In this paper, an improved back-to-back converter is proposed, and the converter is used as a test power source for vector inspection of relay protection in an active distribution network, which effectively solves the problem that the output voltage and current of the test power source cannot be continuously and stably adjusted. Firstly, a three-phase back-to-back cascade converter is established to analyze the impedance characteristics of its DC terminal. Then a feedforward voltage is added to the inverter to improve the input impedance characteristics of the inverter. Secondly, the system stability and parameter stability of the improved back-to-back converter are analyzed. Finally, the improved converter is used as the test power source for vector inspection of relay protection in the active distribution network. The simulation results show that the stability of the improved back-to-back converter system is greatly improved. The experiment shows that the vector check technology based on an improved back-to-back converter can effectively check the vector of relay protection in an active distribution network and find various installation problems. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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11 pages, 619 KiB

Open AccessArticle

GIS Fault Prediction Approach Based on IPSO-LSSVM Algorithm

by Hengyang Zhao, Guobao Zhang and Xi Yang

Sustainability 2023, 15(1), 235; https://doi.org/10.3390/su15010235 - 23 Dec 2022

Cited by 1 | Viewed by 1724

Abstract

With the improvement of industrialization, the importance of equipment failure prediction is increasing day by day. Accurate failure prediction of gas-insulated switchgear (GIS) in advance can reduce the economic loss caused by the failure of the power system to operate normally. Therefore, a [...] Read more.

With the improvement of industrialization, the importance of equipment failure prediction is increasing day by day. Accurate failure prediction of gas-insulated switchgear (GIS) in advance can reduce the economic loss caused by the failure of the power system to operate normally. Therefore, a GIS fault prediction approach based on Improved Particle Swarm Optimization Algorithm (IPSO)-least squares support vector machine (LSSVM) is proposed in this paper. Firstly, the future gas conditions of the GIS to determine the characteristic data of SF6 decomposition gas are analyzed; Secondly, a GIS fault prediction model based on LSSVM is established, and the IPSO algorithm is used to normalize the parameters LSSVM. The parameters of c and radial basis kernel function

σ^{2}

are optimized, which can meet the needs of later search accuracy while ensuring the global search capability in the early stage. Finally, the effectiveness of the proposed method is verified by the fault data of gas-insulated switch. Simulation results shows that, compared with the prediction methods based on IGA-LSSVM and PSO-LSSVM, the prediction accuracy rate of the proposed method reached 92.1%, which has the smallest prediction absolute error, higher accuracy and stronger prediction ability. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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21 pages, 3902 KiB

Open AccessArticle

Tourism Service Scheduling in Smart City Based on Hybrid Genetic Algorithm Simulated Annealing Algorithm

by Pannee Suanpang, Pitchaya Jamjuntr, Kittisak Jermsittiparsert and Phuripoj Kaewyong

Sustainability 2022, 14(23), 16293; https://doi.org/10.3390/su142316293 - 6 Dec 2022

Cited by 22 | Viewed by 3212

Abstract

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in [...] Read more.

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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26 pages, 12867 KiB

Open AccessArticle

Development of Operation Strategy for Battery Energy Storage System into Hybrid AC Microgrids

by Felipe Ramos, Aline Pinheiro, Rafaela Nascimento, Washington de Araujo Silva Junior, Mohamed A. Mohamed, Andres Annuk and Manoel H. N. Marinho

Sustainability 2022, 14(21), 13765; https://doi.org/10.3390/su142113765 - 24 Oct 2022

Cited by 29 | Viewed by 5557

Abstract

With continuous technological advances, increasing competitiveness of renewable sources, and concerns about the environmental impacts of the energy matrix, the use of hybrid microgrids has been promoted. These generation microsystems, historically composed basically of fossil fuels as the main source, have experienced an [...] Read more.

With continuous technological advances, increasing competitiveness of renewable sources, and concerns about the environmental impacts of the energy matrix, the use of hybrid microgrids has been promoted. These generation microsystems, historically composed basically of fossil fuels as the main source, have experienced an energy revolution with the introduction of renewable and intermittent sources. However, with the introduction of these uncontrollable sources, the technical challenges to system stability, low diesel consumption, and security of supply increase. The main objective of this work is to develop an operation and control strategy for energy storage systems intended for application in hybrid microgrids with AC coupling. Throughout the work, a bibliographic review of the existing applications is carried out, as well as a proposal for modification and combination to create a new control strategy. This strategy, based on optimized indirect control of diesel generators, seeks to increase generation efficiency, reduce working time, and increase the introduction of renewable sources in the system. As a result, there is a significant reduction in diesel consumption, a decrease in the power output variance of the diesel generation system, and an increase in the average operating power, which ensures effective control of hybrid plants. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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15 pages, 3593 KiB

Open AccessArticle

An Efficient MPPT Technique-Based Single-Stage Incremental Conductance for Integrated PV Systems Considering Flyback Central-Type PV Inverter

by Ahmed Ismail M. Ali, Zuhair Muhammed Alaas, Mahmoud A. Sayed, Abdulaziz Almalaq, Anouar Farah and Mohamed A. Mohamed

Sustainability 2022, 14(19), 12105; https://doi.org/10.3390/su141912105 - 25 Sep 2022

Cited by 21 | Viewed by 3051

Abstract

Central-type photovoltaic (PV) inverters are used in most large-scale standalone and grid-tied PV applications due to the inverter’s high efficiency and low-cost per kW generated. The perturbation and observation (P&O) and incremental conductance (IncCond) have become the most common techniques for maximum power [...] Read more.

Central-type photovoltaic (PV) inverters are used in most large-scale standalone and grid-tied PV applications due to the inverter’s high efficiency and low-cost per kW generated. The perturbation and observation (P&O) and incremental conductance (IncCond) have become the most common techniques for maximum power point tracking (MPPT) strategies of PV/wind generation systems. Typically, the MPPT technique is applied in a two-stage operation; the first stage tracks the MPP and boosts the PV voltage to a certain level that complies with grid voltage, whereas the second stage represents the inversion stage that ties the PV system to the grid. Therefore, these common configurations increase the system size and cost as well as reduce its overall footprint. As a result, this paper applies two IncCond MPPT techniques on a proposed single-stage three-phase differential-flyback inverter (DFI). In addition, the three-phase DFI is analyzed for grid current negative-sequence harmonic compensation (NSHC). The proposed system efficiently provides a MPPT of the PV system and voltage boosting property of the DC-AC inverter in a single-stage operation. Moreover, the MPPT technique has been applied through the DFI using the conventional and modified IncCond tracking strategies. Furthermore, the system is validated for the grid-tied operation with the negative-sequence harmonic compensation strategy using computer-based simulation and is tested under uniform, step-change, as well as fast-changing irradiance profiles. The average efficiencies of the proposed system, considering the conventional and modified IncCond MPPT techniques, are 94.16% and 96.4% with tracking responses of 0.062 and 0.035 s and maximum overshoot of 46.15% and 15.38%, respectively. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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25 pages, 5163 KiB

Open AccessArticle

Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization

by Suchitra Dayalan, Sheikh Suhaib Gul, Rajarajeswari Rathinam, George Fernandez Savari, Shady H. E. Abdel Aleem, Mohamed A. Mohamed and Ziad M. Ali

Sustainability 2022, 14(17), 10985; https://doi.org/10.3390/su141710985 - 2 Sep 2022

Cited by 26 | Viewed by 2629

Abstract

Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce [...] Read more.

Demand response programs can effectively handle the smart grid’s increasing energy demand and power imbalances. In this regard, price-based DR (PBDR) and incentive-based DR (IBDR) are two broad categories of demand response in which incentives for consumers are provided in IBDR to reduce their demand. This work aims to implement the IBDR strategy from the perspective of the service provider and consumers. The relationship between the different entities concerned is modelled. The incentives offered by the service provider (SP) to its consumers and the consumers’ reduced demand are optimized using Stackelberg–particle swarm optimization (SPSO) as a bi-level problem. Furthermore, the system with a grid operator, the industrial consumers of the grid operator, the service provider and its consumers are analyzed from the service provider’s viewpoint as a tri-level problem. The benefits offered by the service provider to its customers, the incentives provided by the grid operator to its industrial customers, the reduction of customer demand, and the average cost procured by the grid operator are optimized using SPSO and compared with the Stackelberg-distributed algorithm. The problem was analyzed for an hour and 24 h in the MATLAB environment. Besides this, sensitivity analysis and payment analysis were carried out in order to delve into the impact of the demand response program concerning the change in customer parameters. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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28 pages, 10034 KiB

Open AccessArticle

A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System

by Ali M. Jasim, Basil H. Jasim, Habib Kraiem and Aymen Flah

Sustainability 2022, 14(16), 10158; https://doi.org/10.3390/su141610158 - 16 Aug 2022

Cited by 24 | Viewed by 2992

Abstract

In recent years, microgrids (MGs) have been developed to improve the overall management of the power network. This paper examines how a smart MG’s generation and demand sides are managed to improve the MG’s performance in order to minimize operating costs and emissions. [...] Read more.

In recent years, microgrids (MGs) have been developed to improve the overall management of the power network. This paper examines how a smart MG’s generation and demand sides are managed to improve the MG’s performance in order to minimize operating costs and emissions. A binary orientation search algorithm (BOSA)-based optimal demand side management (DSM) program using the load-shifting technique has been proposed, resulting in significant electricity cost savings. The proposed optimal DSM-based energy management strategy considers the MG’s economic and environmental indices to be the key objective functions. Single-objective particle swarm optimization (SOPSO) and multi-objective particle swarm optimization (MOPSO) were adopted in order to optimize MG performance in the presence of renewable energy resources (RERs) with a randomized natural behavior. A PSO algorithm was adopted due to the nonlinearity and complexity of the proposed problem. In addition, fuzzy-based mechanisms and a nonlinear sorting system were used to discover the optimal compromise given the collection of Pareto-front space solutions. To test the proposed method in a more realistic setting, the stochastic behavior of renewable units was also factored in. The simulation findings indicate that the proposed BOSA algorithm-based DSM had the lowest peak demand (88.4 kWh) compared to unscheduled demand (105 kWh); additionally, the operating costs were reduced by 23%, from 660 USD to 508 USD, and the emissions decreased from 840 kg to 725 kg, saving 13.7%. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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15 pages, 3796 KiB

Open AccessArticle

An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination

by Wan Chen, Baolian Liu, Muhammad Shahzad Nazir, Ahmed N. Abdalla, Mohamed A. Mohamed, Zujun Ding, Muhammad Shoaib Bhutta and Mehr Gul

Sustainability 2022, 14(14), 8510; https://doi.org/10.3390/su14148510 - 12 Jul 2022

Cited by 18 | Viewed by 2408

Abstract

To reduce the allocation of energy storage capacity in wind farms and improve economic benefits, this study is focused on the virtual synchronous generator (synchronverter) technology. A system accompanied by wind power, energy storage, a synchronous generator and load is presented in detail. [...] Read more.

To reduce the allocation of energy storage capacity in wind farms and improve economic benefits, this study is focused on the virtual synchronous generator (synchronverter) technology. A system accompanied by wind power, energy storage, a synchronous generator and load is presented in detail. A brief description of the virtual synchronous generator control strategy is given. The capacity allocation is based on different optimization goals and the optimal energy storage capacity configuration of the coordinated frequency modulation (FM) control strategy. The detail of the dual-loop control strategy is carried out by establishing the grid-connected transfer function model of the synchronverter energy storage and a theoretical model of life cycle cost is established. The optimal control strategy of coordinated FM for wind storage is implemented using MATLAB software. The simulation showed that the proposed strategy provided the energy storage capacity at high wind speed, which is configured to be 5.9% of the installed capacity of the wind turbine, marking a reduction of 26% compared with the 8% capacity required for independent support. In addition, the proposed method has improved the energy storage capacity configuration of the coordinated FM control strategy. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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14 pages, 1525 KiB

Open AccessArticle

Management and Policy Modeling of the Market Using Artificial Intelligence

by Qunpeng Fan

Sustainability 2022, 14(14), 8503; https://doi.org/10.3390/su14148503 - 11 Jul 2022

Viewed by 1639

Abstract

This paper investigates the market management and modeling based on advanced artificial intelligence. The proposed model deploys the combination of the support vector machine and fuzzy set theory to provide a practical and powerful prediction model for the market price over the next [...] Read more.

This paper investigates the market management and modeling based on advanced artificial intelligence. The proposed model deploys the combination of the support vector machine and fuzzy set theory to provide a practical and powerful prediction model for the market price over the next day. A realistic and effective model is then introduced to model the market players, such as the renewable energy sources of solar and wind turbines, as well as the fossil-fueled sources of micro turbines and fuel cells. In order to provide an optimal management program, it introduces a stochastic framework based on the point estimate method and adaptive grey wolf optimization algorithm (GWO). The proposed optimization methods use an adaptive strategy to choose the most fitting modification for enhancing the GWO performance. A realistic scenario is simulated to demonstrate the model’s effectiveness and impression on the real market management. The results clearly show the effectiveness of the prediction and management model. The prediction results show the superiority of the proposed model by RMSE of 2.9643 compared to the 3.217 for SVR, 3.2364 for ANN and 3.0621 for the grey model. Moreover, the optimal MAPE is 2.7453 by the proposed method, which is much better than the 3.052 by SVR, 3.1552 by ANN and 2.9285 by the grey model. From point of view of optimization, the most fitting power dispatch has been attained with the total cost of 300.8632 over 24 h. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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23 pages, 3715 KiB

Open AccessArticle

Experimental Investigation of an Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller for Buck DC/DC Converters

by Badreddine Babes, Noureddine Hamouda, Fahad Albalawi, Oualid Aissa, Sherif S. M. Ghoneim and Saad A. Mohamed Abdelwahab

Sustainability 2022, 14(13), 7967; https://doi.org/10.3390/su14137967 - 29 Jun 2022

Cited by 15 | Viewed by 2246

Abstract

This study proposes a way of designing a reliable voltage controller for buck DC/DC converter in which the terminal attractor approach is combined with an enhanced reaching law-based Fast Terminal Synergetic Controller (FTSC). The proposed scheme will overcome the chattering phenomena constraint of [...] Read more.

This study proposes a way of designing a reliable voltage controller for buck DC/DC converter in which the terminal attractor approach is combined with an enhanced reaching law-based Fast Terminal Synergetic Controller (FTSC). The proposed scheme will overcome the chattering phenomena constraint of existing Sliding Mode Controllers (SMCs) and the issue related to the indefinite time convergence of traditional Synergetic Controllers (SCs). In this approach, the FTSC algorithm will ensure the proper tracking of the voltage while the enhanced reaching law will guarantee finite-time convergence. A Fuzzy Neural Network (FNN) structure is exploited here to approximate the unknown converter nonlinear dynamics due to changes in the input voltage and loads. The Fuzzy Neural Network (FNN) weights are adjusted according to the adaptive law in real-time to respond to changes in system uncertainties, enhancing the increasing the system’s robustness. The applicability of the proposed controller, i.e., the Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller (AFN-FTSC), is evaluated through comprehensive analyses in real-time platforms, along with rigorous comparative studies with an existing FTSC. A dSPACE ds1103 platform is used for the implementation of the proposed scheme. All results confirm fast reference tracking capability with low overshoots and robustness against disturbances while comparing with the FTSC. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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14 pages, 3159 KiB

Open AccessArticle

Cost Estimation Process of Green Energy Production and Consumption Using Probability Learning Approach

by Jian Xiao and Wei Hou

Sustainability 2022, 14(12), 7091; https://doi.org/10.3390/su14127091 - 9 Jun 2022

Cited by 2 | Viewed by 1856

Abstract

With electric vehicle (EV) charging, green energy production costs could be reduced, and smart grid (SG) reliability improved. Nevertheless, the vast number of EVs could adversely affect the stability of the voltage and cost of operation. The present study designs a new security-based [...] Read more.

With electric vehicle (EV) charging, green energy production costs could be reduced, and smart grid (SG) reliability improved. Nevertheless, the vast number of EVs could adversely affect the stability of the voltage and cost of operation. The present study designs a new security-based system based on a new EV participation charging method for a decentralized blockchain-enabled SG system. It is aimed at minimizing the level of power alternation in the electrical network and the total charging costs of EVs as mobile systems. In the first step, the power alternation level issue of the SG is formulated based on the capacity of EV batteries, the rate of charging, and EV users’ charging behavior. Next, a new adaptive blockchain-based EV participation (AdBEV) method is proposed, using the Iceberg order execution algorithm for improving EV discharging and charging schedules. Simulated outcomes demonstrate that the suggested method is superior to the genetic algorithm method when it comes to reducing power fluctuation levels and total charging cost. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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21 pages, 4711 KiB

Open AccessArticle

Coordinated Design of Type-2 Fuzzy Lead–Lag-Structured SSSCs and PSSs for Power System Stability Improvement

by Prabodh Khampariya, Sidhartha Panda, Hisham Alharbi, Almoataz Y. Abdelaziz and Sherif S. M. Ghoneim

Sustainability 2022, 14(11), 6656; https://doi.org/10.3390/su14116656 - 29 May 2022

Cited by 10 | Viewed by 2251

Abstract

This work suggests a type-2 fuzzy lead–lag (T2FLL) controller structure for flexible AC transmission system (FACTS)-based damping controllers and power system stabilizers (PSSs) for power system stability improvement. The values of the suggested controller are optimized by a hybrid adaptive differential evolution and [...] Read more.

This work suggests a type-2 fuzzy lead–lag (T2FLL) controller structure for flexible AC transmission system (FACTS)-based damping controllers and power system stabilizers (PSSs) for power system stability improvement. The values of the suggested controller are optimized by a hybrid adaptive differential evolution and pattern search algorithm (hADE-PS) method. Initially, a single-machine infinite-bus (SMIB) system with lead–lag (LL)-structured FACTS and PSS controllers is considered, and the dominance of the hADE-PS method is established over the original differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The supremacy of T2FLL over the lead–lag (LL) controller is established under different large and small disturbance conditions, as well as varied loading conditions and fault positions. Lastly, the effectiveness of T2FLL is evaluated in a multimachine power system (MMPS). It is demonstrated that the suggested T2FLL offers better performance than the LL controller under various large and small disturbance conditions by providing significantly more damping to all modes of oscillations. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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17 pages, 3945 KiB

Open AccessEditor’s ChoiceArticle

Management Optimization of Electricity System with Sustainability Enhancement

by Wei Hou, Rita Yi Man Li and Thanawan Sittihai

Sustainability 2022, 14(11), 6650; https://doi.org/10.3390/su14116650 - 29 May 2022

Cited by 18 | Viewed by 4046

Abstract

Based on new policies and social changes, renewable energies have highly penetrated electrical systems, making the system more vulnerable than before. On the other hand, it leads to congestion and competition within the network. To this end, this paper developed a probabilistic multi-objective-based [...] Read more.

Based on new policies and social changes, renewable energies have highly penetrated electrical systems, making the system more vulnerable than before. On the other hand, it leads to congestion and competition within the network. To this end, this paper developed a probabilistic multi-objective-based congestion management approach and applied it to the optimal transmission switching (OTS) strategies, to maximize system suitability and minimize total production costs. A point estimation economic method (PEM) has been applied, as one of the best management and economic tools to handle the uncertainties associated with a wind turbine’s power production and load demand (LD). Results demonstrate the effectiveness and merit of the proposed technique, compared to the existing one, which can lead to higher reliability and sustainability for the grids. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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27 pages, 3807 KiB

Open AccessArticle

Investigation on New Metaheuristic Algorithms for Solving Dynamic Combined Economic Environmental Dispatch Problems

by Benyekhlef Larouci, Ahmed Nour El Islam Ayad, Hisham Alharbi, Turki E. A. Alharbi, Houari Boudjella, Abdelkader Si Tayeb, Sherif S. M. Ghoneim and Saad A. Mohamed Abdelwahab

Sustainability 2022, 14(9), 5554; https://doi.org/10.3390/su14095554 - 5 May 2022

Cited by 15 | Viewed by 2511

Abstract

In this paper, the dynamic combined economic environmental dispatch problems (DCEED) with variable real transmission losses are tackled using four metaheuristics techniques. Due to the consideration of the valve-point loading effects (VPE), DCEED have become a non-smooth and more complex optimization problem. The [...] Read more.

In this paper, the dynamic combined economic environmental dispatch problems (DCEED) with variable real transmission losses are tackled using four metaheuristics techniques. Due to the consideration of the valve-point loading effects (VPE), DCEED have become a non-smooth and more complex optimization problem. The seagull optimization algorithm (SOA), crow search algorithm (CSA), tunicate swarm algorithm (TSA), and firefly algorithm (FFA), as both nature and biologic phenomena-based algorithms, are investigated to solve DCEED problems. Our proposed algorithms, SOA, TSA, and FFA, were evaluated and applied on the IEEE five-unit test system, and the effectiveness of the proposed CSA approach was applied on two-unit, five-unit, and ten-unit systems by considering VPE. We defined CSA for different objective functions, such as cost of production, emission, and CEED, by considering VPE. The obtained results reveal the efficiency and robustness of the CSA compared to SOA, TSA, FFA, and to other optimization algorithms reported recently in the literature. In addition, Matlab simulation results show the advantages of the proposed approaches for solving DCEED problems. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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23 pages, 7929 KiB

Open AccessArticle

Investigating the Impact of Wind Power Integration on Damping Characteristics of Low Frequency Oscillations in Power Systems

by Jian Chen, Tao Jin, Mohamed A. Mohamed, Andres Annuk and Udaya Dampage

Sustainability 2022, 14(7), 3841; https://doi.org/10.3390/su14073841 - 24 Mar 2022

Cited by 8 | Viewed by 2289

Abstract

This paper investigates the impact of doubly-fed induction generator (DFIG) wind farms on system stability in multi-generator power systems with low-frequency oscillations (LFOs). To this end, this paper establishes the interconnection model of the equivalent generators and derives the system state equation. On [...] Read more.

This paper investigates the impact of doubly-fed induction generator (DFIG) wind farms on system stability in multi-generator power systems with low-frequency oscillations (LFOs). To this end, this paper establishes the interconnection model of the equivalent generators and derives the system state equation. On this basis, an updated system state equation of the new power system with integrated wind power is further derived. Then, according to the updated system state equation, the impact factors that cause changes in the system damping characteristics are presented. The IEEE two-area four-machine power system is used as a simulation model in which the LFOs occur. The simulation results demonstrate that the connection of wind power to the power feeding area (PFA) increases the damping ratio of the dominant mode of inter-area oscillation from −0.0263 to −0.0107, which obviously improves the system stability. Furthermore, the wind power integration into PFA, as the connection distance of the wind power increases, gradually decreases the damping ratio of the dominant mode of the inter-area oscillation to −0.0236, approaching that of no wind power in the system. Meanwhile, with the increase in the wind power output capacity, the damping ratio of the dominant mode of the intra-area and inter-area oscillation increases, and the maximum damping ratios reach 0.1337 and 0.0233, respectively. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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31 pages, 4427 KiB

Open AccessArticle

Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

by Prashant, Anwar Shahzad Siddiqui, Md Sarwar, Ahmed Althobaiti and Sherif S. M. Ghoneim

Sustainability 2022, 14(6), 3305; https://doi.org/10.3390/su14063305 - 11 Mar 2022

Cited by 26 | Viewed by 3460

Abstract

This article presents the selection of location and sizing of multiple distributed generators (DGs) for boosting performance of the radial distribution system in the case of constant power load flow and constant impedance load flow. The consideration of placing and sizing of DGs [...] Read more.

This article presents the selection of location and sizing of multiple distributed generators (DGs) for boosting performance of the radial distribution system in the case of constant power load flow and constant impedance load flow. The consideration of placing and sizing of DGs is to meet the load demand. This article tries to overcome the limitations of existing techniques for determining the appropriate location and size of DGs. The selection of DG location is decided in terms of real power losses, accuracy, and sensitivity. The size of DG is measured in terms of real and reactive power. Both positioning and sizing of DG are analyzed with the genetic algorithm and the heuristic probability distribution method. The results are compared with other existing methods such as ant-lion optimization algorithm, coyote optimizer, modified sine-cosine algorithm, and particle swarm optimization. Further, the power quality improvement of the network is assessed by positioning D-STATCOM, and its location is decided on the basis of the nearby bus having poor voltage profile and high total harmonic distortion (THD). The switching and controlling of D-STATCOM are assessed with fuzzy logic controller (FLC) for improving the performance parameters such as voltage profile and THD at that particular bus. The proposed analytical approach for the system is tested on the IEEE 33 bus system. It is observed that the performance of the system with the genetic algorithm gives a better solution in comparison to heuristic PDF and other existing methods for determining the optimal location and size of DG. The introduction of D-STATCOM into the system with FLC shows better performance in terms of improved voltage profile and THD in comparison to existing techniques. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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18 pages, 6696 KiB

Open AccessArticle

Energy Conservation Measures and Value Engineering for Small Microgrid: New Hospital as a Case Study

by Saleh Abdulaziz Almarzooq, Abdullah M. Al-Shaalan, Hassan M. H. Farh and Tarek Kandil

Sustainability 2022, 14(4), 2390; https://doi.org/10.3390/su14042390 - 19 Feb 2022

Cited by 6 | Viewed by 4039

Abstract

Energy conservation measures can not only improve energy efficiency; it can also enhance microgrid resilience. This paper aims at investigating energy conservation in a small microgrid, using a new hospital in Riyadh city as a case study, to satisfy the Saudi Building Code [...] Read more.

Energy conservation measures can not only improve energy efficiency; it can also enhance microgrid resilience. This paper aims at investigating energy conservation in a small microgrid, using a new hospital in Riyadh city as a case study, to satisfy the Saudi Building Code (SBC part 601) requirement of energy conservation as the first case. The second case study aims to apply and simulate additional advanced energy conservation requirements. The new hospital has considered energy conservation measures uch as upgraded Heating, Ventilation, and Air Conditioning (HVAC), lighting type effect, thermal insulation, and window material. These energy conservation considerations made a difference in the annual energy saving and efficiency of its microgrid. This study used Autodesk Revit software to obtain building information modeling (BIM) and eQUEST to perform energy simulations. The two software programs are integrated together to perform comprehensive energy simulations with detailed building information from the model by Autodesk Green Building Studio (GBS). The energy conservation measures mainly focused on energy management and saving in the building’s electrical installations. All utilized equipment in the hospital should follow the Saudi standards issued by the national authorities. The simulation results revealed a noticeable annual energy saving of up to 19.82% for the second case, using a great thermal resistance building envelope, energy-saving lighting system, and highly rated Energy Efficiency Ratio (EER) HVAC system compared to the first case. More than 100,000 SR in yearly energy saving was achieved by implementing the second case study. Applying the Value Methodology (VM) to the proposed hospital in this study saved more than 700,000 SR in the initial cost of the hospital. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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18 pages, 3588 KiB

Open AccessArticle

Technical and Economic Evaluation for Off-Grid Hybrid Renewable Energy System Using Novel Bonobo Optimizer

by Hassan M. H. Farh, Abdullrahman A. Al-Shamma’a, Abdullah M. Al-Shaalan, Abdulaziz Alkuhayli, Abdullah M. Noman and Tarek Kandil

Sustainability 2022, 14(3), 1533; https://doi.org/10.3390/su14031533 - 28 Jan 2022

Cited by 38 | Viewed by 3478

Abstract

In this study, a novel bonobo optimizer (BO) technique is applied to find the optimal design for an off-grid hybrid renewable energy system (HRES) that contains a diesel generator, photovoltaics (PV), a wind turbine (WT), and batteries as a storage system. The proposed [...] Read more.

In this study, a novel bonobo optimizer (BO) technique is applied to find the optimal design for an off-grid hybrid renewable energy system (HRES) that contains a diesel generator, photovoltaics (PV), a wind turbine (WT), and batteries as a storage system. The proposed HRES aims to electrify a remote region in northern Saudi Arabia based on annualized system cost (ASC) minimization and power system reliability enhancement. To differentiate and evaluate the performance, the BO was compared to four recent metaheuristic algorithms, called big-bang–big-crunch (BBBC), crow search (CS), the genetic algorithm (GA), and the butterfly optimization algorithm (BOA), to find the optimal design for the proposed off-grid HRES in terms of optimal and worst solutions captured, mean, convergence rate, and standard deviation. The obtained results reveal the efficacy of BO compared to the other four metaheuristic algorithms where it achieved the optimal solution of the proposed off-grid HRES with the lowest ASC (USD 149,977.2), quick convergence time, and fewer oscillations, followed by BOA (USD 150,236.4). Both the BBBC and GA algorithms failed to capture the global solution and had high convergence time. In addition, they had high standard deviation, which revealed that their solutions were more dispersed with obvious oscillations. These simulation results proved the supremacy of BO in comparison to the other four metaheuristic algorithms. Full article

(This article belongs to the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids)

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