Smart Island Research Articles

Cloud-fog architecture-based control of smart island microgrid in master-slave organization using disturbance observer-based hybrid backstepping sliding mode controller

Distributed control is an effective method to coordinate the microgrid with various components, and also in a smart microgrid, communication graph layouts are essential since changing the topology unexpectedly could disrupt the operation of the distributed controllers, and also an imbalance may occur between the production and load. Hence, reducing the exchanged data between units and system operator is essential in order to reduce the transmitted data volume and computational burden. For this purpose, an islanded microgrid with multiple agents which is using cloud-fog computing is proposed here, in order to reduce the computing burden on the central control unit as well as reducing data exchange among units. To balance the production power and loads in a smart island with a stable voltage/frequency, a hybrid backstepping sliding mode controller (BSMC) with disturbance observer (DO) is suggested to control voltage/frequency and current in the MG-based master-slave organization. Therefore, this paper proposes a DO-driven BSMC for controlling voltage/frequency, and power of energy sources within a Master-Slave organization; in addition, the study proposes a clod-fog computing for enhancing performance, reducing transferred data volume, and processing information on time. In the extensive simulations, the suggested controller shows a reduction in steady-state error, a fast response, and a lower total harmonic distortion (THD) for nonlinear and linear loads less than 0.33 %. The fog layer serves as a local processing level, so it reduces the exchanged data between cloud and fog nodes.

Modeling energy management of an energy hub with hybrid energy storage systems for a smart island considering water–electricity nexus

Energy hubs (EHs) represent a pivotal paradigm in achieving optimal resource utilization across various energy domains. This paper presents an advanced framework for the optimal management of a smart island, leveraging the synergies within the water-electricity nexus. By integrating diverse resources, including electricity, water, heat, and hydrogen, the proposed EH model aims to meet the multifaceted demands of consumers on the island. To enhance operational efficiency, this study delves into the nuanced impacts of key EH components, elucidating their roles in meeting demand profiles and minimizing operational costs. Formulated as a mixed integer linear programming (MILP) model, the EH optimization problem is addressed using the GAMS optimization tool. The overarching objective is to fulfill consumer demand while concurrently optimizing resource utilization, considering factors such as storage degradation costs and emissions from fossil-fuel-based units. In addition to strategic optimization, this study pioneers a novel approach to stochastic parameter forecasting, integrating convolutional neural networks (CNNs) and long-short-term memory networks (LSTMs). By harnessing the capabilities of these advanced forecasting techniques, the EH model can anticipate dynamic changes in demand patterns with heightened accuracy and precision. The empirical results underscore the transformative potential of the proposed EH framework, showcasing significant reductions—up to 30%—in emission costs. Moreover, the study underscores the pivotal role of EHs as enablers for scaling up renewable energy penetration, offering a robust foundation for sustainable energy transitions in island communities and beyond. Additionally, implementing a load-shifting demand response program can lower total costs by approximately $257 per day, offering significant savings for EHs over extended periods.

An island detection methodology with protection against cyber attack

Unplanned islanding of micro-grids is a significant barrier to supplying continuous power to key customers. The identification of the islanding moments must be rapid to enable the distributed generators (DG) to perform control measures in the shortest possible period. Micro phasor measuring units (µ-PMU) are gaining popularity in distribution systems and micro grids as a result of their ability to produce high-quality data at a high speed. These µ-PMUs can be utilized to detect islands. However, the µ-PMU relies heavily on the communication system for transmission of data, which is vulnerable to cyberattacks. In consideration of the previous technique, this research provides a smart island detection application with µ-PMU having lowered cyberattack probabilities. This representation is equipped with a µ-PMU implemented on the relevant DG’s bus. The voltage data acquired from these µ-PMUs are processed using the sequence transformation in order to simulate the sequence component angle. The angular sum of the negative and positive sequence components is evaluated and the maximum value is deployed for detection of islanding. MATLAB/Simulink tests the proposed approach through an IEEE-34 node distribution network. Multiple simulations demonstrate the robustness of the technique.

Smart Island Lemukutan Through WebGIS Based Benthic Habitat Data Collection

Benthic habitats provide a variety of functions, both ecological and economic, for life in coastal areas and small islands. Smart islands or smart islands are currently being implemented in a number of small island developing countries with one of the challenges being technology based. One technology that can be used in management, including monitoring and supervision of resources, is the Geographic Information System (GIS). The synergy of GIS data and the help of the latest multimedia software technology is necessary to help display an interactive and easily accessible, in this case, webGIS technology. This research was carried out on Lemukutan Island which consists of 6 research stations, each of which has a distribution of coral reef location points based on coordinates. Based on the results of the analysis of the percentage of benthic habitats in the study locations ranging from bad to good categories. Furthermore, the data from field observations and analysis results are displayed on the Lemukan Island smart island website and can be accessed at https://webgissmartisland.com. Regular monitoring of coral reefs is very important and needs to be done to help input data for Smart Island Lemukutan so that it becomes a catalyst in realizing a smart island ecosystem in West Kalimantan Province by providing benthic habitat maps via webgis services and actualizing technological development in coastal areas and small islands. smart island based through the smart destination concept by integrating benthic habitat information via WebGIS services.

An optimal management architecture based on digital twin for smart solar-based islands incorporating deep learning and modified particle swarm optimization

Smart island (SI) energy management is a type of energy management system used to ensure that energy is used efficiently on islands. It is designed to reduce energy consumption and costs while also improving the sustainability of the island. Smart island energy management systems use a combination of technologies such as solar, wind, and other renewable energy sources, energy storage systems, smart meters, and advanced analytics to monitor and manage energy usage. The system can be used to provide islanders with real-time energy usage data, allowing them to make informed decisions about their energy use. Using the effects on the environment and economics of the SI as a basis for fully modeling the optimum performance of the SI in grid-connected operations, the feasibility and balance of the SI power grid are ensured. In addition, taking into account the impact of interruptible loads (IL) on SI operation costs, a power shift for IL can be performed using storage batteries in a digital twin environment. A new ecology-driven optimization algorithm has been developed that continuously adjusts migration rates with habitat suitability indexes of normalized individuals and adds a differential perturbation to the migration operator of the migration process. The enhanced particle swarm optimization algorithm has been implemented as the SI optimization dispatching algorithm. In order to have a precise prediction of the renewable energy sources, their output power is predicted using the recurrent neural network (RNN) deep learning model. Based on the simulation outcomes, it is evident that the suggested power dispatching model could greatly decrease the overall price of the system by implementing the advanced and effective algorithm and model presented in the study.

Bali Smart Island: Smart City Implementation in Bali Province

This study aims to explore and examine SPBE Towards Bali Smart Island in the Provincial Government of Bali. This study used a qualitative descriptive research method with an inductive approach. Data collection was carried out through interviews, observation, and documentation techniques. The results showed that SPBE towards Bali Smart Island has been going well and has implemented smart city development. However, ASN’s digital literacy and competence are still weak, post-pandemic regional budgets are not stable, cyberattacks, and the large number of application systems built by the center and each region are obstacles to implementing SPBE Towards Bali Smart Island. Strategies that can be implemented to overcome the obstacles encountered in implementing SPBE towards Bali Smart Island include: collaborating with pentahelix elements to hold local, national and global scale activities and events based on digital technology scale such as the Bali Digital Festival which is held every year; encouraging international investors to develop the digital technology industry in Bali so as to have a domino effect in developing smart city dimensions in realizing Bali Smart Island; forming an internal audit/assessor team to carry out periodic assessments related to SPBE development so as to create strong control and evaluation; optimizing the Cyber Lantern program using social media platforms in the form of providing training and sharing knowledge related to the use of digital technology, data security, technology ethics and so on as a medium for increasing digital literacy; forming the SPBE Forum throughout the Province of Bali to form integrated development of the Bali Smart Island program and hold training on the use of certified digital technology.

Blockchain Technology to Enhance Integrated Blue Economy: A Case Study in Strengthening Sustainable Tourism on Smart Islands

Smart destinations require a management system that provides convenient real-time use of digital technology in creating, communicating, and delivering value to visitors. Research related to smart island destinations is still limited, let alone those that utilize blockchain technology to create the smartness of the destination. This research is an empirical study that captures the perceptions of stakeholders in the blue economy on smart islands and the use of blockchain technology in order to build smart islands, a destination that consists of many islands that lack connectedness, such as the Seribu Islands in Jakarta, Indonesia. Data were collected using qualitative and quantitative approaches (mixed methods). Qualitative data were collected from scientific journal publications and followed up with VOS viewer analysis, and quantitative data with the questionnaire survey responses from 150 blue economy industry players in the Seribu Islands and structural equation modeling showed that good digital literacy and blue economy management have significant influence on blockchain technology and impact smart islands. This study indicates that, for islands that have challenges in accessibility and connectivity, the presence of blockchain and smart technology is needed to integrate various resources from each stakeholder so that the blue economy in the islands can be developed more effectively and efficiently, while at the same time ensuring the achievement of sustainability.

Modelling and simulation of a thermal storage system based on phase change materials integrated in a tertiary building

Decarbonization of the building sector is one of the key challenges to achieve the ambitious goal of carbon neutrality by 2050, established in the European Green Deal. In this sense, current trends focus on the promotion of onsite renewable energy sources, as well as on the electrification of heating and cooling demands and sector coupling approaches through Power-to-heat strategies. This minimizes energy transportation losses while creating an increased need for storage systems. In this scenario, Thermal Energy Storage (TES) systems gain importance and provide the required flexibility, although the experience with high storage periods and volumes, fast response capacity, easy integration into building facilities and cost-effective and environmentally friendly solutions is still scarce. This paper focuses on a TRNSYS (Transient System Simulation Program) modelling and simulation analysis of different integration strategies of a TES system based on Phase Change Materials (PCM) into a real-scale tertiary building. The target building (CARTIF III) is located in Valladolid, Spain, within a Mediterranean climatic area, and incorporates different energy systems including a local photovoltaic (PV) field and a geothermal heat pump (HP). The combination of the PCM storage with the PV and the geothermal HP is studied for the cooling season (in summer) aiming at maximizing the overall system energy efficiency and minimizing the energy import from the grid, thus pursuing a Smart Island concept. Results from this study will feed the solution design for the actual integration project that will be addressed within the framework of a upcoming EU research project.

Accessibility and Public Transport Mobility for a Smart(er) Island: Evidence from Sardinia (Italy)

• Multimodal integration model for Public Transport Service (PTS) in an island scenario • Mobility-based spatial interpretation for the benefit of the population • Use of real data obtained by the Kriging method • Transport modes do not reflect the socio-physical and economic reality of the island The purpose of this paper is to highlight the critical aspects of islands context transportation accessibility by suggesting a more innovative, safer, and more sustainable framework for public transport service (PTS) and place-based organisation, as well as by integrating the latest tendencies in mobility. Specifically, this research focuses on multimodal integration models for PTS that consider the existing infrastructure system and the socioeconomic issues typical of an island environment. This topic has received inadequate consideration in the scientific literature on islands. To achieve these aims, an analytical-numerical approach is adopted. Starting from initial origin-destination matrices (O/D) surveyed by the National Institute of Statistics (ISTAT), a methodology was implemented to compute the geographical distribution of trips, and thereafter, the values distributed over the whole region were interpolated by Surfer software with the Kriging method. This methodology was applied to the Sardinia case study particularly emblematic because it has seen a tremendous transition over the previous decades, resulting in a massive socio-economic gap between inland and coastal areas, that led to an increase in private vehicles for transportation purposes, primarily for business and pleasure. The application, replicable in other islands that have highlighted the same socio-economic problems linked to poor mobility planning, shows an accessible spatial planning approach, combining PTS and rental for driver services, by considering the principles and issues of island contexts. This research gives an important scientific contribution by emphasising the quality of transport infrastructures, place-based organisation, population distribution, and physical configuration of the Insular Region, as well as by considering the most pressing issues of island contexts. Findings could help island governments in revising their policy and practice of transport, accordingly.

A cost-efficient Based Cooperative Model for Relible Energy Management Of Networked Microgrids Within a Smart Island

This paper mainly focuses on investigating a stochastic energy management approach to enhance reliability of the networked microgrids within a smart island. In this regard, a networked microgrid is considered in the smart island, which is integrated with a multi-energy hub (multi-EH) system aiming to simultaneously support the electrical, thermal, and water demands of the island, isolated from the main grid. The objective function is organized as the cost of operation and reliability of the networked microgrid and is solved using a modified bat algorithm. The uncertainty of stochastic parameters is modelled using the 2-m point estimate method, which has notable advantages in terms of accuracy and simplicity of implementation. Different scenarios are considered both in critical and normal conditions to evaluate the performance of the studied model. Both GAMS and MATLAB software’s were employed to study the proposed model. The proposed model improved the reliability of the networked microgrid system and results reveal that the proposed cooperative approach could decrease total operation and investment cost of the networked microgrid by 13%.

Advanced energy planning of Smart Islands

Advanced energy planning of Smart Islands

Islands of Connectivity [communications - networks and services

Stunning coastlines and verdant mountain scenery make the Cook Islands a popular tourist destination. This tourism industry has contributed to a decade of economic growth, and in 2021 the government of this nation in the South Pacific introduced the SMART Economy Initiative to create 'smart' island networks. Around the world, the Internet of Things (IoT) is used for energy metering, road traffic management, street lighting operation and environmental controls. Not all IoT nodes need the fast data transfer of a 5G network, and in the Cook Islands, the tropical and mountainous terrain means installation is not practical. LoRaWAN (long-range wide area network) technology was selected for the Cook Islands' Smart Island project. The low-power protocol uses the unlicensed industrial, scientific, and medical (ISM) frequency band for bi-directional communication.

A novel energy management framework incorporating multi‐carrier energy hub for smart city

The development of advanced and intelligent measurement instruments in recent years has increased the intelligence of modern energy systems, especially power systems. Besides, with the advancement of energy conversion technologies, these systems benefit from multi-carrier energy resources. Accordingly, this paper presents a model of smart city which considers various components, including smart transportation system (STS), microgrid (MG), and smart energy hub (SEH) with the ability of energy transformation. The proposed model addresses the islanded operation of a smart city that makes it a smart island. This island deploys the energy carriers of electricity, heat, gas and water as well. In addition, STS includes electric vehicle (EV) parking lots as well as metro system (MS) that can interactively exchange energy. More precisely, the different components of the smart island are modelled on the assumption of energy interdependency. In the proposed model, the water supply unit in SEH is provided which can be effective in reducing the cost of components by supplying water to them. In order to exchange energy within STS, metro stations have been optimally allocated using intelligent water drops (IWD) optimization method. In addition to smart island modelling, this paper quantifies the uncertainties within STS and MG using cloud theory. Eventually, the proposed model is simulated to ensure its effectiveness and accuracy.

Adaptive backstepping control for master-slave AC microgrid in smart island

To control a smart-island (which comprises three distributed generation (DG) units with their autonomous converters), based on a nonlinear backstepping control (BSC) scheme, an adaptive reference signal, and a state observer are designed to improve the steady-state performance of the system. An adaptive reference value has been designed and used instead of a constant reference signal to improve the system's steady-state performance, e.g., total harmonic distortion (THD), peak value, and effective value in the voltage control mode. The presented adaptive reference is changed based on the error between the reference signal and the output voltage signal to tend the error to zero. Compared to the classical backstepping controllers, the reference signal is pursued with a fast response and a low steady-state of loads alternations. To demonstrate the efficiency, authenticity, and compatibility of the proposed control strategy, offline digital time-domain simulation studies are carried out on a master-slave organized inverter-based microgrids in various load variations like linear and nonlinear loads in MATLAB/Simulink software environment. The obtained results are compared with previously reported techniques. Moreover, the obtained simulation results are verified by performing laboratory-based experimental tests based on digital signal processing (DSP), which validate the proposed control strategy's accuracy, authenticity, and effectiveness under different conditions.

Control of LPV Modeled AC-Microgrid Based on Mixed H2/H∞ Time-Varying Linear State Feedback and Robust Predictive Algorithm

This paper presents a robust model predictive control (RMPC) method with a new mixed H2/<inline-formula> <tex-math notation="LaTeX">$\text{H}\infty $ </tex-math></inline-formula> linear time-varying state feedback design. In addition, we propose a linear parameter-varying model for inverters in a microgrid (MG), in which disturbances and uncertainty are considered, where the inverters connect in parallel to renewable energy sources (RES). The proposed RMPC can use the gain-scheduled control law and satisfy both the H2 and <inline-formula> <tex-math notation="LaTeX">$\text{H}\infty $ </tex-math></inline-formula> proficiency requirements under various conditions, such as disturbance and load variation. A multistep control method is proposed to reduce the conservativeness caused by the unique feedback control law, enhance the control proficiency, and strengthen the RMPC feasible area. Furthermore, a practical and efficient RMPC is designed to reduce the online computational burden. The presented controller can implement load sharing among distributed generators (DGs) to stabilize the frequency and voltage of an entire smart island. The proposed strategy is implemented and studied in a MG with two DG types and various load types. Specifically, through converters, one type of DGs is used to control frequency and voltage, and the other type is used to control current. These two types of DGs operate in a parallel mode. Simulation results show that the proposed RMPCs are input-to-state practically stable (ISpS). Compared with other controllers in the literature, the proposed strategy can lead to minor total harmonic distortion (THD), lower steady-state error, and faster response to system disturbance and load variation.

A robust risk assessment method for energy planning scenarios on smart islands under the demand uncertainty

Energy systems with a high share of variable renewable energy are more vulnerable to sudden changes in the system operation. This is especially emphasized on small systems such as energy systems on geographical islands. Because of these reasons, there is a need for quantifying the risk of energy scenarios of such systems. This paper presents a novel robust risk assessment method under demand uncertainty for energy planning scenarios for the islands. The method uses graph theory for the representation of power system topology. The Poisson distribution is used for calculating the probability of power system element failure. The robust modelling approach is applied by the introduction of auxiliary variables and compared to the deterministic model results. Four energy planning scenarios for Unije island are modelled and subjugated to several power system outages resulting in a risk vector calculated as the product of probability vector and damage matrix. The study also presents a zero-import risk energy planning scenario for Unije island that is achieved for a system of 0.5 MW photovoltaic plant and 3.55 MWh battery storage system.

Smart Island and Sustainable Tourist Development with the Example of Madeira. Part 2: Analysis of Expectations of Local Community and Tourists

Smart Island and Sustainable Tourist Development with the Example of Madeira. Part 2: Analysis of Expectations of Local Community and Tourists

Smart Island and Sustainable Tourist Development with the example of Madeira. Part 1: Theoretical Contexts and Development Conditions

Smart Island and Sustainable Tourist Development with the example of Madeira. Part 1: Theoretical Contexts and Development Conditions

The Smart Islands method for defining energy planning scenarios on islands

Islands represent areas where it is possible to have a clear overview of resources and needs over a large number of sectors. Because of this, the developed energy planning scenarios on the islands should reflect the possibility of meeting local needs with available resources of a wide range of sectors. This is often not the case in the current studies that analyse only a limited number of sectors. The developed method automatically combines needs and resources based on the quantitative indicators and generates energy planning scenarios with precisely defined types and the capacities of required technologies. The results show that the Smart Islands method provides 7 energy planning scenarios for Krk island with different technology mixes. The case study for Vis island is considered for cases with and without electrical interconnection. When the interconnection is not considered, the method suggests a 5.42 MWh battery system for maintaining grid stability. The results indicate that the Smart Islands method can be applied to islands with different characteristics as well as suggest optimal energy planning scenarios while meeting needs with local resources.

Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids

Cyber-physical threats as false data injection attacks (FDIAs) in islanded smart microgrids (ISMGs) are typical accretion attacks, which need urgent consideration. In this regard, this paper proposes a novel cyber-attack detection model to detect FDIAs based on singular value decomposition (SVD) and fast Fourier transform (FFT). Since new research are mostly focusing on FDIAs detection in DC systems, paying attention to AC systems attack detection is also necessary; hence, AC state estimation (SE) have been used in SI analysis and in considering renewable energy sources effect. Whenever malicious data are added into the system state vectors, vectors’ temporal and spatial datum relations might drift from usual operating conditions. In this approach, switching surface based on sliding mode controllers is dialyzed to regulate detailed FFT’s coefficients to calculate singular values. Indexes are determined according to the composition of FFT and SVD in voltage/current switching surface to distinguish the potential cyber-attack. This protection layout is presented for cyber-attack detection and is studied in various types of FDIA forms like amplitude and vector derivation of signals, which exchanged between agents such as smart sensor, control units, smart loads, etc. The prominent advantage of the proposed detection layout is to reduce the time (less than 10 milliseconds from the attack outset) in several kinds of case studies. The proposed method can detect more than 96% accuracy from 2967 sample tests. The performances of the method are carried out on AC-ISMG in MATLAB/Simulink environment.