Facility Planning Optimization Platform, GGOD, for Expandable Cluster-type Micro-grid Installations and Operations

This paper describes the architecture and the utilization for a facility planning optimization platform called GGOD, “Grid of Grids Optimal Designer” and applies it to expandable cluster-type micro-grid installations and operations. The expandable cluster-type micro-grid is defined as a group of micro-grids that are connected by bi-directional power transfer networks. Furthermore, power sources are also networked. Especially, by networking among power sources, powers necessary for social activities in-demand areas are secured. The proposed architecture is based on service-oriented architecture, meaning that optimization functions are executed as services. For flexibility, these services are executed by requests based on extensible mark-up language texts. The available optimizations are written in meta-data, which are accessible to end-users from the meta-data database system called clearinghouse. The meta-data are of two types, one for single optimization and the other for combined optimization. The processes in GGOD are conducted by the management function which interprets descriptions in meta-data. In meta-data, the names of optimization functions and activation orders are written. The basic executions follow sequential, branch, or loop flow processes, which execute combined optimizations, compare more than two kinds of optimization processes, and perform iterative simulations, respectively. As an application of the proposed architecture, the power generation sites and transmission networks are optimized in a geospatial integrated-resource planning scenario. In this application, a structure and a method for the combination of component functions in GGOD are exemplified. Moreover, GGOD suggests promotions of a lot of applications by effective combinations of basic optimization functions

Modelling and control strategies for hydrokinetic energy harnessing

The high prices and depletion of conventional energy resources and the environmental concern due to the high emission of CO2 gases have encouraged many researchers worldwide to explore a new field in renewable energy resources. The hydrokinetic energy harnessing in the river is one of the potential energies to ensure the continuity of clean, reliable, and sustainable energy for the future generation. The conventional hydropower required a special head, lots of coverage area, and some environmental issues. Conversely, the hydrokinetic system based on free stream flowing is one of the best options to provide the decentralised energy for rural and small-scale energy production. Lately, the effort of energy harnessing based on hydrokinetic technology is emerging significantly. Nevertheless, several challenges and issues need to be considered, such as turbine selection for energy conversion, generalised turbine model and control strategies for the grid and non-grid connection. To date, no detailed information on which turbines and turbine model are most suited to be implemented that match Malaysia’s river characteristics. Besides, a large oscillation has occurred on the output current and power during dynamic steady state due to the water variation and fluctuation in the river. Hence, reducing the energy extraction and controller efficiency for stand-alone and grid-connected systems, respectively. Therefore, the study aims to analyse the different turbine's design, proposed the turbine model, and propose the potential control strategies for stand-alone and grid-connected hydrokinetic energy harnessing in the river. In this work, three types of vertical axis turbines, including the H-Darrieus, Darrieus, and Gorlov with twelve different NACA and NREL hydrofoils, were analysed using the QBlade and MATLAB software, respectively. The effect of symmetrical and non-symmetrical geometry profiles, hydrofoils thicknesses, and turbine solidities have been compared to choose one of the best option turbines based on the highest power coefficient (CP) and a torque coefficient (CM), respectively. Subsequently, the turbine power model generalised equation has been proposed to represent the hydrokinetic turbine characteristic using a polynomial estimation equation. On the other hand, the MPPT control strategy is employed for the off-grid system using the sensorless method. The circuit topology based on an uncontrolled rectifier with the DC boost converter is implemented to regulate the rectifier output voltage through duty ratio. Subsequently, the metaheuristic method based on the combination of the Hill-Climbing Search (HCS) MPPT algorithm and the Fuzzy Logic Controller has been proposed to produce a variable step size compared to the fixed step size in conventional HCS algorithm. On the contrary, the dynamic model of the grid-connected hydrokinetic system has been linearised for small-signal stability analysis. The eigenvalues analysis-based approached has been applied to evaluate the system stability due to the small disturbance. The PI controller with the eigenvalues tracing method has been proposed to improve the system stability by reducing the oscillation frequency. The research outcomes indicated that the H-Darrieus with NACA 0018 was the best turbine for energy conversion in the river. Besides, the HCS-Fuzzy MPPT algorithm improved the energy extraction up to 88.30 % as well as reduced 74.47 % the oscillation compared to the SS-HCS MPPT. The stability of grid-connected hydrokinetic energy harnessing was improved up to 63.63 % by removing the oscillation frequency at states of λ8,9,10,11 as well as reducing 40.1 % oscillation of the generator stator current at the rotor side controller (RSC)

A Review of Energy Management Systems and Organizational Structures of Prosumers

Thisreviewprovidesthestateoftheartofenergymanagementsystems(EMS)and organizationalstructuresofprosumers.Integrationofrenewableenergysources(RES)intothe householdbringsnewchallengesinoptimaloperation,powerquality,participationintheelectricity marketandpowersystemstability.AcommonsolutiontothesechallengesistodevelopanEMSwith differentprosumerorganizationalstructures.EMSdevelopmentisamultidisciplinaryprocessthat needstoinvolveseveralaspectsofobservation.Thispaperprovidesanoverviewoftheprosumer organizationalandcontrolstructures,typesandelements,predictionmethodsofinputparameters, optimizationframeworks,optimizationmethods,objectivefunctions,constraintsandthemarket environment.Specialattentionisgiventotheoptimizationframeworkandpredictionofinput parameters,whichrepresentsroomforimprovement,thatmitigatetheimpactofuncertainties associatedwithRES-basedgeneration,consumptionandmarketpricesonoptimaloperation.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.a - Per a 2030, augmentar la cooperació internacional per tal de facilitar l’accés a la investigació i a les tecnolo­gies energètiques no contaminants, incloses les fonts d’energia renovables, l’eficiència energètica i les tecnologies de combustibles fòssils avançades i menys contaminants, i promoure la inversió en infraestructures energètiques i tecnologies d’energia no contaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Big data analytics for demand response in smart grids

The transition to an intelligent, reliable and efficient smart grid with a high penetration of renewable energy drives the need to maximise the utilisation of customers’ demand response (DR) potential. More so, the increasing popularity of smart meters deployed at customers’ sites provides a vital resource where data driven strategies can be adopted in enhancing the performance of DR programs. This thesis focuses on the development of new methods for enhancing DR in smart grids using big data analtyics techniques on customers smart meter data. One of the main challenges to the effective and efficient roll out of DR programs particularly for peak load reduction is identifying customers with DR potential. This question is answered in this thesis through the proposal of a shape based clustering algorithm along with novel features to target customers. In addition to targeting customers for DR programs, estimating customer demand baseline is one of the key challenges to DR especially for incentive-based DR. Customer baseline estimation is important in that it ensures a fair knowledge of a customers DR contribution and hence enable a fair allocation of benefits between the utility and customers. A Long Short-Term Memory Recurrent Neural Network machine learning technique is proposed for baseline estimation with results showing improved accuracy compared to traditional estimation methods. Given the effect of demand rebound during a DR event day, a novel method is further proposed for baseline estimation that takes into consideration the demand rebound effect. Results show in addition to customers baseline accurately estimated, the functionality of estimating the amount of demand clipped compared to shifted demand is added

Distribuirana estimacija stanja u elektroenergetskimn sistemima upotrebom probabilističkih grafičkih modela

We present a detailed study on application of factor graphs and the belief propagation (BP) algorithm to the power system state estimation (SE) problem. We start from the BP solution for the linear DC model, for which we provide a detailed convergence analysis. Using BPbased DC model we propose a fast real-time state estimator for the power system SE. The proposed estimator is easy to distribute and parallelize, thus alleviating computational limitations and allowing for processing measurements in real time. The presented algorithm may run as a continuous process, with each new measurement being seamlessly processed by the distributed state estimator. In contrast to the matrixbased SE methods, the BP approach is robust to illconditioned scenarios caused by significant differences between measurement variances, thus resulting in a solution that eliminates observability analysis. Using the DC model, we numerically demonstrate the performance of the state estimator in a realistic real-time system model with asynchronous measurements. We note that the extension to the non-linear SE is possible within the same framework. Using insights from the DC model, we use two different approaches to derive the BP algorithm for the non-linear model. The first method directly applies BP methodology, however, providing only approximate BP solution for the non-linear model. In the second approach, we make a key further step by providing the solution in which the BP is applied sequentially over the non-linear model, akin to what is done by the Gauss-Newton method. The resulting iterative Gauss-Newton belief propagation (GN-BP) algorithm can be interpreted as a distributed Gauss- Newton method with the same accuracy as the centralized SE, however, introducing a number of advantages of the BP framework. The thesis provides extensive numerical study of the GN-BP algorithm, provides details on its convergence behavior, and gives a number of useful insights for its implementation. Finally, we define the bad data test based on the BP algorithm for the non-linear model. The presented model establishes local criteria to detect and identify bad data measurements. We numerically demonstrate that the BP-based bad data test significantly improves the bad data detection over the largest normalized residual test.Glavni rezultati ove teze su dizajn i analiza novih algoritama za rešavanje problema estimacije stanja baziranih na faktor grafovima i „Belief Propagation“ (BP) algoritmu koji se mogu primeniti kao centralizovani ili distribuirani estimatori stanja u elektroenergetskim sistemima. Na samom početku, definisan je postupak za rešavanje linearnog (DC) problema korišćenjem BP algoritma. Pored samog algoritma data je analiza konvergencije i predloženo je rešenje za unapređenje konvergencije. Algoritam se može jednostavno distribuirati i paralelizovati, te je pogodan za estimaciju stanja u realnom vremenu, pri čemu se informacije mogu prikupljati na asinhroni način, zaobilazeći neke od postojećih rutina, kao npr. provera observabilnosti sistema. Proširenje algoritma za nelinearnu estimaciju stanja je moguće unutar datog modela. Dalje se predlaže algoritam baziran na probabilističkim grafičkim modelima koji je direktno primenjen na nelinearni problem estimacije stanja, što predstavlja logičan korak u tranziciji od linearnog ka nelinearnom modelu. Zbog nelinearnosti funkcija, izrazi za određenu klasu poruka ne mogu se dobiti u zatvorenoj formi, zbog čega rezultujući algoritam predstavlja aproksimativno rešenje. Nakon toga se predlaže distribuirani Gaus- Njutnov metod baziran na probabilističkim grafičkim modelima i BP algoritmu koji postiže istu tačnost kao i centralizovana verzija Gaus-Njutnovog metoda za estimaciju stanja, te je dat i novi algoritam za otkrivanje nepouzdanih merenja (outliers) prilikom merenja električnih veličina. Predstavljeni algoritam uspostavlja lokalni kriterijum za otkrivanje i identifikaciju nepouzdanih merenja, a numerički je pokazano da algoritam značajno poboljšava detekciju u odnosu na standardne metode

Review of MVDC applications, technologies, and future prospects

This paper presents a complete review of MVDC applications and their required technologies. Four main MVDC applications were investigated: rail, shipboard systems, distribution grids, and offshore collection systems. For each application, the voltage and power levels, grid structures, converter topologies, and protection and control structure were reviewed. Case studies of the varying applications as well as the literature were analyzed to ascertain the common trends and to review suggested future topologies. For rail, ship, and distribution systems, the technology and ability to implement MVDC grids is available, and there are already a number of case studies. Offshore wind collection systems, however, are yet able to be implemented. Across the four applications, the MVDC voltages ranged from 5–50 kV DC and tens of MW, with some papers suggesting an upper limit of 100 kV DC and hundreds of MV for distribution networks and offshore wind farm applications. This enables the use of varying technologies at both the lower and high voltage ranges, giving flexibility in the choice of topology that is required required

Non-invasive inspections: a review on methods and tools

Non-Invasive Inspection (NII) has become a fundamental tool in modern industrial maintenance strategies. Remote and online inspection features keep operators fully aware of the health of industrial assets whilst saving money, lives, production and the environment. This paper conducted crucial research to identify suitable sensing techniques for machine health diagnosis in an NII manner, mainly to detect machine shaft misalignment and gearbox tooth damage for different types of machines, even those installed in a hostile environment, using literature on several sensing tools and techniques. The researched tools are critically reviewed based on the published literature. However, in the absence of a formal definition of NII in the existing literature, we have categorised NII tools and methods into two distinct categories. Later, we describe the use of these tools as contact-based, such as vibration, alternative current (AC), voltage and flux analysis, and non-contact-based, such as laser, imaging, acoustic, thermographic and radar, under each category in detail. The unaddressed issues and challenges are discussed at the end of the paper. The conclusions suggest that one cannot single out an NII technique or method to perform health diagnostics for every machine efficiently. There are limitations with all of the reviewed tools and methods, but good results possible if the machine operational requirements and maintenance needs are considered. It has been noted that the sensors based on radar principles are particularly effective when monitoring assets, but further comprehensive research is required to explore the full potential of these sensors in the context of the NII of machine health. Hence it was identified that the radar sensing technique has excellent features, although it has not been comprehensively employed in machine health diagnosis

Impact of Grid Unbalances on Electric Vehicle Chargers

There is a global trend to reduce emissions from cars through the adoption of other alternatives, such as electric vehicles (EVs). The increasing popularity of EVs has led to a growing demand for electric vehicle chargers. EV chargers are essential for charging the batteries of EVs. Since the EV charger stays connected to the grid for long periods of time to charge the EV battery, it must be able to handle disturbances in the power grid. The goal of this paper is to present an overview of the impact of grid events on EV battery chargers. As well as the impact of grid unbalances on EV chargers, this paper also provides an overview of the impact of grid faults on other, similar power electronics interfaced resources such as PV and energy storage systems

What is a Blockchain? A Definition to Clarify the Role of the Blockchain in the Internet of Things

The use of the term blockchain is documented for disparate projects, from cryptocurrencies to applications for the Internet of Things (IoT), and many more. The concept of blockchain appears therefore blurred, as it is hard to believe that the same technology can empower applications that have extremely different requirements and exhibit dissimilar performance and security. This position paper elaborates on the theory of distributed systems to advance a clear definition of blockchain that allows us to clarify its role in the IoT. This definition inextricably binds together three elements that, as a whole, provide the blockchain with those unique features that distinguish it from other distributed ledger technologies: immutability, transparency and anonimity. We note however that immutability comes at the expense of remarkable resource consumption, transparency demands no confidentiality and anonymity prevents user identification and registration. This is in stark contrast to the requirements of most IoT applications that are made up of resource constrained devices, whose data need to be kept confidential and users to be clearly known. Building on the proposed definition, we derive new guidelines for selecting the proper distributed ledger technology depending on application requirements and trust models, identifying common pitfalls leading to improper applications of the blockchain. We finally indicate a feasible role of the blockchain for the IoT: myriads of local, IoT transactions can be aggregated off-chain and then be successfully recorded on an external blockchain as a means of public accountability when required