Data-driven, metaheuristic-based off-grid microgrid capacity planning optimisation and scenario analysis: Insights from a case study of Aotea-Great Barrier Island

Small privately-purchased off-grid renewable energy systems (RESs) are increasingly used for energy generation in remote areas. However, such privately-purchased stand-alone RESs are often unaffordable for households with lower incomes. While considerable attention has been devoted to a range of off-grid microgrid sizing methods, leveraging the potential of data-driven, artificial intelligence-based metaheuristic optimisation algorithms is less well-explored. Importantly, data-driven metaheuristics have the potential to produce the nearest solution to the globally optimum solution in microgrid sizing applications, which have been recognised as non-deterministic, polynomial time-hard (NP-hard) problems. Furthermore, there is a general lack of electrified transportation interventions considered during long-term grid-independent microgrid planning phases. In response, this paper introduces a novel metaheuristic-based strategic off-grid microgrid capacity planning optimisation model that is applicable to associated integrated energy and e-mobility resource plans. The formulated general off-grid microgrid sizing model is solved using a competitively selected state-of-the-art metaheuristic, namely moth-flame optimisation. To test the effectiveness of the proposed model, three independent microgrid development projects have been considered for three communities residing on Aotea-Great Barrier Island, namely Tryphena, Medlands, and Mulberry Grove. The sites of interest have different demand profiles and renewable energy potentials, with consequent changes in the technologies considered in the associate candidate pools.Comment: Electricity Engineers' Association (EEA) Conference 2022, Hamilton, New Zealand, 19-21 September 202

Electricity Network Scenarios for Great Britain in 2050

The next fifty years are likely to see great developments in the technologies deployed in electricity systems, with consequent changes in the structure and operation of power networks. This paper, which forms a chapter in the forthcoming book Future Electricity Technologies and Systems, develops and presents six possible future electricity industry scenarios for Great Britain, focussed on the year 2050. The paper draws upon discussions of important technologies presented by expert authors in other chapters of the book to consider the impact of different combinations of key influences on the nature of the power system in 2050. For each scenario there is a discussion of the effects of the key parameters, with a description and pictorial illustration. Summary tables identify the role of the technologies presented in other chapters of the book, and list important figures of interest, such as the capacity and energy production of renewable generation technologies

Design And Implementation Of Co-Operative Control Strategy For Hybrid AC/DC Microgrids

This thesis is mainly divided in two major sections: 1) Modelling and control of AC microgrid, DC microgrid, Hybrid AC/DC microgrid using distributed co-operative control, and 2) Development of a four bus laboratory prototype of an AC microgrid system. At first, a distributed cooperative control (DCC) for a DC microgrid considering the state-of-charge (SoC) of the batteries in a typical plug-in-electric-vehicle (PEV) is developed. In DC microgrids, this methodology is developed to assist the load sharing amongst the distributed generation units (DGs), according to their ratings with improved voltage regulation. Subsequently, a DCC based control algorithm for AC microgrid is also investigated to improve the performance of AC microgrid in terms of power sharing among the DGs, voltage regulation and frequency deviation. The results validate the advantages of the proposed methodology as compared to traditional droop control of AC microgrid. The DCC-based control methodology for AC microgrid and DC microgrid are further expanded to develop a DCC-based power management algorithm for hybrid AC/DC microgrid. The developed algorithm for hybrid microgrid controls the power flow through the interfacing converter (IC) between the AC and DC microgrids. This will facilitate the power sharing between the DGs according to their power ratings. Moreover, it enables the fixed scheduled power delivery at different operating conditions, while maintaining good voltage regulation and improved frequency profile. The second section provides a detailed explanation and step-by-step design and development of an AC/DC microgrid testbed. Controllers for the three-phase inverters are designed and tested on different generation units along with their corresponding inductor-capacitor-inductor (LCL) filters to eliminate the switching frequency harmonics. Electric power distribution line models are developed to form the microgrid network topology. Voltage and current sensors are placed in the proper positions to achieve a full visibility over the microgrid. A running average filter (RAF) based enhanced phase-locked-loop (EPLL) is designed and implemented to extract frequency and phase angle information. A PLL-based synchronizing scheme is also developed to synchronize the DGs to the microgrid. The developed laboratory prototype runs on dSpace platform for real time data acquisition, communication and controller implementation

Modelling and simulation of hybrid PV & BES systems as flexible resources in smartgrids - Sundom smart grid case

Ever-growing energy needs and larger penetration of renewable energy in the power grids with higher intermittency in power generation cause the need for flexible energy sources. Flexible sources such as distributed generation, demand response, electric vehicles etc. play a dominant role in providing flexibility in services such as frequency, voltage and power balance control in smart grids. Given the present state of technology and economic maturity of battery energy storage systems (BESS), has a lot of potential to fulfill increasing power systems rapid, short-term flexibility needs. In this paper, a case study on hybrid photovoltaic (PV) arrays & lithium ion based BESS as flexible energy sources are integrated in medium voltage (MV) network side in local pilot network, Sundom Smart Grid (SSG). Vaasa, Finland. Sundom Smart grid is modelled based on real time data on energy consumption and generation streamed from network. Role of batteries as a flexible energy source in the PV & BESS hybrid for power balance flexibility application is demonstrated by means of Matlab simulations.fi=vertaisarvioitu|en=peerReviewed

Intelligent Microgrids

Center for Electromechanic

Research trends on microgrid systems: a bibliometric network analysis

The numeral of academic publications in the microgrid system field has rapidly grown. A microgrid system is a group of interconnected distributed generation, loads, and energy storage operating as a single controllable entity. Many published articles recently focused on distributed generation, system control, system stability, power quality, architectures, and broader focus areas. This work analyzes microgrid: alternating current (AC), direct current (DC), and hybrid AC/DC microgrid systems with bibliometric network analysis through descriptive analysis, authors analysis, sources analysis, words analysis, and evolutionary path based on the Scopus database between 2010 and 2021. The finding helps find out the top authors and most impact sources, most relevant and frequently used in the research title, abstract, and keyword, graphically mapping the research evolved and identifying trend topic

Management of Islanded Operation of Microgirds

Distributed generations with continuously growing penetration levels offer potential solutions to energy security and reliability with minimum environmental impacts. Distributed Generations when connected to the area electric power systems provide numerous advantages. However, grid integration of distributed generations presents several technical challenges which has forced the systems planners and operators to account for the repercussions on the distribution feeders which are no longer passive in the presence of distributed generations. Grid integration of distributed generations requires accurate and reliable islanding detection methodology for secure system operation. Two distributed generation islanding detection methodologies are proposed in this dissertation. First, a passive islanding detection technique for grid-connected distributed generations based on parallel decision trees is proposed. The proposed approach relies on capturing the underlying signature of a wide variety of system events on a set of critical system parameters and utilizes multiple optimal decision tress in a parallel network for classification of system events. Second, a hybrid islanding detection method for grid-connected inverter based distributed generations combining decision trees and Sandia frequency shift method is also proposed. The proposed method combines passive and active islanding detection techniques to aggregate their individual advantages and reduce or eliminate their drawbacks. In smart grid paradigm, microgrids are the enabling engine for systematic integration of distributed generations with the utility grid. A systematic approach for controlled islanding of grid-connected microgrids is also proposed in this dissertation. The objective of the proposed approach is to develop an adaptive controlled islanding methodology to be implemented as a preventive control component in emergency control strategy for microgrid operations. An emergency power management strategy for microgrid autonomous operation subsequent to inadvertent islanding events is also proposed in this dissertation. The proposed approach integrates microgrid resources such as energy storage systems, demand response resources, and controllable micro-sources to layout a comprehensive power management strategy for ensuring secure and stable microgrid operation following an unplanned islanding event. In this dissertation, various case studies are presented to validate the proposed methods. The simulation results demonstrate the effectiveness of the proposed methodologies