MD Shoeb

Variable distributed energy resources (DERs) such as solar and wind are rapidly becoming common in low-inertia microgrids (MGs) worldwide as the world explores cost-efficient and sustainable energy solutions. Additionally, the urgent need for greenhouse gas emission reductions and the availability of vast renewable energy resources serve as motivations to harvest these renewabies. Solar photovoltaic (PV) technology is one of the most utilised due to the significant drop in prices for solar PV systems. However, the variable nature of solar PV generation due to cloud movements introduces rapid ramp events thus affecting power system management. Nowcasting, which is defined as very short-term solar irradiance forecasting, together with controllable DERs, can be integrated into MGs to possibly address these ramp events and enable an increase in PV penetration levels in MGs. This study outlines the benefits and limitations of nowcasting and its applications in the control of MGs obtained from a survey. From the survey, it was evident that sky camera-based nowcasting technology is still new but has potential in MG applications. Applications of nowcasting in MGs included ramp rates control and scheduling of spinning reserves. Additionally, PV penetration levels may be increased if nowcasting tools are incorporated into the control of MGs. However, the main barrier impeding the utilisation of sky camera-based nowcasting technology is the lack of experience and lack of demonstrated reliability.

Photovoltaic (PV) hosting capacity determination for power systems often entails power network simulation work that is onerous due to the typically large number of medium voltage (MV)/low voltage (LV) feeders, even for small isolated diesel networks. Dividing the power system into several parts has proven itself useful as a means of simulating the system as a whole. The availability of consistent data containing important attributes (e.g., network configuration, load and customer data) for each feeder and identifying the relevant attributes is crucial in clustering. These attributes form the input, on which the clustering is performed. This paper presents the observation of clustering a unique remote network in Western Australia in which PV penetration has been significantly increased over the last decade. K-means clustering methodology is utilised in this work to assign the feeders into different groups. Thirteen different attributes for each feeder were available as a basis for the clustering. This paper also identified the suitable representative feeders for each cluster, which can be used for system-wide simulation work.

Large remote areas may consist of multiple microgrids (MG), each owned and operated by a different owner (operator) in standalone mode. However, they can be coupled provisionally to support each other during overloading or excessive generation by renewables. This paper proposes a technique to maintain the voltage and frequency (VF) of each MG within the desired range in such situations. To this end, a multilevel optimization approach is utilized that determines the most suitable actions to recover the troubled MG. At first, the proposed technique utilizes the local resources of an MG, such as adjusting the generation levels of the dispatchable sources, charging or discharging of existing battery energy storages, and determining the best configuration for the microgrid's network. It then proceeds to determine the required support from neighboring healthy MGs if the local supports are not adequate. In that case, the proposed technique optimally selects the MGs (and level of support) to be coupled to support the troubled MG(s). In this regard, it considers the trading cost, reliability, emission and VF of the prospective coupled MG's network while forming an MG cluster. The performance of the developed technique is evaluated through numerical analyses in MATLAB.

Battery energy storage system has become an important part of modern energy system with the growth of renewable energy and electric vehicle (EV). With the development of recent battery technologies, the improvement in overall battery charging efficiency and its cost, have become critical issues because, as the charger efficiency increases, the charging time and electricity cost decreases. Furthermore, power electronics based chargers can cause line current distortions and harmonic issues in the ac power system; and hence efficient and low-distortion smart chargers are needed to minimize power system disturbances. The aim of this paper is to study and analyze the conventional charging algorithms and the power converter topologies available in practice to design a fast, effective and efficient battery charger for EV/Microgrid/Energy storage applications. An LC Parallel Resonant Converter (PRC) can offer an effective solution for designing a fast and reliable battery charger with simple control circuits and techniques. The distinct feature of the proposed topology is that, the converter can operate in open loop while maintaining a constant charging current and hence any of the fast charging algorithms can be easily implemented without any complex controller and sensors.

The optimal operation of a standalone microgrid is often governed by the central controller, which optimizes the set-points of the local controllers of the distributed generators based on the available and predicted data. However, due to the uncertainty of loads and renewable generations, the optimized set-points may not be valid for long periods. This paper proposes a technique to readjust the dispatch of the suitable generation units, between the optimizations, to support load changes. To this end, the potential field concept is used by the loads to select the suitable generation units to make the decision making very quick. The decision is made based on different criteria such as cost, reliability, emission, and power loss. This process requires low computational efforts and can be done instantly. Besides, a periodic optimization is performed by the MG's central controller to retune the whole system and reconfirm the optimal operation. Numerical analysis has been carried out on a sample microgrid to validate the performance of the proposed technique.

The availability of solar energy has encouraged the application of renewable energy technologies in the grid-connected power system. A microgrid (MG) system provides electricity to the local areas based on the load requirements and variations of local renewable energy resources. The objective of this study is to assess the technical and economic feasibility of an MG system. The economics of an MG is investigated with the aim of promoting renewable energy technologies such as photovoltaic and battery system. In view of this, the optimal configuration of the proposed MG system is achieved with the application of the HOMER software. In this study, the green technologies are applied in the grid-connected system that serves a medium-sized residential building. It can be seen from the study results that the proposed configuration of the MG system is cost-effective and environmentally friendly. This leads to the improvement in the annual cost savings and reduction of energy purchased, cost of energy, total net present cost and greenhouse gas emissions respectively.

An autonomous microgrid is susceptible to the uncertainties in the variations of its demand and the generation of its non-dispatchable renewable sources. Such events can result in the voltage or frequency of the microgrid to exceed beyond their allowed ranges. Some methods are proposed in the literature to predict such events a few minutes ahead. As such, the voltage and frequency violation of a microgrid can be predicted and prevented with the introduction of an appropriately designed preventive controller. The key factor to be considered is the accuracy of the prediction tools, based on which the preventive controller is designed. This paper proposes a look-ahead controller that is complemented by considering various scenarios, as the result of considering the prediction inaccuracies and their probabilities. The sensitivity of the performance of such a preventive controller with respect to the prediction error is also studied in this paper through extensive numerical analyses in MATLAB.

This paper proposes a method to prevent the frequency and voltage excursion in autonomous microgrids beyond their predefined desired ranges, following any change in load and/or renewable energy output. The proposed technique utilizes the short-term prediction data of load and renewable generations to determine any prospective occurrence of events that violate the microgrid's predefined range of frequency or voltage. It then determines the optimal generation for dispatchable sources and the MG's most appropriate topology as first options to prevent the violation. If it is not enough, the proposed technique goes on to engage in supporting actions, such as exchanging power with neighboring microgrid, utilizing energy storages, demand response and renewable energy curtailment. It considers the technical, reliability and environmental aspects of the MG along with the operational cost, as well as the cost of supporting actions. The optimized control variables are sent to the local controller before the occurrence of the event to maintain the frequency and voltage within the desired limits.

Conventional RCC uses Type-I compensator or Integrator which cannot track the Maximum Power Point (MPP) for all the operating condition without Adaptive Gain Tuning. The Type-II compensator based closed loop control scheme of the proposed MPPT makes it robust against all types of disturbances, panel and plant parameter variations. The RCC technique is very simple to implement and the total MPPT controller can be easily implemented using analog circuitry only. However, the challenge lies in designing the compensator as the RCC technique, along with PV panel, exhibits highly non-linear dynamics. Conventional Bode plot technique is used for designing the compensator where the plant parameters (Gain and Phase) are obtained by perturbing the panel operation around the MPP at crossover frequency. This paper proposes a compensator based implementation of Dynamic Maximum Power Point Tracking (MPPT) for rapidly changing irradiation and load variation using Ripple Correlation Control (RCC) technique. The proposed control scheme has a very fast convergence and is very prompt in tracking irradiance variation and load disturbance rejection. The efficacy of the proposed MPPT and the compensator are verified by simulation.

A new technique is proposed in this paper to manage the frequency and voltage of standalone remote area microgrids within predefined limits. To this end, a multilayer approach is used to determine the most suitable actions. The proposed technique contemplates the dynamic supply adequacy and sustainability of the microgrid in addition to the operational costs. It takes action instantly after an event that violates the microgrid's voltage and/or frequency and adjusts the generation levels of the dispatchable sources and determining the best configuration for the microgrid's network. It then proceeds to determine the level of power support from available neighboring microgrids and controlling the loads, as well as the charge or discharge power of existing battery storage systems, in addition to curtailing the renewable sources in successive actions. The developed operation-stage technique uses a metaheuristic optimization. The performance of the developed technique is validated through extensive numerical analyses in MATLAB.