Protection of Microgrids

The concept of microgrids goes back to the early years of the electricity industry although the systems then were not formally called microgrids. Today, two types of microgrids can be seen: independent and grid connected. The protection requirement of these two types differs as the protection needs of an independent microgrid are intended for protecting components and systems within the microgrid, whereas a grid connected microgrid demands both internal and external protection. The first part of this chapter is dedicated to independent microgrids. How protection devices such as residual current circuit breakers, miniature and moulded case circuit breakers, and surge protective devices should be selected for an example microgrid is discussed while referring to the relevant standards. In the next section, the protection of a grid connected microgrid is discussed. Particularly, micro-source protection, microgrid protection, loss of mains protection and fault ride-through requirements are discussed while referring to two commonly used distributed generator connection codes. An example with simulations carried out in the IPSA simulation platform was used to explain different protection requirements and calculation procedures. Finally, grounding requirements are discussed while referring to different interfacing transformer connections and voltage source inverter connections

Developing and Delivering a Remote Experiment based on the Experiential Learning framework during COVID-19 Pandemic

The students following Engineering disciplines should acquire a conceptual understanding of the concepts and the processors and attitudes. There are two recognizable learning environments for students: classroom and laboratory environments. With the COVID-19 Pandemic, both environments merged to online environments, impacting students' processes and characteristic attitudes development. This paper introduces a theoretical framework based on experiential learning to plan and deliver processes online. A case study based on the power-factor correction experiment was presented. The traditional experiment that runs for 2 hours was broken into smaller tasks such as pre-lab activity, simulation exercise, PowerPoint presentation, remote laboratory activity, and final report based on the experiential learning approach. The delivery of the lab under online mode delivery was presented. Then students' performance was compared before and after the online mode of delivery. It was found that students' performance on average has a distinct improvement. In order to obtain students' reflections about the online experiential learning approach, a questionnaire that carries close and open-ended questions was administered. The majority of the students liked the approach followed and praised for allowing them to experiment in a novel way during the COVID-19

Optimizing pv-hosting capacity with the integrated employment of dynamic line rating and voltage regulation

A record amount of renewable energy has been added to global electricity generation in recent years. Among the renewable energy sources, solar photovoltaic (PV) is the most popular energy source integrated into low voltage distribution networks. However, the voltage limits and current-carrying capacity of the conductors become a barrier to maximizing the PV-hosting capacity in low voltage distribution networks. This paper presents an optimization approach to maximize the PV-hosting capacity in order to fully utilize the existing low voltage distribution network assets. To achieve the maximum PV-hosting capacity of the network, a novel method based on the dynamic line rating of the low voltage distribution network, the coordinated operation of voltage control methods and the PV re-phasing technique was introduced and validated using a case study. The results show that the proposed methodology can enhance the PV-hosting capacity by 53.5% when compared to existing practices

Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method which incorporates appliance usage patterns (AUPs) to improve performance of active load identi- fication and forecasting. In the first stage, the AUPs of a given residence were learnt using a spectral decomposition based standard NILM algorithm. Then, learnt AUPs were utilized to bias the priori probabilities of the appliances through a specifically constructed fuzzy system. The AUPs contain likelihood measures for each appliance to be active at the present instant based on the recent activity/inactivity of appliances and the time of day. Hence, the priori probabilities determined through the AUPs increase the active load identification accuracy of the NILM algorithm. The proposed method was successfully tested for two standard databases containing real household measurements in USA and Germany. The proposed method demonstrates an improvement in active load estimation when applied to the aforementioned databases as the proposed method augments the smart meter readings with the behavioral trends obtained from AUPs. Furthermore, a residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, five minutes ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique

Novel Energy Management System for a DC MicroGrid

This paper presents a design and simulation of a rule based energy management system for a dc MicroGrid that considers a cost function to reflect the battery degradation and that relates to the actual battery parameters.The derivation of the battery cost function and the utilization of that to ensure an optimum utilization of the battery energy storage were presented. The detailed description of the algorithms used to implement the EMS was presented. Simulation on PSCAD/EMTDC software was used to demonstrate the operation of the EMS both under grid connected and islanded modes. Further, the inertia support provided by the super-capacitor to avoid the collapse of the dc link of the MicroGrid was demonstrated

Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

Non-intrusive load monitoring based on low frequency active power measurements

A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on ac- tive power signal is presented. This method works e ectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the Karhunen Lo ́ eve (KL) expan- sion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible ap- pliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Pos- teriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: tracebase and reference energy disaggregation data set (REDD). The pre- sented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real household

Performance comparison of optimum power flow based on the sequential second-order cone programming in unbalanced low voltage distribution networks with distributed generators

A solution technique using sequential second-order cone programming to solve the optimum power flow problem in low voltage (LV) distribution networks with distributed generation is developed. A novel bound tightening method is suggested to get exact solutions with few iterations. A novel approximation method is suggested to increase exactness by approximating phase angle dependent components. The performance of the suggested solution method is compared with linear programming, genetic algorithm, particle swarm, sequential quadratic programming with multiple start points, and global search-based optimization methods. The exactness of the generated solutions is validated after comparison with a load flow. The proposed algorithm provides better performance in optimality, execution time, and exactness compared to other methods

Enhancing PV hosting capacity using voltage control and employing dynamic line rating

Photovoltaic (PV) system installation has encouraged to be further expedited to minimize climate change and thus, rooftop solar PV systems have been sparkled in every corner of the world. However, due to technological constraints linked to voltage and currents, the PV hosting capacity has been substantially constrained. Therefore, this paper proposes a competent approach to maximize PV hosting capacity in a low voltage distribution network based on voltage control and dynamic line rating of the cables. Coordinated voltage control is applied with an on-load tap changing transformer, and reactive power compensation and active power curtailment of PV inverters. A case study with probabilistic and deterministic assessments is carried out on a real Sri Lankan network to show how the PV hosting capacity is constrained. The findings revealed the capability of integrated voltage control schemes and dynamic line rating in maximizing hosting capacity. The study is expanded by incorporating the PV rephasing approach in conjunction with the aforementioned control techniques, and the effectiveness of PV-rephasing is clearly demonstrated. When compared to voltage control and conductor static rating, the combined rephasing, voltage control, and DLR yielded a 60% increase in PV hosting capacity

Economic feasibility of using agrivoltaics for tomato farming

Agrivoltaics or agrophotovoltaics (APV), which simply describes farming under a canopy of PV panels, has been recently gaining a wider implementation to improve farming yields as well as generate electricity on the same piece of land. The presented study undertakes an economic analysis to justify the implementation of agrivoltaics in a tomato farm. Three research cases are investigated; Case 1 is the control scenario which is just ordinary tomato farming that is used as a baseline. And then there are Cases 2 and 3, which are low‐density and high‐density agrivoltaics, respectively. The farm is irrigated from a borehole using a diesel generator in Case 1 and solar pumps in the Agrivoltaics Cases 2 and 3. The study found that tomato harvest is reduced by a minimum of 16% in agrivoltaics setup. However, this reduced harvest is compensated by the PV output. The payback period has been calculated considering the capital costs of the PV system and other operational costs within the farm, and it is found that Case 2 and Case 3 have 3 years and 3.6 years payback periods, respectively. While on the other hand, ordinary tomato farming is unattractive with a lengthy payback period of 17.5 years. Net present value analysis is also used to determine the profitability of the three scenarios over a 10‐year period, and the agrivoltaics scenarios are calculated to be profitable while ordinary tomato farming is not profitable. Therefore, this study justifies economic investment in agrivoltaics for tomato farming in Botswana