Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

ENERGY JUSTICE AND U.S. ENERGY POLICY: CASE STUDY APPLICATIONS EXPLORING U.S. ENERGY POLICY THROUGH AN ENERGY JUSTICE FRAMEWORK

This thesis presents three examples of U.S. energy policy and demonstrates how these policies violate the principles of energy justice. First, requiring only Federal agencies to obtain a percentage of energy production from renewables violates the distributive energy justice principle through a lack of a federal renewable energy policy which distributes the potential for unequal electrical grid failure to populations. Second, U.S. energy policy violates the procedural energy justice principle through inequitable participation and poor knowledge dissemination that, in some cases, contributes to stagnant renewable targets during the decision-making process and inequitable distribution of the benefits associated with renewable energy arguably resulting from differential representation of economic groups in policy decision making. Third, the United States’ continued reliance on and subsidization of fossil fuel extraction and use, violates the prohibitive energy justice principle by causing physical harm to humans and the environment. Finally, a lack of federal renewable energy policy hinders comprehensive energy policy including diversifying the U.S. renewable energy portfolios. Considering energy policy through the framework of energy justice offers a means of evaluating existing policy and can improve future energy policy decision-making. Demanding energy justice ensures that all populations have equitable distribution, participation, and access to affordable, efficient, and clean energy technologies that contribute to obtaining basic needs

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

: Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid’s security, reliability and resilienc

Exploring Cyber Security Issues and Solutions for Various Components of DC Microgrid System

Nowadays, considering the growing demand for the DC loads and simplified interface with renewable power generation sources, DC microgrids could be cost effective solution for the power supply in small scale area. the supervisory control and data acquisition (SCADA) system maintain the bidirectional power communication through the internet connectivity with the microgrid. However, this intelligent and interactive feature may pose a cyber-security threat to the power grid. this work aims to exploring cyber-security issues and their solutions for the DC microgrid system. To mitigate the adverse effects of various cyber-attacks such as the False Data Injection (FDI) attack, Distributed Denial of Service (DDoS) attack etc., two new techniques based on non-linear and proportional-integral (PI) controllers have been proposed. Simulation results obtained from MATLAB/Simulink software demonstrate the effectiveness of the proposed methods in mitigating the adverse effects of cyber-attacks on the DCMG system performance

Manufacturing of Photovoltaic Devices, Power Electronics and Batteries for Local Direct Current Power Based Nanogrid

To meet the current and future demands of electrical power for household, industrial, commercial and transport sectors, the energy infrastructure has to undergo changes in terms of generation, distribution and consumption. Due to the shortcomings of nuclear and fossil fuel based power generation, the emergence of renewable energy has provided a very lucrative option. With the advent of low-cost photovoltaics (PV) panels and our ability to generate, store and use electrical energy locally without the need for long-range transmission, the world is about to witness transformational changes in electricity infrastructures. For local nano-grids, direct current (DC) -based system has several distinct advantages that are demonstrated through theoretical and experimental results. A PV- powered and local DC power based nano-grids can be more efficient, reliable, cyber secured, and can easily adopt internet of things (IoT) platforms. With DC generation, storage and consumption, significant amount of energy can be saved that are wasted in back and forth conversion between AC and DC. In case of geomagnetic disturbances, such nano-grids will be more resilient compared to centralized distribution network. Free-fuel, i.e. sunlight, based local DC nano-grid can be the sustainable and cost effective solution for underdeveloped, developing and developed economies. To take advantage of this, the manufacturing of PV, power electronics and batteries have to follow the best practices that aid process control, quality improvement and potential cost reduction. Without proper process control, the variation will result in yield loss, inferior performance and higher cost of production. On many instances, these issues were not considered, and some technology such as perovskite solar cell, received a lot of attention as a disruptive technology. Through detailed technical and economic assessments, it was shown that the variability and lack of rigorous process control will result in a lower efficiency when perovskite thin film solar cells are connected together to form a module. Due to stability and performance reasons, it was showed the perovskite solar cell is not ideal for 2-terminal or 4-terminal multi-junction/tandem configuration with silicon cells. Power electronics also play a vital role in PV systems. The challenges and design rules for silicon carbide (SiC) and gallium nitride (GaN) based power device manufacturing were analyzed. Based on it, advanced process control (APC) based single wafer processing (SWP) tools for manufacturing SiC and GaN power devices are proposed. For energy storage, batteries play an important role in PV installation. Li-ion technology will become the preferred storage due to its capabilities. Incorporation of advanced process control, rapid thermal processing, Industrial IoT, etc. can reduce variability, improve performance and reduce quality-check failures and bring down the cost of electrochemical batteries. The combined approaches in manufacturing of PV, power electronics and batteries will have a very positive impact in the growth of PV powered DC –based nano-grids

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Digital Twins and Artificial Intelligence for Applications in Electric Power Distribution Systems

As modern electric power distribution systems (MEPDS) continue to grow in complexity, largely due to the ever-increasing penetration of Distributed Energy Resources (DERs), particularly solar photovoltaics (PVs) at the distribution level, there is a need to facilitate advanced operational and management tasks in the system driven by this complexity, especially in systems with high renewable penetration dependent on complex weather phenomena. Digital twins (DTs), or virtual replicas of the system and its assets, enhanced with AI paradigms can add enormous value to tasks performed by regulators, distribution system operators and energy market analysts, thereby providing cognition to the system. DTs of MEPDS assets and the system can be utilized for real-time and faster-than-real-time operational and management task support, planning studies, scenario analysis, data analytics and other distribution system studies. This study leverages DT and AI to enhance DER integration in an MEPDS as well as operational and management (O&M) tasks and distribution system studies based on a system with high PV penetration. DTs have been used to both estimate and predict the behavior of an existing 1 MW plant in Clemson University by developing asset digital twins of the physical system. Solar irradiance, temperature and wind-speed variations in the area have been modeled using physical weather stations located in and around the Clemson region to develop ten virtual weather stations. Finally, DTs of the system along with virtual and physical weather stations are used to both estimate and predict, in short time intervals, the real-time behavior of potential PV plant installations over the region. Ten virtual PV plants and three hybrid PV plants are studied, for enhanced cognition in the system. These physical, hybrid and virtual PV sources enable situational awareness and situational intelligence of real-time PV production in a distribution system

Distributed energy resources and the application of AI, IoT, and blockchain in smart grids

Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability

Implementation of second-life batteries as energy storage systems enhancing the interoperability and flexibility of the energy infrastructure in tertiary buildings

The main focus of this project is to evaluate the implementation of second-life batteries for a building stock enabling the energy flexibility schemes like Demand Response (DR). This project will focus particularly on how the building stock and its energy infrastructure (energy storage systems, legacy-assets, communication devices and grid architecture, among others) can participate as innovative energy solutions of the next generation of smart-grids, acting as virtual power plants (VPP) in order to deploy the distributed generation (DG) concept in the actual energy field and paving the way to unlock the demand response (DR) market in the distribution energy network. In addition, the implementation of these technologies will led to plan different business models and the scalability of them in the tertiary building sector. Battery energy storage systems (BESSs) are already being deployed for several stationary applications in a techno-economical feasible way. This project focuses in the study to obtain potential revenues from BESSs built from EVs lithium-ion batteries with varying states of health (SoH). For this analysis, a stationary BESS sizing model is done, using the parameters of a 14 kWh new battery, but also doing a comparison with parameters if the same battery would be 11.2 kWh second-life battery. The comprehensive sizing model consists of several detailed sub-models, considering battery specifications, aging and an operational strategy plan, which allow a technical assessment through a determined time frame. Therefore, battery depreciation and energy losses are considered in this techno-economic analysis. Potential economical feasible applications of new and second-life batteries, such the integration of a Building Integrated Photovoltaics (BIPV), self-consumption schemes, feed-in-tariff schemes and frequency regulation as well as their combined operation are compared. The research includes different electricity price scenarios mostly from the current Spanish energy market. The operation and integration of ICT-IoT technology upgrades is found to have the highest economic viability for this specific case study. A detailed study for this project will enhance the relevant importance of these topics in the energy field and how it will be a disruptive solution for the initial problem statement. A general context is given in order to introduce the main and specific objectives thus to trace an adequate way to follow and achieve them. The development of this master thesis will be coupled with the Demand Response Integration technologies (DRIvE) [10] H2020 EU funded project, currently on-going, considering some of the energy consumption data and initial parameters from the selected case study at COMSA Corporación office building in Barcelona, Spain