State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon

Smart Grid Technologies in Europe: An Overview

The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio

Computational intelligence techniques for energy storage management

Ph. D. ThesisThe proliferation of stochastic renewable energy sources (RES) such as photovoltaic and wind power in the power system has made the balancing of generation and demand challenging for the grid operators. This is further compounded with the liberalization of electricity market and the introduction of real-time electricity pricing (RTP) to reflect the dynamics in generation and demand. Energy storage sources (ESS) are widely seen as one of the keys enabling technology to mitigate this problem. Since ESS is a costly and energy-limited resource, it is economical to provide multiple services using a single ESS. This thesis aims to investigate the operation of a single ESS in a grid-connected microgrid with RES under RTP to provide multiple services. First, artificial neural network is proposed for day-ahead forecasting of the RES, demand and RTP. After the day-ahead forecast is obtained, the day-ahead schedule of energy storage is formulated into a mixed-integer linear programming and implemented in AMPL and solved using CPLEX. This method considers the impact of forecasting errors in the day-ahead scheduling. Empirical evidence shows that the proposed nearoptimal day-ahead scheduling of ESS can achieve a lower operating cost and peak demand. Second, a fuzzy logic-based energy management system (FEMS) for a grid-connected microgrid with RES and ESS is proposed. The objectives of the FEMS are energy arbitrage and peak shaving for the microgrid. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state-of-charge (SoC) of ESS, the power difference between RES and demand, and RTP. Instead of using a forecasting-based approach, the proposed FEMS is designed with the historical data of the microgrid. It determines the charge and discharge rate of the ESS in a rolling horizon. A comparison with other controllers with the same objectives shows that the proposed controller can operate at a lower cost and reduce the peak demand of the microgrid. Finally, the effectiveness of the FEMS greatly depends on the membership functions. The fuzzy membership functions of the FEMS are optimized offline using a Pareto based multi-objective evolutionary algorithm, nondominated sorting genetic algorithm- II (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy logic controller. A comparison was made against other control strategies with similar objectives are carried out at a simulation level. Empirical evidence shows that the proposed methodology can find more solutions on the Pareto front in a single run. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, UK. Furthermore, reserve service is added on top of energy arbitrage and peak shaving to the energy management system (EMS). As such multi-service of a single ESS which benefit the grid operator and consumer is achieved

Optimal Control of Hybrid Systems and Renewable Energies

This book is a collection of papers covering various aspects of the optimal control of power and energy production from renewable resources (wind, PV, biomass, hydrogen, etc.). In particular, attention is focused both on the optimal control of new technologies and on their integration in buildings, microgrids, and energy markets. The examples presented in this book are among the most promising technologies for satisfying an increasing share of thermal and electrical demands with renewable sources: from solar cooling plants to offshore wind generation; hybrid plants, combining traditional and renewable sources, are also considered, as well as traditional and innovative storage systems. Innovative solutions for transportation systems are also explored for both railway infrastructures and advanced light rail vehicles. The optimization and control of new solutions for the power network are addressed in detail: specifically, special attention is paid to microgrids as new paradigms for distribution networks, but also in other applications (e.g., shipboards). Finally, optimization and simulation models within SCADA and energy management systems are considered. This book is intended for engineers, researchers, and practitioners that work in the field of energy, smart grid, renewable resources, and their optimization and control

XVII Simposio CEA de Control Inteligente: Reunión anual del grupo de Control Inteligente del comité español de automática (CEA). Libro de Actas, León, 27-29 de junio de 2022

Al igual que en las ediciones anteriores, el XVII Simposio CEA de Control Inteligente ha tratado de mantener los objetivos propuestos por el Grupo Temático de CEA y desarrollar unas jornadas de convivencia en las que se han desarrollado actividades científicas de investigación, de formación de doctores, de relaciones con la industria y, por supuesto, actividades culturales y de relaciones sociales de todos los miembros que formamos esta comunidad científica. Este año, el lugar elegido para la celebración del Simposio ha sido la ciudad de León y le ha correspondido la organización del mismo al Grupo de Investigación SUPPRESS de la Universidad de León, dirigido por el profesor Manuel Domínguez. Con más de 90 asistentes en algunas de las actividades del Simposio, hemos conseguido batir récords de asistencia y generar un ambiente más que propicio para desarrollar distintas discusiones científicas de gran calado. Esto demuestra el interés que suscita nuestra disciplina en estos tiempos. Durante los últimos años el control inteligente está demostrando ser una herramienta esencial para contribuir a solucionar los grandes retos que se nos van a plantear en el futuro. Pero, hasta la fecha no habíamos experimentado, tan de primera mano, los efectos derivados del cambio climático, la falta de recursos energéticos y de materias primas, las pandemias, la falta de recursos hídricos, la ciberseguridad o los incendios. Por ello, más que nunca se antoja necesario reflexionar, reforzar nuestros vínculos o crear nuevas sinergias para contribuir y poner nuestro valioso conocimiento a disposición de nuestra sociedad. En este sentido nossentimos orgullosos de presentar las contribuciones tan valiosas que recoge este documento. Estas han superado todas nuestras expectativas, lo que da muestras del sentido de responsabilidad que tiene el Grupo Temático CEA de Control Inteligente con su tiemp

Design and implementation of machine learning techniques for modeling and managing battery energy storage systems

The fast technological evolution and industrialization that have interested the humankind since the fifties has caused a progressive and exponential increase of CO2 emissions and Earth temperature. Therefore, the research community and the political authorities have recognized the need of a deep technological revolution in both the transportation and the energy distribution systems to hinder climate changes. Thus, pure and hybrid electric powertrains, smart grids, and microgrids are key technologies for achieving the expected goals. Nevertheless, the development of the above mentioned technologies require very effective and performing Battery Energy Storage Systems (BESSs), and even more effective Battery Management Systems (BMSs). Considering the above background, this Ph.D. thesis has focused on the development of an innovative and advanced BMS that involves the use of machine learning techniques for improving the BESS effectiveness and efficiency. Great attention has been paid to the State of Charge (SoC) estimation problem, aiming at investigating solutions for achieving more accurate and reliable estimations. To this aim, the main contribution has concerned the development of accurate and flexible models of electrochemical cells. Three main modeling requirements have been pursued for ensuring accurate SoC estimations: insight on the cell physics, nonlinear approximation capability, and flexible system identification procedures. Thus, the research activity has aimed at fulfilling these requirements by developing and investigating three different modeling approaches, namely black, white, and gray box techniques. Extreme Learning Machines, Radial Basis Function Neural Networks, and Wavelet Neural Networks were considered among the black box models, but none of them were able to achieve satisfactory SoC estimation performances. The white box Equivalent Circuit Models (ECMs) have achieved better results, proving the benefit that the insight on the cell physics provides to the SoC estimation task. Nevertheless, it has appeared clear that the linearity of ECMs has reduced their effectiveness in the SoC task. Thus, the gray box Neural Networks Ensemble (NNE) and the white box Equivalent Neural Networks Circuit (ENNC) models have been developed aiming at exploiting the neural networks theory in order to achieve accurate models, ensuring at the same time very flexible system identification procedures together with nonlinear approximation capabilities. The performances of NNE and ENNC have been compelling. In particular, the white box ENNC has reached the most effective performances, achieving accurate SoC estimations, together with a simple architecture and a flexible system identification procedure. The outcome of this thesis makes it possible the development of an interesting scenario in which a suitable cloud framework provides remote assistance to several BMSs in order to adapt the managing algorithms to the aging of BESSs, even considering different and distinct applications

A novel power management and control design framework for resilient operation of microgrids

This thesis concerns the investigation of the integration of the microgrid, a form of future electric grids, with renewable energy sources, and electric vehicles. It presents an innovative modular tri-level hierarchical management and control design framework for the future grid as a radical departure from the ‘centralised’ paradigm in conventional systems, by capturing and exploiting the unique characteristics of a host of new actors in the energy arena - renewable energy sources, storage systems and electric vehicles. The formulation of the tri-level hierarchical management and control design framework involves a new perspective on the problem description of the power management of EVs within a microgrid, with the consideration of, among others, the bi-directional energy flow between storage and renewable sources. The chronological structure of the tri-level hierarchical management operation facilitates a modular power management and control framework from three levels: Microgrid Operator (MGO), Charging Station Operator (CSO), and Electric Vehicle Operator (EVO). At the top level is the MGO that handles long-term decisions of balancing the power flow between the Distributed Generators (DGs) and the electrical demand for a restructure realistic microgrid model. Optimal scheduling operation of the DGs and EVs is used within the MGO to minimise the total combined operating and emission costs of a hybrid microgrid including the unit commitment strategy. The results have convincingly revealed that discharging EVs could reduce the total cost of the microgrid operation. At the middle level is the CSO that manages medium-term decisions of centralising the operation of aggregated EVs connected to the bus-bar of the microgrid. An energy management concept of charging or discharging the power of EVs in different situations includes the impacts of frequency and voltage deviation on the system, which is developed upon the MGO model above. Comprehensive case studies show that the EVs can act as a regulator of the microgrid, and can control their participating role by discharging active or reactive power in mitigating frequency and/or voltage deviations. Finally, at the low level is the EVO that handles the short-term decisions of decentralising the functioning of an EV and essential power interfacing circuitry, as well as the generation of low-level switching functions. EVO level is a novel Power and Energy Management System (PEMS), which is further structured into three modular, hierarchical processes: Energy Management Shell (EMS), Power Management Shell (PMS), and Power Electronic Shell (PES). The shells operate chronologically with a different object and a different period term. Controlling the power electronics interfacing circuitry is an essential part of the integration of EVs into the microgrid within the EMS. A modified, multi-level, H-bridge cascade inverter without the use of a main (bulky) inductor is proposed to achieve good performance, high power density, and high efficiency. The proposed inverter can operate with multiple energy resources connected in series to create a synergized energy system. In addition, the integration of EVs into a simulated microgrid environment via a modified multi-level architecture with a novel method of Space Vector Modulation (SVM) by the PES is implemented and validated experimentally. The results from the SVM implementation demonstrate a viable alternative switching scheme for high-performance inverters in EV applications. The comprehensive simulation results from the MGO and CSO models, together with the experimental results at the EVO level, not only validate the distinctive functionality of each layer within a novel synergy to harness multiple energy resources, but also serve to provide compelling evidence for the potential of the proposed energy management and control framework in the design of future electric grids. The design framework provides an essential design to for grid modernisation

Contributions for microgrids dynamic modelling and operation

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Control and Optimization of Energy Storage in AC and DC Power Grids

Energy storage attracts attention nowadays due to the critical role it will play in the power generation and transportation sectors. Electric vehicles, as moving energy storage, are going to play a key role in the terrestrial transportation sector and help reduce greenhouse emissions. Bulk hybrid energy storage will play another critical role for feeding the new types of pulsed loads on ship power systems. However, to ensure the successful adoption of energy storage, there is a need to control and optimize the charging/discharging process, taking into consideration the customer preferences and the technical aspects. In this dissertation, novel control and optimization algorithms are developed and presented to address the various challenges that arise with the adoption of energy storage in the electricity and transportation sectors. Different decentralized control algorithms are proposed to manage the charging of a mass number of electric vehicles connected to different points of charging in the power distribution system. The different algorithms successfully satisfy the preferences of the customers without negatively impacting the technical constraints of the power grid. The developed algorithms were experimentally verified at the Energy Systems Research Laboratory at FIU. In addition to the charge control of electric vehicles, the optimal allocation and sizing of commercial parking lots are considered. A bi-layer Pareto multi-objective optimization problem is formulated to optimally allocate and size a commercial parking lot. The optimization formulation tries to maximize the profits of the parking lot investor, as well as minimize the losses and voltage deviations for the distribution system operator. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location is also performed. Furthermore, in this dissertation, energy management strategies of the onboard hybrid energy storage for a medium voltage direct current (MVDC) ship power system are developed. The objectives of the management strategies were to maintain the voltage of the MVDC bus, ensure proper power sharing, and ensure proper use of resources, where supercapacitors are used during the transient periods and batteries are used during the steady state periods. The management strategies were successfully validated through hardware in the loop simulation