Energy Management Strategies in hydrogen Smart-Grids: A laboratory experience

As microgrids gain reputation, nations are making decisions towards a new energetic paradigm where the centralized model is being abandoned in favor of a more sophisticated, reliable, environmentally friendly and decentralized one. The implementation of such sophisticated systems drive to find out new control techniques that make the system “smart”, bringing the Smart-Grid concept. This paper studies the role of Energy Management Strategies (EMSs) in hydrogen microgrids, covering both theoretical and experimental sides. It first describes the commissioning of a new labscale microgrid system to analyze a set of different EMS performance in real-life. This is followed by a summary of the approach used towards obtaining dynamic models to study and refine the different controllers implemented within this work. Then the implementation and validation of the developed EMSs using the new labscale microgrid are discussed. Experimental results are shown comparing the response of simple strategies (hysteresis band) against complex on-line optimization techniques, such as the Model Predictive Control. The difference between both approaches is extensively discussed. Results evidence how different control techniques can greatly influence the plant performance and finally we provide a set of guidelines for designing and operating Smart Grids.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-

Energy management strategies for smart grids

Le monde de l’énergie est en pleine mutation. La production centralisée d’énergie électrique laisse place à une gestion décentralisée, faisant ainsi apparaître de nouveaux acteurs et défis technologiques dans le monde de l’énergie. La principale cause de cette évolution est le taux croissant des sources d'énergies renouvelables porté par la volonté de décarbonisation du système de production pour contribuer à des enjeux environnementaux majeurs. Le développement rapide des systèmes d'information est perçu comme un accélérateur qui rend possible le déploiement à grande échelle de stratégies de contrôle avancées.Cette thèse est dédiée au développement et à la validation de stratégies de contrôle avancées pour la gestion de systèmes énergétiques présents dans un réseau de distribution. Dans l’objectif d’une coordination optimale d’un grand nombre d’acteurs, associé au partage des ressources de chacun, émerge un des principaux défis qui est la gestion à grande échelle de ces systèmes. Pour répondre à ce défi, deux méthodes de commande prédictive distribuées (DMPC) sont proposées et comparées. Les deux méthodes misent sur la division d’un problème d’optimisation de grande échelle en plusieurs contrôleurs MPC locaux et un contrôleur de coordination. Les deux méthodes sont basées sur une décomposition primale et sur une décomposition duale respectivement. L’efficacité en termes de temps de calcul des deux méthodes est démontrée, ceci en vue de la grande échelle des systèmes étudiés. De plus, la modularité, la robustesse et la protection des données sont des avantages qu’offrent ces stratégies de MPC distribuées par rapport à un contrôleur MPC centralisé.Un autre défi important dans la gestion des réseaux électriques est la maitrise des incertitudes croissantes dans les réseaux d’énergie. Ces incertitudes sont principalement dues à l’intermittence des sources d’énergies renouvelables et à l’apparition des véhicules électriques avec leur besoin d’énergie fluctuant. Pour gérer ces incertitudes, des solutions techniques innovantes seront requises pour maintenir la stabilité et la qualité de service des réseaux électriques. Pour répondre à ce défi, deux systèmes de gestion d’énergie prenant en compte l’incertitude explicitement sont proposés dans cette thèse. Le premier est dédié à la gestion et au dimensionnement d’une centrale de production photovoltaïque, et le deuxième à la gestion de stations de recharge de voitures électriques. Dans les deux cas, l’incertitude est prise en compte explicitement dans la stratégie de contrôle en appliquant des algorithmes randomisés. Un comportement plus robuste et prédictible est obtenu par rapport à des approches purement déterministes.Cette thèse a été réalisée au sein de Schneider Electric en partenariat avec le Gipsa-Lab.Electricity grids are currently undergoing a profound transformation away from a centralized towards a decentralized power management paradigm. The two main drivers are the emergence of renewable energy sources and the rapid development of information systems. The latter enables the deployment of advanced control strategies, able to respond to the numerous challenges which arise for the reliable operation of the evolving electricity grids.This thesis is dedicated to the development and assessment of such advanced control strategies at distribution grid level. More precisely, energy management systems using Distributed Model Predictive Control (DMPC) and Stochastic Optimization are proposed. In order to optimally coordinate the operation of a large number of assets in a distribution grid, one challenge is to deal with the large-scale nature of the system. For this purpose, two hierarchical DMPC frameworks for resource sharing problems are proposed and compared with each other. Both of them rely on dividing a large-scale MPC problem into several local MPC problems and one coordinator problem. The two frameworks which are based on a primal- and on a dual decomposition of the initial centralized optimization problem are shown to be computationally tractable despite the large-scale nature of the system. Moreover they come along with a better modularity, safety and data privacy compared to a centralized MPC solution.Another important challenge stems from the increasing amount of uncertainties in the electricity grid. This is mainly due to the high intermittency of renewable energy sources and due to the foreseeable vehicle electrification which comes along with highly fluctuating charging needs. Dealing with those uncertainties requires innovative technical solutions in order to maintain the balance of power production and consumption at all times. In order to address this issue, two energy management systems, one for PV power plants and another one for electric vehicle charging stations, are proposed in this thesis. They explicitly take into account the uncertainties in the control strategy, using randomized algorithms. This way a robust and more predictable behavior of the systems is achieved.This Ph.D. thesis was prepared within the Gipsa-lab in partnership with Schneider-Electric in the scope of the AMBASSADOR project (www.ambassador-fp7.eu)

Energy management strategies for smart grids with renewables

Theoretical thesis.Bibliography: pages 193-210.Chapter 1 Introduction -- Chapter 2 Literature Review -- Chapter 3 Performance Analysis of an Experimental Smart Building -- Chapter 4 Energy Trading in Local Electricity Market with Renewables -- Chapter 5 Optimal Price Based Control of HVAC Systems -- Chapter 6 Conclusion and Future Work.With increasing environmental concerns raised from fossil fuel sources, the prominent feature of next - generation smart grids is to supply power from clean/renewable energy sources (i.e., solar, wind and fuel cell, etc) in order to provide economic, environmental, reliability and security benefits. To achieve these goals , future smart grids will work in highly complex and dynamic environments and will have small - capacity distributed renewable energy generators (DREGs) with non - dispatchable and intermittent characteristics. Moreover, the utilization of DREGs on a large - scale helps to flatten peak demand to avoid substantial overcapacity in the size of a power system due to high aggregated peak demand. However, DREGs need to manage, and they required interaction with each other, with storage systems and an energy provider for improved asset utilization and energy efficiency. In this context, an efficient demand-side management system (DSMS) is essential for coordinate control of DREGs and responsive loads to maximize the system's utilization and reliability in a smart grid. Fundamentally, demand-side management (DSM) is a process of shifting/reshaping electrical loads and utilizing new technologies to reduce power bills, overall operational costs and increase energy efficiency. This thesis addresses the challenges of developing a framework for optimal DSMS by modeling the energy usage behavior of self-interested distributed entities through studying the propriety DREGs and consumers in a smart grid. The major contributions of this research are given below. The first contribution of this research is to develop an algorithm for analyzing the performance of an experimental smart building through real-time data analysis, and then recommend possible measures to improve its energy efficiency. It focusses on the performance gap in terms of energy efficiency and the criticalities related to the characteristics of chosen devices and demand management strategies adopted. In addition, new technologies (to enhance DREGs production), coordinated measures (to improve building energy management system) and transactive control (to control the building's responsive load) are proposed. The scientific analysis of proposed recommendations for an intelligent energy management system demonstrates significant energy and cost savings for smart buildings. The second contribution of this research is to present a three-level hierarchical energy-trading framework for encouraging the owners of DREGs to voluntarily take part in an energy trading process. The developed strategy captures the complex interactions between the owners of geographically DREGs and the aggregator in the smart grid using a non-cooperative contract theoretic approach. Moreover, a dynamic pricing scheme is developed that the aggregator can utilize to incentivize the owners of DREGs and a distributed algorithm is proposed to enable the energy-trading process. Various categories, types, and constraints of DREGs, different trading scenarios and wholesale price impact on trading are considered in the analysis for practical applications. The solution of the developed scheme shows that socially optimal energy management for both trading partners can be achieved. The third contribution of this research is to develop an occupant's comfort aware energy imbalance management scheme for efficiently curtailing responsive loads of commercial buildings with the market price. The aim is to reduce the aggregated and peak demand to deal with energy imbalance problem in case of DREGs intermittency and/or power shortage from the grid while providing the desired quality of service. To achieve this goal, an intelligent and new price-based demand response (PBDR) control strategy is proposed to optimize the responsive load scheduling. Occupants' varying thermal preferences in the response of price signals are considered and modeled using the artificial neural network (ANN) to integrate into the optimal scheduling problem. The performance of the proposed management techniques is tested in real Australian power distribution networks under real load, weather conditions, and electricity tariff structure. The developed models, algorithms, and techniques can capture the different cost-benefit trade-offs that exist for efficiently managing buildings energy in a smart grid. These strategies have shown significant performance improvement when compared with existing solutions. The work in this thesis demonstrates that modeling power usage behavior of distributed entities in a smart grid for robust DSMS is both possible and beneficial for increasing the energy efficiency of smart buildings in a smart grid.1 online resource (xxv, 211 pages) colour illustration

Fundamentals for the design of energy management strategies for smart grids based on predictive control techniques. Methodology and case studies (EMS validation test)

This document compiles all the simulations performed to validate the EMS developed in the paper "Fundamentals for the design of energy management strategies for smart grids based on predictive control techniques. Methodology and case studies"This work was supported in part by grant PID2020-116616RB-C31 and grant PID2021-124908NB-I00 founded by MCIN/AEI/10.13039/501100011033 and by ‘‘ERDF A way of making Europe’’; by the Generalitat Valenciana regional government through project CIAICO/2021/064, by Andalusian Regional Program of R+D+i (P20- 00730), and by the project “The green hydrogen vector. Residential and mobility application”, approved in the call for research projects of the Cepsa Foundation Chair of the University of Huelva. Funding for open access charge: CRUE-Universitat Politècnica de València.Pajares Ferrando, A.; Vivas Fernandez, FJ.; Blasco Ferragud, FX.; Herrero Durá, JM.; Segura Manzano, F.; Andújar Márquez, JM. (2023). Fundamentals for the design of energy management strategies for smart grids based on predictive control techniques. Methodology and case studies (EMS validation test). http://hdl.handle.net/10251/19329

Plug in electric vehicles in smart grids: Energy management

This book highlights the cutting-edge research on energy management within smart grids with significant deployment of Plug-in Electric Vehicles (PEV). These vehicles not only can be a significant electrical power consumer during Grid to Vehicle (G2V) charging mode, they can also be smartly utilized as a controlled source of electrical power when they are used in Vehicle to Grid (V2G) operating mode. Electricity Price, Time of Use Tariffs, Quality of Service, Social Welfare as well as electrical parameters of the network are all different criteria considered by the researchers when developing energy management techniques for PEVs. Risk averse stochastic energy hub management, maximizing profits in ancillary service markets, power market bidding strategies for fleets of PEVs, energy management of PEVs in the presence of renewable energy in distribution lines or microgrids and loss minimization in distribution networks based on smart coordination approaches using real time energy prices are some of the attractive and novel topics explored in this book. It will be an excellent reference for graduate students, researchers and industry professionals who are interested in getting a snapshot view of today’s latest research on applying various smart energy management strategies for smart grids with high penetration of PEVs

Plug In Electric Vehicles in Smart Grids: Energy Management

This book highlights the cutting-edge research on energy management within smart grids with significant deployment of Plug-in Electric Vehicles (PEV). These vehicles not only can be a significant electrical power consumer during Grid to Vehicle (G2V) charging mode, they can also be smartly utilized as a controlled source of electrical power when they are used in Vehicle to Grid (V2G) operating mode. Electricity Price, Time of Use Tariffs, Quality of Service, Social Welfare as well as electrical parameters of the network are all different criteria considered by the researchers when developing energy management techniques for PEVs. Risk averse stochastic energy hub management, maximizing profits in ancillary service markets, power market bidding strategies for fleets of PEVs, energy management of PEVs in the presence of renewable energy in distribution lines or microgrids and loss minimization in distribution networks based on smart coordination approaches using real time energy prices are some of the attractive and novel topics explored in this book. It will be an excellent reference for graduate students, researchers and industry professionals who are interested in getting a snapshot view of today’s latest research on applying various smart energy management strategies for smart grids with high penetration of PEVs