Modelling and control of a hybrid renewable energy system to supply demand of a green-building

The Proceedings of the International Congress on International Environmental Modelling and Software (iEMSs 2010) are available here: http://www.iemss.org/iemss2010/proceedings.htmlInternational audienceRenewable energy sources are an "indigenous" environmental option, economically competitive with conventional power generation where good wind and solar resources are available. Hybrid plants can help in improving the economic and environmental sustainability of renewable energy systems to fulfil the energy demand. The aim of this paper is to present the architecture of a Decision Support System (DSS) that can be used for the optimal energy management at a local scale through the integration of different renewable energy sources. The integrated model representing a hybrid energy generation system connected to the grid is developed. It consists of PV and solar thermal modules, wind turbine and biomass plant. Moreover, a framework is presented for the optimization of the different ways to ensure the electrical and thermal energy demand of the microgrid as well as the water demand, with specific reference to two main cases for the real time energy optimal control: the presence/absence of a storage system. Finally, the optimization model has been applied to a case study

A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events

The increasing electricity use and reliance on intermittent renewable energy sources challenge power grid management during peak demand, making Demand Response programs and energy conservation measures essential. This research combines distributed optimization using ADMM with deep learning models to plan indoor temperature setpoints effectively. A two-layer hierarchical structure is used, with a central building coordinator at the upper layer and local controllers at the thermal zone layer. The coordinator must limit the building's maximum power by translating the building's total power to local power targets for each zone. Local controllers can modify the temperature setpoints to meet the local power targets. While most algorithms are either centralized or require prior knowledge about the building's structure, our approach is distributed and fully data-driven. The proposed algorithm, called Distributed Planning Networks, is designed to be both adaptable and scalable to many types of buildings, tackling two of the main challenges in the development of such systems. The proposed approach is tested on an 18-zone building modeled in EnergyPlus. The algorithm successfully manages Demand Response peak events.Comment: 15 pages, 9 figures, to be published in IEEE Transactions on Automation Science and Engineerin

Reinforcement Learning Based Penetration Testing of a Microgrid Control Algorithm

Microgrids (MGs) are small-scale power systems which interconnect distributed energy resources and loads within clearly defined regions. However, the digital infrastructure used in an MG to relay sensory information and perform control commands can potentially be compromised due to a cyberattack from a capable adversary. An MG operator is interested in knowing the inherent vulnerabilities in their system and should regularly perform Penetration Testing (PT) activities to prepare for such an event. PT generally involves looking for defensive coverage blindspots in software and hardware infrastructure, however the logic in control algorithms which act upon sensory information should also be considered in PT activities. This paper demonstrates a case study of PT for an MG control algorithm by using Reinforcement Learning (RL) to uncover malicious input which compromises the effectiveness of the controller. Through trial-and-error episodic interactions with a simulated MG, we train an RL agent to find malicious input which reduces the effectiveness of the MG controller

A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System

Components of cyber physical systems, which affect real-world processes, are often exposed to the internet. Replacing conventional control methods with Deep Reinforcement Learning (DRL) in energy systems is an active area of research, as these systems become increasingly complex with the advent of renewable energy sources and the desire to improve their efficiency. Artificial Neural Networks (ANN) are vulnerable to specific perturbations of their inputs or features, called adversarial examples. These perturbations are difficult to detect when properly regularized, but have significant effects on the ANN\u27s output. Because DRL uses ANN to map optimal actions to observations, they are similarly vulnerable to adversarial examples. This work proposes a novel attack technique for continuous control using Group Difference Logits loss with a bifurcation layer. By combining aspects of targeted and untargeted attacks, the attack significantly increases the impact compared to an untargeted attack, with drastically smaller distortions than an optimally targeted attack. We demonstrate the impacts of powerful gradient-based attacks in a realistic smart energy environment, show how the impacts change with different DRL agents and training procedures, and use statistical and time-series analysis to evaluate attacks\u27 stealth. The results show that adversarial attacks can have significant impacts on DRL controllers, and constraining an attack\u27s perturbations makes it difficult to detect. However, certain DRL architectures are far more robust, and robust training methods can further reduce the impact.12 pages, 5 figure

Modelling and control of a hybrid renewable energy system to supply demand of a green-building

Renewable energy sources are an “indigenous” environmental option, , economically competitive with conventional power generation where good wind and solar resources are available. Hybrid plants can help in improving the economic and environmental sustainability of renewable energy systems to fulfil the energy demand. The aim of this paper is to present the architecture of a Decision Support System (DSS) that can be used for the optimal energy management at a local scale through the integration of different renewable energy sources. The integrated model representing a hybrid energy generation system connected to the grid is developed. It consists of PV and solar thermal modules, wind turbine and biomass plant. Moreover, a framework is presented for the optimization of the different ways to ensure the electrical and thermal energy demand of the microgrid as well as the water demand, with specific reference to two main cases for the real time energy optimal control: the presence/absence of a storage system. Finally, the optimization model has been applied to a case study

Système d'aide à la décision pour la durabilité des systèmes énergétiques renouvelables et des infrastructures d'hydrogène : modélisation, contrôle et analyse de risques

Thèse en co-tutelle : école nationale supérieure des mines de Paris, France et Università degli studi di Genova, ItaliaA transition to a renewable based energy system is crucial. Hydrogen produced from renewable energy sources (RES) offers the promise of a clean, sustainable energy carrier that can be produced from domestic energy resources around the globe. The production of hydrogen from renewable energy resources is not well understood. This complexity that exists comes from many facts such that related to the intermittent behaviour of renewable energy resources. The alternative fuels and energy carriers that are produced from the RES are challenging for the sustainable development of renewable energy. These systems need to be better investigated to first manage the flux of renewable energy and hence produce alternative fuel and energy. In addition, attention must be given to the technical feasibility of the hydrogen supply chain, which is mainly driven by uncertain, but clean solar and wind energy resources. Furthermore, the infrastructure of hydrogen presents many challenges and defies that need to be overcome for a successful transition to a future hydrogen economy. These challenges are mainly due to the existence of many technological options for the production, storage, transportation and end users. Given this main reason, it is essential to understand and analyse the hydrogen supply chain (HSC) in advance, in order to detect the important factors that may play increasing role in obtaining the optimal configuration.Compte tenu du caractère non durable des systèmes énergétiques actuel, il est nécessaire d'engager une transition énergétique durable fondée sur les ressources d'énergies renouvelables. L'hydrogène produit à partir des énergies renouvelables offre une solution prometteuse pour satisfaire les objectifs mondiaux de réduction des émissions de gaz à effet de serre et assurer une sécurité énergétique d'approvisionnement. L'utilisation d'un vecteur énergétique tel que l'hydrogène couplé aux ressources renouvelables offre une variété d'avantages sur plusieurs échelles. L'hydrogène a le potentiel de permettre l'exploitation des ressources renouvelables dans le secteur de transport. En effet, l'hydrogène dispose d'un potentiel de remplacer les carburants fossiles, assurant ainsi une réduction des émissions polluantes. L'hydrogène peut être alors une solution pour les défis énergétiques actuels, mais pour cela des barrières doivent être encore surmontées. Cette transition s'accompagne de plusieurs défis qui devraient être surmontés comme ceux liés aux caractères intermittents des ressources renouvelables. Une attention particulière doit être accordée à la faisabilité technique de la chaîne d'approvisionnement en hydrogène, qui est principalement entourée par le caractère intermittent des ressources renouvelables. Par ailleurs, l'infrastructure d'hydrogène présente de nombreuses difficultés qui doivent être surmontées pour une transition réussite vers une économie dépendante de l'hydrogène. Ces difficultés sont principalement dues à des obstacles purement économiques ainsi qu'à l'existence de nombreuses options technologiques pour la production, le stockage, le transport et l'utilisation d'hydrogène. Pour cette raison principale, il est primordial de comprendre et d'analyser la chaîne logistique d'hydrogène à l'avance, afin de détecter les facteurs importants qui peuvent jouer un rôle croissant dans l'élaboration d'une configuration optimale. Notre recherche est essentiellement focalisée sur la question suivante : Comment mettre en place un futur énergétique durable intégrant les énergies renouvelables et l'hydrogène?. Cette question est abordée par le biais du développement de nouveaux systèmes énergétiques fondés sur des innovations radicales. Ainsi, dans quel contexte l'infrastructure de l'hydrogène doit être développée en intégrant les critères liés aux risques de l'hydrogène

Models, methods and approaches for the planning and design of the future hydrogen supply chain

International audienceThe infrastructure of hydrogen presents many challenges and defies that need to be overcome for a successful transition to a future hydrogen economy. These challenges are mainly due to the existence of many technological options for the production, storage, transportation and end users. Given this main reason, it is essential to understand and analyze the hydrogen supply chain (HSC) in advance, in order to detect the important factors that may play increasing role in obtaining the optimal configuration. The objective of this paper is to review the current state of the available approaches for the planning and modeling of the hydrogen infrastructure. The decision support systems for the HSC may vary from paper to paper. In this paper, a classification of models and approaches has been done, and which includes mathematical optimization methods, decision support system based on geographic information system (GIS) and assessment plans to a better transition to HSC. The paper also highlights future challenges for the introduction of hydrogen. Overcoming these challenges may solve problems related to the transition to the future hydrogen economy