391477 Optimum Management of Energy Decisions Empowered By Internet of Things (IoT) Architecture - Application on a Smart Grid with Renewable Energy Sources and Hydrogen Storage
In a networked environment where various energy sources, including renewable (RES) such as photovoltaic and wind generators, are present, there is an increased need for flexible and adaptive energy management. Besides the varying supply, due to the nature of RES, the demand also varies through time by the end user. The combination of the requirements that arise by the dynamic supply and demand in the grid formulates a highly complex environment that can be modeled as a complex system of interconnecting processes. Such a complex system has a large number of design variables and its decision-makings require real time data collected from devices, systems and processes. Thus design and operation of a multi-source energy network involves numerous decision-making at various levels and domains. As systems become more interconnected and diverse, it is important to be able to design the interactions among the various components in an efficient and robust way, prior to their deployment to a specific environment. To address this challenge it is important to rely on an architecture that considers the heterogeneity and the dynamic behavior of the involved components. The Internet of things (IoT) is considered as a promising networking paradigm that bridges the gap between the cyber and physical world and can be used as an alternative approach to the management of energy decisions in a networked environment. The implementation of an IoT enabled architecture can provide a better insight about the physical systems and their interconnection to the cyber world. In this work a generic scheme is proposed that facilitates the design and deployment of scalable, flexible and easily maintainable modular systems for the energy domain. The proposed scheme is not limited to a particular technical implementation and can be used for a wide range of applications.
The objective of this work is to develop a reliable, sustainable and adaptive architecture that will implement the optimum energy decisions in a complex networked ecosystem. More specifically, this work investigates the impact of emerging IoT on energy networked systems in a smart grid environment. To achieve this objective, the modeling of the IoT objects is discussed in order to identify the requirements of decision support systems in dynamic and distributed environments. Furthermore, the relation of the developed IoT enabled architecture and the energy management decisions are overviewed. Finally, some preliminary results are presented that show the implementation of the proposed scheme to a smart grid system with RES.
In order to implement a generic and context-aware framework that will be able to accommodate the needs of the various devices within the smart grid domain, an architectural reference model is adopted that describes the structure of the system and an application specific model is developed that describes the requirement of the energy domain. The subsystems are modeled as objects or entities of a Unified Modeling Language (UML) diagram. The operations are modeled by a Finite State Machine (FSM) and the rules of operation are represented by propositional logic. The implementation is realized by a middleware, which is situated between the low level device drivers and the upper level industrial automation software.
The developed energy management scheme (EMS), which is empowered by the IoT architecture, is implemented for the operation of a smart grid that involves three autonomous stations (nodes) with local battery storage capabilities, hydrogen generation and long term hydrogen storage options. Each station has a PV array, wind generators and a diesel generator for backup purposes. Also at one of the stations there is a Polymer Electrolyte Membrane (PEM) fuel cell and a PEM electrolyzer for the production of hydrogen. This smart grid is located at Xanthi, Greece and its operation is monitored by a supervisory control station while the Machine to Machine (M2M) communication for the decision making is implemented by the IoT enabled architecture. The performance of the involved nodes is explored and the overall operation of the grid under various operating scenarios is presented. Besides the typical operation, a series of unplanned events are also explored in order to verify the response of the developed EMS in terms of flexibility and robustness of the energy exchange at a network level.