Design and Early Simulations of Next Generation Intelligent Energy Systems

Design and Early Simulations of Next Generation Intelligent Energy Systems

Rafik Fainti (Department of Electrical and Computer Engineering, University of Thessaly, Volos, Greece), Antonia Nasiakou (Department of Electrical and Computer Engineering, University of Thessaly, Volos, Greece), Eleftherios Tsoukalas (Department of Electrical and Computer Engineering, University of Thessaly, Volos, Greece) and Manolis Vavalis (Department of Electrical and Computer Engineering, University of Thessaly, Volos, Greece)
DOI: 10.4018/ijmstr.2014040104
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Abstract

The aim of this paper is twofold. Firstly, to briefly present the overall objectives and the expected outcome of an on-going effort concerning the design the implementation and the analysis of next generation intelligent energy systems based on anticipatory control and a set of ICT emerging technologies and innovations. Secondly, to describe an early proof-of-concept implementation and the preliminary experimentation of a simulation platform focused on holistic detailed studies of electric energy markets. The proposed platform allows us to elucidate issues related to the open and smart participation of producers and consumers on large-scale energy e-markets. Based on an existing simulation system we present the required theoretical studies, the enabling technologies, and the practical tools that contribute to the development of such a platform capable of truly large scale simulations that cover real life scenarios and stress most components and modules of next generation smart energy markets. Elements of game theory are utilized to solve the optimization problem related to the maximization of the social welfare of producers and consumers. Selected simulation results associated with the basic required characteristics of our platform are presented.
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1. Introduction

Concerns about global climate change, resource limitations and economic stability drive a worldwide transition to smart energy. In advanced power systems, or smart grids, renewable generation, smart meters and novel storage technologies are modernizing the electricity infrastructure at an unprecedented rate. No one can really predict what the power infrastructure of the future would look like. It will likely be not a single network, but a network of networks, a network of smart grids (a system referred to as energy internet). The result will be a more difficult network to manage – in addition to stochasticity on the demand side; it will have stochasticity on the generation side. Yet, like existing power networks, the advanced power systems of the future would have to balance efficiency and reliability. And when this happens, reliability will always be given the priority. Even with this assumption, reliability is not guaranteed. It has been estimated that annual loss due to service interruption in the US is about $80 billion. A comparable quantity is estimated in Europe. The cause for each interruption might be different. But the principal reason is that generation and delivery capacity fails to provide sufficient safety margins, which may be further reduced due to poor management and bad regulation. Investing on more generators and better delivery systems (transmission and distribution) is an answer, which suffers from some severe limitations.

It is expected that there will be an unavoidable and unprecedented increase on power demand (particularly due to electrification of transportation) and consumers will have higher expectations for the service, both on quality and quantity. On the other hand, resources are limited and the investment on exploiting them is a lengthy and expensive process. In this view of the future, it is very likely that safety margins may be reduced even further and more service interruptions will occur. A growing number of basic and applied research projects are underway to address this challenge. Most are focusing on responding to a plethora of emerging needs in smart grids components, sensors and devices. A significant number considers system-level management issues (Dimeas, Hatziargiriou, & Weidlich, 2011). The proposed approach, however, is fundamentally different from past and ongoing ones. Inspired by the observation that a significant portion of the energy generated is lost due to inefficiency in operations, it considers energy efficiency as a new energy resource the development of which has dual storage and generation characteristics with significant benefits to both network reliability and efficiency.

Demand based dynamic pricing for both residential and commercial consumers is one of the main characteristics of liberalized electricity markets with crucial impact on the whole energy network and drastic effects on virtually all of its components. To assess such an overall impact a plethora of theoretical studies have been published and a series of on-site case studies have already be performed and analyzed. To the best of our knowledge not many associated simulation studies have been performed. In particular, no truly large scale simulations results that involve real-life configuration data concerning the transmission and distribution network over extensive geographical areas with thousands or even millions of customers are available.

The aim of the second part of this paper is to present the design, the initial implementation and the preliminary experimentation of an integrated simulation platform that

  • Pays special attention to decentralized markets built on the principles of competitiveness where producers and consumers can participate via auctions, in order to determine the price and the quantity of the electric energy to be exchanged.

  • Involves all market participants and therefore provides support for both the transmission and the distribution networks and their common activities.

  • Enjoys several crucial characteristics such as accuracy, efficiency, expandability, robustness and follows the basic physical, technical and societal principles involved.

  • It is open architecture and open source allowing software reuse and collaboration at large.

  • Uses game theory techniques to develop optimal market strategies that maximize its participants’ social welfare and focus on the producers’ profit.

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