Parham A. Mirzaei, Pengarang

An innovative soft computing system for smart energy grids cybersecurity Konstantinos Demertzis, Lazaros S. Iliadis and Vardis-Dimitrios Anezakis Department of Forestry and Management of the Environment and Natural Resources, Lab of ForestEnvironmental Informatics and Computational Intelligence, Democritus University of Thrace, Orestiada, Greece ABSTRACT The upgrade of energy infrastructures by the incorporation of communication and Internet technologies might introduce new risks for the security and for the smooth operation of electricity networks. Exploitation of the potential vulnerabilities of the heterogeneous systems used in smart energy grids (SEGs) may lead to the loss of control of critical electronic devices and, moreover, to the interception of confidential information. This may result in the disruption of essential services or even in total power failures. Addressing security issues that can ensure the confidentiality, the integrity, and availability of energy information is the primary objective for a transition to a new energy shape. This research paper presents an innovative system that can effectively offer SEG cybersecurity. It employs soft computing approaches, fuzzy cognitive maps, and a Mamdani fuzzy inference system in order to model overall security level. Three of the 27 scenarios considered herein have low overall security level, 21 of them have middle overall security, whereas only 3 are characterized as secure. The system automates the strategic planning of high security standards, as it allows a thorough audit of digital systems related to potential infrastructures and it contributes towards accurate decision-making in cases of threats. KEYWORD S Smart energy grids; smart grids domains; smart grid cybersecurity; logical interface categories; fuzzy cognitive maps; Mamdani fuzzy inference system; fuzzy rules Applying extended Kalman filters to adaptive thermal modelling in homes Muddasser Alama, Alex Rogersb, James Scottc, Kamran Alid and Frederik Auffenberge aDepartment of Engineering Science, University of Oxford, Oxford, UK; bDepartment of Computer Science, University of Oxford, Oxford, UK; cMicrosoft Research Cambridge, Cambridge UK; dUsman Institute of Technology, Karachi, Pakistan; eElectronics and Computer Science, University of Southampton, Southampton, UK ABSTRACT Space-heating accounts for more than 40% of residential energy consumption in some countries (e.g. the UK and the US) and thus is a key area to address for energy efficiency improvement. To do so, intelligent domestic heating systems (IDHS) equipped with sensors and technologies that minimize user-input, have been proposed for optimal heating control in homes. However, a key challenge for IDHS is to obtain sufficient knowledge of the thermal dynamics of the home to build a thermal model that can reliably predict the spatial and temporal effects of its actions (e.g. turning the heating on or off or use of multiple heaters). This challenge of learning a thermal model has been studied extensively for decades in large purpose-built buildings (such as offices, educational, commercial or communal residential buildings) where machine learning is used to infer suitable thermal models. However, we believe that the technological gap between homes and buildings is fast vanishing with the advent of home automation and cloud computing, and the techniques and lessons learned in purpose-built buildings are increasingly applicable to homes too; with necessary modifications to tackle the challenges unique to homes (e.g. impact of household activities, diverse heating systems, more lenient occupancy schedule). Following this philosophy, we present a methodical study where stochastic grey-box modelling is used to develop thermal models and an extended Kalman filter (EKF) is used for parameter estimation. To demonstrate the applicability in homes, we present the case-study of a room in a family house equipped with underfloor heating and custom-built .NET Gadgeteer hardware. We built grey-box models and use the EKF to infer the thermal model of the room. In doing so, we use our in-house collected data to show that, in this instance, our thermal model predicts the indoor air temperature where the 95th percentile of the absolute prediction error is 0.95 ◦C and 1 .37 ◦C for 2 and 4 hours predictions, respectively; in contrast to the corresponding 2.09 ◦C and 3.11 ◦C errors of the existing (historical-average based) thermal model. KEYWORD S Heating systems; thermal modelling; extended Kalman filter; underfloor heating Energy management in solar microgrid via reinforcement learning using fuzzy reward Panagiotis Kofinasa,b, George Vourosa and Anastasios I. Dounis b aDepartment of Digital Systems 80, University of Piraeus, Piraeus, Greece; bDepartment of Automation Engineering 250, Piraeus University of Applied Sciences (T.E.I. of Piraeus), Athens, Greece ABSTRACT This paper proposes a single-agent system towards solving energy management issues in solar microgrids. The proposed system consists of a photovoltaic (PV) source, a battery bank, a desalination unit (responsible for providing the demanded water) and a local consumer. The trade-offs and complexities involved in the operation of the different units, and the quality of services’ demanded from energy consumer units (e.g. the desalination unit), makes the energy management a challenging task. The goal of the agent is to satisfy the energy demand in the solar microgrid, optimizing the battery usage, in conjunction to satisfying the quality of services provided. It is assumed that the solar microgrid operates in island-mode. Thus, no connection to the electrical grid is considered. The agent collects data from the elements of the system and learns the suitable policy towards optimizing system performance by using the Q-Learning algorithm. The reward function is implemented by fuzzy system Sugeno type for improving the learning efficiency. Simulation results provided show the performance of the system. KEYWORD S Reinforcement learning; Q-learning; microgrid; energy management; fuzzy reward Introduction of plug-in hybrid electric vehicles in an isolated island system Christos S. Ioakimidis a,b,c and Konstantinos N. Genikomsakis a,d,e aNet-Zero Energy Efficiency on City Districts (NZED) Unit, Research Institute for Energy, University of Mons, Mons, Belgium; bMIT Portugal Program, Sustainable Energy Systems, Porto Salvo, Portugal; cIN+, Department of Mechanical Engineering, Instituto Superior Técnico (UTL), Lisbon, Portugal; dDeustoTech – Deusto Foundation, Bilbao, Spain; eFaculty of Engineering, University of Deusto, Bilbao, Spain ABSTRACT This paper considers the case of São Miguel in the Azores archipelago as a typical example of an isolated island with high renewable energy potential, but largely dependent on fossil fuels incurring high import costs, in order to assess and analyse the potential impact of the plug-in hybrid electric vehicle (PHEV) technology on the local power supply system. To this end, the present work employs The Integrated MARKAL-EFOM System (TIMES) to examine a number of scenarios with different levels of PHEVs penetration under the grid-to-vehicle (G2V) approach, taking into account the established Government policies, regarding the increase in renewable energy production quotas, for the evolution of demand and supply over time. The results obtained indicate that the PHEVs integration into the local grid system under the G2V energy transferring paradigm can be realized without immediate technical barriers and bears the potential to yield significant benefits to the energy mix, reducing thus the environmental impact. KEYWORD S Energy system analysis; gridto-vehicle (G2V); plug-in hybrid electric vehicle (PHEV); TIMES model generator; transportation sector electrification Modelling of household electricity consumption with the aid of computational intelligence methods Kostas Karatzas and Nikos Katsifarakis Department of Mechanical Engineering, Environmental Informatics Research Group, Aristotle University, Thessaloniki, Greece ABSTRACT The installation of smart meters for electricity consumption monitoring is common practice in many countries. Such meters usually provide information for the temporal variation of electricity consumption-related parameters, at an aggregated (household) level. In some cases, such meters may monitor individual appliances, or appliance groups installed in household departments. In the current study, a Computational Intelligence approach is used to analyse and model appliance group electricity consumption and to investigate the best possible computational approaches for improving consumption model performance. For this purpose, meta-features are used, a new feature prioritization method is introduced and a set of selected algorithms is employed. Results indicate an improvement in modelling capacity and an ability to construct models that effectively perform partial electricity consumption disaggregation. Overall, such methods may be used for the support of household electricity consumption modelling and for related demand management. KEYWORD S Household electricity consumption; smart metering; computational intelligence; partial disaggregation Predicting agent performance in large-scale electricity demand shifting Charilaos Akasiadis and Alexandros Georgogiannis School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece ABSTRACT A variety of multi-agent systems methods has been proposed for forming cooperatives of interconnected agents representing electricity producers or consumers in the Smart Grid. One major problem that arises in this domain is assessing participating agents’ uncertainty, and correctly predicting their future behaviour regarding power consumption shifting actions. In this paper we adopt various machine learning techniques and use these to effectively monitor the trustworthiness of agent statements regarding their final shifting actions. In particular, we evaluate the performance of four approaches, one based on a Histogram Filter, and three on regression approaches, that is, Gaussian Process, k-Nearest Neighbours, and Kernel Regression. We incorporate these to aggregate individual forecasts within a directly applicable scheme for providing cooperative electricity demand shifting services. Experiments were conducted on realworld datasets from thousands of users located in Kissamos, a municipality of Crete. Our results confirm that the adoption of machine learning techniques provides tangible benefits regarding enhanced cooperative performance, and increased financial gains for the participants. KEYWORD S Multi-agent; cooperatives; predictions; demand shifting; smart grid