Application of multiobjective genetic programming to the design of robot failure recognition systems

We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge

Energy Management and Privacy in Smart Grids

Despite the importance of power systems in today’s societies, they suffer from aging infrastructure and need to improve the efficiency, reliability, and security. Two issues that significantly limit the current grid’s efficient energy delivery and consumption are: loadollowing generation dispatch, and energy theft. A loadollowing generation dispatch is usually employed in power systems, which makes continuous small changes so as to account for differences between the actual energy demand and the predicted values. This approach has led to an average utilization of energy generation capacity below 55% [49]. Moreover, energy theft causes several billion dollar losses to U.S. utility companies [31] [16], while in developing countries it can amount to 50% of the total energy delivered [48]. Recently, the Smart Grid has been proposed as a new electric grid to modernize current power grids and enhance its efficiency, reliability, and sustainability. Particularly, in the Smart Grid, a digital communication network is deployed to enable two-way communications between users and system operators. It thus makes it possible to shape the users’ load demand curves by means of demand response strategies. Additionally, in the Smart Grid, traditional meters will be replaced with cyber-physical devices, called smart meters, capable of recording and transmitting users’ real-time power consumption. Due to their monitoring capabilities, smart meters offer a great opportunity to detect energy theft in smart grids, but also raise serious concerns about users’ privacy. In this dissertation, we design optimal load scheduling schemes to enhance system efficiency, and develop energy theft detection algorithms that can preserve users’ privacy

Energy Management and Privacy in Smart Grids

Despite the importance of power systems in today’s societies, they suffer from aging infrastructure and need to improve the efficiency, reliability, and security. Two issues that significantly limit the current grid’s efficient energy delivery and consumption are: loadollowing generation dispatch, and energy theft. A loadollowing generation dispatch is usually employed in power systems, which makes continuous small changes so as to account for differences between the actual energy demand and the predicted values. This approach has led to an average utilization of energy generation capacity below 55% [49]. Moreover, energy theft causes several billion dollar losses to U.S. utility companies [31] [16], while in developing countries it can amount to 50% of the total energy delivered [48]. Recently, the Smart Grid has been proposed as a new electric grid to modernize current power grids and enhance its efficiency, reliability, and sustainability. Particularly, in the Smart Grid, a digital communication network is deployed to enable two-way communications between users and system operators. It thus makes it possible to shape the users’ load demand curves by means of demand response strategies. Additionally, in the Smart Grid, traditional meters will be replaced with cyber-physical devices, called smart meters, capable of recording and transmitting users’ real-time power consumption. Due to their monitoring capabilities, smart meters offer a great opportunity to detect energy theft in smart grids, but also raise serious concerns about users’ privacy. In this dissertation, we design optimal load scheduling schemes to enhance system efficiency, and develop energy theft detection algorithms that can preserve users’ privacy

Smart Grid Enabling Low Carbon Future Power Systems Towards Prosumers Era

In efforts to meet the targets of carbon emissions reduction in power systems, policy makers formulate measures for facilitating the integration of renewable energy sources and demand side carbon mitigation. Smart grid provides an opportunity for bidirectional communication among policy makers, generators and consumers. With the help of smart meters, increasing number of consumers is able to produce, store, and consume energy, giving them the new role of prosumers. This thesis aims to address how smart grid enables prosumers to be appropriately integrated into energy markets for decarbonising power systems. This thesis firstly proposes a Stackelberg game-theoretic model for dynamic negotiation of policy measures and determining optimal power profiles of generators and consumers in day-ahead market. Simulation results show that the proposed model is capable of saving electricity bills, reducing carbon emissions, and increasing the penetration of renewable energy sources. Secondly, a data-driven prosumer-centric energy scheduling tool is developed by using learning approaches to reduce computational complexity from model-based optimisation. This scheduling tool exploits convolutional neural networks to extract prosumption patterns, and uses scenarios to analyse possible variations of uncertainties caused by the intermittency of renewable energy sources and flexible demand. Case studies confirm that the proposed scheduling tool can accurately predict optimal scheduling decisions under various system scales and uncertain scenarios. Thirdly, a blockchain-based peer-to-peer trading framework is designed to trade energy and carbon allowance. The bidding/selling prices of individual prosumers can directly incentivise the reshaping of prosumption behaviours. Case studies demonstrate the execution of smart contract on the Ethereum blockchain and testify that the proposed trading framework outperforms the centralised trading and aggregator-based trading in terms of regional energy balance and reducing carbon emissions caused by long-distance transmissions

Analytic Conditions for Energy Neutrality in Uniformly-Formed Wireless Sensor Networks

Future deployments of wireless sensor network (WSN) infrastructures for environmental or event monitoring are expected to be equipped with energy harvesters (e.g. piezoelectric, thermal, photovoltaic) in order to substantially increase their autonomy. In this paper we derive conditions for energy neutrality, i.e. perpetual energy autonomy per sensor node, by balancing the node's expected energy consumption with its expected energy harvesting capability. Our analysis assumes a uniformly-formed WSN, i.e. a network comprising identical transmitter sensor nodes and identical receiver/relay sensor nodes with a balanced cluster-tree topology. The proposed framework is parametric to: (i) the duty cycle for the network activation; (ii) the number of nodes in the same tier of the cluster-tree topology; (iii) the consumption rate of the receiver node(s) that collect (and possibly relay) data along with their own; (iv) the marginal probability density function (PDF) characterizing the data transmission rate per node; (v) the expected amount of energy harvested by each node. Based on our analysis, we obtain the number of nodes leading to the minimumenergy harvestingrequirement for each tier of the WSN cluster-tree topology. We also derive closed-form expressions for the difference in the minimum energy harvesting requirements between four transmission rate PDFs in function of the WSN parameters. Our analytic results are validated via experiments using TelosB sensor nodes and an energy measurement testbed. Our framework is useful for feasibility studies on energy harvesting technologies in WSNs and for optimizing the operational settings of hierarchical WSN-based monitoring infrastructures prior to time-consuming testing and deployment within the application environment

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin