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Definition of the demand response events duration using differential search algorithm for aggregated consumption shifting and generation scheduling

Definition of the demand response events duration using diff...

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International Conference on Intelligent Systems Applications to Power Systems (ISAP)

作者： Pedro Faria João Soares Zita Vale GECAD - Knowledge Engineering and Decision Support Research Center IPP - Polytechnic Institute of Porto Porto Portugal

An adequate integration of demand response programs requires the definition of distinct models covering a wide diversity of operation scenarios. In this paper, the proposed methodology addresses the definition of demand response programs considering the demand shifting between periods, in scenarios of multi-period demand response events. The optimization problem refers to the minimization of the operation costs for a Virtual Power Player in the energy resources schedule. Differential search algorithm has been used in order to obtain the solutions for the optimization problem in a large set of operation scenarios. The case study illustrates the use of the proposed methodology to support the decisions concerning the duration of each demand response event and considering the consumption shifting modeling as constraints of the optimization problem.

关键词： Optimization Mathematical model Schedules Load management Context Minimization Organisms

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Two-stage stochastic day-ahead optimal resource scheduling in a distribution network with intensive use of distributed energy resources

Two-stage stochastic day-ahead optimal resource scheduling i...

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IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America)

作者： Tiago Sousa Mohammad Ali Fotouhi Ghazvini Hugo Morais Rui Castro Zita Vale GECAD - Knowledge Engineering and Decision Support Research Center IPP - Polytechnic Institute of Porto Porto Portugal GECAD - Knowledge Engineering and Decision Support Research Center IPP - Polytechnic Institute of Porto Portugal GECAD - Knowledge Engineering and Decision Support Research Center IPP - Polytechnic Institute of Porto Lyngby Denmark INESC-ID / IST Instituto Superior Técnico University of Lisbon Lisbon Portugal

The integration of renewable sources and electric vehicles will introduce new uncertainties to the optimal resource scheduling, namely at the distribution level. These uncertainties are mainly originated by the power generated by renewables sources and by the electric vehicles charge requirements. This paper proposes a two-state stochastic programming approach to solve the day-ahead optimal resource scheduling problem. The case study considers a 33-bus distribution network with 66 distributed generation units and 1000 electric vehicles.

关键词： Stochastic processes Uncertainty Nickel Batteries Programming Load flow

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A survey on smart meeting rooms and open issues

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International Journal of Smart Home 2015年第9期9卷 13-20页

作者： Freitas, Carlos Filipe Barroso, João Ramos, Carlos GECAD - Knowledge Engineering and Decision Support Research Center ISEP - Institute of Engineering Polytechnic of Porto Porto Portugal UTAD - Universidade de Trás os Montes e Alto Douto Vila Real Portugal

decision Making is one of the most important activities of the human being. Nowadays decisions imply to consider many different points of view, so decisions are commonly taken by formal or informal groups of persons. Groups exchange ideas or engage in a process of argumentation and counter-argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. Smart Meeting Rooms (SMR) are the election spaces where groups can perform these activities in a local or distributed setting. SMR definition has evolved and in this article we present its evolution side by side with several projects that have been developed during the last decade. We also present the features and evaluation methods present in literature and we make a discussion on the open issues. © 2015 SERSC.

关键词： Problem solving

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Selection constructive based hyper-heuristic for dynamic scheduling

Selection constructive based hyper-heuristic for dynamic sch...

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Iberian Conference on Information Systems and Technologies (CISTI)

作者： S. Gomes A. Madureira B. Cunha GECAD - Knowledge Engineering and Decision Support Research Center School of Engineering from Polytechnic of Porto (ISEP/IPP) Porto Portugal

Manufacturing environments require a real-time adaptation and optimization method to dynamically and intelligently maintain the current scheduling plan feasible. This way, the organization keeps clients satisfied and achieves its objectives (costs are minimized and profits maximized). This paper proposes an optimization approach - Selection Constructive based Hyper-heuristic for Dynamic Scheduling - to deal with these dynamic events, with the main goal of maintaining the current scheduling plan feasible and robust as possible. The development of this dynamic adaptation approach is inspired on evolutionary computation and hyper-heuristics. Our empirical results show that a selection constructive hyper-heuristic could be advantageous on solving dynamic adaptation optimization problems.

关键词： Dynamic scheduling Job shop scheduling Optimization Genetic algorithms Processor scheduling Evolutionary computation

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Racing based approach for Metaheuristics parameter tuning

Racing based approach for Metaheuristics parameter tuning

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Iberian Conference on Information Systems and Technologies (CISTI)

作者： Ivo Pereira Ana Madureira GECAD - Knowledge Engineering and Decision Support Research Center School of Engineering from Polytechnic of Porto (ISEP/IPP) Porto Portugal

Metaheuristics are very useful to achieve good solutions in reasonable execution times. Sometimes they even obtain optimal solutions. However, to achieve near-optimal solutions, the appropriate tuning of parameters is required. This paper presents a Racing based learning module proposal for an autonomous parameter tuning of Metaheuristics. After a literature review on Metaheuristics parameter tuning and Racing approaches, the learning module is presented. A computational study for the resolution of the Scheduling problem is also presented. Comparing the preliminary obtained results with previous published results allow to conclude about the effectiveness and efficiency of this proposal.

关键词： Tuning Processor scheduling Job shop scheduling Optimization Bibliographies Genetic algorithms

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Multi-agent based metalearner using genetic algorithm for decision support in electricity markets

Multi-agent based metalearner using genetic algorithm for de...

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International Conference on Intelligent Systems Applications to Power Systems (ISAP)

作者： Tiago Pinto João Barreto Isabel Praça Gabriel Santos Zita Vale E. J. Solteiro Pires GECAD - Knowledge Engineering and Decision Support Research Center IPP - Polytechnic Institute of Porto Porto Portugal UTAD - Universidade de Trás-os-Montes e Alto Douro Portugal

The continuous changes in electricity markets' mechanisms and operations turn this environment into a challenging domain for the participating entities. Simulation tools are increasingly being used for decision support purposes of such entities. In particular, multi-agent based simulation, which facilitates the modeling of different types of mechanisms and players, is being fruitfully applied to the study of worldwide electricity markets. An effective decision support to market players' negotiations is, however, still not properly reached due to the uncertainty that results from the increasing penetration of renewable generation and the complexity of market mechanisms themselves. In this scope, this paper proposes a novel metalearner that provides decision support to market players in their negotiations. The proposed metalearner uses as input the output of several other market negotiation strategies, which are used to create a new, enhanced response. The final result is achieved through the combination and evolution of the strategies' learning results by applying a genetic algorithm.

关键词： Electricity supply industry Genetic algorithms Context Sociology Statistics Genetics Analytical models

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Characterize a step using machine learning

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous engineering 2015年第11期10卷 69-84页

作者： Anacleto, Ricardo Figueiredo, Lino Almeida, Ana Novais, Paulo Meireles, António GECAD - Knowledge Engineering and Decision Support Research Center School of Engineering - Polytechnic of Porto Porto Portugal DI/CCTC - Computer Science and Technology Center University of Minho Braga Portugal

Most of pedestrian inertial navigation system estimates displacement based on the integration of inertial sensors measurements. However, due to low-cost sensors and pedestrian dead reckoning inherent characteristics these systems provide huge location estimation errors. To suppress some of these limitations we propose a pedestrian inertial navigation system based on low-cost sensors and on information fusion and learning techniques. The proposed system introduces a step characterization module that characterizes the step according to the activity that the pedestrian is performing. This module performs three characterizations: terrain, direction and length. Thus, in this work are presented and evaluated several machine learning approaches that perform the terrain characterization. The inclusion of this machine learning module led to a significantly better performance of the pedestrian inertial navigation system. © 2015 SERSC.

关键词： Inertial navigation systems

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Portfolio Optimization for Electricity Market Participation with Particle Swarm

Portfolio Optimization for Electricity Market Participation ...

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International Workshop on Database and Expert Systems Applications

作者： Ricardo Faia Tiago Pinto Zita Vale E.J. Solteiro Pires GECAD - Knowledge Engineering and Decision Support Research Center Polytechnic of Porto (ISEP IIPP) Porto Portugal UTAD - Universidade de Tras-os-Montes e Alto Douro Vila Real Portugal

The liberalization of energy markets has imposed several modifications in the electricity market environment. The paradigm of monopoly market ceased to exist, and new models have been put into practice. The new models have increased the incentive on competitiveness, making market players struggle to achieve the best outcomes out of market participation. Producers aim at reaching the maximum profit on the sale of energy, while consumers try to minimize their spending on electrical energy. The proposed methodology considers the optimization of players' participation in multiple market opportunities. Reference prices that are expected in each market type at each moment are achieved through the application of neural networks. Using the forecasted prices, the proposed portfolio optimization method allocates the sale and purchase of electrical energy to different markets throughout the time, with the aim at achieving the most advantageous participation profile. A particle swarm approach is used to reduce the execution time while guaranteeing the minimum degradation of the results. Results of the swarm methodology are compared to those of a deterministic approach, using real data from the Iberian electricity market - MIBEL.

关键词： Optimization Portfolios Electricity supply industry Adaptation models Particle swarm optimization Artificial neural networks Context

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Energy resource management under the influence of the weekend transition considering an intensive use of electric vehicles

Energy resource management under the influence of the weeken...

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Clemson University Power Systems Conference (PSC)

作者： T. Sousa H. Morais T. Pinto Z. Vale GECAD –Knowledge Engineering and Decision Support Research Center IPP –Polytechnic Institute of Porto Porto Portugal Department of Electrical Engineering Technical University of Denmark (DTU) Lyngby Denmark

Energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and of massive electric vehicle is envisaged. The present paper proposes a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and Vehicle-to-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with their owners. It takes into account these contracts, the users' requirements subjected to the VPP, and several discharge price steps. The full AC power flow calculation included in the model takes into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33-bus distribution network and V2G is used to illustrate the good performance of the proposed method.

关键词： Batteries Load modeling Discharges (electric) Energy resources Optimal scheduling Scheduling Electric vehicles

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Hour-ahead Energy Resource Management in University Campus Microgrid

Hour-ahead Energy Resource Management in University Campus M...

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IEEE Eindhoven PowerTech Conference

作者： M. Silva F. Fernandes H. Morais S. Ramos Z. Vale GECAD - Knowledge Engineering and Decision Support Research Center IPP - Polytechnic Institute of Porto Porto Portugal Department of Electrical Engineering Technical University of Denmark (DTU) Lyngby Denmark

The management of intermittent energy resources by a microgrid operator (MGO) are a key aspect of future smart grids. A microgrid allows act as a single controllable entity and can operate in both grid-connected or in islanded mode. The present paper proposes a platform to be used by MGO regarding the energy resource management in smart grids and considering day-ahead and hour-ahead time-horizons. This method considers that the energy resources are managed by a MGO that establishes contracts with their client. The operator is able to manage the consumption and generation resources, also including demand response programs, obtaining profits from selling electricity to the main network. The model uses full AC power flow and the energy buying and selling from/to the main network. The case study considers the internal low voltage distribution network of a real university campus, and the projection of distributed energy resources based on the evolution scenario for the year 2050.

关键词： Day-ahead scheduling Distributed Energy Resources Hour-ahead scheduling Microgrid operator

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