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Mexican International Conference on Artificial Intelligence, MICAI

作者： João Soares Zita Vale Hugo Morais Nuno Borges GECAD - Knowledge Engineering and Decision-Support Research Centre Polytechnic of Porto (ISEP/ IPP) Porto Portugal

Demand Response (DR) has gained attention in the past few years. With the mass introduction of Electric Vehicles (EVs) the opportunity for DR programs can grow even more. This paper concentrates on a specific type of DR, namely evaluating different scenarios with respect to end-user EVs tariffs. The effects of different tariffs are compared in several scenarios in terms of the Virtual Power Plant (VPP) operation costs and also the EVs' perspective, i.e. considering the optimal use of end-user contracts. To solve the Mixed Integer Non-Linear Problem (MINLP) a modernized optimization approach is used by combining a deterministic method in the first stage, relaxing the problem to a Mixed Integer Linear Problem (MILP) with the use of a computational intelligence method in the second stage, namely Particle Swarm Optimization (PSO). The case study presents different price scenarios, namely, singletariff, bi-tariff, tri-tariff, tetra-tariff, and Real-Time Pricing (RTP). The network used for this application is a 33-bus distribution network with high penetration of renewables and a fleet of 30 electric buses.

关键词： Energy resources Contracts Power generation Vehicles Load management Optimization Pricing

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Data Mining Approach for decision support in Real Data Based Smart Grid Scenario

Data Mining Approach for Decision Support in Real Data Based...

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International Workshop on Database and Expert Systems Applications

作者： Catarina Ribeiro Tiago Pinto Marco Silva Sérgio Ramos Zita Vale GECAD - Knowledge Engineering and Decision Support Research Center Polytechnic of Porto (ISEP/IPP) Porto Portugal

The increasing use of renewable energy sources and distributed generation brought several changes to the power system operation, with huge implications to the competitive electricity markets. With the eminent implementation of microgrids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players` benefits. The contribution of this paper is a clustering methodology regarding the remuneration and tariff of VPP. It proposes a model to implement fair and strategic remuneration and tariff methodologies, using a clustering algorithm, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to the players characteristics. The proposed clustering methodology has been tested in a real distribution network with 16 bus, including residential and commercial consumers, PV generation and storage units.

关键词： Context Electricity supply industry Remuneration Smart grids Clustering algorithms Analytical models Contracts

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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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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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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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MASCEM: EPEX SPOT Day-Ahead market integration and simulation

MASCEM: EPEX SPOT Day-Ahead market integration and simulatio...

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

作者： Gabriel Santos Ricardo Fernandes Tiago Pinto Isabel Praça Zita Vale Hugo Morais GECAD - Knowledge Engineering and Decision Support Research Center Polytechnic of Porto (ISEP/IPP) Porto Portugal Automation and Control Group Technical University of Denmark (DTU) Lyngby Denmark

The energy sector restructuring process in industrialized countries had the aim of reducing electricity prices by increasing competitiveness, and facilitate the integration of distributed energy resources. However, the complexity in market players' interactions has increased, and new problems have emerged. decision support tools that facilitate the study and comprehension of these markets became extremely useful, providing players with competitive advantage. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) arises in this context, modeling and simulating real electricity markets. It is crucial to MASCEM to have the ability to simulate as many market models and player types as possible, thus enhancing the ability to recreate the electricity markets reality in its maximum possible extent. This paper presents the EPEX Spot Day-Ahead market integration in MASCEM. EPEX Spot SE's mission is to lead European markets coupling in a single unified market, thus being crucial for the study of competitive electricity markets.

关键词： Europe Power markets Context Companies Adaptive systems Power systems

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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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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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