Decision Support Concerning Demand Response Programs Design and Use – A Conceptual Framework and Simulation Tool

Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

Multicriteria decision making: a case study in the automobile industry

Multi-criteria decision analysis(MCDA) has been one of the fastest-growing areas of operations research during the last decades. The academic attention devoted to MCDA motivated the development of a great variety of approaches and methods within the field. These methods distinguish themselves in terms of procedures, theoretical assumptions and type of decision addressed. This diversity poses challenges to the process of selecting the most suited method for a specific real-world decision problem. In this paper we present a case study in a real-world decision problem arising in the painting sector of an automobile plant. We tackle the problem by resorting to the well-known AHP method and to the MCDA method proposed by Pereira and Fontes (2012) (MMASSI). By relying on two, rather than one, MCDA methods we expect to improve the confidence and robustness of the obtained results. The contributions of this paper are twofold: first, we intend to investigate the contrasts and similarities of the results obtained by distinct MCDA approaches (AHP and MMASSI); secondly, we expect to enrich the literature of the field with a real-world MCDA case study on a complex decision making problem since there is a paucity of applied research work addressing real decision problems faced by organizations.

Multicriteria decision making: a case study in the automobile industry

Multi-criteria decision analysis (MCDA) has been one of the fastest-growing areas of operations research during the last decades. The academic attention devoted to MCDA motivated the development of a great variety of approaches and methods within the field. These methods distinguish themselves in terms of procedures, theoretical assumptions and type of decision addressed. This diversity poses challenges to the process of selecting the most suited method for a specific real-world decision problem. In this paper we present a case study in a real-world decision problem arising in the painting sector of an automobile plant. We tackle the problem by resorting to the well-known AHP method and to the MCDA method proposed by Pereira and Fontes (2012) (MMASSI). By relying on two, rather than one, MCDA methods we expect to improve the confidence and robustness of the obtained results. The contributions of this paper are twofold: first, we intend to investigate the contrasts and similarities of the results obtained by distinct MCDA approaches (AHP and MMASSI); secondly, we expect to enrich the literature of the field with a real-world MCDA case study on a complex decision making problem since there is a paucity of applied research work addressing real decision problems faced by organizations.

Group decision making for selection of an information system in a business context

The main objective of this work is to report on the development of a multi-criteria methodology to support the assessment and selection of an Information System (IS) framework in a business context. The objective is to select a technological partner that provides the engine to be the basis for the development of a customized application for shrinkage reduction on the supply chains management. Furthermore, the proposed methodology di ers from most of the ones previously proposed in the sense that 1) it provides the decision makers with a set of pre-defined criteria along with their description and suggestions on how to measure them and 2)it uses a continuous scale with two reference levels and thus no normalization of the valuations is required. The methodology here proposed is has been designed to be easy to understand and use, without a specific support of a decision making analyst.

Adapting Meeting Tools to Agent Decision

Electricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Impact of Distributed Generation in the Transmission System Expansion Planning

In this work, the impact of distributed generation in the transmission expansion planning will be simulated through the performance of an optimization process for three different scenarios: the first without distributed generation, the second with distributed generation equivalent to 1% of the load, and the third with 5% of distributed generation. For modeling the expanding problem the load flow linearized method using genetic algorithms for optimization has been chosen. The test circuit used is a simplification of the south eastern Brazilian electricity system with 46 buses.

Expatriates’ influence on knowledge sharing: an empirical study with International portuguese companies

Despite the abundant literature in knowledge management, few empirical studies have explored knowledge management in connection with international assignees. This phenomenon has a special relevance in the Portuguese context, since (a) there are no empirical studies concerning this issue that involves international Portuguese companies; (b) the national business reality is incipient as far as internationalisation is concerned, and; (c) the organisational and national culture presents characteristics that are distinctive from the most highly studied contexts (e.g., Asia, USA, Scandinavian countries, Spain, France, The Netherlands, Germany, England and Russia). We examine the role of expatriates in transfer and knowledge sharing within the Portuguese companies with operations abroad. We focus specifically on expatriates’ role on knowledge sharing connected to international Portuguese companies and our findings take into account organizational representatives’ and expatriates’ perspectives. Using a comparative case study approach, we examine how three main dimensions influence the role of expatriates in knowledge sharing among headquarters and their subsidiaries (types of international assignment, reasons for using expatriation and international assignment characteristics). Data were collected using semi‐structured interviews to 30 Portuguese repatriates and 14 organizational representatives from seven Portuguese companies. The findings suggest that the reasons that lead Portuguese companies to expatriating employees are connected to: (1) business expansion needs; (2) control of international operations and; (3) transfer and knowledge sharing. Our study also shows that Portuguese companies use international assignments in order to positively respond to the increasingly decaying domestic market in the economic areas in which they operate. Evidence also reveals that expatriation is seen as a strategy to fulfill main organizational objectives through their expatriates (e.g., business internationalization, improvement of the coordination and control level of the units/subsidiaries abroad, replication of aspects of the home base, development and incorporation of new organizational techniques and processes). We also conclude that Portuguese companies have developed an International Human Resources Management strategy, based on an ethnocentric approach, typically associated with companies in early stages of internationalization, i.e., the authority and decision making are centered in the home base. Expatriates have a central role in transmitting culture and technical knowledge from company’s headquarters to the company’s branches. Based on the findings, the article will discuss in detail the main theoretical and managerial implications. Suggestions for further research will also be presented.

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

Six thinking hats: A novel metalearner for intelligent decision support in electricity markets

The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

Desenvolvimento de uma framework de argumentação Para apoio à tomada de decisão ubíqua

Os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) surgiram com o objetivo de apoiar um conjunto de decisores no processo de tomada de decisão. Uma das abordagens mais comuns na literatura para a implementação dos SADG é a utilização de Sistemas Multi-Agente (SMA). Os SMA permitem refletir com maior transparência o contexto real, tanto na representação que cada agente faz do decisor que representa como no formato de comunicação utilizado. Com o crescimento das organizações, atualmente vive-se uma viragem no conceito de tomada de decisão. Cada vez mais, devido a questões como: o estilo de vida, os mercados globais e o tipo de tecnologias disponíveis, faz sentido falar de decisão ubíqua. Isto significa que o decisor deverá poder utilizar o sistema a partir de qualquer local, a qualquer altura e através dos mais variados tipos de dispositivos eletrónicos tais como tablets, smartphones, etc. Neste trabalho é proposto um novo modelo de argumentação, adaptado ao contexto da tomada de decisão ubíqua para ser utilizado por um SMA na resolução de problemas multi-critério. É assumido que cada agente poderá utilizar um estilo de comportamento que afeta o modo como esse agente interage com outros agentes em situações de conflito. Sendo assim, pretende-se estudar o impacto da utilização de estilos de comportamento ao longo do processo da tomada de decisão e perceber se os agentes modelados com estilos de comportamento conseguem atingir o consenso mais facilmente quando comparados com agentes que não apresentam nenhum estilo de comportamento. Pretende-se ainda estudar se o número de argumentos trocados entre os agentes é proporcional ao nível de consenso final após o processo de tomada de decisão. De forma a poder estudar as hipóteses de investigação desenvolveu-se um protótipo de um SADG, utilizando um SMA. Desenvolveu-se ainda uma framework de argumentação que foi adaptada ao protótipo desenvolvido. Os resultados obtidos permitiram validar as hipóteses definidas neste trabalho tendo-se concluído que os agentes modelados com estilos de comportamento conseguem na maioria das vezes atingir um consenso mais facilmente comparado com agentes que não apresentam nenhum estilo de comportamento e que o número de argumentos trocados entre os agentes durante o processo de tomada de decisão não é proporcional ao nível de consenso final.