Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

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.

Municipal solid waste management system: decision support through systems analysis

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering

Strategic Noise Mapping with GIS for the Universitat Jaume I Smart Campus: best methodology practices

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Lean management implementation: a decision making framework

Submitted to the graduate faculty Universidade Nova de Lisboa – Faculdade de Ciências e Tecnologia in partial fulfillment of the requirements for the degree of Master in Industrial Engineering

A strategic plan for “ADHD Project”

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

How reporting and controlling systems can be effectively designed to support the decision-making process: The case of a Software SME

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Foresight exercises as a tool for decision-making: the example of two case studies in health

Based on the report for the unit “Foresight Methods Analysis” of the PhD programme on Technology Assessment at the Universidade Nova de Lisboa, under the supervision of Prof. Dr. António B. Moniz

Strategic planning of electricity systems: Integrating renewable energies

Tese de Doutoramento - Programa Doutoral em Engenharia Industrial e Sistemas (PDEIS)

Economising, Strategising and the Decision to Outsource

We study the make-or-buy decision of oligopolistic firms in an industry in which final good production requires specialised inputs. Firms’ mode of operation decision depends on both the incentive to economize on costs and on strategic considerations. We explore the strategic incentives to outsource and show that asymmetric equilibria emerge, with firms choosing different modes of operation, even when they are ex-ante identical. With ex-ante asymmetries, higher cost firms are more likely to outsource. We apply our model to a number of different international trading setups.

Strategic mass killings

Since World War II there have been about fifty episodes of large-scale mass killings of civilians and massive forced displacements. They were usually meticulously planned and independent of military goals. We provide a model where conflict onset, conflict intensity and the decision to commit mass killings are all endogenous, with two main goals: (1) to identify the key variables and situations that make mass killings more likely to occur; and (2) to distinguish conditions under which mass killings and military conflict intensity reinforce each other from situations where they are substitute modes of strategic violence. We predict that mass killings are most likely in societies with large natural resources, significant proportionality constraints for rent sharing, low productivity and low state capacity. Further, massacres are more likely in a civil than in an interstate war, as in the latter group sizes matter less for future rents. In non polarized societies there are asymmetric equilibria with only the larger group wanting to engage in massacres. In such settings the smaller group compensates for this by fighting harder in the first place. In this case we can talk of mass killings and fighting efforts to be substitutes. In contrast, in polarized societies either both or none of the groups can be ready to do mass killings in case of victory. Under the "shadow of mass killings" groups fight harder. Hence, in this case massacres and fighting are complements. We also present novel empirical results on the role of natural resources in mass killings and on what kinds of ethnic groups are most likely to be victimized in massacres and forced resettlements, using group level panel data.

Playing Second Fiddle: Expert Advice and Decision-Making in Switzerland

This thesis concerns the role of scientific expertise in the decision-making process at the Swiss federal level of government. It aims to understand how institutional and issue-specific factors influence three things: the distribution of access to scientific expertise, its valuation by participants in policy for- mulation, and the consequence(s) its mobilization has on policy politics and design. The theoretical framework developed builds on the assumption that scientific expertise is a strategic resource. In order to effectively mobilize this resource, actors require financial and organizational resources, as well as the conviction that it can advance their instrumental interests within a particular action situation. Institutions of the political system allocate these financial and organizational resources, influence the supply of scientific expertise, and help shape the venue of its deployment. Issue structures, in turn, condition both interaction configurations and the way in which these are anticipated by actors. This affects the perceived utility of expertise mobilization, mediating its consequences. The findings of this study show that the ability to access and control scientific expertise is strongly concentrated in the hands of the federal administration. Civil society actors have weak capacities to mobilize it, and the autonomy of institutionalized advisory bodies is limited. Moreover, the production of scientific expertise is undergoing a process of professionalization which strengthens the position of the federal administration as the (main) mandating agent. Despite increased political polarization and less inclu- sive decision-making, scientific expertise remains anchored in the policy subsystem, rather than being used to legitimate policy through appeals to the wider population. Finally, the structure of a policy problem matters both for expertise mobilization and for the latter's impact on the policy process, be- cause it conditions conflict structures and their anticipation. Structured problems result in a greater overlap between the principal of expertise mobilization and its intended audience, thereby increasing the chance that expertise shapes policy design. Conversely, less structured problems, especially those that involve conflicts about values and goals, reduce the impact of expertise.

Accepting Methadone: The Evolution of an Institutional Decision

This is a study of organisational decision making among senior civil servants in the Department of Health (DOH) in relation to the acceptance of methadone maintenance as a valid treatment modality for opiate misuse in Ireland. A qualitative strategy was adopted with an emergent design and grounded theory perspective. The data was collected using a naturalistic mode of inquiry and comprised of documentary analysis and semi-structured interviews. The aspects of decision making chosen for the study were: 1. Identifying the actors involved considering the heretofore dominant 'corporation sole' culture of the Irish public administration. 2. Identifying two (out of the myriad) processes involved in decision making. 3. Identifying what theoretical model(s) of decision making most closely approximates to this case. The findings were as follows: 1. Actors involved at all levels of the decision making could be identified, albeit with some difficulty. This as a result of the strategic management initiative. Previously, it may not have been possible. Stages or phases could not, in this case, be readily identified though limitations of this study may prove significant. 2. Both the processes selected in decision-making in this case were confirmed. Personal and professional support provided by peers and seniors is crucial to decision making. Decision making does occur within networks: these tend to be those that are formally appointed rather than informal ones. 3. The model closest to that of this case was that of incremental decision making within network settings.This resource was contributed by The National Documentation Centre on Drug Use.