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.

Conceiving modern engineers within the framework of the sustainability action plan (PASUS) of ISEP: sustainability matters!

Engineering education practices have evolved not only due to the natural changes in the contents of the curricula and skills but also, and more recently, due to the requirements imposed by the Bologna revision process. In addition, industry is becoming more demanding, as society is becoming more and more aware of the global needs and consequences of industrial practices. Under this scope, higher education needs not only to follow but also to lead these trends. Therefore, the School of Engineering of the Polytechnic Institute of Porto (ISEP), a Global Reporting Initiative (GRI) training partner in Portugal, prepared and presented its Sustainability Action Plan (PASUS), with the main objective of creating a new kind of engineers, with Sustainable Development at the core of their graduation and MsC degrees. In this paper, the main strategies and activities of the referred plan along with the strategic approach, which guided its development and implementation, will be presented in detail. Additionally, a reflection about the above mentioned bridge between concept and application will be established and justified, in the framework of the action plan. Although in most of the situations, there was no prior discussion or specific request, many of the graduation and post-graduation programmes offered by ISEP already include courses that attend to PASUS philosophy. As a consequence, the number of Master thesis, Graduation projects and R&D projects that address sustainability problems has grown substantially, a proof that for ISEP community, sustainability really matters!

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.

Strategic Drivers for Implementing Sustainability Programs in Portuguese Organizations– Let’s Listen to Aristotle: From Triple to Quadruple Bottom Line

This article describes a study that investigated the main strategic drivers that influence the implementation of sustainability/social responsibility programs. An online survey was administered to managers of Portuguese organizations with certified management systems. The findings suggest that the implementation of such programs is mainly correlated to: 1.) the approach to understanding and working toward the satisfaction of the community’s needs (in the broad sense of social responsibility); 2.) how systematically sustainability within the organization is identified and managed (e.g., pollution prevention, improved environmental performance, and compliance with the applicable environmental laws); and 3.) the degree to which the organization tries to understand the needs of the employees and works toward satisfying them. In addition to the survey, five interviews with top managers of the surveyed organizations provided some useful insights. There was no consensus on the meaning of sustainability and social responsibility: some described it as an instrumental approach for obtaining better organizational results, while others regarded it as the right thing to do (i.e., it is values driven). In all cases, however, the managers supported a kind of umbrella construct under which different size corporations use different models (for example, the Dow Jones Sustainability Index (DJSI), Global Reporting Initiative (GRI), ISO 14001 environmental management systems), although some managers reported that they simply do not know what to do. All of those surveyed agreed that the lack of a systematic approach could represent a major threat to their organization, making them willing to pay more attention and take more action on the issue of sustainability. An additional suggestion made by managers was to change from a triple bottom line (economic dimension, environmental dimension, social equity dimension) to a quadruple bottom line by adding another dimension: personal and family happiness. This fourth dimension was recognized by the Greek philosopher/thinker Aristotle (384-322 BCE) who thought of happiness as the highest good (virtue) and ultimate goal and purpose of life, achieved through living well, in harmony. Such harmony suggests a balance and a lack of excess—in other words a sustainable existence.

Accounting education in an action-based learning environment: lessons learned

In the context of the Bologna Declaration a change is taking place in the teaching/learning paradigm. From teaching-centered education, which emphasizes the acquisition and transmission of knowledge, we now speak of learning-centered education, which is more demanding for students. This paradigm promotes a continuum of lifelong learning, where the individual needs to be able to handle knowledge, to select what is appropriate for a particular context, to learn permanently and to understand how to learn in new and rapidly changing situations. One attempt to face these challenges has been the experience of ISCAP regarding the teaching/learning of accounting in the course Managerial Simulation. This paper describes the process of teaching, learning and assessment in an action-based learning environment. After a brief general framework that focuses on education objectives, we report the strengths and limitations of this teaching/learning tool. We conclude with some lessons from the implementation of the project.

Metalearning in ALBidS: A Strategic Bidding System for electricity markets

Metalearning is a subfield of machine learning with special pro-pensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotia-tion entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that pro-vides decision support to electricity markets’ participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed meth-od are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity market´s data.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Strategic bidding methodology for electricity markets using adaptive learning

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.

ß-Lactams: chemical structure, mode of action and mechanisms of resistance

This synopsis summarizes the key chemical and bacteriological characteristics of β-lactams, penicillins, cephalosporins, carbanpenems, monobactams and others. Particular notice is given to first-generation to fifth-generation cephalosporins. This review also summarizes the main resistance mechanism to antibiotics, focusing particular attention to those conferring resistance to broad-spectrum cephalosporins by means of production of emerging cephalosporinases (extended-spectrum β-lactamases and AmpC β-lactamases), target alteration (penicillin-binding proteins from methicillin-resistant Staphylococcus aureus) and membrane transporters that pump β-lactams out of the bacterial cell.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

β-Lactams: chemical structure, mode of action and mechanisms of resistance

This synopsis summarizes the key chemical and bacteriological characteristics of β-lactams, penicillins, cephalosporins, carbanpenems, monobactams and others. Particular notice is given to first-generation to fifth-generation cephalosporins. This reviewalso summarizes the main resistancemechanism to antibiotics, focusing particular attention to those conferring resistance to broad-spectrum cephalosporins by means of production of emerging cephalosporinases (extended-spectrum β-lactamases and AmpC β-lactamases), target alteration (penicillin-binding proteins from methicillin-resistant Staphylococcus aureus) and membrane transporters that pump β-lactams out of the bacterial cell.

Evaluation of the noise exposure of symphonic orchestra musicians

For musicians, the impact of noise exposure is not yet fully characterized. Some inconsistencies can be found in the methodology used to evaluate noise exposure. This study aims to analyze the noise exposure of musicians in a symphonic orchestra to understand their risk for hearing loss, applying the methodology proposed by ISO 9612:2009. Noise levels were monitored among musicians during the rehearsal of eight different repertoires. Test subjects were selected according to their instrument and position in the orchestra. Participants wore noise dosimeters throughout the rehearsals. A sound meter was used to analyze the exposure of the conductor. The results showed that musicians are exposed to high noise levels that can damage hearing. Brass, woodwind and percussion and timpani musicians were exposed to noise levels in excess of the upper exposure action level of 85 dB (A), while the other instrumental groups had a lower exposure action level of 80 dB (A). Percussion musicians were exposed to high peak noise levels of 135 dB (C). Sound levels varied by instrument, repertoire and position. Octave frequency analyses showed differences among musicians. This study suggests that musicians are at risk for hearing loss. There is a need for more effective guidelines applicable to all countries, which should define standardized procedures for determining musician noise exposure and should allow exposure level normalization to the year, including different repertoires.

Porto Polytechnical Engineering School sustainable development action plan

Porto Polytechnical Engineering School (ISEP), a Global Reporting Initiative training partner in Portugal, has just presented its Sustainable Development Action Plan (PASUS), which main objective is the formation of a new kind of engineers, with a Sustainable Development (SD) philosophy in the core of their academic curricula courses.