Review of Serious Energy Games : Objectives, Approaches, Applications, Data Integration, and Performance Assessment

In recent years, serious energy games (SEGs) garnered increasing attention as an innovative and effective approach to tackling energy-related challenges. This review delves into the multifaceted landscape of SEG, specifically focusing on their wide-ranging applications in various contexts. The study investigates potential enhancements in user engagement achieved through integrating social connections, personalization, and data integration. Among the main challenges identified, previous studies overlooked the full potential of serious games in addressing emerging needs in energy systems, opting for oversimplified approaches. Further, these studies exhibit limited scalability and constrained generalizability, which poses challenges in applying their findings to larger energy systems and diverse scenarios. By incorporating lessons learned from prior experiences, this review aims to propel the development of SEG toward more innovative and impactful directions. It is firmly believed that positive behavior changes among individuals can be effectively encouraged by using SEG

A Systemic Approach to Next Generation Infrastructure Data Elicitation and Planning Using Serious Gaming Methods

Infrastructure systems are vital to the functioning of our society and economy. However, these systems are increasingly complex and are more interdependent than ever, making them difficult to manage. In order to respond to increasing demand, environmental concerns, and natural and man-made threats, infrastructure systems have to adapt and transform. Traditional engineering design approaches and planning tools have proven to be inadequate when planning and managing these complex socio-technical system transitions. The design and implementation of next generation infrastructure systems require holistic methodologies, encompassing organizational and societal aspects in addition to technical factors. In order to do so, a serious gaming based risk assessment methodology is developed to assist infrastructure data elicitation and planning. The methodology combines the use of various models, commercial-off-the-shelf solutions and a gaming approach to aggregate the inputs of various subject matter experts (SMEs) to predict future system characteristics. The serious gaming based approach enables experts to obtain a thorough understanding of the complexity and interdependency of the system while offering a platform to experiment with various strategies and scenarios. In order to demonstrate its abilities, the methodology was applied to National Airspace System (NAS) overhaul and its transformation to Next Generation Air Transportation System (NextGen). The implemented methodology yielded a comprehensive safety assessment and data generation mechanism, embracing the social and technical aspects of the NAS transformation for the next 15 years

Evolving game theory based decision making systems for NETA power market modelling, analysis and trading strategy development

In this thesis, current work carried out on analyzing the strategic behaviours in electricity trading is first reviewed. An intelligent decision-making and support technique, game theory, is often used in the market practice. Game theory is a discipline concerned with how individuals make decisions when they are partly aware of how their action might affect each other and when each individual might take this into account. Deficiencies and limitations of traditional game theory based methods developed for decision-making in electricity trading are also investigated. This research then explores to discover the impact of intelligent systems based trading strategies in the UK power markets. To model these behaviours and the New Electricity Trading Arrangements (NETA) system of the UK, traditional competitive and cooperative game theory strategies are taken into account in the work reported in this thesis. An improved methodology, “trigger price strategy”, is introduced to simulate power generation companies’ enhanced gaming strategies. Such modelling problem is, however, intractable and hence an extra-numerical search technique, Evolutionary Computation, is employed to solve the game theory based system modelling problem. An encoded Genetic Algorithm based technique is developed to search for an effective model for the complex decision-making process and to help decision-makers evaluate their strategies and bidding parameters. A novel and effective electricity trading simulation model is thus developed, where its design features are close to the NETA. The model scale is as close as possible to NETA. A complex and more realistic two-sided transaction mechanism with demand fully incorporated is incorporated in this model. These are a world first in this research area

Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

Multi-energy retail market simulation with autonomous intelligent agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2005. Faculdade de Engenharia. Universidade do Port

Reinforcement Learning and Game Theory for Smart Grid Security

This dissertation focuses on one of the most critical and complicated challenges facing electric power transmission and distribution systems which is their vulnerability against failure and attacks. Large scale power outages in Australia (2016), Ukraine (2015), India (2013), Nigeria (2018), and the United States (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. These incidents clearly indicate the necessity of extensive research efforts to protect the power system from external intrusion and to reduce the damages from post-attack effects. We analyze the vulnerability of smart power grids to cyber and physical attacks and failures, design different gametheoretic approaches to identify the critical components vulnerable to attack and propose their associated defense strategy, and utilizes machine learning techniques to solve the game-theoretic problems in adversarial and collaborative adversarial power grid environment. Our contributions can be divided into three major parts:Vulnerability identification: Power grid outages have disastrous impacts on almost every aspect of modern life. Despite their inevitability, the effects of failures on power grids’ performance can be limited if the system operator can predict and identify the vulnerable elements of power grids. To enable these capabilities we study machine learning algorithms to identify critical power system elements adopting a cascaded failure simulator as a threat and attack model. We use generation loss, time to reach a certain percentage of line outage/generation loss, number of line outages, etc. as evaluation metrics to evaluate the consequences of threat and attacks on the smart power grid.Adversarial gaming in power system: With the advancement of the technologies, the smart attackers are deploying different techniques to supersede the existing protection scheme. In order to defend the power grid from these smart attackers, we introduce an adversarial gaming environment using machine learning techniques which is capable of replicating the complex interaction between the attacker and the power system operators. The numerical results show that a learned defender successfully narrows down the attackers’ attack window and reduce damages. The results also show that considering some crucial factors, the players can independently execute actions without detailed information about each other.Deep learning for adversarial gaming: The learning and gaming techniques to identify vulnerable components in the power grid become computationally expensive for large scale power systems. The power system operator needs to have the advanced skills to deal with the large dimensionality of the problem. In order to aid the power system operator in finding and analyzing vulnerability for large scale power systems, we study a deep learning technique for adversary game which is capable of dealing with high dimensional power system state space with less computational time and increased computational efficiency. Overall, the results provided in this dissertation advance power grids’ resilience and security by providing a better understanding of the systems’ vulnerability and by developing efficient algorithms to identify vulnerable components and appropriate defensive strategies to reduce the damages of the attack

Stakeholder-driven design and appraisal in hydroelectric projects: a participatory gaming approach

Hydropower is an important source of renewable energy, but large hydropower multipurpose river basin projects can displace communities and have serious adverse effects on the local environment and livelihoods. The Sardar Sarovar Dam in India and other similar projects have provoked local and international protest culminating in the temporary withdrawal of the World Bank from large hydropower project finance. It would appear to be a better option for powerful stakeholders to engage seriously with weaker ones. As well as ethical concerns, economic theory would suggest that there is a flawed basis for costbenefit analysis which omits input from local stakeholders, particularly that of indigenous peoples who also have a role to play in project design. It is argued that the Kaldor-Hicks criterion should be abandoned and that decisions should be made based on a multi-criteria analysis of which cost-benefit analysis is but one component. It is suggested that full stakeholder engagement could best be conducted through participatory role-playing games which are being increasingly found in use as a means of exploring and resolving stakeholder conflicts. Such use is still relatively recent and safeguards such as a neutral moderator, advocates and ethical gaming rules are required to protect weaker and non-expert stakeholders

A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)

In the 1990s, Agent-Based Modeling (ABM) began gaining popularity and represents a departure from the more classical simulation approaches. This departure, its recent development and its increasing application by non-traditional simulation disciplines indicates the need to continuously assess the current state of ABM and identify opportunities for improvement. To begin to satisfy this need, we surveyed and collected data from 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an agent-based model. From this large data set we establish the current practice of ABM in terms of year of publication, field of study, simulation software used, purpose of the simulation, acceptable validation criteria, validation techniques and complete description of the simulation. Based on the current practice we discuss six improvements needed to advance ABM as an analysis tool. These improvements include the development of ABM specific tools that are independent of software, the development of ABM as an independent discipline with a common language that extends across domains, the establishment of expectations for ABM that match their intended purposes, the requirement of complete descriptions of the simulation so others can independently replicate the results, the requirement that all models be completely validated and the development and application of statistical and non-statistical validation techniques specifically for ABM.Agent-Based Modeling, Survey, Current Practices, Simulation Validation, Simulation Purpose