1,456 research outputs found

    Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

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    The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach

    Defy the Game: Automated Market Making using Deep Reinforcement Learning

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    Automated market makers have gained popularity in the financial market for their ability to provide liquidity without needing a centralized intermediary (market maker). However, they suffer from the problems of slippage and impermanent loss, which can lead to losses for both liquidity providers and takers. This work implements a pseudo-arbitrage rule to solve the impermanent loss issues related to arbitrage opportunities. The mechanism implements a trusted external oracle to get the market conditions, put them on the automated market maker, and match the bonding curve to them. Next, the application of a Double Deep Q-Learning reinforcement learning algorithm is proposed to reduce these issues in automated market makers. The algorithm adjusts the curvature of the bonding curve function to adapt to market conditions quickly. This work describes the model, the simulation environment used to learn and test the proposed approach, and the metrics used to evaluate its performance. Finally, it explains the results of the experiments and analysis of their implications. The approach shows promise in reducing slippage and impermanent loss and recommending improvements and future works

    Genuinely Service-Oriented Enterprises: Using Work System Theory to See Beyond the Promise of Efficient Software Architecture

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    The concept of service-oriented enterprise has great potential. Taken literally, however, it raises many issues, including practical difficulties of creating a service-oriented enterprise in the computer science sense and the huge leap from flexible IT infrastructure to an enterprise that is genuinely oriented toward providing services for customers and employees. This paper is a conceptual contribution showing how work system theory can help in seeing analysis and design issues beyond technical architectures that have dominated research to date. After summarizing background concepts related to service, service systems, and the vision of service-oriented enterprises, this paper explains how work system theory can help in recognizing many obstacles on the path toward that ideal. Recognition of those obstacles supports analysis and design by illuminating the amount of change required to move to a genuinely service-oriented enterprise and by helping analysts and designers decide where service-orientation in its various guises is really appropriate

    AI-driven approaches for optimizing the energy efficiency of integrated energy system

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    To decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions. This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue. The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion

    Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm

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    The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of a MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes a MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework. To enhance the generalization ability of dealing with various uncertainties, we also propose an improved multi-agent soft actor-critic (MASAC) algorithm, which facilitates en-ergy transactions between multi-agents in MMG, and employs automated machine learning (AutoML) to optimize the MASAC hyperparameters to further improve the generalization of deep reinforcement learning (DRL). The test results demonstrate that the proposed method successfully achieves power complementarity between different entities, and reduces the MMG system operating cost. Additionally, the proposal significantly outperforms other state-of-the-art reinforcement learning algorithms with better economy and higher calculation efficiency.Comment: Accepted by Energie

    A novel multi-level and community-based agent ecosystem to support customers dynamic decision-making in smart grids

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    Electrical systems have evolved at a fast pace over the past years, particularly in response to the current environmental and climate challenges. Consequently, the European Union and the United Nations have encouraged the development of a more sustainable energy strategy. This strategy triggered a paradigm shift in energy consumption and production, which becoming increasingly distributed, resulted in the development and emergence of smart energy grids. Multi-agent systems are one of the most widely used artificial intelligence concepts in smart grids. Both multi-agent systems and smart grids are distributed, so there is correspondence between the used technology and the network's complex reality. Due to the wide variety of multi-agent systems applied to smart grids, which typically have very specific goals, the ability to model the network as a whole may be compromised, as communication between systems is typically non-existent. This dissertation, therefore, proposes an agent-based ecosystem to model smart grids in which different agent-based systems can coexist. This dissertation aims to conceive, implement, test, and validate a new agent-based ecosystem, entitled A4SG (agent-based ecosystem for smart grids modelling), which combines the concepts of multi-agent systems and agent communities to enable the modelling and representation of smart grids and the entities that compose them. The proposed ecosystem employs an innovative methodology for managing static or dynamic interactions present in smart grids. The creation of a solution that allows the integration of existing systems into an ecosystem, enables the representation of smart grids in a realistic and comprehensive manner. A4SG integrates several functionalities that support the ecosystem's management, also conceived, implemented, tested, and validated in this dissertation. Two mobility functionalities are proposed: one that allows agents to move between physical machines and another that allows "virtual" mobility, where agents move between agent communities to improve the context for the achievement of their objectives. In order to prevent an agent from becoming overloaded, a novel functionality is proposed to enable the creation of agents that function as extensions of the main agent (i.e., branch agents), allowing the distribution of objectives among the various extensions of the main agent. Several case studies, which test the proposed services and functionalities individually and the ecosystem as a whole, were used to test and validate the proposed solution. These case studies were conducted in realistic contexts using data from multiple sources, including energy communities. The results indicate that the used methodologies can increase participation in demand response events, increasing the fitting between consumers and aggregators from 12 % to 69 %, and improve the strategies used in energy transaction markets, allowing an energy community of 50 customers to save 77.0 EUR per week.Os últimos anos têm sido de mudança nos sistemas elétricos, especialmente devido aos atuais desafios ambientais e climáticos. A procura por uma estratégia mais sustentável para o domínio da energia tem sido promovida pela União Europeia e pela Organização das Nações Unidas. A mudança de paradigma no que toca ao consumo e produção de energia, que acontece, cada vez mais, de forma distribuída, tem levado à emergência das redes elétricas inteligentes. Os sistemas multi-agente são um dos conceitos, no domínio da inteligência artificial, mais aplicados em redes inteligentes. Tanto os sistemas multi-agente como as redes inteligentes têm uma natureza distribuída, existindo por isso um alinhamento entre a tecnologia usada e a realidade complexa da rede. Devido a existir uma vasta oferta de sistemas multi-agente aplicados a redes inteligentes, normalmente com objetivos bastante específicos, a capacidade de modelar a rede como um todo pode ficar comprometida, porque a comunicação entre sistemas é, geralmente, inexistente. Por isso, esta dissertação propõe um ecossistema baseado em agentes para modelar as redes inteligentes, onde vários sistemas de agentes coexistem. Esta dissertação pretende conceber, implementar, testar, e validar um novo ecossistema multiagente, intitulado A4SG (agent-based ecosystem for smart grids modelling), que combina os conceitos de sistemas multi-agente e comunidades de agentes, permitindo a modelação e representação de redes inteligentes e das suas entidades. O ecossistema proposto utiliza uma metodologia inovadora para gerir as interações presentes nas redes inteligentes, sejam elas estáticas ou dinâmicas. A criação de um ecossistema que permite a integração de sistemas já existentes, cria a possibilidade de uma representação realista e detalhada das redes de energia. O A4SG integra diversas funcionalidades, também estas concebidas, implementadas, testadas, e validadas nesta dissertação, que suportam a gestão do próprio ecossistema. São propostas duas funcionalidades de mobilidade, uma que permite aos agentes mover-se entre máquinas físicas, e uma que permite uma mobilidade “virtual”, onde os agentes se movem entre comunidades de agentes, de forma a melhorar o contexto para a execução dos seus objetivos. É também proposta uma nova funcionalidade que permite a criação de agentes que funcionam como uma extensão de um agente principal, com o objetivo de evitar a sobrecarga de um agente, permitindo a distribuição de objetivos entre as várias extensões do agente principal. A solução proposta foi testada e validada por vários casos de estudo, que testam os serviços e funcionalidades propostas individualmente, e o ecossistema como um todo. Estes casos de estudo foram executados em contextos realistas, usando dados provenientes de diversas fontes, tais como comunidades de energia. Os resultados demonstram que as metodologias utilizadas podem melhorar a participação em eventos de demand response, subindo a adequação entre consumidores e agregadores de 12 % para 69 %, e melhorar as estratégias utilizadas em mercados de transações de energia, permitindo a uma comunidade de energia com 50 consumidores poupar 77,0 EUR por semana

    FlexEnergy - A Prosumer-based Approach For The Automated Marketing Of Manufacturing Companies' Energy Flexibility

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    The transition to renewable energy sources and the need to address climate change has significantly changed the energy landscape. However, the fluctuating nature of renewables and increased electricity price volatility pose challenges to power grids and companies. This study focuses on energy flexibility achieved through industrial demand-side management (DSM) as a solution. Information technology (IT) and standardization are vital for enabling energy flexibility by optimizing energy consumption and facilitating interoperability. Digital energy platforms allow energy-intensive industries to optimize energy usage, thus enabling industrial demand optimization and effective communication within the energy ecosystem. Standardization ensures the efficient implementation of energy flexibilitymeasures across diverse energymarkets.Thisstudy proposes a process model to streamline the integration of energy flexibility measures into production processes. This model eliminates the labor-intensive manual implementation process, enabling seamless adoption of energy flexibility measures and participation in energy markets. Marketing energy flexibility is addressed through the prosumer-based process that leverages standardized communication facilitated by the energy flexibility data model (EFDM), optimizing the energy consumption of manufacturing companies. The contributions of this research lie in the proposed processmodelfor marketing energy flexibility,streamlining energy flexibility implementation through automated EFDM modeling. The findings provide insights for researchers and practitioners, guiding the adoption of energy flexibility measures and supporting a sustainable energy future

    A New Belt and Road Framework Method Based on the Internet of Things (IoT) for Industrial Applications

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    A network called the Internet of things (IoT) enables data communication amongst all autonomous inanimate devices. It is a network that enables users to practice object communication. Its development marks a significant turning point in digital technologies. The IoT is growing, and with it, the amount of research being done on its foundation. The western region's economic growth should take advantage of this growth and find a path that works for it. There are many flaws and issues in the accounting system of enterprises as a consequence of the development of internal management aspects. In order to aid the west area's quick development, this article is focused on the optimization of the financial growth trajectory of that region against the "Belt and Road" framework. The interaction development route of related industries and modern urbanization is presented in this research. The future total GDP and per capita GDP of the west area are studied and reported using simulated data. According to the data, the west area's total GDP and per capita GDP will expand annually over the following 4 years and afterwards continue to grow

    FINANCIAL RISK AND FINANCIAL RISK MANAGEMENT TECHNOLOGY (RMT): ISSUES AND ADVANCES

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    Methods for sound risk management are of increasing interest among Wall Street investment banking and brokerage firms in the aftermath of the October 1987 crash of the stock market. We present an overview of the basic definitions and issues related to risk, and the management of financial risk and financial risk management technology (RMT) for information systems (IS) technology professionals. We discuss of the content of risk management technology, including the models, the software and hardware, and the market data required to track risk. We also discuss the identification of risky events, alternative approaches to the measurement of risk, and how investment firms go about formulating strategies to control financial risk. We next show how changes in the information technologies supporting these tasks have led to improvements in the control of risk and in the design of products which involve financial risk. Advances in five areas that are of interest are: communications software, object-oriented programming, the use of parallel processors and supercomputers, and the application of artificial intelligence and neural nets. Although these new information technologies create significant opportunities to improve global and departmental risk management, a basic question that must be addressed involves the costs associated with their implementation. Thus, a third contribution of this paper is to analyze the extent to which the implementation of these technologies will affect firm costs. To this end, we evaluate the components of the cost function for risk management, and consider some ways that the new technologies can be applied to reduce overall costs.Information Systems Working Papers Serie

    PROGNOSIS - Historical Pattern Matching for Economic Forecasting and Trading

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    In recent years financial markets have become complex environments that continuously change and they change quickly. The strong link between the continuous change in the markets and the danger of losing money when trading in them, has made financial studies a domain that concentrates increasing scientific and business attention. In this context, the development of computational techniques that can monitor recent financial events can process them according to their similarity with historical data recordings, and can support financial decision making, is a challenging problem. In this work, the principal idea for tackling this problem is the integration of 'current' market information as derived from the market's recent past and historical information. A robust technique which is based on flexible pattern matching, segmented data representations, time warping, and time series embedding dimension measures is proposed. Complementary time series derived features, concerning trend structures, temporal considerations and statistical measures are systematically combined in this technique. All these components have been integrated into a software package, which I called PROGNOSIS, that can selectively monitor its application and allows systematic evaluation in terms of financial forecasting and trading performance. In addition, two other topics are discussed in this thesis. Firstly, in chapter 3, a neural network, that is known as the Growing Neural Gas network, is employed for financial forecasting and trading. To my knowledge, this network has never been applied before to financial problems. Based on this a neural network forecasting and trading benchmark was constructed for comparison purposes. Secondly, a novel method of approaching the well established co-integraton theory is proposed in the last chapter of the thesis. This method enhances the co-integration theory by integrating into it local time relations between two time series. These local time dependencies are identified using dynamic time warping. The hypothesis that is tested is that local time shifts, delays, shrinks or stretches, if identified, may help to reveal co-integrating movement between the two time series. I called this type of co-integration time-warped co-integration. To this end, the time-warped co-integration framework is presented as an error correction model and it is tested on arbitrage trading opportunities within PROGNOSIS
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