Location Awareness in Multi-Agent Control of Distributed Energy Resources

The integration of Distributed Energy Resource (DER) technologies such as heat pumps, electric vehicles and small-scale generation into the electricity grid at the household level is limited by technical constraints. This work argues that location is an important aspect for the control and integration of DER and that network topology can inferred without the use of a centralised network model. It addresses DER integration challenges by presenting a novel approach that uses a decentralised multi-agent system where equipment controllers learn and use their location within the low-voltage section of the power system. Models of electrical networks exhibiting technical constraints were developed. Through theoretical analysis and real network data collection, various sources of location data were identified and new geographical and electrical techniques were developed for deriving network topology using Global Positioning System (GPS) and 24-hour voltage logs. The multi-agent system paradigm and societal structures were examined as an approach to a multi-stakeholder domain and congregations were used as an aid to decentralisation in a non-hierarchical, non-market-based approach. Through formal description of the agent attitude INTEND2, the novel technique of Intention Transfer was applied to an agent congregation to provide an opt-in, collaborative system. Test facilities for multi-agent systems were developed and culminated in a new embedded controller test platform that integrated a real-time dynamic electrical network simulator to provide a full-feedback system integrated with control hardware. Finally, a multi-agent control system was developed and implemented that used location data in providing demand-side response to a voltage excursion, with the goals of improving power quality, reducing generator disconnections, and deferring network reinforcement. The resulting communicating and self-organising energy agent community, as demonstrated on a unique hardware-in-the-loop platform, provides an application model and test facility to inspire agent-based, location-aware smart grid applications across the power systems domain

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

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

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

Cyclic blackout mitigation and prevention

Severe and long-lasting power shortages plague many countries, resulting in cyclic blackouts affecting the life of millions of people. This research focuses on the design, development and evolution of a computer-controlled system for chronic cyclic blackouts mitigation based on the use of an agent-based distributed power management system integrating Supply Demand Matching (SDM) with the dynamic management of Heat, Ventilation, and Air Conditioning (HVAC) appliances. The principle is supported through interlocking different types of HVAC appliances within an adaptive cluster, the composition of which is dynamically updated according to the level of power secured from aggregating the surplus power from underutilised standby generation which is assumed to be changing throughout the day. The surplus power aggregation provides a dynamically changing flow, used to power a basic set of appliances and one HVAC per household. The proposed solution has two modes, cyclic blackout mitigation and prevention modes, selecting either one depends on the size of the power shortage. If the power shortage is severe, the system works in its cyclic blackout mitigation mode during the power OFF periods of a cyclic blackout. The system changes the composition of the HVAC cluster so that its demand added to the demand of basic household appliances matches the amount of secured supply. The system provides the best possible air conditioning/cooling service and distributes the usage right and duration of each type of HVAC appliance either equally among all houses or according to house temperature. However if the power shortage is limited and centred around the peak, the system works in its prevention mode, in such case, the system trades a minimum number of operational air conditioners (ACs) with air cooling counterparts in so doing reducing the overall demand. The solution assumes the use of a new breed of smart meters, suggested in this research, capable of dynamically rationing power provided to each household through a centrally specified power allocation for each family. This smart meter dynamically monitors each customer’s demand and ensures their allocation is never exceeded. The system implementation is evaluated utilising input power usage patterns collected through a field survey conducted in a residential quarter in Basra City, Iraq. The results of the mapping formed the foundation for a residential demand generator integrated in a custom platform (DDSM-IDEA) built as the development environment dedicated for implementing and evaluating the power management strategies. Simulation results show that the proposed solution provides an equitably distributed, comfortable quality of life level during cyclic blackout periods.Severe and long-lasting power shortages plague many countries, resulting in cyclic blackouts affecting the life of millions of people. This research focuses on the design, development and evolution of a computer-controlled system for chronic cyclic blackouts mitigation based on the use of an agent-based distributed power management system integrating Supply Demand Matching (SDM) with the dynamic management of Heat, Ventilation, and Air Conditioning (HVAC) appliances. The principle is supported through interlocking different types of HVAC appliances within an adaptive cluster, the composition of which is dynamically updated according to the level of power secured from aggregating the surplus power from underutilised standby generation which is assumed to be changing throughout the day. The surplus power aggregation provides a dynamically changing flow, used to power a basic set of appliances and one HVAC per household. The proposed solution has two modes, cyclic blackout mitigation and prevention modes, selecting either one depends on the size of the power shortage. If the power shortage is severe, the system works in its cyclic blackout mitigation mode during the power OFF periods of a cyclic blackout. The system changes the composition of the HVAC cluster so that its demand added to the demand of basic household appliances matches the amount of secured supply. The system provides the best possible air conditioning/cooling service and distributes the usage right and duration of each type of HVAC appliance either equally among all houses or according to house temperature. However if the power shortage is limited and centred around the peak, the system works in its prevention mode, in such case, the system trades a minimum number of operational air conditioners (ACs) with air cooling counterparts in so doing reducing the overall demand. The solution assumes the use of a new breed of smart meters, suggested in this research, capable of dynamically rationing power provided to each household through a centrally specified power allocation for each family. This smart meter dynamically monitors each customer’s demand and ensures their allocation is never exceeded. The system implementation is evaluated utilising input power usage patterns collected through a field survey conducted in a residential quarter in Basra City, Iraq. The results of the mapping formed the foundation for a residential demand generator integrated in a custom platform (DDSM-IDEA) built as the development environment dedicated for implementing and evaluating the power management strategies. Simulation results show that the proposed solution provides an equitably distributed, comfortable quality of life level during cyclic blackout periods

Implementation of a novel multi-agent system for demand response management in low-voltage distribution networks

In this era of advanced distribution automation technologies, demand response is becoming an important tool for electricity network management. The available flexible loads can efficiently help in alleviating the network constraints and achieving demand-supply balance. Therefore, this forms the rationale behind this paper, which aims to implement a multi-agent system framework in order to achieve flexible price-based demand response. A genetic algorithm-based multi-objective optimization technique is applied to determine the optimal locations and the amount of required demand reduction in order to keep the network within statutory limits. The methodology is based on probabilistic estimation of the granularity of total available flexible demand from shiftable home appliances in each low-voltage feeder. Moreover, an optimal decision making for the start time of appliances upon receiving a real-time price signal is proposed. This is accomplished by considering the willingness to participate as well as price demand elasticity of the different clusters of customers. To fully demonstrate the feasibility and effectiveness of the proposed framework, a modified IEEE 69 bus distribution network comprising 1824 low voltage residential customers has been implemented and analyzed

A systematic review of machine learning techniques related to local energy communities

In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.publishedVersio

Consumer behavior modeling for electrical energy systems : a complex systems approach

Orientador: Alexandre Rasi AokiCoorientador: Germano Lambert-TorresTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 27/02/2019Inclui referências: p. 141-154Resumo: Um sistema complexo é um sistema composto de muitas partes que interagem entre si, de modo que o comportamento coletivo emergente dessas partes é mais do que a soma de seus comportamentos individuais. O sistema elétrico de potência pode ser considerado um sistema complexo devido à sua diversidade de agentes heterogêneos inter-relacionados e a emergência de comportamento complexo. Sistemas de potência estão aumentando em complexidade com novos avanços relacionados à redes elétricas inteligentes tais como tecnologia de informação e comunicação, geração distribuída, veículos elétricos, armazenamento de energia e, especialmente, uma crescente interação e participação de um grande número de consumidores heterogêneos dispersos geograficamente. O sistema elétrico de potência pode ser estudado como um sistema técnico-socioeconômico complexo com múltiplas facetas, e a teoria de sistemas complexos pode fornecer uma base teórica sólida para seus desafios de modelagem e análise. O presente trabalho trata da aplicação da teoria de sistemas complexos em sistemas de potência, focando a análise no consumidor e no seu comportamento relacionado ao consumo de eletricidade, utilizando técnicas do campo da economia comportamental. Comportamentos complexos e emergentes sobre o consumo de eletricidade, bem como seu impacto nas redes elétricas, são analisados através da modelagem do comportamento dos cliente em uma simulação baseada em agentes, considerando quatro categorias de consumidores. A análise da simulação, aplicada a um estudo de caso em uma rede de distribuição de média tensão radial com dados reais, mostrou que premissas ligeiramente diferentes sobre o comportamento do consumidor no nível micro levam a resultados macro muito distintos e com comportamento não linear. Entender e modelar adequadamente o comportamento dos consumidores é de grande importância para o planejamento e operação de redes de energia, e a economia comportamental serve como uma base teórica promissora para modelar o comportamento no consumo de eletricidade. Os resultados deste trabalho mostraram que a teorias de sistemas complexos fornece ferramentas adequadas para lidar com sistemas de potência cada vez mais complexos, considerando-os não mais como um sistema independente agregado, mas como um sistema complexo integrado. Palavras-chave: distribuição de energia; consumo de eletricidade; teoria de sistemas complexos; simulação baseada em agentes; economia comportamental.Abstract: A complex system is a system composed of many interacting parts, such that the collective emergent behavior of those parts is more than the sum of their individual behaviors. Electrical energy systems may be considered a complex system due to its diversity of interrelated heterogeneous agents and emergent complex behavior. Energy systems are increasing in complexity with new advances related to the smart grid such as information and communication technology, distributed generation, electric vehicles, energy storage, and, especially, increasing interaction and participation of a large number of geographically distributed heterogeneous consumers. Power systems can be studied as a complex techno-socio-economical system with multiple facets, and Complex System Theory (CST) may provide a solid theoretical background for these modeling and analysis challenges. The present work deals with the application of CST into electrical energy systems, focusing the analysis on the consumer and their behavior on electricity consumption, using insights from the field of behavioral economics. Emergent complex behaviors on electricity consumption as well as its impact on power grids are analyzed by modeling customer behavior on an agent-based simulation, considering four different consumer categories. The analysis of the simulation, applied on a case study on a radial medium voltage distribution grid with real-world data, showed that slightly different assumptions on consumer behavior at the micro-level lead to very different and non-linear macro outcomes. To properly understand and model consumer behavior is of great importance to the planning and operation of electrical grids, and behavioral economics serves as a promising theoretical background to model behavior on electricity consumption. The results of this work showed that CST provides suitable tools to tackle electrical energy systems' increasing complexity, by considering the electrical power systems not as an aggregated independent system anymore, but as an integrated complex system. Keywords: power distribution; electricity consumption; complex systems theory; agent-based simulation; behavioral economics