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- Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization AlgorithmPublication . Gomes, Luis; Faria, Pedro; Fernandes, Filipe; Vale, Zita; Ramos, CarlosThe recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.
- Smart Grid Ecosystem Modeling Using a Novel Framework for Heterogenous Agent CommunitiesPublication . Pereira, Helder; Ribeiro, Bruno; Gomes, Luis; Vale, ZitaThe modeling of smart grids using multi-agent systems is a common approach due to the ability to model complex and distributed systems using an agent-based solution. However, the use of a multi-agent system framework can limit the integration of new operation and management models, especially artificial intelligence algorithms. Therefore, this paper presents a study of available open-source multi-agent systems frameworks developed in Python, as it is a growing programming language and is largely used for data analytics and artificial intelligence models. As a consequence of the presented study, the authors proposed a novel open-source multi-agent system framework built for smart grid modeling, entitled Python-based framework for heterogeneous agent communities (PEAK). This framework enables the use of simulation environments but also allows real integration at pilot sites using a real-time clock. To demonstrate the capabilities of the PEAK framework, a novel agent ecosystem based on agent communities is shown and tested. This novel ecosystem, entitled Agent-based ecosystem for Smart Grid modeling (A4SG), takes full advantage of the PEAK framework and enables agent mobility, agent branching, and dynamic agent communities. An energy community of 20 prosumers, of which six have energy storage systems, that can share energy among them, using a peer-to-peer market, is used to test and validate the PEAK and A4SG solutions.
- Demonstration of an Energy Consumption Forecasting System for Energy Management in BuildingsPublication . Jozi, Aria; Ramos, Daniel; Gomes, Luis; Faria, Pedro; Pinto, Tiago; Vale, ZitaDue to the increment of the energy consumption and dependency of the nowadays lifestyle to the electrical appliances, the essential role of an energy management system in the buildings is realized more than ever. With this motivation, predicting energy consumption is very relevant to support the energy management in buildings. In this paper, the use of an energy management system supported by forecasting models applied to energy consumption prediction is demonstrated. The real-time automatic forecasting system is running separately but integrated with the existing SCADA system. Nine different forecasting approaches to obtain the most reliable estimated energy consumption of the building during the following hours are implemented.
- GAIC: Um sistema inteligente e flexível para simulação e apoio à participação de pequenos e médios consumidores na gestão ativa de cargas no âmbito de smart gridsPublication . Gomes, Luís Filipe de Oliveira; Vale, Zita; Ramos, CarlosA liberalização dos mercados de energia e a utilização intensiva de produção distribuída tem vindo a provocar uma alteração no paradigma de operação das redes de distribuição de energia elétrica. A continuidade da fiabilidade das redes de distribuição no contexto destes novos paradigmas requer alterações estruturais e funcionais. O conceito de Smart Grid vem permitir a adaptação das redes de distribuição ao novo contexto. Numa Smart Grid os pequenos e médios consumidores são chamados ao plano ativo das participações. Este processo é conseguido através da aplicação de programas de demand response e da existência de players agregadores. O uso de programas de demand response para alcançar benefícios para a rede encontra-se atualmente a ser estudado no meio científico. Porém, existe a necessidade de estudos que procurem benefícios para os pequenos e médios consumidores. O alcance dos benefícios para os pequenos e médios consumidores não é apenas vantajoso para o consumidor, como também o é para a rede elétrica de distribuição. A participação, dos pequenos e médios consumidores, em programas de demand response acontece significativamente através da redução de consumos energéticos. De modo a evitar os impactos negativos que podem provir dessas reduções, o trabalho aqui proposto faz uso de otimizações que recorrem a técnicas de aprendizagem através da utilização redes neuronais artificiais. Para poder efetuar um melhor enquadramento do trabalho com as Smart Grids, será desenvolvido um sistema multiagente capaz de simular os principais players de uma Smart Grid. O foco deste sistema multiagente será o agente responsável pela simulação do pequeno e médio consumidor. Este agente terá não só que replicar um pequeno e médio consumidor, como terá ainda que possibilitar a integração de cargas reais e virtuais. Como meio de interação com o pequeno e médio consumidor, foi desenvolvida no âmbito desta dissertação um sistema móvel. No final do trabalho obteve-se um sistema multiagente capaz de simular uma Smart Grid e a execução de programas de demand response, sSendo o agente representante do pequeno e médio consumidor capaz de tomar ações e reações de modo a poder responder autonomamente aos programas de demand response lançados na rede. O desenvolvimento do sistema permite: o estudo e análise da integração dos pequenos e médios consumidores nas Smart Grids por meio de programas de demand response; a comparação entre múltiplos algoritmos de otimização; e a integração de métodos de aprendizagem. De modo a demonstrar e viabilizar as capacidades de todo o sistema, a dissertação inclui casos de estudo para as várias vertentes que podem ser exploradas com o sistema desenvolvido.
- Real-Time Simulation of Renewable Energy Transactions in Microgrid Context Using Real Hardware ResourcesPublication . Abrishambaf, Omid; Gomes, Luís; Faria, Pedro; Afonso, João L.; Vale, ZitaThe recent changes on the electrical power systems make the role of distributed generation more important. Employing distributed generation in demand side allows the consumers to have active participation in the electricity markets. This paper implements the real-time simulation of a local microgrid that consists of two subsystems: home area network and neighborhood area network. In this system, the home area network is the electrical grid of a house and the neighborhood area network is the low voltage electrical distribution grid of the neighborhood. The main contribution of this paper is to assess scenarios for energy transactions between these two areas using real resources. In the case studies, several real profiles have been employed for simulating the consumption and generation of this local microgrid.
- Impact of Forecasting Models Errors in a Peer-to-Peer Energy Sharing MarketPublication . Gomes, Luis; Morais, Hugo; Goncalves, Calvin; Gomes, Eduardo; Pereira, Lucas; Vale, ZitaThe use of energy sharing models in smart grids has been widely addressed in the literature. However, feasible technical solutions that can deploy these models into reality, as well as the correct use of energy forecasts are not properly addressed. This paper proposes a simple, yet viable and feasible, solution to deploy energy management systems on the end-user-side in order to enable not only energy forecasting but also a distributed discriminatory-price auction peer-to-peer energy transaction market. This work also analyses the impact of four energy forecasting models on energy transactions: a mathematical model, a support-vector machine model, an eXtreme Gradient Boosting model, and a TabNet model. To test the proposed solution and models, the system was deployed in five small offices and three residential households, achieving a maximum of energy costs reduction of 10.89% within the community, ranging from 0.24% to 57.43% for each individual agent. The results demonstrated the potential of peer-to-peer energy transactions to promote energy cost reductions and enable the validation of auction-based energy transactions and the use of energy forecasting models in today’s buildings and end-users.
- Distributed Intelligent Management of Microgrids using a Multi-Agent Simulation PlatformPublication . Gomes, Luis; Pinto, Tiago; Faria, Pedro; Vale, ZitaMulti-agent approaches have been widely used to model complex systems of distributed nature with a large amount of interactions between the involved entities. Power systems are a reference case, mainly due to the increasing use of distributed energy sources, largely based on renewable sources, which have potentiated huge changes in the power systems’ sector. Dealing with such a large scale integration of intermittent generation sources led to the emergence of several new players, as well as the development of new paradigms, such as the microgrid concept, and the evolution of demand response programs, which potentiate the active participation of consumers. This paper presents a multi-agent based simulation platform which models a microgrid environment, considering several different types of simulated players. These players interact with real physical installations, creating a realistic simulation environment with results that can be observed directly in the reality. A case study is presented considering players’ responses to a demand response event, resulting in an intelligent increase of consumption in order to face the wind generation surplus.
- Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand ResponsePublication . Barreto, Rúben; Goncalves, Calvin; Gomes, Luis; Faria, Pedro; Vale, ZitaIn the energy sector, prosumers are becoming relevant entities for energy management systems since they can share energy with their citizen energy community (CEC). Thus, this paper proposes a novel methodology based on demand response (DR) participation in a CEC context, where unsupervised learning algorithms such as convolutional neural networks and k-means are used. This novel methodology can analyze future events on the grid and balance the consumption and generation using end-user flexibility. The end-users’ invitations to the DR event were according to their ranking obtained through three metrics. These metrics were energy flexibility, participation ratio, and flexibility history of the end-users. During the DR event, a continuous balancing assessment is performed to allow the invitation of additional end-users. Real data from a CEC with 50 buildings were used, where the results demonstrated that the end-users’ participation in two DR events allows reduction of energy costs by EUR 1.31, balancing the CEC energy resources.
- Agricultural irrigation scheduling for a crop management system considering water and energy use optimizationPublication . Abrishambaf, Omid; Faria, Pedro; Gomes, Luis; Vale, ZitaCenter pivot systems are widely used to overcome the irrigation needs of agricultural fields. In this paper, an autonomous approach is proposed in order to improve the low efficiency of irrigation by developing a system based on the water requirement of the plantations, through field data. The data are local temperature, local wind, soil moisture, precipitation forecast, and soil evapotranspiration calculation. This information enables the system to calculate the real evapotranspiration for not being necessary to restrict to lysimetric measures. By this way, the system schedules the irrigation for the lower cost periods, considering the produced energy by the local resources, and the price of energy purchased from the utility grid. Also, it is considered that the irrigation must be carried out within the time interval in which the plantations do not reach the wilding point, so it will be carried out at the periods with the lowest cost. This will optimize the overall operational costs of the irrigation.
- Real-Time Simulation of Real-Time Pricing Demand Response to Meet Wind VariationsPublication . Gomes, Luis; Fernandes, Filipe; Faria, Pedro; Silva, Marco; Vale, Zita; Ramos, CarlosRecent changes of paradigm in power systems opened the opportunity to the active participation of new players. The small and medium players gain new opportunities while participating in demand response programs. This paper explores the optimal resources scheduling in two distinct levels. First, the network operator facing large wind power variations makes use of real time pricing to induce consumers to meet wind power variations. Then, at the consumer level, each load is managed according to the consumer preferences. The two-level resources schedule has been implemented in a real-time simulation platform, which uses hardware for consumer’ loads control. The illustrative example includes a situation of large lack of wind power and focuses on a consumer with 18 loads.