Multi-Agent Systems SemantiC Interoperability for simulation and dEcision supporT in complex energY systems

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Publications

BRICKS: Building’s reasoning for intelligent control knowledge-based system

Publication . Santos, Gabriel; Vale, Zita; Faria, Pedro; Gomes, Luis

Building energy management systems have been largely implemented, focusing on specific domains. When installed together, they lack interoperability to make them work correctly and to achieve a centralized user interface. The Building's Reasoning for Intelligent Control Knowledge-based System (BRICKS) overcomes these issues by developing an interoperable building management system able to aggregate different interest domains. It is a context-aware semantic rule-based system for intelligent management of buildings' energy and security. Its output can be a set of alarms, notifications, or control actions to take. BRICKS itself, and its features are the innovative contribution of the present paper. It is very important for buildings' energy management, namely in the scope of demand response programs. In this paper, it is shown how semantics is used to enable the knowledge exchange between different devices, algorithms, and models, without the need for reprogramming the system. A scenario is deployed in a real building for demonstration.

2020Journal article

Open access

Day-ahead electricity market price forecasting using artificial neural network with spearman data correlation

Publication . Nascimento, Joao; Pinto, Tiago; Vale, Zita

Electricity markets are complex environments with very dynamic characteristics. The large-scale penetration of renewable energy sources has brought an increased uncertainty to generation, which is consequently, reflected in electricity market prices. In this way, novel advanced forecasting methods that are able to predict electricity market prices taking into account the new variables that influence prices variation are required. This paper proposes a new model for day-ahead electricity market prices forecasting based on the application of an artificial neural network. The main novelty of this paper relates to the pre-processing phase, in which the relevant data referring to the different variables that have a direct influence on market prices such as generation, temperature, consumption, among others, is analysed. The association between these variables is performed using spearman correlation, from which results the identification of which data has a larger influence on the market prices variation. This pre-analysis is then used to adapt the training process of the artificial neural network, leading to improved forecasting results, by using the most relevant data in an appropriate way.

2019Journal article

Open access

Air conditioner consumption optimization in an office building considering user comfort

Publication . Khorram Ghahfarrokhi, Mahsa; Faria, Pedro; Abrishambaf, Omid; Vale, Zita

The rapid growth of energy consumption and its consequences in the last decades, made the world persuaded to energy optimization and energy management. Therefore, producers and prosumers should be equipped with the automation infrastructures to perform the management programs, such as demand response programs. Office buildings are considering as a proper case for implementing energy consumption minimization since they are responsible for a huge portion of total energy consumption in the world. This paper proposes a multi-period optimization algorithm implemented in Supervisory Control and Data Acquisition system of an office building. The developed optimization algorithm is an efficient solution considered for minimizing the power consumption of air conditioners by considering the user comfort constraints. Two determinative parameters are defined to prevent over-power reduction from certain devices. In order to respect to user preferences, priority numbers are dedicated to each air conditioner to present the importance of each device. A case study with several scenarios is implemented to verify the performance of the proposed algorithm in real life using real data of the building. The obtained results show the impacts of proposed parameters and different comfort constraints of algorithm while the main target of the optimization has been reached.

2020-03Journal article

Open access

Learning Bidding Strategies in Local Electricity Markets using a Nature-Inspired Algorithm

Publication . Lezama, Fernando; Soares, João; Faia, Ricardo; Faria, Pedro; Vale, Zita

Local electricity markets (LEM) are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, how these local markets will be integrated into existing market structures, and to make the most profit from them, is still unclear. In this work, we propose a LEM framework based on bi-level optimization. In the upper level, end-users aim at maximizing profits, while the lower level represents the clearing market process. Moreover, a cascade integration to the wholesale market through an aggregator that acts after the LEM has been cleared is considered. Learning strategies using only available information can be a powerful tool to take the most advantage of LEM. To this end, we advocate the use of ant colony optimization (ACO), a nature-inspired technique, similar to that employed in machine learning. By using ACO, consumers, producers and prosumers, can learn the best strategies to maximize their profits without sharing private information and based solely on their experience.

2020Conference object

Open access

Demand response and dispatchable generation as ancillary services to support the low voltage distribution network operation

Publication . Canizes, Bruno; Silveira, Vitor; Vale, Zita

The current power systems, namely the low voltage distribution networks, have been suffering considerable changes in recent years. What appeared to be innovation trends nowadays due to technological advances and manufacturing cost reduction has become the new reality in the coming years. Thus, the growing trend of power generation by renewable sources has posed new challenges and new opportunities. Furthermore, the wide installation of “smart meters” and the interest in placing the citizens as core players into the future energy markets and systems operation improves the role of the distribution system operator. In this way, developing new and innovative methodologies to explore the potential mechanisms for providing ancillary services in distribution networks becomes of great importance, namely in low voltage levels. This research paper proposes an innovative methodology to enhance the demand response participation of small consumers and dispatchable distributed renewable energy sources flexibility as ancillary services to mitigate the voltage and congestion issues in low voltage distribution networks. A realistic low voltage distribution network with 236 buses is used to illustrate the application of the proposed model. The results demonstrate a considerable voltage profile and congestion improvements.

2022Journal article

Open access