Zita Vale

Polytechnic Institute of Porto | IPP · GECAD - Grupo de Investigação em Engenharia do Conhecimento e Apoio à Decisão

Call for publications, Applied Energy, ELSEVIER Changes in technology, climate issues, and the need to replace fossil fuels with renewables are forcing a major energy sector restructuring. The widespread adoption of local energy communities enables the combined local generation of different energy vectors, including electricity, heat, cooling power...

Adopting distributed energy resources (DERs) is the key to a low-carbon future in electrical distribution systems (EDS). However, integrating DERs increases the uncertainties in the distribution system expansion planning (DSEP). Thus, the long-term DSEP faces a planning risk brought by the uncertainty of demand, electric vehicle (EV) demand, renewa...

Metaheuristic optimization algorithms are increasingly used to reach near-optimal solutions for complex and large-scale problems that cannot be solved in due time by exact methods. Metaheuristics’ performance is, however, deeply dependent on their effective configuration and fine-tuning to align the algorithm’s search process with the specific char...

Renewable energy sources have transformed the electricity market, allowing virtual power players or aggregators to participate and benefit from selling surplus energy. However, meeting demand and ensuring energy production stability can be challenging due to the intermittent nature of renewable sources. Accurate forecasting of energy consumption, g...

There is a growing trend towards consumer-focused approaches integrating distributed generation resources in the power and energy sector. This, however, adds complexity to community management as new players are introduced. The authors have designed a trustworthy rate (TR) system to address this issue of selecting participants for demand response e...

The privacy and security of users’ data have been a concern in the last few years. New techniques like federated learning have appeared to allow the training of machine learning models without sharing the users’ personal data. These systems have a lot of variables that can change the outcome of the models. Studies have been made to explore the effe...

Smart grid is a revolutionary concept that came to renew the way the power systems are thought. However, for these systems to be possible it is necessary and recommended that the consumers have the right technology integrated in their houses, in other words, have smart homes. This paper describes the demonstration of a smart home solution based on...

Multiagent systems promote a decentralized and distributed approach that enable the division of complex problems into smaller parts. The use of multiagent systems also enables the representation of physical entities, such as persons, pursuing their own goals in an active and proactive society. Currently developments are promoting the idea of having...

In order to encourage energy saving and the adoption of renewable sources, this study provides a comprehensive experimental framework that integrates socioeconomic and behavioral objectives for the local energy community. The experiment aims to find out how successfully using behavioral interventions might encourage customers to save electrical ene...

The authors review the efforts made in the last five years to implement Demand Response (DR) programs, considering and studying several models and countries. As motivation, climate change has been a topic widely discussed in the last decades, namely in the power and energy sectors. Therefore, it is crucial to substitute non-renewable fuels with mor...

A challenge that consistently arises when reviewing and justifying novel energy models and theorems is the accuracy of the electrical data used. Therefore, this paper presents a dataset representing a complete European residential community based on real-life data. In this case, a community of 250 residential households was constructed with actual...

This paper proposes a novel approach for the provision of non-frequency ancillary service (AS) by consumers connected to low-voltage distribution networks. The proposed approach considers an asymmetric pool-based local market for AS negotiation, allowing consumers to set a flexibility quantity and desired price to trade. A case study with 98 consum...

The share of renewable generation is growing worldwide, increasing the complexity of the grids operation to maintain its stability and balance. This leads to an increased need for designing new electricity markets (EMs) suited to this new reality. Simulation tools are widely used to experiment and analyze the potential impacts of new solutions, suc...

The transition from the current energy architecture to a new model is evident and inevitable. The coming future promises innovative and increasingly rigorous projects and challenges for everyone involved in this value chain. Technological developments have allowed the emergence of new concepts, such as renewable energy communities, decentralized re...

The stochastic nature of renewable energy resources and consumption has the potential to threaten the balance between generation and consumption as well as to cause instability in power systems. The microgrid operators (MGOs) are financially responsible for compensating for the imbalance of power within their portfolio. The imbalance of power can b...

The Special Issue “Demand Response in Smart Grids” includes 10 papers [...]

Changes will be required to handle the increased power flow in the network as the distribution infrastructure ages and the number of EVs and renewable grows. Designing and operating an intelligent network that reacts to changing power flows to ensure the optimal operation is the most economical option than fortifying the network with heavier cables...

There is a growing complexity, volatility, and unpredictability in the electric sector that hardens the decision-making process. To this end, the use of proper decision support tools and simulation platforms becomes essential. This paper presents the Multi-Agent based Real-Time INfrastructure for Energy (MARTINE) platform that allows real-time simu...

Hydrogen is a promising commodity, a renewable secondary energy source, and feedstock alike, to meet greenhouse gas emissions targets and promote economic decarbonization. A common goal pursued by many countries, the hydrogen economy receives a blending of public and private capital. After European Green Deal, state members created national policie...

One of the primary challenges in solving the State Estimation (SE) problem in low voltage networks is the presence of Gross Errors (GE). If the SE model fails to accurately estimate state variables in the presence of GE, the system operator may receive a distorted image of the power network, potentially leading to unforeseen disruptions, blackouts,...

Uncertainty in renewable energy generation and energy demand reduces generation flexibility, which should be compensated by increasing flexibility on the supply grid and demand sides. Dynamic line rating (DLR) forecasting, in which the maximum current carrying capacity of overhead transmission lines is accurately forecasted, is important to enable...

Electric Vehicles (EV) are emerging into electricity grid, where the Vehicle-to-Grid (V2G) feature is a major flexibility opportunity for Demand Response (DR) programs. Optimized and fair management of EVs flexibility activation is then required. In the present paper, the authors propose a methodology to deal with the complex management of the Loca...

By empowering consumers and enabling them as active players in the power and energy sector, demand flexibility requires more precise and sophisticated load modeling. In this paper, a laboratory testbed was designed and implemented for surveying the behavior of laboratory loads in different network conditions by using real-time simulation. Power har...

The adoption of smart grids is becoming a common reality worldwide. This new reality is starting to impact energy customers as they face a dynamic grid in which they can actively participate. However, if energy customers are not prepared to participate actively, they can have their energy costs increased. This paper provides a review of acceptance...

In the past decade, the global distribution of energy resources has expanded significantly. The increasing number of prosumers creates the prospect for a more decentralized and accessible energy market, where the peer-to-peer energy trading paradigm emerges. This paper proposes a methodology to optimize the participation in peer-to-peer markets bas...

Globally, the amount of renewable energy generation is increasing, which raises the complexity of operating electrical grids to maintain stability and balance and boosts the need for developing new electricity market (EM) models fitting this new reality. To test, study, and validate the possible effects of novel EM designs, simulation techniques ar...

The rapid developments in Internet‐of‐Things (IoT), cloud computing, and big data technologies have increased the popularity of machine learning (ML) techniques. As a result, of all ML techniques, deep learning (DL) is at the forefront of innovation, outperforming all other techniques in many application domains. DL has made breakthroughs in speech...

The large‐scale integration of electric vehicles (EVs) can contribute to the better use of renewable resources and the emergence of new technologies. However, if not properly controlled, it has several downsides. Several strategies make it possible to perform this control by making use of data mining models to deal with the large amounts of data as...

Due to the increasing penetration level of local generating resources at the distribution network, the transmission system operator (TSO) and distribution system operators (DSOs) need to coordinate their operations to use these resources and provide the required energy and regulation for the power system. This work proposes a hierarchy model for sc...

The 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 pape...

Prosumers are emerging in the power and energy market to provide load flexibility to smooth the use of distributed generation. The volatile behavior increases the production prediction complexity, and the demand side must take a step forward to participate in demand response events triggered by a community manager. If balance is achieved, the parti...

The flexibility provided by the demand side will be crucial to take a step forward to increase the penetration of renewable energy resources in the system. The proposed methodology provides the aggregator with information about the most reliable consumers, attributing a trustworthy rate that characterizes their performance on Demand Response (DR) e...

Active consumers, as new players that can contribute for the market transactions, will provide flexibility through demand response events to deal with the volatile behavior from the distributed generation resources such as solar and wind. However, their lack of knowledge and uncertain behavior will add a new level of complexity to the community man...

This chapter proposes an electricity customers characterization framework supported by knowledge discovery in a database (KDD). Data mining (DM) techniques are explored to be applied to the power system field. The proposed DM techniques include clustering algorithms approaches, electrical customers' characterization models, and forecasting techniqu...

Data mining approaches are increasingly important to enable dealing with the constantly rising challenges in power and energy systems. Classification models, in particular, are suitable for predicting classes of new observations based on previous cases. This chapter illustrates the advantages of the use of classification models, namely artificial n...

The present deliverable was developed as part of the research activities of the TradeRES project Task 5.3 – Performance assessment of current and new market designs and trad�ing mechanisms for National and Regional Markets. This report presents the first edition of deliverable 5.3, which provides an initial assessment of the market performance usin...

New challenges arise with the upsurge of a Big Data era. Huge volumes of data, from the most varied natures, gathered from different sources, collected in different timings, often with high associated uncertainty, make the decision-making process a harsher task. Current methods are not ready to deal with characteristics of the new problems. This pa...

Scheduling forecasting activities and improving the forecasting accuracy is important to deliver energy efficiency to the customers. However, it is also important to reduce the computational effort dedicated to these forecasting activities to ensure more effective environment sustainability. This paper proposes two forecasting algorithms known as a...

This paper presents an innovative multi-agent ecosystem framework designed to simulate various energy communities and smart grids while providing an easy and practical solution to manage and control each simulation. This framework allows the coexistence of various multi-agent systems and provides tools to enable the management of the ecosystem and...

Empowering the consumers will increase the complexity of local communities’ management. Enabling bidirectional communication and appliances to become smarter can be a huge step toward implementing demand response. However, a solution capable of providing the right knowledge and tools must be developed. The authors thereby propose a methodology to m...

Complex optimization problems are often associated to large search spaces and consequent prohibitive execution times in finding the optimal results. This is especially relevant when dealing with dynamic real problems, such as those in the field of power and energy systems. Solving this type of problems requires new models that are able to find near...

Energy data measured on-site from buildings can help describe the consumption behavior of end-users and can be used to examine and prove certain theorems and models, that require a large volume of data to be gathered. However, the direct extraction of this data can often be a lengthily and costly process. As a result, a dataset of a residential com...

Energy management in buildings can be largely improved by considering adequate forecasting techniques to find load consumption patterns. While these forecasting techniques are relevant, decision making is needed to decide the forecasting technique that suits best each context, thus improving the accuracy of predictions. In this paper, two forecasti...

Energy enables the functioning of modern society. However, humanity's reliance on fossil fuels since the industrial revolution has contributed to many societal problems including climate change, environmental degradation and pollution, and the transition to a renewable and carbon-free energy system is one of the grand challenges for the 21st centur...

Flexible demand management for residential load scheduling, which considers constraints, such as load operating time window and order between them, is a key aspect in demand response. This paper aims to address constraints imposed on the operation schedule of appliances while also participating in demand response events. An innovative crossover met...

The increased penetration of renewables in power distribution networks has motivated significant interest in local energy systems. One of the main goals of local energy markets is to promote the participation of small consumers in energy transactions. Such transactions in local energy markets can be modeled as a bi-level optimization problem in whi...