Zita Vale

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

Changes are currently unfolding within electric distribution networks as renewable energy integration grows. This paper introduces a novel stochastic approach for expansion planning in distribution networks with high renewable energy penetration. The model addresses uncertainties, daily and seasonal impacts, distributed generation remuneration, car...

The discussion of fairness is gaining considerable attention in the context of Local Energy Systems (LES). This is partially motivated by the energy transition, which has put more attention on technologies and production closer to the end-user. In other words, we are evolving towards a more user-centric approach, which requires dealing with fairnes...

The distribution system is undergoing profound transformations with decarbonization efforts through the rapid integration of renewable energy and the consequent transition of the distribution network from passive to active. Nonetheless, environmental and economic urgency requires redesigning the existing distribution system. New operational standar...

Energy communities are of the utmost importance for the promotion of active participation in local energy markets under the current agenda of the European Union. Thus, it is crucial that all operations requiring energy transactions keep the stability of electrical grids. To solve this problem, this paper proposes a methodology capable of preventing...

The purpose of this study is to discuss and highlight some important considerations of using Virtual Power Plants as innovative solutions for managing energy resources from a financial and technical point of view. Virtual Power Plants are examined concerning their unique characteristics and their potential role in optimizing energy supply in compar...

The reduction of CO2 emissions is a critical imperative in the pursuit of sustainable energy solutions. A viable avenue for mitigating CO2 emissions within the power sector is adopting energy communities, which empowers communities to achieve decentralization and sustainability. Furthermore, the convergence of energy communities and machine learnin...

Given the growth of domotics and home automation, there is a need to use smart devices that integrate energy management systems and enable the automation of the environment. Considering the need to study the relationship between the environmental parameters in which the equipment is located and the energy parameters, an Environmental Awareness smar...

The transportation electrification is growing fast, especially with batteries and electric vehicles (EV) decreasing prices. Based on the growing number of EV close to end-of-life, a topic became popular, second-life batteries (SLB). SLB are batteries removed from EV when their energy and power density degraded below required level but are still per...

Local energy communities enhance energy self- sufficiency and sustainability by promoting local renewable generation and consumption. However, variations in renewable power generation and consumption are inevitable. Using flexible resources is crucial for ensuring uninterrupted energy supply during interruptions, enhancing local sustainability, and...

The adoption and implementation of smart grid technologies raise new challenges while also creating opportunities. The concept of energy communities motivates customers to become closer, collaborate and achieve common goals. In these communities, global energy management models, demand response programs, and transactive energy models can be deploye...

The energy sector explores various paths to improve the energy management of buildings. Nowadays a frequent path is to schedule load forecasting activities due to the accessibility of reliable forecasting algorithms. Data scientists usually take advantage of a large historic of consumption with weekly patterns and sensors data presenting a higher c...

The energy management of electrical buildings takes an active role in the energy market. Researchers tasked with the primary goal of reducing the energy costs take advantage of machine learning algorithms to predict how much energy should be bought and sold in the market ahead of time. Some researchers take account green computing approaches to red...

The energy resource management problem in energy systems is hard to optimize, mainly due to non-linear restrictions and a large number of variables involved. This is partly because of the increased integration of distributed energy resources. Computational intelligence optimization techniques, namely evolutionary algorithms, are regarded as efficie...

With the current state of the electrical power system, regarding the increase of renewable generation integration and electric vehicle penetration to reduce gas emissions, the energy resource management problem becomes extremely complex to optimize to the significant dimensionality and uncertainty. Metaheuristic optimization algorithms become effic...

The current landscape of the electric world is shifting toward a cleaner and more sustainable pattern. While this is a known fact, there is still very little consideration for how distributed generators (DG) should be compensated when studying the network, whether through planning or operation/reconfiguration. The DG remuneration needs to be consid...

The intensification of environmental impacts and the increased economic risks are triggering a technological race towards a low-carbon economy. In this socioeconomic scenario of increasing changes and environmental concerns, microgrids (MGs) play an important role in integrating distributed energy resources. Thus, a planning strategy for grid-conne...

The Optimal Power Flow (OPF) problem, used to obtain efficient operation conditions with the lowest cost or the minimum power loss in electrical power systems, is a non-polynomial problem that becomes even harder to analyze when considering renewable energy sources (RES) with uncertain behavior. Therefore, establishing a manageable number of RES sc...

One of the challenges of renewable energy is its uncertain nature. Community shared energy storage (CSES) is a solution to alleviate the uncertainty of renewable resources by aggregating excess energy during appropriate periods and discharging it when renewable generation is low. CSES involves multiple consumers or producers sharing an energy stora...

The widespread of distributed renewable energy is leading to an increased need for advanced energy management solutions in buildings. The variability of generation needs to be balanced by consumer flexibility, which needs to be accomplished by keeping the consumption cost as low as possible, while guaranteeing consumer comfort. This paper proposes...

The combination of genetic programming with federated learning could solve the computational distribution while promoting a collaborative learning environment. This paper proposes a federated learning configuration that enables the use of genetic programming for its global model. In addition, this paper also proposes a new aggregation algorithm tha...

In the new paradigm of smart grids, load flexibility remuneration strategies play a key role in defining innovative business models and market frameworks using distributed energy resources and demand response. This article establishes a set of use cases used to support the application of remuneration strategies considering contextual load flexibili...

The change in the electric grid is a well-known and addressed topic. To achieve the very ambitious goals to prevent climate crises and to increase the participation of renewable generation without decreasing the reliability and security of the power system, demand side flexibility and demand response presents themselves as effective solutions to in...

The use of smart building solutions can bring advantages to both a building and its users. The main motivation of this work is to propose a solution based on internet of things devices to retrieve knowledge from historic data and, using the knowledge gained, contribute to the comfort of the user and the building’s sustainability. The use of interne...

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...

Home energy management systems are essential for the optimization of resources in complex demand scheduling problems that require energy efficiency in homes. This can be achieved through the use of Renewable Electricity Sources (RES), for cleaner and more sustainable energy generation, as well as participation in Demand Response (DR) programs, for...

Despite the several advantages that distributed ledgers provide to end-users and the system, it also gives access to all data in the chain to every user with a participating ledger node, even if encrypted. While modifying data in the blockchain is a difficult and complex process, the sole existence of data in this way raises some security concerns....

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...

In local energy markets, Demand Response (DR) concept plays an essential role in balancing the generation and demand at a local level. Consumers and prosumers, assisted by aggregators, participate in DR events by responding to signals to adjust their energy consumption patterns. Aggregators act as intermediaries of small consumers, coordinating the...

This paper proposes a demand response-based energy management model for energy communities, considering the respective members’ data privacy. Through forecasting and clustering algorithms, this model can identify demand response opportunities for the next day, rank and select the participants for the event and monitor and evaluate the respective ev...

Nowadays, data science and machine learning areas play a crucial role by performing predictions of energy with reliable historic of consumptions and other sensors data. This is crucial for decision makers to formulate an optimized plan to buy and sell energy ahead of time. According to many data scientists the decision criteria to select sensors to...

In the future power grid, a local electricity market (LEM) with renewable energy sources and a smart grid will play a key role. Consumers, prosumers and small distributed energy resources on the other hand, play an important role in local energy transactions. In the LEM under study, the system costs are decreased and prosumers are given additional...

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...

Developing innovative electricity market designs to facilitate a sustainable transition to (near) 100% renewable power systems while meeting societal needs is a crucial and actual topic of research. This article presents preliminary key findings from the H2020 European project TradeRES, addressing this critical topic. The project uses agent-based a...

Increasing the number of participants in energy communities leads to a new challenge in power systems, which is finding the optimal strategy for community members. Accordingly, this paper presents a distributed model for determining the optimal energy trading strategy of community participants such as buyers, sellers, and the community manager (CM)...

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...

As the population continues to age, the number of elderly individuals is increasing at an unprecedented rate as the active age group in developed countries continues to shrink. This demographic shift has resulted in a shortage of resources and limitations in the provision of adequate elderly care. Among the many health risks faced by the elderly, f...

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...

It is clear and inevitable that the current electricity sector will change to a new model. For everyone in this value chain, the future holds new projects and challenges that will get harder and harder. Technological advances have led to the creation of new ideas, such as renewable energy communities, decentralized renewable energy generation, and...

Scenario-based stochastic programming (SBSP) methods have been used broadly to cope with power system operation and planning uncertainties. For SBSP, probability density functions (PDFs) of uncertain parameters must be known and many scenarios are typically generated to precisely approximate the PDFs causing computational burden. On the other hand,...

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...

Aggregators, as intermediaries between consumers, prosumers, and local market operators, manage their clients’ resources for participation in the multi-markets. Various resources of consumers and prosumers have different technical characteristics, which shall be considered in the scheduling strategy. The fast response and controllability of some re...

On the one hand, natural gas-fired dispatchable distributed generation (DG) units and batteries can be used in microgrids (MG) to cope with the intermittency of renewable energy resources such as wind turbines and photovoltaic units. On the other hand, the uncertainties in MG influence the gas system through the gas-fired DG units, making these sys...

The EU is encouraging the creation of local energy communities (LECs) for electricity trading, promoting local balance and a self-sustained community while reducing electricity bills. Local electricity markets (LEMs) ease the electricity trading of distributed energy resources while incentivizing the integration of renewable energy sources into the...

This article analysis, through simulation, the impact that local transactions and the penetration of EVs have on the distribution network and the costs and incomes of local market participants. An auction-based competitive market and mathematical model are provided to simulate end-user transactions with EVs on the low-voltage grid. The proposed fra...

For a successful implementation of Demand Response (DR) in the real markets, the uncertainty from the players' response must be reduced. From the community manager's perspective, the proposed methodology focuses on selecting trustworthy participants according to their availability for the triggered DR event. As innovation from previous works, the a...

This paper proposes a demand response-based model to manage the energy resources of an energy community, considering the data privacy of the respective community members and electric vehicles. First, through a day-ahead forecasting algorithm, the implemented model identifies possible demand response events to be launched in the energy community. Th...

Massive advancements have been noticed on the Internet of Things (IoT) integrating smart Homes Energy Management Systems (HEMSs). In the literature, many reviews have been carried out regarding the technological upgrades in the HEMSs. However, a comprehensive review of energy management technologies at the smart cities scale is lacking in the liter...

The structural changes in the energy sector caused by renewable sources and digitization have resulted in an increased use of Artificial Intelligence (AI), including Machine Learning (ML) models. However, these models’ black-box nature and complexity can create issues with transparency and trust, thereby hindering their interpretability. The use of...

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...