• Energy Research
• 2016-2025close

These data were released under the 2022-3 Competition on solar generation forecasting. Please check our competitions: www.gecad.isep.ipp.pt/smartgridcompetitions The data set comprises the power generated by photovoltaic panels and data collected from a near weather station. The data was collected in 5 minutes periods. Data comprises: Hour Starting minute (inclusive) Ending minute (exclusive) Generated power (kW) Temperature (ºC) Dewpoint (ºC) Pressure (hPa) Wind Direction (Degrees) Wind Speed (KM/h) Wind Speed Gust (KM/h) Humidity (%) Hourly Precipitation (mm) Daily rain (mm) Solar Radiation (Watts/m2) The data set represents raw data without any treatment, this means that it is possible to find errors. Data can have missing data or missing reading periods, and a fixed zero (0) value, indicating a failure in the system readings. We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work. The present work has received funding from National Funds through FCT under the project UIDB/00760/2020.

The environmental impact and the scarcity of limited fossil fuels led to the need of investment in energy based on renewable sources. This has driven Europe to implement several policies that changed the energy market's paradigm, namely the incentive to microgeneration. The penetration of energy sources from intermittent nature has increased the unpredictability of the system, which makes simulation and analysis tools essential in order to provide decision support to entities in this sector. This paper presents the Tools Control Center (TOOCC) as a solution to increase the interoperability between heterogeneous agent-based systems, in the energy field. The proposed approach acts as a facilitator in the interaction between different systems through the usage of ontologies, allowing them to communicate in the same language. To understand the real applicability of this tool, a case study is presented concerning the interaction between several systems, with the purpose of enabling the energy resource scheduling of a microgrid, and the reaction of a house managed by a house management system.

Building management systems (BMSs) are being implemented broadly by industries in recent decades. However, BMSs focus on specific domains, and when installed on the same building, they lack interoperability to work on a centralized user interface. On the other hand, BMSs interoperability allows the implementation of complex rules based on multi-domain contexts. The Building’s Reasoning for Intelligent Control Knowledge-based System (BRICKS) is a context-aware semantic rule-based system for the intelligent management of buildings’ energy and security. It uses ontologies and semantic web technologies to interact with different domains, taking advantage of cross-domain knowledge to apply context-based rules. This work upgrades the previously presented version of BRICKS by including services for energy consumption and generation forecast, demand response, a configuration user interface (UI), and a dynamic building monitoring and management UI. The case study demonstrates BRICKS deployed at different aggregation levels in the authors’ laboratory building, managing a demand response event and interacting autonomously with other BRICKS instances. The results validate the correct functioning of the proposed tool, which contributes to the flexibility, efficiency, and security of building energy systems.

The increase of variable renewable energy generation has brought several new challenges to power and energy systems. Solutions based on storage systems and consumption flexibility are being proposed to balance the variability from generation sources that depend directly on environmental conditions. The widespread use of electric vehicles is seen as a resource that includes both distributed storage capabilities and the potential for consumption (charging) flexibility. However, to take advantage of the full potential of electric vehicles’ flexibility, it is essential that proper incentives are provided and that the management is performed with the variation of generation. This paper presents a research study on the impact of the variation of the electricity prices on the behavior of electric vehicle’s users. This study compared the benefits when using the variable and fixed charging prices. The variable prices are determined based on the calculation of distribution locational marginal pricing, which are recalculated and adapted continuously accordingly to the users’ trips and behavior. A travel simulation tool was developed for simulating real environments taking into account the behavior of real users. Results show that variable-rate of electricity prices demonstrate to be more advantageous to the users, enabling them to reduce charging costs while contributing to the required flexibility for the system.

Electricity markets are evolving into a local trading setting, which makes it for unexperienced players to achieve good agreements and obtain profits. One of the solutions to deal with this issue is to provide players with decision support solutions capable of identifying opponents' negotiation profiles, so that negotiation strategies can be adapted to those profiles in order to reach the best possible results from negotiations. This paper presents an approach that classifies opponents' proposals during a negotiation, to determine which is the typical negotiation profile in which the opponent most relates. The classification process is performed using an artificial neural network approach, and it is able to adapt at each new proposal during the negotiation process, by re-classifying the opponents' negotiation profile according to the most recent actions. In this way, effective decision support is provided to market players, enabling them to adapt the negotiation strategy throughout the negotiations.

The use of metaheuristics to solve real-life problems has increased in recent years since they are easy to implement, and the problems become easy to model when applying metaheuristic approaches. However, arguably the most important aspect is the simulation time since results can be obtained from metaheuristic methods in a much smaller time, and with a good approximation to the results obtained with exact methods. In this work, the Genetic Algorithm (GA) metaheuristic is adapted and apphed to solve the optimization of electricity markets participation portfolios. This work considers a multiobjective model that incorporates the calculation of the profit and the risk incurred in the electricity negotiations. Results of the proposed approach are compared to those achieved with an exact method, and it can be concluded that the proposed GA model can achieve very close results to those of the deterministic approach, in much quicker simulation time.

Agriculture being an essential activity sector for the survival and prosperity of humanity, it is fundamental to use sustainable technologies in this field. With this in mind, some statistical data are analyzed regarding the food price rise and sustainable development indicators, with a special focus on the Portugal region. It is determined that one of the main factors that influences agriculture’s success is the soil’s characteristics, namely in terms of moisture and nutrients. In this regard, irrigation processes have become indispensable, and their technological management brings countless economic advantages. Like other branches of agriculture, the wine sector needs an adequate concentration of nutrients and moisture in the soil to provide the most efficient results, considering the appropriate and intelligent use of available water and energy resources. Given these facts, the use of renewable energies is a very important aspect of this study, which also synthesizes the main irrigation methods and examines the importance of evaluating the evapotranspiration of crops. Furthermore, the control of irrigation processes and the implementation of optimization and resource management models are of utmost importance to allow maximum efficiency and sustainability in this field.

This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so‐called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large‐scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.