• Energy Research

The first step in order to comply with the European Union goals of Near to Zero Energy Buildings is to reduce the energy consumption in buildings. Most of the building consumption is related to the use of active systems to maintain the interior comfort. Passive design strategies contribute to improve the interior comfort conditions, increasing the energy efficiency in buildings and reducing their energy consumption. In this work, an analysis of the passive strategies used in Net Energy Plus Houses has been made. The participating houses of the Solar Decathlon Europe 2012 competition were used as case studies. The passive design strategies of these houses were compared with the annual simulations, and the competition monitored data, especially during the Passive Monitored Period. The analysis included the thermal properties of the building envelope, geometric parameters, ratios and others passive solutions such as Thermal Energy Storage systems, evaporative cooling, night ventilation, solar gains and night sky radiation cooling. The results reflect the impact of passive design strategies on the houses' comfort and efficiency, as well as their influence in helping to achieve the Zero Energy Buildings category.

The electrical system is undergoing a structural change, dissolving into a new concept of a distributed environment, based on renewable energies, demand management and resource sharing [...]

Self-consumption is modifying the classical structure of the electrical grids worldwide. This energy supply method allows a distributed energy generation and the possibility of involving citizens in the electrical grid. Many countries have defined or are defining rules regarding self-consumption because of the evidence of its unstoppable growth. From the technical point of view, there are numerous associated advantages to the self-consumption, nevertheless it represents a new challenge in the management and design of the electrical grids. In general, the main generation technology for self-consumption is the PV energy. The PV generators are installed in a facility and their generation can be considered as a reduction on the local consumption or even negative consumption. Therefore, high penetration of PV self-consumption will modify the aggregated consumption shape of the electrical grid. The electrical grid should be able to respond to this new shape by adapting generation, controlling consumption or using storage systems. In this paper, we analyze the effects of high penetration of PV self-consumption on the aggregated consumption of the Spanish electrical grid. For this analysis we use historical solar resource data from different cities of Spain and historical data of the aggregated consumption of the country. The results show that PV self-consumption can smooth the aggregated consumption shape, mainly during summer periods. On the other hand, the PV self-consumption can increase the variability of the aggregated consumption shape for high penetration levels.

In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.

With the rising prices of the retail electricity and the decreasing cost of the PV technology, grid parity with commercial electricity will soon become a reality in Europe. This fact, together with less attractive PV feed-in-tariffs in the near future and incentives to promote self-consumption suggest, that new operation modes for the PV Distributed Generation should be explored; differently from the traditional approach which is only based on maximizing the exported electricity to the grid. The smart metering is experiencing a growth in Europe and the United States but the possibilities of its use are still uncertain, in our system we propose their use to manage the storage and to allow the user to know their electrical power and energy balances. The ADSM has many benefits studied previously but also it has important challenges, in this paper we can observe and ADSM implementation example where we propose a solution to these challenges. In this paper we study the effects of the Active Demand-Side Management (ADSM) and storage systems in the amount of consumed local electrical energy. It has been developed on a prototype of a self-sufficient solar house called “MagicBox” equipped with grid connection, PV generation, lead–acid batteries, controllable appliances and smart metering. We carried out simulations for long-time experiments (yearly studies) and real measures for short and mid-time experiments (daily and weekly studies). Results show the relationship between the electricity flows and the storage capacity, which is not linear and becomes an important design criterion.

In this paper we present a heterogeneous collaborative sensor network for electrical management in the residential sector. Improving demand-side management is very important in distributed energy generation applications. Sensing and control are the foundations of the “Smart Grid” which is the future of large-scale energy management. The system presented in this paper has been developed on a self-sufficient solar house called “MagicBox” equipped with grid connection, PV generation, lead-acid batteries, controllable appliances and smart metering. Therefore, there is a large number of energy variables to be monitored that allow us to precisely manage the energy performance of the house by means of collaborative sensors. The experimental results, performed on a real house, demonstrate the feasibility of the proposed collaborative system to reduce the consumption of electrical power and to increase energy efficiency.

Photovoltaic power forecasting is an important problem for renewable energy integration in the grid. The purpose of this review is to analyze current methods to predict photovoltaic power or solar irradiance, with the aim of summarizing them, identifying gaps and trends, and providing an overview of what has been achieved in recent years. A search on Web of Science was performed, obtaining 60 articles published from 2020 onwards. These articles were analyzed, gathering information about the forecasting methods used, the horizon, time step, and parameters. The most used forecasting methods are machine learning and deep learning based, especially artificial neural networks. Most of the articles make predictions for one hour or less ahead and predict power instead of irradiance, although both parameters are strongly correlated, and output power depends on received irradiance. Finally, they use weather variables as inputs, consisting mainly of irradiance, temperature, wind speed and humidity. Overall, there is a lack of hardware implementations for real-time predictions, being an important line of development in future decades with the use of embedded prediction systems at the photovoltaic installations.