• Energy Research

Stochastic models for predicting human behavior have become an essential part of the development of demand planning strategies, as well as a high-resolution base information for building simulation software. Due to the close relationship between human presence and consumption, occupancy patterns allow for the recognition of activity peaks, and subsequently, potential maximum demand hours. This contributes to the improvement of control strategies, which combined with the active participation of consumers will drive to major energy savings. In this paper, a novel behavior model for nine economic sectors in Spain has been developed using a Markov Chain methodology that can easily be extrapolated to other locations. The model can generate daily occupancy profiles with a 10-min resolution for the selected sectors, distinguishing between the type of day and type of working hours. The results, which have been validated and compared with other works showing good accuracy, have highlighted the characteristic patterns and maximum occupancy hours of each studied sector. Furthermore, these simulated profiles have been used as input datasets for the estimation of consumption in some selected sectors, illustrating the potential link that can be established between occupancy profiles and energy usage by means of different modeling techniques.

Stochastic models for predicting human behavior have become an essential part of the development of demand planning strategies, as well as a high-resolution base information for building simulation software. Due to the close relationship between human presence and consumption, occupancy patterns allow for the recognition of activity peaks, and subsequently, potential maximum demand hours. This contributes to the improvement of control strategies, which combined with the active participation of consumers will drive to major energy savings. In this paper, a novel behavior model for nine economic sectors in Spain has been developed using a Markov Chain methodology that can easily be extrapolated to other locations. The model can generate daily occupancy profiles with a 10-min resolution for the selected sectors, distinguishing between the type of day and type of working hours. The results, which have been validated and compared with other works showing good accuracy, have highlighted the characteristic patterns and maximum occupancy hours of each studied sector. Furthermore, these simulated profiles have been used as input datasets for the estimation of consumption in some selected sectors, illustrating the potential link that can be established between occupancy profiles and energy usage by means of different modeling techniques.

Heating and cooling consumption is one of the most significant terms in the total supply, which may come to represent half of the total demand in European countries. These appliances are supplied by a wide range of sources, being electrical devices of special interest in the Smart Grid. Current tools allow the assessment of the consumption with a high accuracy, nevertheless, they lack the temporal resolution or low-level details to study advanced control techniques. In this context, bottom-up stochastic models are a main tool to simulate high-resolution demand profiles. This paper presents a 1-min resolution model for electricity demand of heating and cooling appliances. The system is based on the simulation of individual households considering variables such as the number of residents, location, type of day (weekday or weekend) and date. It was used to simulate daily profiles which showed two main demand peaks, one during mornings and another during dinner time, for heating, and a high demand during midday for cooling consumption. Moreover, annual simulations depicted the importance of cooling appliances, which despite having a lower annual demand, can overcharge the grid with their concurrent utilisation, highlighting the usefulness of this tool for studying the impact of these devices.

Heating and cooling consumption is one of the most significant terms in the total supply, which may come to represent half of the total demand in European countries. These appliances are supplied by a wide range of sources, being electrical devices of special interest in the Smart Grid. Current tools allow the assessment of the consumption with a high accuracy, nevertheless, they lack the temporal resolution or low-level details to study advanced control techniques. In this context, bottom-up stochastic models are a main tool to simulate high-resolution demand profiles. This paper presents a 1-min resolution model for electricity demand of heating and cooling appliances. The system is based on the simulation of individual households considering variables such as the number of residents, location, type of day (weekday or weekend) and date. It was used to simulate daily profiles which showed two main demand peaks, one during mornings and another during dinner time, for heating, and a high demand during midday for cooling consumption. Moreover, annual simulations depicted the importance of cooling appliances, which despite having a lower annual demand, can overcharge the grid with their concurrent utilisation, highlighting the usefulness of this tool for studying the impact of these devices.

This paper proposes real-time Energy Management System (EMS) for a residential hybrid ac/dc microgrid. The residential microgrid is organized in two different distribution systems. A dc distribution bus which interconnect the renewable energy sources (RES), energy storage systems (ESS) and the building’s common facilities; while the apartments are supplied by an ac distribution system connected to the grid. This architecture avoids any modifications in the electrical installation that supplies energy to the apartments. A pure dc voltage supply is not yet a feasible approach for residential buildings. This architecture increases the overall efficiency of the distribution by interconnecting the RES and ESS thorough a dc distribution bus, and therefore avoiding unnecessary dc/ac conversion stages. The real-time EMS performs an 24 hours ahead optimizationin order to schedule the charge/discharge of the ESS, and the energy injection/consumption from the grid. The EMS estimates the RES generation based on the weather forecasting, together with stochastic consumption modelling of the building. The EMS architecture and the residential microgrid have been implemented and tested in a laboratory scale setup. The results shown how the operational costs of the system are effectively decreased by 28%, even with non-accurate estimation of the RES generation or building parameters.

This paper proposes real-time Energy Management System (EMS) for a residential hybrid ac/dc microgrid. The residential microgrid is organized in two different distribution systems. A dc distribution bus which interconnect the renewable energy sources (RES), energy storage systems (ESS) and the building’s common facilities; while the apartments are supplied by an ac distribution system connected to the grid. This architecture avoids any modifications in the electrical installation that supplies energy to the apartments. A pure dc voltage supply is not yet a feasible approach for residential buildings. This architecture increases the overall efficiency of the distribution by interconnecting the RES and ESS thorough a dc distribution bus, and therefore avoiding unnecessary dc/ac conversion stages. The real-time EMS performs an 24 hours ahead optimizationin order to schedule the charge/discharge of the ESS, and the energy injection/consumption from the grid. The EMS estimates the RES generation based on the weather forecasting, together with stochastic consumption modelling of the building. The EMS architecture and the residential microgrid have been implemented and tested in a laboratory scale setup. The results shown how the operational costs of the system are effectively decreased by 28%, even with non-accurate estimation of the RES generation or building parameters.

In the current move towards a decarbonising energy generation, cities and residential users can play a significant role. Solar photovoltaic has emerged as one of the main clean energy sources in this transition. Therefore, this work investigates the large electricity generation capacity for collective photovoltaic self-consumption in blocks of flats in a densely populated city in southern Spain. Three neighbourhoods were studied according to their income levels (low, medium, and high), by modelling their different energy demand profiles. The demand cover factor and the energy exchanged with the electricity grid were calculated. Subsequently, seven regulatory frameworks for self-consumption and surplus photovoltaic injection were compiled and analysed for each income level, focusing on the economic profitability of these renewable installations for different power ratings (270 and 570 W) and different domestic electricity tariffs. Although some regulations provide more economic benefits, with the constant growth in the nominal power of photovoltaic modules and rising electricity prices, the profitability of self-consumption photovoltaic installations is practically guaranteed regardless of the regulatory framework. Therefore, subsidies should target low-income buildings, where self-consumption installations could be the solution to energy poverty problems.