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Huge amount of energy resources greenhouse gas (GHG) emissions is devoted to the built environment. Therefore, an accurate assessment method of these indicators is compulsory. To take advantage of the temporal variation in the primary energy use and associated GHG emissions of the energy supply, we propose two ways of integrating hourly life-cycle conversion factors in building energy systems. First, to appraise the energy system design, we developed a versatile modelling and performance assessment framework using a multi-criteria approach. The simulated performances of possible energy systems are compared, and help designers systematically choose appropriate energy production and storage systems. Second, for the operation of the building, we propose an energy management procedure (EMP) that always feeds in the energy source with the least global warming potential (GWP). These two developments are tested on a case study consisting in an architectural project that should respect the 2000 W society targets. The running conditions of several energy systems are simulated and compared. The most promising scenario is identified. Compared to traditional methods, the assessment framework has a promising future but the GWP-based energy management procedure offers, in the context of the case study, limited GHG emissions mitigation at very high primary energy cost.

In large cities, pollution composed of many different chemical components and small particles is an important public health problem that affects especially children and people presenting breathing difficulties.

This paper presents an optimization method based on genetic algorithm for the energy management inside buildings developed in the frame of the project Smart Living Lab (SLL) in Fribourg (Switzerland). This algorithm optimizes the interaction between renewable energy production, storage systems and energy consumers. In comparison with standard algorithms, the innovative aspect of this project is the extension of the smart regulation over three simultaneous criteria: the energy self-consumption, the decrease of greenhouse gas emissions and operating costs. The genetic algorithm approach was chosen due to the large quantity of optimization variables and the non-linearity of the optimization function. The optimization process includes also real time data of the building as well as weather forecast and users habits. This information is used by a physical model of the building energy resources to predict the future energy production and needs, to select the best energetic strategy, to combine production or storage of energy in order to guarantee the demand of electrical and thermal energy. The principle of operation of the algorithm as well as typical output example of the algorithm is presented.

In the frame of a research project conducted at the Smart Living Lab (SLL), a research center dedicated to the building of the future, this paper presents an algorithm that optimizes the coupling of local renewable energy production systems with energy storage devices and the different consumers both at the level of the building and of its peripherals. The main goal is to improve the energy self-sufficiency of a building by combining three aspects in the same time. The optimization criteria are the renewable energy based independence and the ecological (greenhouses gases emissions) and economical (costs) aspects. The underlying approach to perform the global optimization is first presented, explaining how the algorithm combines and optimizes these three criteria. For this purpose, it takes into account the current value of the state variables (temperature, etc.) and the forecasts future values. These data represent the input of a genetic optimization algorithm that computes the best use of each element of the production and storage systems to ensure the electrical and thermal energy demand. The choice of genetic algorithm is motivated by the large amount of optimization variables and the non-linearity of the score function. The typical computation time for this kind of optimization is short enough to allow a real time regulation. The composition of the energy production and storage is flexible allowing to integrate many technologies types, thus increasing its portability.

The use of renewable energy sources towards autonomous buildings is promising, not only for electrical energy generation but also for heating purposes. However, it presents one fundamental issue: the shift between energy production and heating energy demand. This paper presents a solution to this problem in the form of an optimized, thermally controlled storage using phase change materials integrated to building walls. In contrast to the standard applications of phase change materials, which are currently used to solely improve the thermal inertia of buildings, this paper introduces an active management of heat storage. In particular, it allows the heat energy discharge through the activation of forced convection on demand. A test bench was designed and manufactured. Measurements performed showed excellent agreements with simulated results.

The paper presents the complete results regarding the energy performance and reliability of a new heating/cooling system designed and developed around an economic and ecological concept: self-consume a maximum amount of energy produced by solar photovoltaic panels. To achieve this goal, a unique regulation system which takes advantage of each kW produced by the PV system was developed and implemented. This technology, compared to a conventional system, increases the performance coefficient for the HP, as well as for the overall installation. These improvements are justified by the accurate regulation of different components of the installation - power modulation of the HP and circulation pumps- and by use of photovoltaic panels allowing a reduction of the electrical energy consumed from the grid. To quantify the contribution of the new system, the energyefficiency was establishedfor the 2 cases: with and without solar PV panels. The results indicate that new system provides a significant gain in performance. This new technology, HP directly driven by PV, produces significant annual improvement compared with a conventional heating system, HP, of 22%.

This paper presents thedevelopment of a compact and cheap solar heating system based on photovoltaic panels, heat pump and heating storage through a smart regulation from a technical and economical point of view. The heat pump can be standard and have its own regulation. The building's owner may interact with the regulation through a web service to visualize the monitoring of the installation, update the calendar of the presence and change the operation mode. One of the goals of the regulation is to optimize the consumption of his own electricity production and therefore minimize the electricity taken from the grid. Several means are developed to reach the main objective. On one hand, the regulation takes into account the weather forecast to anticipate the future heating's needs and the photovoltaic production. Weather forecast data allows also to optimize the storage in hot water tanks and in the building structure. On the other hand, the project includes the development of an electronic module which collects the monitoring data and gives the commands to the heat pump and the pumps. This is accomplished without any intrusion in the heat pump's own regulation and therefore the user keeps the constructor's warranty. The new regulation was validated numerically. The expected energy gain due to the weather forecast is about 10%, while the one due to the self-consumption and the heat storage management will bring another 10-15%.

This paper investigates the profitability of a battery energy storage system coupled with a rooftop photovoltaic power plant. In particular, an ageing/cost model accounting for the capacitance fading of the battery is proposed. The model is deployed for proposing an optimal sizing of the battery storage and optimal operating conditions constraints with a smart-building scenario focused on the increase of the energy self-consumption. The results shown 20% of impact of the ageing on the overall profitability. Moreover, by changing the above constraints it is possible to keep the same profitability of the battery storage system and reducing its ageing of 45%.

Today, cooling systems are widely used, notably with the unprecedented growth of data centres and building space cooling. These thermodynamic systems are powered mainly with electricity, and their peak loads are generally associated with very high carbon footprints. At the same time, congestion of the grid due to high load or renewable power injection is becoming an issue for all actors involved with electricity (producers, providers, consumers, and prosumers). Actually, both the price and associated carbon footprint of electricity usually fluctuates along with the charge of the network. This paper discusses the integration of power flexibility (PF) in new and existing cooling systems to avoid a possible cold crunch in the near future. After defining PF, several cooling systems archetypes are presented. Three possible ways to integrate PF are explained: flexibility by thermal inertia and energy storage (thermal and electrochemical). While PF principally targets the reduction of stress on the electric grid, other benefits can also be achieved, e.g. mitigation of direct carbon emissions and decrease of costs related to operating the refrigeration system. We explain how better management of energy transits and possible imbalance in electricity networks can be achieved by thermal inertia. The choice of integrating thermal storage or electric battery is discussed, and both solutions are considered in a specific case study. The study aims at better management of power loads on electricity network caused by the cooling system and could be useful for anyone involved with grid management and/or refrigeration systems.

The rapid economic and population growth in developing countries, effective and efficient energy usage has turned out to be crucial due to the rising concern of depleting fossil fuels, of which, one-third of primary energy is consumed in buildings and expected to rise by 53% up to 2030. This roaring sector posing a challenge, due to 90% of people spend most of their time in buildings, requires enhanced well-being of indoor environment and living standards. Therefore, building operations require more energy because most of the energy is consumed to make the indoor environment comfortable. Consequently, there is the need of improved energy efficiency to decrease energy consumption in buildings. In relation to this, the primary challenge of building control systems is the energy consumption and comfort level are generally conflicting to each other. Therefore, an important problem of sustainable smart buildings is to effectively manage the energy consumption and comfort and attain the trade-off between the two. Thus, smart buildings are becoming a trend of future construction that facilitates intelligent control in buildings for the fulfillment of occupant’s comfort level. In this study, an intelligent multi-objective system has been developed with evolutionary multi-objective genetic algorithm (MOGA) optimization method. The corresponding case study simulation results for the effective management of users’ comfort and energy efficiency have been carried out. The case study results show the management of energy supply for each comfort parameter and maintain high comfort index achieving balance between the energy consumption and comfort level. Keywords: Energy, Buildings, Comfort, Management, Optimization, Trade-off

Standard algorithm of building’s energy strategy often use electricity and its tariff as the sole criterion of choice. This paper introduced an algorithmic regulation using global warming potential (GWP) of energy flux, to select which installation will satisfy the building energy demand (BED). In the frame of the Correlation Carbon project conducted by the Smart Living Lab (SLL), a research center dedicated to the building of the future, this paper presents the algorithm behind the design, the selection and the operation of appropriate energy installations for low-carbon buildings. The control strategy governing the building’s energy flow takes into account demand, supply and storage. The latter being mainly linked to economic or energy benefits, which standard algorithms often use as the only criterion of choice, thus increasing the environmental impact of the energy flowing through the building. In contrast to those, the algorithm introduced in this paper controls the energy fluxes of the electrical grid, photovoltaic (PV) panels, static and vehicle batteries according to their GWP. A simulation over one year at an hourly time step – enabling one to take into account small but significant variations of the GWP of the electricity from grid – is presented for different scenarios, in order to draw an annual balance for a given combination of energy systems. The algorithm chooses the “cleanest” instantaneous energy source combination to meet the BED. Simulations show that a building using this algorithm can satisfy its BED while reducing its GWP by as much as 40% compared to an energy supply using the grid only.