Self-consumption In Buildings Research Articles

Solar governance for the transborder agglomeration of the Greater Geneva based on the solar cadaster development

As cities are major energy consumers challenges arise in densely populated areas that limit solar resources, hindering the deployment of urban solar power plants. To address this, the paper underscores the importance of digital tools for modeling solar energy accessibility within the urban fabric, focusing on rooftops, building facades, and other relevant locations. In this context, the development of the solar cadaster in Greater Geneva is presented in the paper within the framework of the European INTERREG G2 Solar project (2019–2022). The solar cadaster relies on a rich database from the Geneva Land Information System (SITG), offering comprehensive data on Digital Surface Models, building and roof cadasters, land cover, and more. Notably, this data, including the solar cadaster outputs, is accessible to the public as open data, fostering its extensive use in academia for teaching and research. It serves as a living laboratory for urban solar studies, with its open data approach contributing to widespread adoption. The project, a collaborative effort involving academia, energy utilities, and public institutions on both sides of the border, aims to unite stakeholders and foster a dialogue on solar energy in the region, in line with its commitment to addressing climate change, striving for carbon neutrality by 2050. The paper articulates the steps, methods, and outcomes of the project, offering insights into the challenges of implementing a shared online application tool in a cross-border territory. The propositions are based on feedbacks from stakeholders and lessons learned from case studies. The paper also delves into the shift in the business model for solar photovoltaic installations, emphasizing the need to model self-consumption in buildings. It concludes by highlighting the solar cadaster as a unifying means, fostering cohesion among diverse stakeholders in the region’s energy sector and propelling them toward common governance around solar energy.

Contextual Rule-Based System for Brightness Energy Management in Buildings

The increase in renewable generation of a distributed nature has brought significant new challenges to power and energy system management and operation. Self-consumption in buildings is widespread, and with it rises the need for novel, adaptive and intelligent building energy management systems. Although there is already extensive research and development work regarding building energy management solutions, the capabilities for adaptation and contextualization of decisions are still limited. Consequently, this paper proposes a novel contextual rule-based system for energy management in buildings, which incorporates a contextual dimension that enables the adaptability of the system according to diverse contextual situations and the presence of multiple users with different preferences. Results of a case study based on real data show that the contextualization of the energy management process can maintain energy costs as low as possible, while respecting user preferences and guaranteeing their comfort.

Efficiency evaluation of photovoltaic systems with batteries considering different voltage levels

The incorporation of batteries into photovoltaic (PV) self-consumption systems in buildings has a high potential to improve the degree of decarbonization and consumer benefits. However, very few studies have addressed the evaluation and comparison of the energy performance of PV systems with storage for self-consumption in buildings. Furthermore, studies have omitted the influence of energy storage at different voltage levels, which is an important parameter in the development of High Voltage (HV) lithium batteries.Thus, a load control system was designed and connected to the output of two self-consumption PV systems with batteries operating at different voltages, to compare the energy efficiency under the same energy demand conditions and with identical solar irradiation levels.The results show the importance of considering the voltage level parameter, as the average energy efficiency of High Voltage Installation (HVI) was higher than that of Low Voltage Installation (LVI) by 3 % to 10 % over the range of load powers analysed. Furthermore, it is shown that the most significant energy losses were influenced by the inverter's energy conversion process. Therefore, to achieve good system optimization, manufacturers should decrease the losses in this process and ensure high energy efficiency in the operating voltage and power ranges.

Analysis and optimisation of collective self-consumption in residential buildings in Spain

Studies on self-consumption in residential use are constantly evolving, and society claims new solutions in response to changes to new paradigms. This paper aims to optimise the sizing of a self-consumption installation for collective self-consumption in a residential building under the Spanish regulatory framework. The study considers the current price volatility in the retail electricity Spanish market and focuses on the techno-economic analysis of self-consumption in a building consisting of 12 dwellings using photovoltaic energy and the possible support of a storage system. The three aspects analysed are the sizing of the self-consumption facility, the use of smart appliances to shift their consumption to more cost-effective times and the sharing of the renewable energy generated by the facility among the consumers participating in the collective self-consumption. This study employs two models, an average-day model and a whole-year model. The techno-economic analysis yields that between the two self-consumption modalities for residential consumption, the modality with surplus simplified compensation is more profitable than the one without surpluses. Finally, the study focuses on sharing the total renewable energy generated among the participants. The results indicate that both ex-ante and ex-post dynamic sharing brings few benefits.

Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization

The application of Photovoltaic (PV) system in buildings is growing rapidly in response to the need for clean energy sources and building decarbonization targets. Nonetheless, enhancing PV self-consumption through technical solutions such as Energy Storage Systems (ESS) is getting higher importance to increase the profitability of PV plants, by minimizing the building-grid interaction. In this context, analyzing PV self-consumption of different energy storage configurations becomes more relevant and crucial in building energy modeling although it is heavily time-consuming and complicated, particularly within a multi-objective optimization related to the ESS design. As a solution to resolve this issue, this paper evaluates the accuracy, training, and prediction speed of 24 Machine Learning (ML) models to be used as surrogate models for analyzing PV self-consumption in smart buildings. Furthermore, the performance of short-term Thermal Energy Storage (TES) to increase PV self-consumption is assessed and presented using ML models. The results showed the Gaussian Process Regression (GPR), Neural Networks (NN) including bilayered and trilayered NN models, Support Vector Machines (SVM) including the fine gaussian and cubic SVM models, and Ensembles of Trees (EoT) as superior ML models. The results also revealed that TES systems can efficiently increase PV self-consumption in the building equipped with electric heat pumps to provide heating, cooling, and domestic hot water. Moreover, the TES size optimization regarding the Life Cycle Cost (LCC) showed that the LCC-based optimum TES size can yield 7.1% savings within 30 years of the building service life. The novelties of this research are first to provide a reference to select the most suitable ML models in predicting PV self-consumption, second to implement machine learning for analyzing the performance of short-term thermal energy storage to enhance PV self-consumption in buildings, and third to carry out an LCC-based optimization on TES size using ML-based prediction models.

A demand side management approach to increase self-consumption in buildings

There is a growing interest in increasing the presence of renewable energy in the electric network. Photovoltaic production from grid-connected systems is leading this growth in terms of households. Alongside this development, concern about network security has emerged, because excesses of intermittent renewable energy on the grid could exceed voltage limits. Self-consumption, understood as the capacity of the producer to consume his or her own production, can partially solve these problems. Thermostatic controllable loads, such as heating and cooling, represent 50% of the total amount of energy consumed by buildings; the proper allocation of these loads could be a driving force for self-consumption. In this study, a demand side management strategy is proposed based on a building energy model equipped with an inverter heat pump coupled with a photovoltaic plant. The goal is to maximize the use of local energy from the photovoltaic plant (self-consumption), reducing the export and import of energy to and from the grid. This goal is achieved by optimizing the set-points in each room. An array of optimal set-points over six years is presented. The results show the capacity of the methodology to match similar values of self-consumption (70% in winter and 50% in summer) obtained by strategies based on chemical batteries. The findings are shown in an energy matching chart at different levels of detail (yearly and monthly). Color bubbles are added to the matching chart to help visualize the unmatched energy of the system graphically. In comparison with actual model predictive control technologies, this study’s strategy offers great simplicity and a large saving in computational time.

Solar Heat Pumps and Self-Consumption Can (and should) electricity suppliers encourage thermal storage?

Heat pumps and water tanks can be used to increase PV self-consumption in buildings without any additional equipment, but there is sometimes a lack of economic incentives to maximize it that limits economic gains. Therefore, pricing conditions need to change in order to make self-consumption strategies more interesting for prosumers. This study aims at determining what, if any, unsubsidized market conditions could lead to economically motivated self-consumption control strategies with solar heat pumps. A sensitivity analysis is used on multiple pricing models based on current market conditions for a solar PV and ground source heat pump system for a single-family house in Norrköping, Sweden. The results show that control strategies aimed at maximizing self-consumption have very little impact on net costs, regardless of pricing model or variation in price. Feed-in-bonus is the most important aspect when comparing different pricing schemes, and no other sensitivity comes close.

Multi-scale roof characterization from LiDAR data and aerial orthoimagery: Automatic computation of building photovoltaic capacity

Abstract Photovoltaic self-consumption in buildings requires the installation of photovoltaic (PV) systems mostly on roofs, taking the advantage of these building locations for both the available surface area and the certainty of a big amount of annual incoming solar radiation. Since the parameters required for a proper PV system design are mainly geometric: azimuth-orientation, tilt angle and effective dimensions of the different roof slopes; aerial LiDAR data and orthoimagery offered by the National Geographic Agencies became suitable data sources for this purpose, ensuring its availability for any city regardless of its location. This paper presents a novel automatic methodology that combines LiDAR and orthoimage data processing to geometrically characterize roofs at slope level and calculate their PV solar potential. The methodology developed has been validated against results obtained from a higher-resolution aerial 3D point cloud of the roofs under study. Different locations and roof types have been tested in order to confirm the performance of the methodology under different conditions, being able to accurately characterize the geometry of most types of roofs, such as flat roofs, gable or saddle roofs, single pitched roofs and pyramid roofs at city, neighbourhood and building level.

Optimal Sizing of PV System Combined to Cooling Load Management Strategy towards Photovoltaic Self-Consumption in Buildings

Optimal Sizing of PV System Combined to Cooling Load Management Strategy towards Photovoltaic Self-Consumption in Buildings

Influence of Spatially Varying Costs on Structure and Operation of Energy Storage and Supply Facilities in a Local Energy System

Thermal and Electrical Energy Storage (TES and EES) systems can balance the fluctuating energy supply of renewable energy sources for satisfying the energy demands. The extension of central TES systems is an appropriate measure for buffering surplus heat which can be produced by Combined Heat and Power Plants (CHP) or Heat Pumps (HP) for acting optimally on the electricity market. In addition, EES systems installed in combination with roof-top photovoltaic (PV) power plants can increase the self-consumption in buildings and reduce surplus electricity in the distribution grids. This research applies a spatially resolved techno-economical optimization method at a residential area for studying the future development of total costs, CO2 emissions, energy exchange and stored amounts of energy for TES and EES systems in various scenarios. By the implementation of cost penalties in the model, the behavior of various operators for PV-battery systems is emulated. An increasing self-consumption and decreasing overall electricity exchange is stimulated by creating incentives for trading electricity within the studied local area.