Photovoltaic potential in building façades

Tese de doutoramento, Sistemas Sustentáveis de Energia, Universidade de Lisboa, Faculdade de Ciências, 2018Consistent reductions in the costs of photovoltaic (PV) systems have prompted interest in applications with less-than-optimum inclinations and orientations. That is the case of building façades, with plenty of free area for the deployment of solar systems. Lower sun heights benefit vertical façades, whereas rooftops are favoured when the sun is near the zenith, therefore the PV potential in urban environments can increase twofold when the contribution from building façades is added to that of the rooftops. This complementarity between façades and rooftops is helpful for a better match between electricity demand and supply. This thesis focuses on: i) the modelling of façade PV potential; ii) the optimization of façade PV yields; and iii) underlining the overall role that building façades will play in future solar cities. Digital surface and solar radiation modelling methodologies were reviewed. Special focus is given to the 3D LiDAR-based model SOL and the CAD/plugin models DIVA and LadyBug. Model SOL was validated against measurements from the BIPV system in the façade of the Solar XXI building (Lisbon), and used to evaluate façade PV potential in different urban sites in Lisbon and Geneva. The plugins DIVA and LadyBug helped assessing the potential for PV glare from façade integrated photovoltaics in distinct urban blocks. Technologies for PV integration in façades were also reviewed. Alternative façade designs, including louvers, geometric forms and balconies, were explored and optimized for the maximization of annual solar irradiation using DIVA. Partial shading impacts on rooftops and façades were addressed through SOL simulations and the interconnections between PV modules were optimized using a custom Multi-Objective Genetic Algorithm. The contribution of PV façades to the solar potential of two dissimilar neighbourhoods in Lisbon was quantified using SOL, considering local electricity consumption. Cost-efficient rooftop/façade PV mixes are proposed based on combined payback times. Impacts of larger scale PV deployment on the spare capacity of power distribution transformers were studied through LadyBug and SolarAnalyst simulations. A new empirical solar factor was proposed to account for PV potential in future upgrade interventions. The combined effect of aggregating building demand, photovoltaic generation and storage on the self-consumption of PV and net load variance was analysed using irradiation results from DIVA, metered distribution transformer loads and custom optimization algorithms. SOL is shown to be an accurate LiDAR-based model (nMBE ranging from around 7% to 51%, nMAE from 20% to 58% and nRMSE from 29% to 81%), being the isotropic diffuse radiation algorithm its current main limitation. In addition, building surface material properties should be regarded when handling façades, for both irradiance simulation and PV glare evaluation. The latter appears to be negligible in comparison to glare from typical glaze/mirror skins used in high-rises. Irradiation levels in the more sunlit façades reach about 50-60% of the rooftop levels. Latitude biases the potential towards the vertical surfaces, which can be enhanced when the proportion of diffuse radiation is high. Façade PV potential can be increased in about 30% if horizontal folded louvers becomes a more common design and in another 6 to 24% if the interconnection of PV modules are optimized. In 2030, a mix of PV systems featuring around 40% façade and 60% rooftop occupation is shown to comprehend a combined financial payback time of 10 years, if conventional module efficiencies reach 20%. This will trigger large-scale PV deployment that might overwhelm current grid assets and lead to electricity grid instability. This challenge can be resolved if the placement of PV modules is optimized to increase self-sufficiency while keeping low net load variance. Aggregated storage within solar communities might help resolving the conflicting interests between prosumers and grid, although the former can achieve self-sufficiency levels above 50% with storage capacities as small as 0.25kWh/kWpv. Business models ought to adapt in order to create conditions for both parts to share the added value of peak power reduction due to optimized solar façades.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/52363/201

IEA ECES Annex 31 Final Report - Energy Storage with Energy Efficient Buildings and Districts: Optimization and Automation

At present, the energy requirements in buildings are majorly met from non-renewable sources where the contribution of renewable sources is still in its initial stage. Meeting the peak energy demand by non-renewable energy sources is highly expensive for the utility companies and it critically influences the environment through GHG emissions. In addition, renewable energy sources are inherently intermittent in nature. Therefore, to make both renewable and nonrenewable energy sources more efficient in building/district applications, they should be integrated with energy storage systems. Nevertheless, determination of the optimal operation and integration of energy storage with buildings/districts are not straightforward. The real strength of integrating energy storage technologies with buildings/districts is stalled by the high computational demand (or even lack of) tools and optimization techniques. Annex 31 aims to resolve this gap by critically addressing the challenges in integrating energy storage systems in buildings/districts from the perspective of design, development of simplified modeling tools and optimization techniques

D4.3 - Report on infrastructure requirements for developing sustainably PEDs,summarizing the outcome of the techno-economic modelling activities

Implementation of PEDs requires immense infrastructure investments in energy efficiency measures, energy generation, transformation and storage as well as in new mobility solutions. On the other hand, it is crucial to create affordable living arrangements despite the high costs of the aforementioned measures. Thus, this work aims to answer the overarching question of which infrastructure will be required to turn an existing neighbourhood into a PED. As existing districts in Europe are highly heterogeneous and, thus, difficult to analyse, this study uses a case study with a specific district archetype. To address this issue, this study utilises the district comparability tool, a framework created as part of the SMARTBEEjS project to enable technical comparability across districts in Europe. In addition, the cost of the necessary infrastructure is estimated, which leads to the discussion of inclusion and affordability issues as potential barriers of PEDs. An integrated techno-economic modelling consisting of different modelling approaches for the power, heating and cooling and mobility sectors as well as demand-side measures are applied. Those include tailor-made mixed integer linear programming, synPro simulations, agent based modelling and statistical correlations. Using these modelling methods, several plausible transition scenarios are analysed based on the building, climate and socio-demographic data of an archetype district of Griesheim-Mitte in Frankfurt am Main (Germany). The results show that envelope retrofitting is crucial to fulfil the PED requirement of energy positivity and to reduce the capacities of the energy generation and storage technology. Furthermore, the PED concept can be more economical than the business-as-usual scenario of importing the required energy. However, high upfront costs can be a barrier for less wealthy societies. This barrier needs to be reduced by public schemes or smart business models to avoid creating a neighbourhood concept that does not uphold the principles of energy justice and inclusion. This study has its limitations in terms of the scope of the study. Only one district archetype (based on Griesheim-Mitte in Frankfurt am Main and relevant for districts in Nottingham and Amsterdam) is analysed. Further work could look into different archetypes and the transition scenarios for those. Moreover, not all scenarios, i.e. transition measures could be modelled quantitatively within the frame of this work. In particular, the waste heat from data centers in the selected district was not considered. Hence, applying industrial waste heat for powering district heat networks should be investigated more thoroughly in the future

Decarbonising fishery ports through smart cluster energy systems

The rising energy prices at seaports and fishing industries pose a major challenge because the pace of work and high demand for fish products has increased dra‐matically. This comes at a time of growing international pressure and global moti‐vations to address climate change and reduce carbon emissions in many different sectors of the economy. In the literature review, a few research studies were found to highlight the op‐timal use of power energy in ports, while some studies proposed certain measures that contribute to some extent to reducing energy consumption and carbon emis‐sions. However, there is an absence of a study that discusses the possibility of de‐veloping a holistic energy analysis and management that can be scaled from a site to a community level to achieve economically and environmentally viable benefits to the community. The research study that is described in this thesis aims to develop a compre‐ hensive integrated system for the optimal use of energy in seaports through the de‐velopment of a smart grid system that is based on the renewable energy at Milford Haven Port, which was developed and used as an applied case stud. It is hoped that this study will contribute to reducing energy prices and that the port will achieve economic benefits by sharing its surplus power with the national grid. A five‐stage research methodology has been developed, starting with the pro‐ cess of collecting and analysing data on fishery buildings, known as and energy audit. It then develops energy simulation models at the port using energy simula‐tion software. The next stage aims to propose a smart grid model at multi‐levels, namely a building, port and a community of 200 houses around a fishery port. The next stage consists of the development of two smart decision‐making systems: the first aimed at sharing surplus power with the neighbours of the port through a Peerto Peer (P2P) energy sharing approach; and the second aims to achieve financial in‐comes for the port by selling surplus power to the national grid when energy prices rise, a price‐based control strategy is used in this system The model was developed and tested within 24 hours on randomly selected days during the four seasons of the year. The simulation was characterised by the fact that it was carried out instantaneously to get an accurate result, which resem‐ bles a real‐life system. In addition, the optimal number of energy storage systems was determined at multi‐levels, which achieve the self‐sufficiency of the electric power that is needed to meet the energy demand during the day. Finally, a proposed road map has been developed to achieve nearly zero carbon fishery ports that can be applied to different ports in different locations

Applications, Operational Architectures and Development of Virtual Power Plants as a Strategy to Facilitate the Integration of Distributed Energy Resources

In this article, we focus on the development and scope of virtual power plants (VPPs) as a strategy to facilitate the integration of distributed energy resources (DERs) in the power system. Firstly, the concepts about VPPs and their scope and limitations are introduced. Secondly, smart management systems for the integration of DERs are considered and a scheme of DER management through a bottom-up strategy is proposed. Then, we analyze the coordination of VPPs with the system operators and their commercial integration in the electricity markets. Finally, the challenges that must be overcome to achieve the large-scale implementation of VPPs in the power system are identified and discussed.The authors acknowledge the support from GISEL research group IT1191-19, as well as from the University of the Basque Country UPV/EHU (research group funding 181/18)

Pathways to Net-Zero Energy Buildings: An Optimization Methodology

Building Performance Simulation (BPS) is frequently used by decision-makers to estimate building energy consumption at the design stage. However, the true potential of BPS remains unrealized if trial and error methods of building simulation are used to identify combinations of parameters to reduce energy use. Optimization techniques combined with BPS offer many benefits such as: (i) identification of potential optimal designs which best achieve desired performance objectives; (ii) system level component integration by simultaneously considering conflicting trade-offs; and (iii) a process-oriented simulation tool that is complementary to BPS, eliminating the need for repetitive userinitiated model evaluations. However, the capability of optimization algorithms to effectively map out the entire solution space and discover information is farther reaching than building design. As shown in this thesis, optimization datasets are also a valuable resource for conducting uncertainty and sensitivity analyses and evaluating policies to incentivize low-energy building design. Two performance criteria are considered in this thesis: net-energy consumption and life-cycle cost. The term ‘performance-optimized’ refers to the extreme of these two criteria that is Net-Zero Energy (NZE) and cost-optimized buildings. A Net-Zero Energy Building (NZEB) generates at least as much renewable energy on-site as it consumes in a given year. A cost-optimized building has the lowest life-cycle cost over a considered period. A focus of this thesis is identifying optimal pathways to NZE and cost-optimized building designs. This thesis proposes the following approaches to identify pathways to net-zero energy: (i) a redesign case-study of an existing near-Net-Zero Energy Home (NZEH) archetype using a proposed optimization methodology; (ii) the development of an information-driven hybrid evolutionary algorithm for optimal building design; (iii) a methodology for identifying the influence of design variations on building energy performance; (iv) a methodology to evaluate the effect of incentives on life-cycle energy-cost curves; and (v) effect of a time-of-use feed-in tariff on optimal net-zero energy home design. The optimization methodology consists of: (i) an energy model; (ii) a cost model; (iii) a custom optimization algorithm; (iv) a database; and (v) a statistics module. Several new simulation techniques are proposed to identify pathways to performanceoptimized net-zero energy buildings: (i) probability distribution functions extracted from previous simulations; (ii) back-tracking searches; and (iii) importance factors to summarize back-tracking search results. This thesis provides valuable information related to: (i) the development of performancebased energy codes for buildings; (ii) systematic design of cost-optimized NZEHs; (iii) systematic analysis of the impact of different design parameters on energy consumption and cost; (iv) the study of incentive measures for NZEHs

Energy Efficiency in Buildings: Both New and Rehabilitated

Buildings are one of the main causes of the emission of greenhouse gases in the world. Europe alone is responsible for more than 30% of emissions, or about 900 million tons of CO2 per year. Heating and air conditioning are the main cause of greenhouse gas emissions in buildings. Most buildings currently in use were built with poor energy efficiency criteria or, depending on the country and the date of construction, none at all. Therefore, regardless of whether construction regulations are becoming stricter, the real challenge nowadays is the energy rehabilitation of existing buildings. It is currently a priority to reduce (or, ideally, eliminate) the waste of energy in buildings and, at the same time, supply the necessary energy through renewable sources. The first can be achieved by improving the architectural design, construction methods, and materials used, as well as the efficiency of the facilities and systems; the second can be achieved through the integration of renewable energy (wind, solar, geothermal, etc.) in buildings. In any case, regardless of whether the energy used is renewable or not, the efficiency must always be taken into account. The most profitable and clean energy is that which is not consumed

A heuristic search approach for sizing battery storage of a neighbourhood including nearly Zero Energy Buildings (nZEB)

With the changes introduced by the transition towards sustainable energy supply, major concerns like energy efficiency and system resilience can be handled by integrating adequate energy storage with renewable energy sources to realize net-zero energy in the neighbourhoods. In this paper, the neighbourhood surrounding VU medical center and university campus in Amsterdam, is selected as a case study to understand the impact of battery storage and photovoltaic generation in achieving the goal of energy neutrality. An optimization method is developed to identify the optimal size of battery storage by minimizing the net import energy from the grid. The proposed algorithm consists of two stages. The first stage uses mixed integer linear programming (MILP) to find the minimum cost associated with net import energy from the grid and the associated cost of a particular battery. The second stage applies heuristic search approach to identify the optimal size of the battery, which contributes to the most economical dispatch. The simulation results of the case study validate the proposed approach

Open Data and Models for Energy and Environment

This Special Issue aims at providing recent advancements on open data and models. Energy and environment are the fields of application.For all the aforementioned reasons, we encourage researchers and professionals to share their original works. Topics of primary interest include, but are not limited to:Open data and models for energy sustainability;Open data science and environment applications;Open science and open governance for Sustainable Development Goals;Key performance indicators of data-aware energy modelling, planning and policy;Energy, water and sustainability database for building, district and regional systems; andBest practices and case studies