An agent-based simulator to estimate domestic energy use

Throughout recent years, there has been an increase in the population size, as well as a fast economic growth, which has led to an increase of the energy demand that comes mainly from fossil fuels. In order to reduce the ecological footprint, governments have implemented sustainable measures and it is expected that by 2035 the energy produced from renewable energy sources, such as wind and solar would be responsible for one-third of the energy produced globally. However, since the energy produced from renewable sources is governed by the availability of the respective primary energy source there is often a mismatch between production and demand, which could be solved by adding flexibility on the demand side through demand response (DR). DR programs influence the end-user electricity usage by changing its cost along the time. Under this scenario the user needs to estimate the energy demand and on-site production in advance to plan its energy demand according to the energy price. This work focuses on the development of an agent-based electrical simulator, capable of: (a) estimating the energy demand and on-site generation with a 1-min time resolution for a 24-h period, (b) calculating the energy price for a given scenario, (c) making suggestions on how to maximize the usage of renewable energy produced on-site and to lower the electricity costs by rescheduling the use of certain appliances. The results show that this simulator allows reducing the energy bill by 11% and almost doubling the use of renewable energy produced on-site.

The effect of key process operational conditions on enhanced biological phosphorus removal from wastewater

Enhanced biological phosphorus removal (EBPR) is the most economic and sustainable option used in wastewater treatment plants (WWTPs) for phosphorus removal. In this process it is important to control the competition between polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs), since EBPR deterioration or failure can be related with the proliferation of GAOs over PAOs. This thesis is focused on the effect of operational conditions (volatile fatty acid (VFA) composition, dissolved oxygen (DO) concentration and organic carbon loading) on PAO and GAO metabolism. The knowledge about the effect of these operational conditions on EBPR metabolism is very important, since they represent key factors that impact WWTPs performance and sustainability. Substrate competition between the anaerobic uptake of acetate and propionate (the main VFAs present in WWTPs) was shown in this work to be a relevant factor affecting PAO metabolism, and a metabolic model was developed that successfully describes this effect. Interestingly, the aerobic metabolism of PAOs was not affected by different VFA compositions, since the aerobic kinetic parameters for phosphorus uptake, polyhydroxyalkanoates (PHAs) degradation and glycogen production were relatively independent of acetate or propionate concentration. This is very relevant for WWTPs, since it will simplify the calibration procedure for metabolic models, facilitating their use for full-scale systems. The DO concentration and aerobic hydraulic retention time (HRT) affected the PAO-GAO competition, where low DO levels or lower aerobic HRT was more favourable for PAOs than GAOs. Indeed, the oxygen affinity coefficient was significantly higher for GAOs than PAOs, showing that PAOs were far superior at scavenging for the often limited oxygen levels in WWTPs. The operation of WWTPs with low aeration is of high importance for full-scale systems, since it decreases the energetic costs and can potentially improve WWTP sustainability. Extended periods of low organic carbon load, which are the most common conditions that exist in full-scale WWTPs, also had an impact on PAO and GAO activity. GAOs exhibited a substantially higher biomass decay rate as compared to PAOs under these conditions, which revealed a higher survival capacity for PAOs, representing an advantage for PAOs in EBPR processes. This superior survival capacity of PAOs under conditions more closely resembling a full-scale environment was linked with their ability to maintain a residual level of PHA reserves for longer than GAOs, providing them with an effective energy source for aerobic maintenance processes. Overall, this work shows that each of these key operational conditions play an important role in the PAO-GAO competition and should be considered in WWTP models in order to improve EBPR processes.

Distributed solar micro generation

Taking into account the fact that the sun’s radiation is estimated to be enough to cover 10.000 times the world’s total energy needs (BRAKMANN & ARINGHOFF, 2003), it is difficult to understand how solar photovoltaic systems (PV) are still such a small part of the energy source matrix across the globe. Though there is an ongoing debate as to whether energy consumption leads to economic growth or whether it is the other way around, the two variables appear correlated and it is clear that ensuring the availability of energy to match a country’s growth targets is one of the prime concerns for any government. The topic of centralized vs distributed electricity generation is also approached, especially in what regards the latter fit to developing countries needs, namely the lack of investment capabilities and infrastructure, scattered population, and other factors. Finally, Brazil’s case is reviewed, showing that the current cost of electricity from the grid versus the cost from PV solutions still places an investment of this nature with 9 to 16 years to reach breakeven (from a 25 year panel lifespan), which is too high compared to the required 4 years for most Brazilians. Still, recently passed legislation opened the door, even if unknowingly, to the development of co-owned solar farms, which could reduce the implementation costs by as much as 20% and hence reduce the number of years to breakeven by 3 years.

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.