Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration

The integration of renewable energy technologies and building renovation are the two main procedures for improving energy sustainability of buildings at neighborhood scale. It is difficult, however, to optimize these procedures simultaneously. This study focuses on improving energy sustainability of Hemberg, a Swiss village with a population of about 900, through optimizing these two procedures. For this purpose a computational platform was developed, combining software CitySim, HOMER Pro, QGIS and Rhinoceros. The energy demand on hourly basis for the buildings in the village was analyzed through comparing the current demand with that after retrofitting according to the Swiss energy labels (i) Minergie and (ii) Minergie-P. Swiss energy maps were used to identify the most promising renewable energy sources while three scenarios were considered for solar PV integration and energy system improvements. The first scenario presents the current condition in the village, while the second scenario explores improvements in electricity generation and the third in both electricity and heat generation. The results show that retrofitting of all buildings according to Minergie reduces the space heating demand by 70–85% and reduces the fluctuations in energy demand, thereby allowing the integration of more renewable energy. According to the simulations, building-integrated solar PV panels can cover the total annual energy demand of the village when considering the Minergie and Minergie-P scenarios. However, the energy system assessment shows that it is difficult to reach beyond 60% when integrating non-dispatchable renewable energy technologies. Finally, and more importantly, integration of wind energy at system level has an important impact in the hub.

Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid

Dasaraden Mauree, Vahid Moussavi Nik, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

A paradigm change in energy system design tools, energy market, and energy policy is required to attain the target levels in renewable energy integration and in minimizing pollutant emissions in power generation. Integrating non-dispatchable renewable energy sources such as solar and wind energy is vital in this context. Distributed generation has been identified as a promising method to integrate Solar PV (SPV) and wind energy into grid in recent literature. Distributed generation using grid-tied electrical hubs, which consist of Internal Combustion Generator (ICG), non-dispatchable energy sources (i.e., wind turbines and SPV panels) and energy storage for providing the electricity demand in Sri Lanka is considered in this study. A novel dispatch strategy is introduced to address the limitations in the existing methods in optimizing grid-integrated electrical hubs considering real time pricing of the electricity grid and curtailments in grid integration. Multi-objective optimization is conducted for the system design considering grid integration level and Levelized Energy Cost (LEC) as objective functions to evaluate the potential of electrical hubs to integrate SPV and wind energy. The sensitivity of grid curtailments, energy market, price of wind turbines and SPV panels on Pareto front is evaluated subsequently. Results from the Pareto analysis demonstrate the potential of electrical hubs to cover more than 60% of the annual electricity demand from SPV and wind energy considering stringent grid curtailments. Such a share from SPV and wind energy is quite significant when compared to direct grid integration of non-dispatchable renewable energy technologies.

Elsevier Sci Ltd2017

Modeling and Assessment of Urban Energy Systems

Amarasinghage Tharindu Dasun Perera

Improving the energy sustainability of our cities involves the integration of multiple renewable energy technologies into existing energy infrastructure, stretching the capabilities of traditional energy systems to the limit. To consider the transition take place in distributed energy system sector, complex cyber physical interactions need to be adequately modelled, which is not possible with currently used white box models. Furthermore, the volatility of climate conditions and frequent extreme climate events due to climate change as well as climate phenomena at urban scale make it essential to improve the robustness and resilience of energy systems. Again, white box models do not allow sufficient flexibility in the modelling approach. To address these limitations, thesis seeks to optimize distributed energy system design with the help of grey- and black-box techniques. A grey box model based on fuzzy logic is introduced to consider the dispatch strategy when designing electrical hubs. The grey box model shows better performance when optimizing electrical hubs. It has been shown that the method can achieve a renewable energy integration level of up to 80%. However, the grey box model fails to handle complex energy flows within the energy system. Therefore, a black-box method based on reinforcement learning is introduced to consider complex energy systems catering multi-energy services. Reinforcement learning based on a fully connected neural network (FNN), outperforms the grey box model by improving the objective function values by 60%. A convolution neural network improves the objective function values further (by up to 20% compared to FNN). The results reveal that black box models are competent when conducting optimization for complex energy systems. Distributed optimization is introduced to move from a single energy system to an energy internet consisting of several interacting energy systems. The energy internet is optimized considering fully cooperative and non-cooperative scenarios. The optimization algorithm shows a good capability to reach the Epsilon-Nash equilibrium when conducting the optimization. Finally, supervised and transfer learning methods have been introduced when conducting energy system optimization at the regional and national scale, which reduced the computation time by 84%. Stochastic and robust programing methods are introduced to improve the climate flexibility of energy systems. A hybrid stochastic-robust optimization algorithm is developed by extending the novel approach to consider both climate uncertainty and extreme events. A regional climate model provides climate scenarios for the stochastic optimization. The results of the study show that renewable energy technologies such as solar PV and wind can be used to cater 50% of the annual energy demand while guaranteeing a robust operation of the energy system during extreme climate events. The model is then further extended by integrating computational models for urban energy simulation considering urban climate. Results of the analysis show that a performance gap of up to 40 % can be observed when neglecting the influences of urban climate in the design of urban energy systems. In the final part of this thesis, a multi-criterion decision making technique is introduced into the energy system optimization model. This helps decision makers to weight a number of conflicting objectives and to consider impacts at the urban scale.

EPFL2019

Comparative Assessment of Electrical Generating Systems for a Greek Island

Lucas Mosca

This master thesis is the last part of the master studies in the department of Energy Management and Sustainability, at the Swiss Federal Institute of Technology (EPFL) in Lausanne. This work has been performed in collaboration with the company Renenig Energy, in Athens. The reader should note that the report is an academical work. Results and conclusion are influenced by hypothesis and should not be taken as general truths. The case study is a fictive project but it is composed of values adapted from another real but confidential project, which makes the analysis plausible. The owner of a private island in Greece wants to build a luxury resort on his island. Therefore, he will also need an electrical generating system that must cover the future electricity demand. Usually, diesel generators are used in remote areas in order to produce electricity. However, this technology is very expensive, and harmful for the environment. This master thesis covers the estimation of the electricity demand and the technico-economical assessment of different energy systems. The objective is to evaluate the potential of a microgrid composed of different renewable energy technologies and compare it to the a baseline system composed of generators only. The hourly electricity demand of the entire island has been modeled using a bottom-up approach with a list of representative types of locals. The consumption of these locals have been divided into three categories : The appliances, the lighting and the heating/cooling. For the energy system which must produce electricity, supply strategies have been defined based on the type of technology. For all strategies, an optimization of the technology installed capacities is performed with the software HOMERPro. It is used as a tool to minimize the net present cost of the energy system and analyze the microgrid energetic behavior. The yearly electricity demand of the island has been estimated to around 11 GWh, with a peak load of 2,5 MW during the summer. The cost of serving this load with generators is

63 million with a levelized cost of electricity of 0.467

/kWh. When integrating solar panels, the system cost decreases to

51.3 million with a LCOE of 0.378

/kWh. Moreover, wind turbines and a battery storage system are also economically viable. The last strategy consists of distributed PV panels on building roofs, wind turbines and a battery storage system. Results show that this energy system can decrease the cost to $ 39.8 million while emitting half of the carbon dioxide compared with the baseline system. However, the battery bank has a large impact on CO2 emissions due to its manufacturing processes. Further work can be done for this case study, especially on the network stability assessment, which was not part of this master thesis. Moreover, the potential of implementing hydrogen in the microgrid could be interesting. This could bring a good storage alternative to batteries.

2019