Integrating Renewable Energy Technologies into Distributed Energy Systems Maintaining System Flexibility

Flexibility of the energy system plays a vital role when integrating non-dispatchable renewable energy technologies. However, flexibility of the energy system has been often discussed only focusing on the operation of the energy system. This study extends the flexibility concept considering both design and operation of the energy system. In order to achieve this, pseudo chronological scenarios used for stochastic optimization is used to define system flexibility. Multiple criterions are considered when evaluating the flexibility of the system and fuzzy logic is used to consider the ambiguity in the assessment process when localizing into a specific application. Subsequently, multi objective optimization is conducted to design a multi-energy hub considering net present value (NPV), system flexibility and renewable energy generation. GPU-accelerated computing is introduced to speed up the computing when evaluating the objective functions for number of scenarios. Results of the study show that poor system flexibility can leads to poor utilization of renewable energy generated. More importantly, penetration levels of non-dispatchable renewable energy technologies notably reduce by 20-30% when considering the flexibility of the energy system which guarantees robust operation.

Climate resilient interconnected infrastructure: Co-optimization of energy systems and urban morphology

Kavan Javanroodi, Amarasinghage Tharindu Dasun Perera, Vahid Moussavi Nik

Co-optimization of urban morphology and distributed energy systems is key to curb energy consumption and optimally exploit renewable energy in cities. Currently available optimization techniques focus on either buildings or energy systems, mostly neglecting the impact of their interactions, which limits the renewable energy integration and robustness of the energy infrastructure; particularly in extreme weather conditions. To move beyond the current state-of-the-art, this study proposes a novel methodology to optimize urban energy systems as interconnected urban infrastructures affected by urban morphology. A set of urban morphologies representing twenty distinct neighborhoods is generated based on fifteen influencing parameters. The energy performance of each urban morphology is assessed and optimized for typical and extreme warm and cold weather datasets in three time periods from 2010 to 2039, 2040 to 2069, and 2070 to 2099 for Athens, Greece. Pareto optimization is conducted to generate an optimal energy system and urban morphology. The results show that a thus optimized urban morphology can reduce the levelized cost for energy infrastructure by up to 30%. The study reveals further that the current building form and urban density of the modelled neighborhoods will lead to an increase in the energy demand by 10% and 27% respectively. Furthermore, extreme climate conditions will increase energy demand by 20%, which will lead to an increment in the levelized cost of energy infrastructure by 40%. Finally, it is shown that co-optimization of both urban morphology and energy system will guarantee climate resilience of urban energy systems with a minimum investment.

2021

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

Energy and Exergy Analysis of a Cruise Ship

Tuong-Van Nguyen, Francesco Baldi, Karin Andersson

In recent years, the International Maritime Organization agreed on aiming to reduce shipping’s greenhouse gas emissions by 50% with respect to 2009 levels. Meanwhile, cruise ship tourism is growing at a fast pace, making the challenge of achieving this goal even harder. The complexity of the energy system of these ships makes them of particular interest from an energy systems perspective. To illustrate this, we analyzed the energy and exergy flow rates of a cruise ship sailing in the Baltic Sea based on measurements from one year of the ship’s operations. The energy analysis allows identifying propulsion as the main energy user (46% of the total) followed by heat (27%) and electric power (27%) generation; the exergy analysis allowed instead identifying the main inefficiencies of the system: while exergy is primarily destroyed in all processes involving combustion (76% of the total), the other main causes of exergy destruction are the turbochargers, the heat recovery steam generators, the steam heaters, the preheater in the accommodation heating systems, the sea water coolers, and the electric generators; the main exergy losses take place in the exhaust gas of the engines not equipped with heat recovery devices. The application of clustering of the ship’s operations based on the concept of typical operational days suggests that the use of five typical days provides a good approximation of the yearly ship’s operations and can hence be used for the design and optimization of the energy systems of the ship.

2018

Modeling and Assessment of Urban Energy Systems

Amarasinghage Tharindu Dasun Perera

EPFL2019