District heating and cooling systems to integrate renewable energy in urban areas

The building sector plays a crucial role in the ongoing energy transition due to its significant share of global energy consumption and carbon dioxide emissions, especially when combined with the estimation that 70% of the world's population will be living in urban areas by 2050.District heating and cooling (DHC) systems have been widely recognized among the viable options as an effective solution for achieving the challenging mid-century targets. In the context of decarbonizing and electrifying the thermal energy requirements in urban areas, this thesis provides a methodology for optimally designing district energy systems (DES).The thermal demand of existing non-residential buildings was first estimated through the development and calibration of grey-box models. Despite challenges and limitations of relying on measured data, doing so allows the definition of optimal operating temperatures for a future energy system. Comparing models calibrated with measurements obtained over different time resolutions revealed the poor performance of heating signature approaches when reconstructing hourly profiles and the potential of the clustering process of balancing modelling errors at the annual level. A flexible mixed-integer linear programming superstructure was then developed to systematically investigate optimal DHC configurations, including the sizing and operation of conversion technologies, as well as the type, layout, diameter, and operating temperatures of the thermal network. Comparing different solutions under economic, environmental and exergy-based key-performance indicators, demonstrated the potential of the anergy network to reduce investment costs without deteriorating systems performance. Additionally, a sensitivity analysis revealed that hybrid configurations with an intermediate degree of decentralization are the most robust to cost uncertainties.Finally, given that 90% of the existing DES worldwide are based on fossil fuels, the developed methods were further enhanced to address the optimal retrofit and expansion of existing systems. Particular focus was given to network expansion, replacing fossil-based technologies with more sustainable heat pumps, and synergies between heating and cooling requirements. Applying the developed methods to a case study further demonstrated both the sustainability and improved economics of the multi-temperature level configuration compared with the fossil-based, high temperature option.The work presented was carried out in collaboration with an international airport to assist their transition towards a fossil-free energy system based on a low temperature network.

Strategic energy planning under uncertainty

Stefano Moret

Various countries and communities are defining or rethinking their energy strategy driven by concerns for climate change and security of energy supply. Energy models, often based on optimization, can support this decision-making process. In the current energy planning practice, most models are deterministic, i.e. they do not consider uncertainty and rely on long-term forecasts for important parameters. However, over the long time horizons of energy planning, forecasts often prove to be inaccurate, which can lead to overcapacity and underutilization of the installed technologies. Although this shows the need of considering uncertainty in energy planning, uncertainty is to date seldom integrated in energy models. The main barriers to a wider penetration of uncertainty are i) the complexity and computational expense of energy models; ii) the issue of quantifying input uncertainties and determining their nature; iii) the selection of appropriate methods for incorporating uncertainties in energy models. To overcome these limitations, this thesis answers the following research question How does uncertainty impact strategic energy planning and how can we facilitate the integration of uncertainty in the energy modeling practice? with four novel methodological contributions. First, a mixed-integer linear programming modeling framework for large-scale energy systems is presented. Given the energy demand, the efficiency and cost of energy conversion technologies, the availability and cost of resources, the model identifies the optimal investment and operation strategies to meet the demand and minimize the total annual cost or greenhouse gas emissions. The concise formulation and low computational time make it suitable for uncertainty applications. Second, a method is introduced to characterize input uncertainties in energy planning models. Third, the adoption of a two-stage global sensitivity analysis approach is proposed to deal with the large number of uncertain parameters in energy planning models. Fourth, a complete robust optimization framework is developed to incorporate uncertainty in optimization-based energy models, allowing to consider uncertainty both in the objective function and in the other constraints. To evaluate the impact of uncertainty, the presentation of the methods is systematically associated to their validation on the real case study of the Swiss energy system. In this context, a novelty is represented by the consideration of all uncertain parameters in the analysis. The main finding is that uncertainty dramatically impacts energy planning decisions. The results reveal that uncertainty levels vary significantly for different parameters, and that the way in which uncertainty is characterized has a strong impact on the results. In the case study, economic parameters, such as the discount rate and the price of imported resources, are the most impacting inputs; also, parameters which are commonly considered as fixed assumptions in energy models emerge as critical factors, which shows that it is crucial to avoid an a priori exclusion of parameters from the analysis. The energy strategy drastically changes if uncertainty is considered. In particular, it is demonstrated that robust solutions, characterized by a higher penetration of renewables and of efficient technologies, can offer more reliability and stability compared to investment plans made without accounting for uncertainty, at the price of a marginally higher cost.

EPFL2017

Sustainability assessment of lignocellulosic biorefineries under uncertainty

Juan David Villegas Gomez

Biorefinery concepts refer to the integrated production of food, fuel, power, commodity chemicals, and polymers from biomass, mimicking the structure of a fossil-based refinery. The use of lignocellulosic biomass as feedstock for biorefineries has been promoted as a long-term strategy to mitigate climate change and foster food security. One of the critical bottlenecks for the development of these systems is the recalcitrance of the feedstock. Costly and energy intensive physicochemical and/or enzymatic pretreatment are therefore needed to release simple sugars from the lignocellulose matrix. This and other challenges would penalise the conversion efficiency or the environmental impact. This thesis puts the focus on the techno-economic and environmental assessment of sustainability of biorefinery concepts. On the one hand, it proposes an alternative approach to the conventional economic performance indicators for biorefineries. The proposed economic evaluation method addresses the lack of a formal market for crop residues and other intermediate agro-industrial streams. The approach allows for the comparison of process variants in terms of the maximum willingness-to-pay of a process unit for the bioresource that enters the system. This is calculated as the global biorefinery revenues, estimated from the definition of competitive target prices, minus the processing costs. This thesis also investigated the inclusion of parameter and scenario uncertainty into the LCA of lignocellulosic biorefineries. Scale and state-of-technology issues behind modelling assumptions are not always explicitly presented and justified. In order to facilitate decision-making, a robust comparison among different biorefinery options must include an explicit estimation of the uncertainty associated to process design parameters. The sensitivity of the LCA indicators to methodological choices such as allocation of impacts among co-products, choice of system boundaries and uncertainty in the values of background flows to the system has been evaluated by using systematic process simulation and global sensitivity analysis techniques.The proposed methods are illustrated by two case studies based on process flowsheets adapted from the second generation (2G) ethanol model developed by the US National Renewable Energy Laboratory (NREL). Different alternatives for feedstock, biomass pretreatment and valorization of extracted sugars were considered. They were simulated with Aspen Plus® process simulator. Case study 1 was used to illustrate the techno-economic assessment method. It consists in a basic 2G ethanol facility, refining sugarcane bagasse. Two technological variants were considered: The first corresponded to the NREL benchmark design, using dilute acid pretreatment and producing ethanol (only fuel-OF). The second variant consisted of a low-severity liquid hot water (LHW) pretreatment producing ethanol from cellulose and a pentoses syrup (Feed and Fuel – FF). [...]

EPFL2014

Strategic Energy Planning under Uncertainty: a Mixed-Integer Linear Programming Modeling Framework for Large-Scale Energy Systems

Michel Bierlaire, François Maréchal, Stefano Moret

Various countries and communities are defining strategic energy plans driven by concerns related to climate change and security of energy supply. Energy models are needed to support this decision-making process. The long time horizon inherent to strategic energy planning requires uncertainty to be accounted for. Most energy models available today are too complex or computationally expensive for uncertainty analyses to be carried out. This study proposes a concise multi-period Mixed-Integer Linear Programming (MILP) formulation for strategic energy planning under uncertainty. The modeling framework allows optimizing the energy system in a snapshot future year having as objective the total annual cost and assessing as well the global CO2-equivalent emissions. Key features of the model are a clear distinction both between demand and supply and between resources and technologies, a low computational time and a multiperiod resolution to account for issues related to seasonality and energy storage. The model is applied to a real case study and a Global Sensitivity Analysis (GSA) highlights the impact of uncertain parameters in energy planning.