• Energy Research
• Open Access close

Extreme conditions caused by climate change and other crises call for enhancing the resilience of buildings and urban energy systems. This paper investigates the role of collaborative decision-making to improve the performance of single buildings and the unified whole in the form of a cohesive cluster of energy consumers to enhance resilience. CIRLEM, the previously developed energy management approach, provides flexibility in energy systems through collective behavior of entities and deploying a lightweight Reinforcement Learning algorithm. This research contributes to developing a novel signal generation structure including price- and demand-based function to stimulate the cohesion attribute. Extended thermal comfort margins are introduced to broaden the flexibility potential, and reward function includes thermal zones categories. The energy management approach and extended comfort constraints is tested under an extreme cold winter in a pilot ecosystem located in Norway made of several buildings characterized by different sizes, use types, performance and energy systems. Acting individually, buildings could save 28 % and 13 % energy and cost, while acting as a collaborative cluster, energy use and cost are reduced by 42 % and 40 %. Through collaboration between buildings, high-performance buildings could help others under high energy demand periods to keep their functionality toward the cluster’s goal.

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.

In recent years, climate change and the corresponding expected extreme weather conditions have been widely recognized as potential problems. The building industry is taking various actions to achieve sustainable development, implement energy conservation strategies, and provide climate change mitigation. In addition to mitigation, it is crucial to adapt to climate change, and to investigate the possible risks and limitations of mitigation strategies. Although the importance of climate change adaptation is well-understood, there are still challenges in understanding and modeling the impacts of climate change, and the consequent risks and extremes. This work provides a comprehensive study of the impacts of climate change on the energy performances and thermal comfort of European residential building stocks. To perform an unbiased assessment and account for climate uncertainties and extreme events, a large set of future climate data was used for a 90-year period (2010–2099). Climate data for 38 European cities in five different climate zones, downscaled by the “RCA4” regional climate model, were synthesized and applied to simulate the respective energy performances of the residential building stocks in the cities. The results suggest that there will be larger needs for cooling buildings in the future and less heating demand; however, there are differences in the variation rates between zones and cities. Discomfort hours will increase notably in cities within cooling-dominated zones, but will not be affected considerably in cities within heating-dominated zones. In addition to long-term changes, climate-induced extremes can considerably affect future energy demands, especially the cooling demand; this may become challenging for both buildings and energy systems.

Abstract Climate change and increased urban population are two major concerns for society. Moving towards more sustainable energy solutions in the urban context by integrating renewable energy technologies supports decarbonizing the energy sector and climate change mitigation. A successful transition also needs adequate consideration of climate change including extreme events to ensure the reliable performance of energy systems in the long run. This review provides an overview of and insight into the progress achieved in the energy sector to adapt to climate change, focusing on the climate resilience of urban energy systems. The state-of-the-art methodology to assess impacts of climate change including extreme events and uncertainties on the design and performance of energy systems is described and discussed. Climate resilience is an emerging concept that is increasingly used to represent the durability and stable performance of energy systems against extreme climate events. However, it has not yet been adequately explored and widely used, as its definition has not been clearly articulated and assessment is mostly based on qualitative aspects. This study reveals that a major limitation in the state-of-the-art is the inadequacy of climate change adaptation approaches in designing and preparing urban energy systems to satisfactorily address plausible extreme climate events. Furthermore, the complexity of the climate and energy models and the mismatch between their temporal and spatial resolutions are the major limitations in linking these models. Therefore, few studies have focused on the design and operation of urban energy infrastructure in terms of climate resilience. Considering the occurrence of extreme climate events and increasing demand for implementing climate adaptation strategies, the study highlights the importance of improving energy system models to consider future climate variations including extreme events to identify climate resilient energy transition pathways.

AbstractSince the beginning of the 21st century, straw-bale buildings are reappearing in the world; however, their thermal performances were not thoroughly investigated up to now. The purpose of this study is to analyze thermal behavior and energy performance of a straw-bale building in Switzerland. Using Pleiades+Comfie Software, building designs have been studied to understand the best way to mitigate overheating risks due to the low heat capacity of straw. Thermal-dynamic results and Life Cycle Assessment conclude that straw bale buildings can be a sustainable alternative in the energy evolution of building construction, due to its low embodied energy and excellent thermal performance.

Distributed energy systems play a significant role in the integration of renewable energy technologies. The Energy Internet links a fleet of distributed energy systems to each other and with the grid. Interactions between the distributed energy systems via information sharing could significantly enhance the efficiency of their real-time operation. However, privacy and security concerns hinder such interactions. A game-theoretic approach can help in this regard, and enable consideration of some of these factors when maintaining interactions between energy systems. Although a game-theoretic approach is used to understand energy systems' operation, such complex interactions between the energy systems are not considered at the early design phase, leading to many practical problems, and often leading to suboptimal designs. The present study introduces a game-theoretic approach that enables consideration of complex interactions among energy systems at the early design phase. Three different architectures are considered in the study, i.e., energy eystem prior to grid (ESPG), fully cooperative (FCS), and non-cooperative (NCS) scenarios, in which each distributed energy system is taken as an agent. A novel distributed optimization algorithm is developed for both FCS and NCS. The study reveals that FCS and NCS reduce the cost, respectively, by 30% and 15% compared to ESPG. In addition to cost reduction, there is a significant change in the energy system design when moving from FCS to NCS scenarios, clearly indicating the requirement for a scenario that lies between NCS and FCS. This will lead to reducing design costs while maintaining privacy.

An integrated approach is presented in this study to design electrical hubs combining optimization, multi-criterion assessment and decision making. Levelized Energy Cost (LEC), Initial Capital Cost (ICC), Grid Integration Level (GI), Levelized CO2 emission (LCO2), utilization of renewable energy, flexibility of the system, loss of load probability (LOLP) are considered as criteria used to assess the design. The novel approach consists of several steps. Pareto analysis is conducted initially using 2D Pareto fronts to reduce the dimensions of the optimization problem. Subsequently, Pareto multi objective optimization is conducted considering LEC, GI and ICC which were identified as the best set of objective functions to represent the design requirements. Next, fuzzy TOPSIS and level diagrams are used for multi-criterion decision making (MCDM) considering the set of criteria and the boundary matrix that represents the design requirements of the application. Pareto analysis shows that 5D optimization problem can be reduced to a 3D optimization problem when considering LEC, ICC and GI as the objective functions. Finally, results obtained from the case study shows that the novel method can be used design distributed energy systems considering a set of criteria which is beyond the reach of Pareto optimization with different priority levels.