Ivalin Petkov

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Petkov

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Ivalin

ORCID

0000-0002-2973-5751

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Publications 1 - 10 of 12

The real estate decarbonization pathway and its relationship to asset-level data quality
Petkov, Ivalin (2025)
SBE Conference Series ~ Sustainable Built Environment Conference 2025 Zurich - Extended Abstracts
Power-to-hydrogen as seasonal energy storage: an uncertainty analysis for optimal design of low-carbon multi-energy systems
Petkov, Ivalin; Gabrielli, Paolo (2020)
Applied Energy
This study analyzes the factors leading to the deployment of Power-to-Hydrogen (PtH2) within the optimal design of district-scale Multi-Energy Systems (MES). To this end, we utilize an optimization framework based on a mixed integer linear program that selects, sizes, and operates technologies in the MES to satisfy electric and thermal demands, while minimizing annual costs and CO2 emissions. We conduct a comprehensive uncertainty analysis that encompasses the entire set of technology (e.g. cost, efficiency, lifetime) and context (e.g. economic, policy, grid carbon footprint) input parameters, as well as various climate-referenced districts (e.g. environmental data and energy demands) at a European-scope. Minimum-emissions MES, with large amounts of renewable energy generation and high ratios of seasonal thermal-to-electrical demand, optimally achieve zero operational CO2 emissions by utilizing PtH2 seasonally to offset the long-term mismatch between renewable generation and energy demand. PtH2 is only used to abate the last 5–10% emissions, and it is installed along with a large battery capacity to maximize renewable self-consumption and completely electrify thermal demand with heat pumps and fuel cells. However, this incurs additional cost. Additionally, we show that ‘traditional’ MES comprised of renewables and short-term energy storage are able to decrease emissions by 90% with manageable cost increases. The impact of uncertainty on the optimal system design reveals that the most influential parameter for PtH2 implementation is (1) heat pump efficiency as it is the main competitor in providing renewable-powered heat in winter. Further, battery (2) capital cost and (3) lifetime prove to be significant as the competing electrical energy storage technology. In the face of policy uncertainties, a CO2 tax shows large potential to reduce emissions in district MES without cost implications. The results illustrate the importance of capturing the dynamics and uncertainties of short- and long-term energy storage technologies for assessing cost and CO2 emissions in optimal MES designs over districts with different geographical scopes.
Optimal decarbonization strategies for existing districts considering energy systems and retrofits
Lerbinger, Alicia; Petkov, Ivalin; Mavromatidis, Georgios; et al. (2023)
Applied Energy
Integrated energy system planning at the district level can contribute towards the sustainable transformation of the building sector by unlocking solutions beyond individual buildings. This is particularly true for existing districts, whose older buildings have a low energy performance and for which measures to reduce the energy demand and ensure a low-emission energy supply must be implemented. In the urban context, district heating networks (DHN) are a promising way of doing the latter, especially with carbon capture and storage (CCS) on the horizon. However, investment decisions for both types of measures – energy supply and demand reduction – must consider individual buildings as part of district-scale considerations as building-level demand-side interventions affect energy demand patterns and densities. These can in turn affect energy supply decisions at the district-level. This study presents a comprehensive methodology for determining optimal decarbonization strategies for existing districts while considering building-level energy supply and retrofitting investment decisions and the expansion of existing DHN. We do so by extending the MANGOret (Multi-stAge eNerGy Optimization - retrofitting) optimization framework for the long-term investment planning of building-level multi-energy systems and envelope retrofits. In addition, the study presents an approach for modeling CCS as an emission-reduction technology for the waste incinerator supplying the marginal expansion of the district heating network. The developed optimization model considers a long-term time horizon with multiple investment stages, allowing it to represent economic and technological developments over the model time horizon. It incorporates a multi-objective perspective, capturing the trade-offs between the total system costs and emissions. The model is applied to two existing case study neighborhoods in the city of Chur, Switzerland. The long-term energy system design and operation for the two neighborhood typologies – mixed-use and residential – are analyzed as part of the DHN expansion investment decision. The results show that retrofitting is the main cost driver of any decarbonization strategy. Therefore the choice and the size of the energy systems offer better leverage for reducing emissions with a moderate increase in costs. In dense inner-city neighborhoods with high heating demands, district heating is the cost-optimal heating choice. Together with a low-emission district heating source such as a waste incinerator with CCS or biomass, it also becomes the CO2-optimal choice. Furthermore, the case studies demonstrate that a combination of heat pumps, hot water thermal storage, and solar PV is not only the CO2-optimal but also the cost-optimal decentralized heating option if no DHN is available. This highlights the attractiveness of energy-efficient heating systems already today.
Optimal decarbonization conditions for buildings & districts: a model-based techno-economic perspective
Petkov, Ivalin (2022)
MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems
Mavromatidis, Georgios; Petkov, Ivalin (2021)
Applied Energy
This study presents MANGO (Multi-stAge eNerGy Optimization), a novel optimization model that incorporates a multi-year planning horizon, along with flexible, multi-stage investment strategies for the effective, long-term design of decentralized multi-energy systems (D-MES). By considering the dynamic surrounding energy and techno-economic landscape that evolves over time, MANGO harnesses the strategic value of investment flexibility and can optimally phase D-MES investments in order to benefit, for instance, from projected future reduced technology costs and technical improvements. To achieve this, the model considers the most relevant dynamic aspects, such as year-to-year variations in energy demands, changing energy carrier and technology prices, technical improvements and equipment degradation. MANGO is also capable of optimizing the design of complex configurations composed of multiple, interconnected D-MES installed at different locations. Finally, the model’s formulation also addresses end-of-horizon effects that can distort solutions in multi-stage energy system models. Besides presenting the key aspects and the mathematical formulation of MANGO, this study also uses the model to develop a six-stage energy design plan, along a 30-year project horizon, for an urban district composed of 3 sites in Zurich, Switzerland. One candidate D-MES is considered per site and different scenarios are examined regarding building retrofitting and D-MES interconnections. Results overall show that retrofitting leads to lower emission levels, but significantly higher costs. On the other hand, D-MES interconnections improve both the economic and the environmental system performance. Finally, regarding optimal D-MES configurations, a variety of technologies is used, with combinations of air-source heat pumps and natural gas boilers leading to better economic performance and combinations of ground-source heat pumps and biomass boilers to more environmentally-friendly designs. Overall, MANGO facilitates D-MES decision-making at the strategic level by delivering flexible multi-stage investment strategies, at the economic level by providing detailed information about the systems’ economic performance during each project year and, finally, at the technical level by specifying the optimal technical configurations of each D-MES and their optimal operating schedules. With its long-term perspective, MANGO can offer insights that closely match the dynamic class of real-world energy system design projects led by energy developers.
MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits
Petkov, Ivalin; Mavromatidis, Georgios; Knoeri, Christof; et al. (2022)
Applied Energy
This study presents MANGOret (Multi-stAge eNerGy Optimization — retrofitting), a novel optimization framework and model for the long-term investment planning of existing building retrofits. MANGOret bridges the methodological gaps between energy system modeling and real estate management to present a scalable framework to optimize both energy and non-energy costs while considering building value. With a 2050 horizon, MANGOret is able to harness the strategic value of investment flexibility to optimally phase investments across the multi-objective cost and CO2 emission decision space considering both operational and embodied emissions. From the energy perspective, the model generates long-term investment strategies for decentralized multi-energy systems and envelope retrofits. The model considers the interdependent trade-offs between demand- and supply-side measures for a number of technologies across time. Technology scheduling is informed by condition degradation functions from utilizing the Schroeder method. From the real estate management perspective, the framework digitalizes the multi-year investment planning process. The model is supported by series of automated data retrieval and processing steps to consider each contextual building project. Importantly, we develop an archetypal energy demand database to reference demands of various retrofitting packages. By considering all retrofitting-relevant investments, the model incorporates the critical budgeting elements of rental revenues to calculate building value. We demonstrate the value of the MANGOret framework across various building types and sizes in different Swiss real estate markets. Our results demonstrate relevance for energy engineers and building owners relating to the long-term design, operation, and investment scheduling of existing buildings. We present multiple optimal strategies considering the trade-offs between cost, value, and CO2 emissions. Aligning with previous studies, our results show that higher investment costs are necessary to achieve low-CO2 retrofits relative to minimum cost strategies. Higher costs are, to a large extent, influenced by envelope retrofits and non-energy internal renovations while energy supply systems contribute to a smaller share of the budget. To achieve low-CO2, retrofits utilize lower embodied emission technology choices and are scheduled early on. Nevertheless, we show that these trade-offs do not necessarily have to be weighed at the extremes of the Pareto front, instead presenting ‘minimal regret’ solutions which reduce CO2 at negligible cost increases. Considering both embodied and operational CO2 emissions over the building life-cycle, our results demonstrate that optimal emission reductions necessitate subsequent reductions in energy consumption. Low-CO2 retrofitting strategies are typified by reducing energy demands as much as possible in order to self-consume as much renewable energy as possible, typically by solar PV and heat pump coupled systems with grid reliance.
The impact of urban district composition on storage technology reliance: trade-offs between thermal storage, batteries, and power-to-hydrogen
Petkov, Ivalin; Gabrielli, Paolo; Spokaite, Marija (2021)
Energy
The interplay of policy and energy retrofit decision-making for real estate decarbonization
Petkov, Ivalin; Knoeri, Christof; Hoffmann, Volker H. (2021)
Environmental Research: Infrastructure and Sustainability
Retrofitting existing buildings is critical for meeting global and institutional net-zero CO2 emissions goals. Prominent energy and climate policy strategies are aiming to increase notoriously low retrofitting rates by triggering energy efficient and/or decarbonized real estate investments. Although many real estate assets are owned by large-scale investors (LSIs), the interplay of their retrofit decision-making and policies are under researched. Relying on interviews with four major owner types, industry experts, and policymakers, we unpack the 'black box' of retrofit investment and demonstrate how LSIs can transform retrofit decision-making processes to meet emissions goals. We show that to accelerate deep retrofits, policymakers should focus on integrated policy mixes, and consider the cross-impacts of policy instruments from various domains on the value-driven retrofitting decision. Instruments indirectly influencing retrofits, such as those targeting affordability or densification, represent a critical avenue for improving the retrofitting policy mix by moving away from single instruments directly targeting energy or emissions aspects. This policy mix should specifically target asset management budgetary decisions, which mainly drive investment planning relevant for deep retrofits.
An uncertainty and sensitivity analysis of Power-to-Hydrogen as a seasonal storage option in a district multi-energy system
Petkov, Ivalin; Gabrielli, Paolo (2019)
Journal of Physics: Conference Series
Seasonal energy storage plays a key role in low-carbon multi-energy systems (MES) by storing renewable generation in times of excess supply in order to meet energy demands months in the future. Power-to-Hydrogen (PtH2) is being investigated as a promising long-term storage solution for integrated MES. In this preliminary work we investigate under which conditions does PtH2 become a seasonal storage option in district MES through an optimization framework including an uncertainty and sensitivity analysis to evaluate the effect of technological and contextual uncertainty on PtH2 execution. PtH2 becomes vital in low-carbon, renewable-heavy, MES for meeting high thermal demands in winter.
Decarbonizing real estate portfolios considering optimal retrofit investment and policy conditions to 2050
Petkov, Ivalin; Lerbinger, Alicia; Mavromatidis, Georgios; et al. (2023)
iScience
Retrofitting existing buildings is crucial for achieving Net Zero emissions. Institutional real estate owners play a key role because of their significant ownership, especially of large buildings. We utilize an interdisciplinary approach to evaluate cost-optimal decarbonization conditions for three Swiss real estate portfolios owned by a global institutional investor. We leverage a bottom-up optimization framework for building asset retrofitting, scaled to the portfolio-level, to study the effect of policy scenarios and implementations. Results indicate that achieving Net Zero necessitates significant investments, largely through thermal energy efficiency measures and low-CO2 energy systems, as early as possible to avoid locked-in emissions. Owners will be challenged to smooth long-term capital investments, pointing to a potential liquidity crisis. Consequently, hard-to-decarbonize assets are unable to reach regulatory benchmarks largely because of lingering embodied emissions. To lower transition risk, we recommend that policymakers move toward average CO2 benchmarks at the real estate portfolio-level, emulating automotive fleets.

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Ivalin Petkov

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