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MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits

Applied Energy(2022)

Cited 15|Views31
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Abstract
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.& nbsp;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.& nbsp;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.& nbsp;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.
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Key words
Building retrofitting,Decentralized multi-energy systems,Real estate investment planning,Multi-stage optimization,Renewable energy,Mixed-integer linear programming
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