Joint optimization of building-envelope and heating-system retrofits at territory scale to enhance decision-aiding

Abstract

Reduction of energy consumption in the building sector has been identified as a major instrument to tackle global climate change and improve sustainability. In this paper, we propose a methodology to address a retrofit planning problem at a community level, with a building resolution. The resulting tool helps local decision-makers identify pertinent actions to improve the environmental behavior of their territories. Two building retrofit levers are considered, namely envelope insulation and heating systems replacement. Retrofit planning is treated here as a single-objective optimization problem aimed at reducing the total costs of retrofit actions by minimizing their net present value. A multidimensional multiple-choice knapsack problem formulation is proposed through the adoption of adequate decision variables. It suitably balances the complexity induced by the large number of potential retrofit action combinations and the number of variables in the problem and permits a linear formulation. An optimization of virtual building stocks is performed to highlight the developed model's capacity to tackle large problems (5,000 buildings) in a few minutes. Finally, three analyses finally are led on a real case-study territory, featuring both appropriate retrofit solutions and building stock information. Long-term evaluation of retrofit strategies over the shortterm results in an additional 10% reduction of energy consumption and greenhouse gases emissions and encourages thermal insulation. When targeting a 40% reduction in energy demand, retrofit costs ranging from 20 to 800e/m 2 are observed. Finally, the developed method was used to draw a CO 2 abatement cost curve at territory level. A 70% reduction of emissions can be achieved with costs under 50 e/tCO 2 e.

Identifying optimal renovation schedules for building portfolios: Application in a social housing context under multi-year funding constraints

“…1.3.1 Building stock modelling Building stocks studied in previous articles are various in terms of size and type. The smallest building stocks consisted in two buildings [7,36], while some studies considered thousands of buildings [13,23,31,32,38]. Often 5 to 50 different buildings were considered [8][9][10][11]24,34,35,37].…”

“…As highlighted in Table 1, building stocks belong to one owner (e.g. private real estate property owner or municipal owner) or to multiple owners, as in Murray et al [12] and Rogeau et al [13] where statistical data for countries were used to model the building stock. Buildings were grouped in one district or one city, or they were spread over a large territory such as a region or a country.…”

“…Buildings were grouped in one district or one city, or they were spread over a large territory such as a region or a country. In most cases, buildings in a stock had the same use, and several types of stock were studied: housing stocks [6,8,13,23,30,32,35,37,38], stocks of schools or university buildings [9,24,36], or stocks of offices or commercial buildings [10,11,33,34].…”

“…Optimal refurbishment scenarios were then identified for each representative building and in some cases, the savings potential were scaled up to get the savings potential of the entire stock [6,8,9,11]. Statistical bottom-up methods were also applied in order to model cities or countries [12,13,23,[31][32][33]38]. In this case, national databases, created from surveys, were employed to rebuild the stock.…”

“…[5]. Several tools have already been developed to solve such complex optimisation problem in a context of neighbourhoods, districts, portfolios or cities refurbishment [6][7][8][9][10][11][12][13], but most of them fail to answer to all of these questions. In this work, a tool associating dynamic building energy simulation, cost assessment and multi-objective optimisation was developed to answer the "which action", "which building" and "which year" questions.…”