Energy planning at the neighborhood level is a major development axis for the energy transition. This scale allows the pooling of production and storage equipment, as well as new possibilities for demand-side management such as flexibility. To manage this growing complexity, one needs two tools. The first concerns modeling, allowing exhaustive simulation analyses of buildings and their energy systems. The second concerns optimization, making it possible to decide on the sizing or control of energy systems. In this article, we analyze, in the case of an existing residential neighborhood, the ability to study by modeling and optimization tools two scenarios of energy flexibility of indoor heating. We propose in particular a method allowing to rely on a varied set of data available to build the various models necessary for optimization tools or dynamic simulation. A study was conducted to identify the neighborhood’s flexibility potential in minimizing $C{O}_2$ emissions, through shared physical storage, or storage in the building envelope. The results of this optimization study were then compared to their application to the virtual neighborhood by simulation.

This paper investigates the impact of load shedding strategies on a block of multiple buildings. It particularly deals with the quantification of the factors i.e., peak shaving, occupants’ thermal comfort or CO ${}_2$ emission reduction and how to quickly quantify them. To achieve this goal, the paper focuses on a new residential district, thermally fed by heat pumps. Four modeling approaches were implemented in order to estimate buildings’ response towards load shedding. Two schemes were combined in order to study an overall load shedding. This strategy for the neighborhood has proved itself efficient for both peak shaving and thermal comfort. Most of the clipped heating load during the peak period is shifted to low-consumption periods, providing an effective peak shaving. The thermal comfort is guaranteed for at least 96% of the time. For CO ${}_2$ emissions reduction, the link between consumption reduction and CO ${}_2$ emissions savings should be realized carefully, since shifting the consumption could increase these emissions.