Demand-side flexibility is becoming increasingly important as power systems integrate more intermittent renewable generation and reduce reliance on centralized dispatchable units. In the residential sector, controllable resources such as Electric Vehicles (EVs) charging systems, Battery Energy Storage Systems (BESS), and electric water heaters can provide meaningful adjustment capability when coordinated within Renewable Energy Communities (RECs). This work compares two methodologies to quantify the technical flexibility of these resources under identical technical and economic conditions: a bottom-up (i) and a top-down (ii) approaches, requiring a consistent benchmarking under the same assumptions.

In the bottom-up approach, each Home Energy Management System (HEMS) individually computes the optimal operating profile (baseline) for each controllable resource using mixed integer optimization, considering energy tariffs, photovoltaic generation forecasts, and expected non-flexible consumption. Technical flexibility upper (upward) and lower (downward) bands is then obtained through a price-based sensitivity analysis of the optimization model. In addition, an offer price is assigned to these bands based on their marginal cost response, which is submitted to the Community Manager (CM) for negotiation with the DSO.

In the top-down approach, the CM starts with individual baselines obtained as in the bottom-up method. It then performs a joint optimization of all prosumers, allowing controlled deviations from the baselines whenever these improve total community cost or social welfare. These deviations define the potential community flexibility. Flexibility valuation is carried out iteratively by introducing it as an explicit variable linked to a price parameter, which the CM progressively adjusts to build the community price–flexibility curve representing the aggregated flexibility available to the DSO.

Methodologies i. and ii. are applied to the same residential community under identical technical and tariff conditions. The analysis compares the flexibility bands (upward and downward) obtained for each resource, identifying which approach yields greater quantities of flexible energy across the day and with what impact on total community cost. A qualitative assessment of the resulting price-flexibility curves is also included.

Results indicate that the bottom-up approach offers higher granularity and preserves prosumer autonomy, while the top-down approach achieves more efficient community-level coordination and optimized flexibility allocation. However, the top-down method entails greater computational effort, higher coordination requirements, and a need for extensive data sharing between prosumers and the community.