Quantifying the Flexibility and Forecasting Performance of Urban Virtual Power Plants: Introducing the Community Imbalance Neutralisation Index (CINI)

Source abstract

The rapid decarbonisation of urban districts is accelerating the deployment of rooftop photovoltaic (PV) systems, household battery energy storage systems (BESSs) and electric vehicles (EVs). However, the high variability of urban micro-generation creates substantial forecasting errors at the urban–grid interface, imposing high balancing costs on distribution system operators (DSOs) and local energy communities. Traditional metrics fail to assess how effectively internal peer-to-peer (P2P) flexibility offsets these forecast mismatches prior to grid settlement. To address this gap, this paper presents a novel methodological framework and a non-parametric indicator: the Community Imbalance Neutralisation Index (CINI). Formulated in a generalised, scalable matrix structure applicable to any heterogeneous urban neighbourhood (N households), CINI quantifies the relative reduction in net community-level imbalance relative to the cumulative sum of uncoordinated individual forecast errors. CINI ranges from 0 per cent (no collective mitigation) to 100 per cent (perfect internal neutralisation). Complementing this index, an adaptive day-ahead scheduling algorithm is introduced to determine the optimal community energy purchase requirement (Eforecast). The proposed framework is numerically evaluated using a high-resolution synthetic benchmark annual dataset with 15 min intervals (35,040 intervals) representing a Central European urban residential cluster equipped with diverse combinations of PV, BESS and managed EV charging infrastructure. The simulation results demonstrate that active cVPP coordination reduces annual grid-facing imbalance energy from 188.73 MWh to 143.88 MWh, increasing the annual CINI score from 57.22% to 67.39% (+10.17 percentage points) compared with the uncoordinated baseline. Notably, the framework reveals a ‘Winter Flexibility Paradox’, achieving its highest relative efficacy during the winter months (+13.88 percentage points in December). Furthermore, sensitivity analyses show that scaling flexibility up to 40 kW achieves a CINI score of 91.22%, revealing diminishing marginal returns and critical technological saturation thresholds. The proposed CINI metric and Eforecast dispatch algorithm provide city planners, municipal energy managers and DSOs with a transparent diagnostic tool to design dynamic socio-economic tariff incentives, optimise urban micro-grid sizing, prevent free-rider dynamics, and foster resilient, self-balancing smart cities.