arXiv subjects

Luigi Grossi

Publications and source records attributed to Luigi Grossi.

5 recordsLinked to original sources

arXiv2026

The Impact of Renewable Energy Communities in the Italian Day-Ahead Electricity Market: A Scenario Analysis

This paper evaluates the economic impact of Renewable Energy Communities (RECs) on the Italian wholesale power market. Combining a bottom-up engineering approach with a short-run economic impact assessment, the study begins by mapping existing and emerging RECs in Italy. We identify key characteristics of RECs, such as average installed capacity, institutional profiles of members, types of renewable systems used, and transmission across Italy's electricity market zones. This mapping yields representative REC configurations, which are employed within a bottom-up engineering model to generate energy injection and self-consumption profiles for different REC prosumer and producer categories (residential, public, small and medium enterprise, non-profit organization, and standalone installation), considering the different levels of solar irradiance in Italy based on latitude. These zonal results, aggregated on an hourly basis, inform the implementation of the synthetic counterfactual approach, which develops alternative scenarios (e.g., 5 GW target for REC-driven capacity set by Italian policy for 2027) to assess the impact of REC-driven injection and self-consumption on the Italian day-ahead power market. The findings suggest that REC deployment can increase equilibrium quantities during daylight in most of the time, while decreasing equilibrium quantities mostly during the cold months, as electrified heating drives greater self-consumption and offsets lower grid injections. Both positive and negative effects on equilibrium quantities suggest that REC deployment also has a potential to reduce wholesale electricity prices. Moreover, by reducing grid exchanges through higher self-consumption, REC proliferation can alleviate pressure on the distribution system.

stat.AP↗

arXiv2026

State-dependent marginal emission factors with autoregressive components

Accurate estimation of Marginal Emission Factors (MEFs) is critical for evaluating the decarbonization potential of low-carbon technologies and demand-side management. However, canonical methodologies, predominantly relying on linear regression and differencing techniques, fail to capture the structural non-linearities inherent in the merit order, i.e. the marginal technology setting electricity prices. Utilizing Markov switching autoregressive models with exogenous regressors (MS-ARX) and hourly US data (2019-2025), we identify distinct, mutually exclusive regimes governed by fuel-price dynamics. We find that linear models overestimate abatement potential by masking the dichotomy between a gas-driven and coal-driven marginal system. Furthermore, using robust structural break detection, we link regime instability to a specific structural shift in natural gas pricing in May 2022. Our results indicate that post-2022, the grid has transitioned into a correction phase where the coal-driven regime is less persistent but highly volatile, necessitating state-dependent policy metrics rather than static annual averages.

stat.AP↗

arXiv2024

Green bubbles: a four-stage paradigm for detection and propagation

Climate change has emerged as a significant global concern, attracting increasing attention worldwide. While green bubbles may be examined through a social bubble hypothesis, it is essential not to neglect a Climate Minsky moment triggered by sudden asset price changes. The significant increase in green investments highlights the urgent need for a comprehensive understanding of these market dynamics. Therefore, the current paper introduces a novel paradigm for studying such phenomena. Focusing on the renewable energy sector, Statistical Process Control (SPC) methodologies are employed to identify green bubbles within time series data. Furthermore, search volume indexes and social factors are incorporated into established econometric models to reveal potential implications for the financial system. Inspired by Joseph Schumpeter's perspectives on business cycles, this study recognizes green bubbles as a necessary evil for facilitating a successful transition towards a more sustainable future.

econ.EM↗

arXiv2024

On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices

Probabilistic electricity price forecasting (PEPF) is subject of increasing interest, following the demand for proper quantification of prediction uncertainty, to support the operation in complex power markets with increasing share of renewable generation. Distributional neural networks ensembles have been recently shown to outperform state of the art PEPF benchmarks. Still, they require critical reliability enhancements, as fail to pass the coverage tests at various steps on the prediction horizon. In this work, we propose a novel approach to PEPF, extending the state of the art neural networks ensembles based methods through conformal inference based techniques, deployed within an on-line recalibration procedure. Experiments have been conducted on multiple market regions, achieving day-ahead forecasts with improved hourly coverage and stable probabilistic scores.

cs.LG↗

arXiv2022

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

stat.AP↗