• Energy Research

Abstract We consider the problem faced by a large consumer that has to define the procurement plan to cover its energy needs. The uncertain nature of the problem, related to the spot price and energy needs, is dealt by the stochastic programming framework. The proposed approach provides the decision maker with a proactive strategy that covers the energy needs with a high reliability level and integrates the Conditional Value at Risk (CVaR) measure to control potential losses. We apply the approach to a real case study and emphasize the effect of the reliability value choice and the difference between risk neutral and adverse positions.

Abstract We consider the problem faced by a large consumer that has to define the procurement plan to cover its energy needs. The uncertain nature of the problem, related to the spot price and energy needs, is dealt by the stochastic programming framework. The proposed approach provides the decision maker with a proactive strategy that covers the energy needs with a high reliability level and integrates the Conditional Value at Risk (CVaR) measure to control potential losses. We apply the approach to a real case study and emphasize the effect of the reliability value choice and the difference between risk neutral and adverse positions.

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In this study, we explore the integration of machine learning algorithms into a decision support system for climate finance, focusing on the impact of rainfall on wineries in Italy. Wineries are particularly vulnerable to climate change, and accurate rainfall forecasting is critical to their success; lack of rain can reduce the quantity and quality of grapes, while flooding can damage vineyards. We identify relevant weather characteristics that cause rainfall and predict quarterly rainfall intensity using machine learning techniques. The dataset was collected from the agrometeorological office of the Piedmont region in Italy to measure the performance of three machine learning techniques (Multivariate Linear Regression, Random Forest, and Neural Network). Mean square error and mean absolute error methods were used to measure the performance of the machine learning models. A comparative analysis between precipitation estimation models based on conventional machine learning algorithms and deep learning architectures with models based on Long Short-Term Memory (LSTM) networks is performed. It shows how the Random Forest algorithm presents the best performances, both in the accuracy and explainability of the predictions. Our study contributes to the climate finance literature by showing how machine learning can support decision-makers in managing climate risks in the food chain, specifically in the wine industry in Italy.

In this study, we explore the integration of machine learning algorithms into a decision support system for climate finance, focusing on the impact of rainfall on wineries in Italy. Wineries are particularly vulnerable to climate change, and accurate rainfall forecasting is critical to their success; lack of rain can reduce the quantity and quality of grapes, while flooding can damage vineyards. We identify relevant weather characteristics that cause rainfall and predict quarterly rainfall intensity using machine learning techniques. The dataset was collected from the agrometeorological office of the Piedmont region in Italy to measure the performance of three machine learning techniques (Multivariate Linear Regression, Random Forest, and Neural Network). Mean square error and mean absolute error methods were used to measure the performance of the machine learning models. A comparative analysis between precipitation estimation models based on conventional machine learning algorithms and deep learning architectures with models based on Long Short-Term Memory (LSTM) networks is performed. It shows how the Random Forest algorithm presents the best performances, both in the accuracy and explainability of the predictions. Our study contributes to the climate finance literature by showing how machine learning can support decision-makers in managing climate risks in the food chain, specifically in the wine industry in Italy.

The definition of the electric energy procurement plan represents a fundamental problem that any consumer has to deal with. Bilateral contracts, electricity market and self-production are the main supply sources that should be properly combined to satisfy the energy demand over a given time horizon at the minimum cost. The problem is made more complex by the presence of uncertainty, mainly related to the energy requirements and electricity market prices. Ignoring the uncertain nature of these elements can lead to the definition of procurement plans which are infeasible or overly expensive in a real setting. In this paper, we deal with the procurement problem under uncertainty by adopting the paradigm of joint chance constraints to define reliable plans that are feasible with a high probability level. Moreover, the proposed model includes in the objective function a risk measure to control undesirable effects caused by the random variations of the electricity market prices. The proposed model is applied to a real test case. The results show the benefit deriving from the stochastic optimization approach and the effect of considering different levels of risk aversion.

The definition of the electric energy procurement plan represents a fundamental problem that any consumer has to deal with. Bilateral contracts, electricity market and self-production are the main supply sources that should be properly combined to satisfy the energy demand over a given time horizon at the minimum cost. The problem is made more complex by the presence of uncertainty, mainly related to the energy requirements and electricity market prices. Ignoring the uncertain nature of these elements can lead to the definition of procurement plans which are infeasible or overly expensive in a real setting. In this paper, we deal with the procurement problem under uncertainty by adopting the paradigm of joint chance constraints to define reliable plans that are feasible with a high probability level. Moreover, the proposed model includes in the objective function a risk measure to control undesirable effects caused by the random variations of the electricity market prices. The proposed model is applied to a real test case. The results show the benefit deriving from the stochastic optimization approach and the effect of considering different levels of risk aversion.