Nabangshu Sinha

This paper introduces a novel modeling setup for forecasting day‐ahead electricity market demand and supply curves, seen as bid prices as a function of bid volumes. These curves are different for each different next‐day hour. The daily data structures on which these curves are assembled have very peculiar features, which make them very difficult to handle. One of these features is that each curve can be seen as defined on a set of volume values that each day and for each hour change in position and number in a non‐deterministic way. Thus, modeling, comparing, and forecasting such curves cannot be made in standard econometric ways. In this paper, it is proposed to carry on the forecasting task by looking at the curves as sequences of bid points, and by tokenizing them into three structurally significant special points and a special segment, without relying on the functional approaches that are so common in related electricity finance literature. The modeling setup is mainly based on linear forecasting methods, with a very modest addition of nonlinearity by means of a simple feedforward neural network. The setup is modular and can be used both in what is called a “pure variant” and a “combined variant”. Three standard and important benchmarks, namely the naive and smarter naive models adapted to curves, and the stochastic‐functional autoregressive model already present in the literature, are used for comparison. The forecasting ability of the proposed setup is comparatively tested on data from the NORD zone of the Italian IPEX electricity market. Overall, numerical results demonstrate that the “combined variant” of the setup is the most effective forecaster among all benchmarks, for both demand and supply curves, and across all hours.

A model is proposed to investigate the effects of power generation source diversification and CO₂ emission control in the presence of dispatchable fossil fuel sources and non-dispatchable carbon-free renewables. In a stochastic environment in which three random factors are considered, namely fossil fuels (gas and coal) and CO₂ prices, we discuss a planning methodology for power system portfolio selection that integrates the non-dispatchable renewables available in a given energy system and optimally combines cost, risk and CO₂ emissions. By combining the deep neural network probabilistic forecasting of fossil fuel path prices with a geometric Brownian motion model for describing the CO₂ price dynamics, we simulate a wide range of plausible market scenarios. Results show that under CO₂ price volatility, optimal portfolios shift toward cleaner energy sources, even in the absence of explicit emission targets, highlighting the implicit regulatory power of volatility. The results suggest that incorporating CO₂ price volatility through market mechanisms can serve as an effective policy tool for driving decarbonization. Our model offers a flexible and reproducible approach to support policy design in energy planning under uncertainty.

This work presents a novel variant of Echo State Network (ESN) known as Time-Varying Echo State Network (TV-ESN) and conducts a comprehensive comparative analysis with the standard ESN model. TV-ESN introduces dynamic variations in the leaking rate (a) and spectral radius (rho) over time, offering adaptability to diverse temporal patterns. The evaluation is performed on benchmark datasets, including the Mackey Glass system and actual electricity demand in the Spanish Electricity Market. Results indicate that TV-ESN achieves superior forecasting performance, as evidenced by lower Normalized Root Mean Square Error (NRMSE) and Mean Absolute Error (MAE) compared to the conventional ESN. Moreover, TV-ESN exhibits enhanced stability in forecasting. The findings suggest the potential of TV-ESN in capturing rich temporal characteristics.