• Energy Research

We study an islanded microgrid system designed to supply a small village with the power produced by photovoltaic panels, wind turbines and a diesel generator. A battery storage system device is used to shift power from times of high renewable production to times of high demand. We build on the mathematical model introduced in [14] and optimize the diesel consumption under a “no-blackout” constraint. We introduce a methodology to solve microgrid management problem using different variants of Regression Monte Carlo algorithms and use numerical simulations to infer results about the optimal design of the grid.

We build and evaluate a probabilistic model designed for forecasting the distribution of the daily mean wind speed at the seasonal timescale in France. On such long-term timescales, the variability of the surface wind speed is strongly influenced by the atmosphere large-scale situation. Our aim is to predict the daily mean wind speed distribution at a specific location using the information on the atmosphere large-scale situation, summarized by an index. To this end, we estimate, over 20 years of daily data, the conditional probability density function of the wind speed given the index. We next use the ECMWF seasonal forecast ensemble to predict the atmosphere large-scale situation and the index at the seasonal timescale. We show that the model is sharper than the climatology at the monthly horizon, even if it displays a strong loss of precision after 15 days. Using a statistical postprocessing method to recalibrate the ensemble forecast leads to further improvement of our probabilistic forecast, which then remains sharper than the climatology at the seasonal horizon.

In recent years, environmental concerns resulted in an increase in the use of renewable resources such as wind energy. However, high penetration of the wind power is a challenge due to the intermittency of this resource. In this context, the wind energy forecasting has become a major issue. In particular, for the end users of wind energy forecasts, a critical but often neglected issue is the economic value of the forecast. In this work, we investigate the economic value of forecasting from 30 min to 3 h ahead, also known as nowcasting. Nowcasting is mainly used to inform trading decisions in the intraday market. Two sources of uncertainty affecting wind farm revenues are investigated, namely forecasting errors and price variations. The impact of these uncertainties is assessed for six wind farms and several balancing strategies using market data. Results are compared with the baseline case of no nowcasting and with the idealized case of perfect nowcast. The three settings show significant differences while the impact of the choice of a specific balancing strategy appears minor.

We study the optimal trading policies for a wind energy producer who aims to sell the future production in the open forward, spot, intraday and adjustment markets, and who has access to imperfect dynamically updated forecasts of the future production. We construct a stochastic model for the forecast evolution and determine the optimal trading policies which are updated dynamically as new forecast information becomes available. Our results allow to quantify the expected future gain of the wind producer and to determine the economic value of the forecasts.

Risks and opportunities related to environmental transition are usually evaluated through the use of scenarios, produced and maintained by international bodies such as the International Energy Agency. This approach assumes perfect knowledge of the scenario by the agent, but in reality, scenario uncertainty is an important obstacle for making optimal investment or divestment decisions. In this paper, we develop a real-options approach to evaluate assets and potential investment projects under dynamic climate transition scenario uncertainty. We use off-the-shelf Integrated Assessment Model (IAM) scenarios and assume that the economic agent acquires the information about the scenario progressively by observing a signal, such as the carbon price or the greenhouse gas emissions. The problem of valuing an investment is formulated as an American option pricing problem, where the optimal exercise time corresponds to the time of entering a potential investment project or the time of selling a potentially stranded asset. To illustrate our approach, we employ representative scenarios from the scenario database of the Network for Greening the Financial System in two energy-related examples: the divestment decision from a coal-fired power plant without Carbon Capture and Storage (CCS) technology and the potential investment into a green coal-fired power plant with CCS. In both cases, we find that the real option value is very sensitive to scenario uncertainty: the value of the coal-fired power plant is reduced by 25% and that of the green coal investment project is reduced by 7% when the agent deduces the scenario by observing carbon emissions, compared to the setting when the true scenario is known. We also find that scenario uncertainty can lead to considerable delays in the implementation of green investment projects, emphasizing the importance of timely and precise climate policy information.

Investments into wind power generation may be hampered by the uncertainty of future revenues caused by the natural variability of the wind resource, the impact of climate change on wind potential and future electricity prices, and the regulatory risks. We quantify the uncertainty of the economic value of wind farms in France, Germany, and Denmark, and evaluate the cost of support mechanisms needed to ensure the profitability of wind farms under present and future climates. To this end, we built a localised model for wind power output and a country-level model for electricity demand and prices. Our study reveals that support mechanisms are needed for current market conditions and the current climate, as well as under future climate conditions according to several scenarios for climate change and energy transition. The cost of support mechanisms during a 15-year period is evaluated to EUR 3.8 to EUR 11.5 billion per year in France, from EUR 15.5 to EUR 26.5 billion per year in Germany, and from EUR 1.2 to EUR 3.3 billion per year in Denmark, depending on the scenario considered and the level of penetration of wind energy.