Dealing with multi-factor uncertainty in electricity markets by combining Monte Carlo simulation with spatial interpolation techniques

Assessing the value of wind generation in future carbon constrained electricity industries

“…The tool determines a probability distribution of overall industry costs and CO 2 emissions for each possible generation portfolio from the MCS. Since the technique is based on MCS, it does not depend upon only normal distributions being used to model uncertainties -arbitrarily complex and interacting probability distributions can also be applied (Duenas et al, 2011;Roques et al, 2006;Spinney and Watkins, 1996). For simplicity, log-normal probability distributions are used to represent fuel cost, carbon costs, and plant capital costs in the case study in section 3.…”

Encompassing Electricity Market Predictions and Weather Forecasts in Power Plan O&M Strategies

“…Monte Carlo methods are especially useful for simulating phenomena with significant uncertainty in inputs. When Monte Carlo simulations have been applied in space exploration and oil exploration, their predictions of failures, cost overruns and schedule overruns are routinely better than human intuition or alternative soft computing methods [70][71][72][73][74][75][76].…”

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

“…In order to use Monte Carlo simulation to tackle uncertainty in the medium term, a large number of realizations of the model are needed, usually entailing a huge computational time and effort. In order to cope with this inconvenience, we have adapted an efficient method proposed in [35] for making market equilibrium models tractable (a practical implementation can be also found in [7]) to other forecasting techniques. This method, which is illustrated schematically in Figure 1, allows one to compute a huge number of simulations by decreasing the computational time and without a major loss of accuracy.…”