In a truly smart grid, system load would be known in advance with a high degree of confidence. Currently, this goal of “smart forecasting” is far from being realized. In the Pacific Gas and Electric (PG&E) aggregation area managed by the California Independent System Operator (ISO), the root mean squared day-ahead forecast error was about 3.8 percent of mean actual load over the period 1 April ...

There is an intrinsic value in higher integration of multi-carrier energy systems (especially gas and electricity), to increase operational flexibility the electricity system improve allocation resources networks. The different carrier markets challenging due existence physical economic dependencies between carriers. We propose this paper integrated day-ahead gas, heat market clearing which inc...

Purely financial players without any physical assets can participate in day-ahead electricity markets as virtual bidders. They arbitrage the price difference between (DA) and real-time (RT) to maximize profits. Virtual bidders encounter various monetary risks uncertainties their decision-making due high volatility of difference. Therefore, this paper proposes a max-min two-level optimization mo...

The traditional deterministic day-ahead (DA) market clearing does not accommodate the uncertainty from variable renewable energy sources, resulting in an increasing activation of expensive reserves and curtailment events. Robust optimization (RO) has been proposed to mitigate this uncertainty. However, as RO considers worst-case scenarios, it results highly conservative solutions. This paper pr...

We model the supply and demand curves of electricity day-ahead auctions in a parsimonious way by building an appropriate algorithm to present information about prices with far fewer parameters than existing algorithm. represent each curve using mesh-free interpolation techniques based on radial basis function approximation. describe results this method for IPEX spot price Italy then use these r...

Market agents with renewable resources face amplified uncertainty when forecasting energy production to securely place bids in electricity markets. To deal uncertainties, stochastic modelling has been applied optimize the bidding strategy of these market agents. However, studies found literature usually focus on day-ahead and balancing markets, leaving aside intraday markets that could be used ...