The implementation of demand-response programs (DRP) has gained interest as a means to alleviate energy consumption during peak-hours. Two explanations account for the success of such programs which involve both utilities and electricity consumers, with the latter often organized into coalitions: the system operator meets its goal of reducing the load peak; simultaneously, electricity consumers achieve economic benefits when reducing consumption during peak hours. In this paper, a Monte Carlo-based algorithm has been proposed for the formulation of multiple purchase offers in the day-ahead energy market (DAEM) by coalitions in which consumers vary in their sensitivity to DRP, manifesting different responsiveness to hourly tariffs based on the hourly market clearing prices. Being able to monitor how coalition members use air-conditioning in the presence of variable hourly energy tariffs, the coordinator can then define a purchase-bidding strategy, depending on how price-sensitive the coalition is. Simulation results show that the presence of a price-sensitive demand leads not only to a subsequent reduction in energy prices during peak-hours but also leads to a decrease in their inter-hour volatility.Index Terms-Demand-response, inter-hour volatility, power demand, power system economics, price volatility.

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In this paper, we deal with the definition of a decision model for a producer operating in a multi-auction electricity market. The decisions to be taken concern the commitment of the generation plants and the quantity of energy required to offer to each auction and to cover the bilateral contracts. We propose a multistage stochastic programming model in which the randomness of the clearing prices is represented by means of a scenario tree. The risk is modelled using a Conditional Value at Risk term in the objective function. Experimental results are reported to show the validity of our model and to discuss the influence of the risk parameters on the optimal value.