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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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Reducing greenhouse gas emissions, limiting the effects of climate change and decreasing the environmental, social and economic costs of energy production are some of the main issues related to the sustainable development of modern society. Energy communities, envisioned to enable local energy exchange between consumers and producers of renewable energy, represent a possible scenario towards a cleaner and sustainable energy system. In this paper, an energy community management model called Power Cloud and presented in previous papers is proposed for a real-world practical application at the University of Calabria. In particular, the implementation of the information and communication technology (ICT) architecture and other enabling technologies, such as the nanogrid and the smart energy box, are discussed in detail. The experiment results show that by adopting the Power Cloud management model it is possible to obtain significant savings in terms of energy cost, which provide benefit for a community, such as a university campus.