• Energy Research

The growing production capacity from renewable energy sources requires a rethinking of the role of users and electric utilities. In this direction, in Italy, the energy authority recently envisaged the opening of the dispatching market to renewable plants and small users. The present work describes the main technical characteristics and first results of the Smart City Vizze project, a field experimentation carried out on a real medium voltage network of the Italian Autonomous Province of Bolzano. The project aims to develop an innovative monitoring and control architecture, supporting the distribution system operator in managing the distribution grid and the future aggregator in dispatching the local generation (and load).

The economic management of a microgrid can greatly benefit from energy storage systems (ESSs), which may act as virtual load deferral systems to take advantage of the fluctuations of energy prices and accommodate for demand-production mismatches caused by the scarce predictability of renewable sources. In a distributed energy management scenario, an ESS may serve multiple users, a setting which calls for the development of suitable resource allocation policies for the storage capacity. In particular, distributed control policies are of interest, where each user operates independently with the least exchange of information with the other users. A methodology is developed in the paper for such purpose, based on an iterative resource allocation mechanism, realized by means of a negotiation process among users, resembling stock exchange dynamics. The resulting distributed strategy for the management of the shared resource comes close to optimality at a low computational cost, which is affordable in large scale practical applications. It is also robust to communication failures between users.

This paper concerns the design of an energy management system for a building cooling system that includes a chiller plant (with two or more chiller units), a thermal storage unit, and a cooling load. The latter is modeled in a probabilistic framework to account for the uncertainty in the building occupancy. The energy management task essentially consists in the minimization of the energy consumption of the cooling system, while preserving comfort in the building. This is achieved by a twofold strategy. The cooling power request is optimally distributed among the chillers and the thermal storage unit. At the same time, a slight modulation of the temperature set-point of the zone is allowed, trading energy saving for comfort. The problem can be decoupled into a static optimization problem (mainly addressing the chiller plant optimization) and a dynamic programming (DP) problem for a discrete time stochastic hybrid system (SHS) that takes care of the overall energy minimization. The DP problem is solved by abstracting the SHS to a (finite) controlled Markov chain, where costs associated with state transitions are computed by simulating the original model and determining the corresponding energy consumption. A numerical example shows the efficacy of the approach.

Abstract This paper describes an adaptive scheme for temperature control in household freezers with low-end sensing and actuation equipment, like on–off compressors and dampers. The scheme is based on an auxiliary filter introduced in the relay loop to alter and improve the characteristics of the induced oscillation. The filter is tuned adaptively to cope with system variability and uncertainty. Some tests on detailed simulation models of a commercial appliance are reported to validate the approach and show its effectiveness with respect to both food preservation and energy consumption requirements.