Showing papers by "Alessandra Parisio published in 2011"

Energy efficient microgrid management using Model Predictive Control

Abstract: Microgrids are subsystems of the distribution grid which comprises small generation capacities, storage devices and controllable loads, operating as a single controllable system that can operate either connected or isolated from the utility grid. In this paper we present a preliminary study on applying a Model Predictive Control (MPC) approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using Mixed-Integer Linear Programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a typical microgrid is employed to assess the performance of the on-line optimization-based control strategy: simulation results show the feasibility and the effectiveness of the proposed approach.

A mixed integer linear formulation for microgrid economic scheduling

Abstract: Microgrids are subsystems of the distribution grid which comprises small generation capacities, storage devices and controllable loads, which can operate either connected or isolated from the utility grid. This paper studies the microgrid economic scheduling, i.e. the problem of optimize microgrid operations to fulfil a time-varying energy demand and operational constraints while minimizing the costs of internal production and imported energy from the utility grid. The problem is posed as a mixed-integer linear programming model. The key difference in the proposed modeling approach is that no complex heuristics or decompositions are used; the full model is formulated and solved in an efficient way by using commercial solvers. This leads to significant improvements in schedule quality and in computational burden. A case study of a typical microgrid is investigated: simulation results show the feasibility and the effectiveness of the proposed approach.

Robust Optimization of operations in energy hub

Abstract: In this paper a robust optimization problem of an energy hub operations is presented. An energy hub is a multi-generation system where multiple energy carriers input to the hub are converted, stored and distributed in order to satisfy energy demands. The solution to energy hub operation problem determines the energy carriers to be purchased and stored in order to satisfy the energy requests while minimizing a cost function. A control approach using Robust Optimization (RO) techniques is proposed; bounded uncertainties on energy hub parameters are taken into account and RO methods are exploited to gain robust solutions which are feasible for all values, or for a selected subset, of uncertain data. Simulation results underline the benefits resulting from the application of the proposed approach to an energy hub structure located in Waterloo, Canada.