Combined environmental and economic dispatch of smart grids using distributed model predictive control

• Model predictive control is applied to solve environmental and economic dispatch of smart grids.
• Distributed MPC is extended to account for hard-soft constraints and ramp rate limits.
• On-line energy pricing is included in the optimization framework.
• Simulations show reduction of generation costs up to 40% due to forecast treatment.
• Simulation of forecast errors results in up to 8% generation overcost.

This paper presents an extended distributed model predictive control (DMPC) framework and its application to a smart grid case study. Specifically, a combined environmental and economic dispatch (EED) problem is formulated and solved, which is a non-trivial multi-objective optimization problem given the high number of agents, information exchanges and constraints associated to large-scale smart grids.In this line, the work proposed herein adopts a distributed Lagrange-based model predictive control with reduced computational demand making use of robust mixed-integer quadratic programming (MIQP) solvers. In addition, the model predictive control (MPC) nature of the framework accounts for renewable resource forecast while physical constraints are included in the formulation. The DMPC is herein extended to calculate market-based on-line energy pricing while minimizing the generation cost and emissions,and to include hard and soft constraints and ramp rate limits.The aforementioned control framework is applied to a smart grid composed of 11 consumer centers, 6 energy storages, 11 generation systems and 31 transmission lines. Simulation results show reductions of generation costs up to 40% when predictions are included in the formulation. Furthermore, the simulation of forecast errors results in up to 8% generation overcost. These results show that DMPC can be considered as an alternative versus other heuristic methods, which do not guarantee an optimal solution to the problem.