459989 Economic Model Predictive Control for Integrating Scheduling and Dispatch of Microgrid Power Systems

Sunday, November 13, 2016: 3:30 PM
Monterey I (Hotel Nikko San Francisco)
Michael Zachar, Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN and Prodromos Daoutidis, Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN

Microgrids are small, autonomous power systems that utilize local generation, storage, and controllable loads to serve local energy demands. Microgrids combine the ideas of demand response, renewable power generation, and cogeneration to offer a future energy supply paradigm with a focus on sustainability. However, an important issue in the operation of these microgrid systems is their potential negative impact on the operation and stability of the macrogrid. In short, the integration of distributed generation may exacerbate the stochasticity of the residual and lead to increased reserve requirement and operational cost for the macrogrid [1]. Existing microgrid control literature has addressed this issue in a variety of ways, e.g. penalizing real-time energy purchases [2], designing auction-based markets [3], incentivizing demand response [4], and operating microgrids as islanded systems [5,6]. In this work, we propose an approach based on economic model predictive control (E-MPC) which can be used to integrate scheduling and control decisions so that energy exchange can be carried out in a grid-friendly manner. In particular, energy exchange commitments will be scheduled 24-hours in advance and real-time deviations will be kept small.

A case study is considered for a prototype microgrid consisting of a bi-directional connection to the macrogrid, photovoltaics, microturbines, a battery bank, flexible air conditioning, and an auxiliary electric boiler. This microgrid regulates indoor air temperature, supplies electricity, and generates hot water for a medium office building. A scheduling problem is considered for meeting these energy demands and coordinating energy exchange with the macrogrid. However, the inherent time scales involved in microgrid scheduling (i.e. hours to days) are not significantly separated from the time scales involved in control (i.e. seconds to minutes). Thus, a mixed integer linear E-MPC problem which incorporates low order process models is formulated for scheduling this microgrid on an hourly basis. Chance constraints are used to reduce the probability of commitment violations due to uncertainties in weather processes and local demands. Within each hour, frequent recourse optimization is used to update microgrid dispatch as uncertain conditions are realized.

The performance of this control approach is analyzed with respect to the operational cost, curtailment of renewable power, frequency and magnitude of commitment violations, and satisfaction of thermal demands. Computation time and differences between realized dispatch decisions and scheduling predictions are also considered. In addition, this presentation will highlight some of the important differences between integration of scheduling and control in traditional chemical systems and in these small power systems.

[1] Bayod-Rújula, A. A. (2009). Future development of the electricity systems with distributed generation. Energy, 34(3), 377–383.

[2] Atzeni, I., Ordonez, L. G., Scutari, G., Palomar, D. P., & Fonollosa, J. R. (2014). Noncooperative Day-Ahead Bidding Strategies for Demand-Side Expected Cost Minimization with Real-Time Adjustments: A GNEP Approach. IEEE Transactions on Signal Processing, 62(9), 2397–2412.

[3] Liu, G., Xu, Y., & Tomsovic, K. (2016). Bidding Strategy for Microgrid in Day-Ahead Market Based on Hybrid Stochastic/Robust Optimization. IEEE Transactions on Smart Grid, 7(1), 227–237.

[4] Nguyen, D. T., & Le, L. B. (2015). Risk-Constrained Profit Maximization for Microgrid Aggregators With Demand Response. IEEE Transactions on Smart Grid, 6(1), 135–146.

[5] Trifkovic, M., Marvin, W. A., Daoutidis, P., & Sheikhzadeh, M. (2014). Dynamic real-time optimization and control of a hybrid energy system. AIChE Journal, 60(7), 2546–2556.

[6] Hosseinzadeh, M., & Salmasi, F. R. (2015). Robust Optimal Power Management System for a Hybrid AC/DC Micro-Grid. IEEE Transactions on Sustainable Energy, 6(3), 675–687.

Extended Abstract: File Not Uploaded