(42d) Power Management in Microgrids with Controllable Loads and Energy Exchange Commitments

Microgrids are local energy systems which contain a variety of dispatchable units (e.g. microturbines, batteries), non-dispatchable units (e.g. rooftop photovoltaics), and stochastic loads (e.g. power, space cooling, and hot water demands). The primary operational goal in these systems is to ensure that local energy demands are met in a robust and cost-effective manner. Due to the inherently stochastic nature of this problem, power exchange with the macrogrid is typically used to correct any local power imbalance. However, microgrid operators may want to actively regulate this energy exchange due to demand charges, unappealing real-time prices, or energy exchange commitments [1]. This leads to a challenging control problem since one cannot rely on the grid connection to mitigate stochasticity in renewables and local demands. Previous authors have used approaches like dynamic programming [2], robust optimization [3], bidding on the day-ahead market [4,5], and coordinating intra-microgrid energy transfers [6] to address this challenge.

In this work, we focus on the dispatch of a microgrid given a unit commitment and energy exchange schedule (i.e. energy exchange should lie between some upper and lower bound in each hour). In particular, 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. We propose and formulate a nonlinear economic model predictive control (E-MPC) problem for this system to regulate indoor air temperature, supply electricity and hot water demands, and satisfy the scheduled energy exchange bounds. This E-MPC problem can be resolved frequently so that disturbances due to weather and load forecasting errors are rejected.

Then, a dynamic microgrid and building thermal model is built in the Simulink software environment to test this control approach. We analyze the performance in terms of operational cost, utilization of available PV power, satisfaction of energy exchange bounds, and thermal comfort for occupants. Particular attention is paid to the ability to capture the important nonlinear dynamics in the E-MPC while maintaining a practical solution time. The benefit of incorporating flexible loads (i.e. the AC unit and auxiliary boiler) and leveraging the natural thermal inertia of building elements is also addressed.

[1] Zachar, M. & Daoutidis, P. Microgrid/Macrogrid Energy Exchange: A Novel Market Structure and Stochastic Scheduling. IEEE Transactions on Smart Grid, submitted.

[2] Riffonneau, Y., Bacha, S., Barruel, F., & Ploix, S. (2011). Optimal Power Flow Management for Grid Connected PV Systems with Batteries. IEEE Transactions on Sustainable Energy, 2(3), 309â??320.

[3] Zhang, Y., Gatsis, N., & Giannakis, G. B. (2013). Robust Energy Management for Microgrids With High-Penetration Renewables. IEEE Transactions on Sustainable Energy, 4(4), 944â??953.

[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), 135â??146.

[5] 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), 227â??237.

[6] Kuznetsova, E., Li, Y.-F., Ruiz, C., & Zio, E. (2014). An Integrated Framework of Agent-Based Modelling and Robust Optimization for Microgrid Energy Management. Applied Energy, 129, 70â??88.