Stochastic Model Predictive Control for Demand Response in a Home Energy Management System

Final Report: TIP-337 Home Battery System for Cybersecure Energy Efficiency and Demand Response

“…If the random variable within the chance constraint is Normally distributed, an exact analytical reformulation of the chance constraint can be used; otherwise, another approach must be taken, such as scenariobased approaches or distributionally robust approaches (Dall'Anese, Baker, and Summers 2017). The work on integrating chance constraints into the foresee code base is currently underway; preliminary results can be seen in (Garifi et al 2018b) where the HEMS aims to satisfy a DR request with a certain probability as well as maintain comfort bounds with a high probability.…”

“…Note that much of this chapter has been previously published in(Jin et al 2017) and(Garifi et al 2018b), and we duplicate that prior work here for the reader's convenience.…”

Stochastic model predictive control for central HVAC plants

“…In the context of energy systems, it has been recently reported that stochastic MPC can systematically mitigate constraint violations and improve economic performance [14,25]. The benefits of stochastic MPC have also been widely reported in the context of building climate (airside) control [26][27][28][29] and energy management [30][31][32][33][34]. We highlight that these studies have focused on the building (airside); to the best of our knowledge, stochastic MPC formulations for HVAC central plants have not been reported.…”

A Chance Constrained MPC-Based Microgrid Central Controller for Building-Sized Hybrid Microgrids

“…Concerning the implementation of the MGCC, its inherent complexity and multivariable characteristic requires the use of an advanced control approach. Model Predictive Controllers present themselves as a well accepted option in the literature (Adamek et al, 2014;Fortenbacher et al, 2014;Jabr et al, 2015;Bruni et al, 2015a,b;DU et al, 2017;Dall'Anese et al, 2017;Dongol et al, 2018;Morstyn et al, 2018;Garifi et al, 2018;Jiang et al, 2019). MPCs are largely adopted on the industry due to their ability of treating constraints and multivariable system naturally.…”

“…In a MGCC it is also relevant that MPC strategies are flexible and accept different objective functions that may include cost and performance metrics of the MG. MPC based strategies for MGCCs have been used for both small MGs, like Bruni et al (2015a) who used it on a single-family house MG, and large microgrids, like Jabr et al (2015) who adopted a optimal power flow approach. Some of these works on hybrid microgrids, however, adopted simplifications on the modelling of the MG, considering only the DC bus behavior (Bruni et al, 2015b), neglecting reactive power (Adamek et al, 2014;Garifi et al, 2018;Dongol et al, 2018), or not taking into account limitations on the power factor for the power consumed from or sold to the main grid (Fortenbacher et al, 2014;Dall'Anese et al, 2017;Morstyn et al, 2018). Power factor issues should be addressed as failing to comply to power factor standards may incur in financial penalties.…”