01863nas a2200205 4500008004100000245009300041210006900134260000900203520116400212653002401376653001801400653003501418653001601453653002301469100002401492700001701516700001601533700001901549856008901568 2019 eng d00aComparison of MPC Formulations for Building Control under Commercial Time-of-Use Tariffs0 aComparison of MPC Formulations for Building Control under Commer c20193 a

Most medium and large commercial buildings in the U.S. are subject to complex electricity tariffs that combine both Time-of-Use (TOU) energy and demand charges. This study analyses the performances of different economic Model Predictive Control (MPC) formulations, from the standpoints of monthly bill reduction, load shifting, and peak demand reduction. Simulations are performed on many simplified commercial building models, with multiple TOU demand charges, and under various summer conditions. Results show that compared to energy-only MPC, the traditional method for dealing with demand charges significantly
reduces peak demand and owner bill, however, highlight a lack of load shifting capability. A proposed incremental approach
is presented, which better balances the bill components in the objective function. In the case study presented, this method
can improve monthly bill savings and increase load shifting during demand response events, while keeping a similarly low
peak demand, compared to traditional MPC methods taking into account demand charges.

10acommercial building10ademand charge10aModel predictive control (MPC)10apeak demand10atime-of-use tarrif1 aVan Cutsem, Olivier1 aKayal, Maher1 aBlum, David1 aPritoni, Marco uhttps://simulationresearch.lbl.gov/publications/comparison-mpc-formulations-building01392nas a2200133 4500008004100000245008600041210006900127260002700196520090500223653003501128100001601163700002001179856005901199 2017 eng d00aMPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings0 aMPCPy An OpenSource Software Platform for Model Predictive Contr aSan Franciscoc08/20173 a

Within the last decade, needs for building control systems that reduce cost, energy, or peak demand, and that facilitate building-grid integration, district-energy system optimization, and occupant interaction, while maintaining thermal comfort and indoor air quality, have come about.  Current PID and schedule-based control systems are not capable of fulfilling these needs, while Model Predictive Control (MPC) could.  Despite the critical role MPC-enabled buildings can play in future energy infrastructures, widespread adoption of MPC within the building industry has yet to occur.  To address barriers associated with system setup and configuration, this paper introduces an open-source software platform that emphasizes use of self-tuning adaptive models, usability by non-experts of MPC, and a flexible architecture that enables application across projects.

10aModel predictive control (MPC)1 aBlum, David1 aWetter, Michael uhttp://www.ibpsa.org/proceedings/BS2017/BS2017_351.pdf