A linear optimization based controller method for real-time load shifting in industrial and commercial buildings

Abstract

Effective demand responsiveness (DR) is crucial to the stability of the electrical grid. With increasing penetration of renewable energy sources demands higher load variation adaptability. Therefore, consumer-side flexibility is required for responding to abrupt DR signals. Real-time pricing (RTP) offer a direct approach for continually communicating DR signals. RTP has shown effectiveness in residential applications, however, its implications are impaired in industrial buildings which are less price-elastic due to stresses imposed by just in time (JIT) manufacturing and market competition. In this paper, we propose an instantaneous demand control methodology for industrial and commercial buildings, where the DR action is continually updated as new DR signals are received. We utilize the hour-ahead RTP (RTP-HA) tariffs and the demand shifting concept. The instantaneous approach is independent of price prediction uncertainty and scheduling approaches. The controller algorithm is converted to a linear optimization problem which is solved optimally and saves computational time, making it practical for real-time use. The method is robust and verified using MATLAB/SIMULINK with actual, one week, data from eight industrial and commercial buildings in Florida. Results show modest reductions in consumers' electricity bills while maintaining required comfort standards. Results also address the load synchronization problem associated with RTP.

Optimal price based control of HVAC systems in multizone office buildings for demand response

“…Using the proposed controller, the electricity cost saving with high thermal discomfort during peak and off-peak hours are 27.2 and 20.3% respectively as shown in Table III. This result demonstrates a further reduction in energy use at a high priced period than the controller developed in [40].…”

“…Unlike, the proposed controller uses the price difference between the current retail price and the dynamic bidding price that occupants change at each time step in various zones. This way the proposed strategy can control the HVAC thermostat setting considering fluctuations in electricity retail prices for more saving of energy cost than the controller in [40]. Using the proposed controller, the electricity cost saving with high thermal discomfort during peak and off-peak hours are 27.2 and 20.3% respectively as shown in Table III.…”

“…As a result, electricity consumers may not have benefits to save electricity costs by reducing their consumption during peak periods. The electricity cost saving of an HVAC unit with a high thermal discomfort level using a price-based strategy in [40] is 17.3%. Unlike, the proposed controller uses the price difference between the current retail price and the dynamic bidding price that occupants change at each time step in various zones.…”

“…Several studies highlighted the advantages for optimal utilization of the HVAC systems in multizone buildings for Price-based DR. A precooling strategy is considered in [37][38][39] to utilize commercial buildings' thermal storage potential for demand shifting during peak hours. In [40] a control strategy has been proposed for aggregation and coordination of industrial and commercial loads for DR using RTP. In [41] a predictive model of HVAC air-circulation fan power consumption is developed to use the commercial HVAC system for fast DR. Reference [42] investigates event-driven DR strategies to reduce commercial HVAC energy consumption.…”

Economic Impact of an Optimization-Based SCADA Model for an Office Building

“…An optimal DR scheduling model for HVAC has been presented in [15], considering the thermal comfort of the users. [16] proposes an energy optimization controller algorithm which is used for industrial and commercial equipment such as HVAC, based on hour-ahead RTP programs. In [17], a SCADA system is implemented which is connected to MATLAB software to control and integrate different information of intelligent buildings such as temperature, ventilation, and illumination to manage and maintain the user satisfaction.…”