Green scheduling of control systems for peak demand reduction

Abstract

Building systems such as heating, air quality control and refrigeration operate independently of each other and frequently result in temporally correlated energy demand surges. As peak power prices are 200-400 times that of the nominal rate, this uncoordinated activity is both expensive and operationally inefficient. We present an approach to fine-grained coordination of energy demand by scheduling the control systems within a constrained peak while ensuring custom climate environments are facilitated. The peak constraint is minimized for energy efficiency, while we provide feasibility conditions for the constraint to be realizable by a scheduling policy for the control systems. The physical systems are then coordinated by the scheduling controller so as both the peak constraint and the climate/safety constraint are satisfied. We also introduce a simple scheduling approach called lazy scheduling. The proposed control and scheduling strategy is implemented in simulation examples from small to large scales, which show that it can achieve significant peak demand reduction while being efficient and scalable.

Optimal scheduling for constant-rate multi-mode systems

“…Nghiem et al [17,18] on energy peak demand reduction within a large organization by synchronizing switching decisions of various "heating, ventilation, and air conditioning" (HVAC) systems. The correlation between extreme weather and energy demand peaks is well documented [6], and hence reducing the energy peak demand due to HVAC systems can potentially significantly reduce the total energy peak demand.…”

“…The correlation between extreme weather and energy demand peaks is well documented [6], and hence reducing the energy peak demand due to HVAC systems can potentially significantly reduce the total energy peak demand. In [17] Nghiem et al considered a model of an organization where at any given time the HVAC system of a zone can be in either ON or OFF mode, and in each mode the temperature of the corresponding zone changes with a mode-dependent constant rate. In order to minimize peak energy-usage they studied the following schedulability problem: find a switching schedule of HVAC systems across different zones so as to maintain the temperature in each zone within a given temperature interval, with the restriction that simultaneously at most a fix number of HVAC systems are switched ON.…”

“…However, as noted by Nghiem et al [18], generally these algorithms are restricted to tasks whose worst case execution times are fixed and known in advance, and hence they are not directly applicable to energy peak reduction problem as posed in [18,17].…”

“…The work most closely related to our is of Nghiem et al [17]. The energy peak reduction problem was first posed in [17] and safe schedulability checking for this problem was reduced to checking a simple formula on temperature rates. Authors also presented a lazy scheduling algorithm where scheduler is required to take decisions only at multiples of a given sampling rate.…”

Smoothing the energy consumption: Peak demand reduction in smart grid

“…Usually, p d is significantly higher than p u , e.g., in Pennsylvania, p d is 240 times p u [21]. The penalty for high peak power consumption is particularly significant, which motivates research on reduction of peak power consumption.…”