Application of an energy management and control system to assess the potential of different control strategies in HVAC systems

Abstract

ElsevierEscrivá-Escrivá, G.; Segura Heras, I.; Alcázar-Ortega, M. (2010). Application of an energy management and control system to assess the potential of different control strategies in HVAC systems. Energy and Buildings. 42(11):2258-2267. doi:10.1016/j.enbuild.2010 AbstractThe significant and continuous increment in the global electricity consumption is asking for energy saving strategies. Efficient control for heating, ventilation and air-conditioning systems (HVAC) is the most cost-effective way to minimize the use of energy in buildings. In this framework, an energy management and control system (EMCS) has been developed to schedule electricity end-uses in the campus of the Universidad Politécnica de Valencia (UPV), Spain. This paper presents an evaluation performed by using the EMCS of different control strategies for HVAC split systems. It is analyzed the effect of different schedules for a common air-conditioning device and demand response strategies are tested in several situations. The economic saving is calculated taking into account the electricity contract clauses.Finally, a test is made for the control of a group of similar devices in order to reduce the maximum peak power in consumption and to obtain a flexible load shape with the HVAC loads.The studies are then extrapolated to a larger system, the whole University campus, for which energy and economic savings are quantified.

Distributed adaptive sensor fault tolerant control for smart buildings

“…The desired values of the temperatures are set up as follows: y s re f =55 o C and y (i) re f =24 o C, i ∈ N . Fig.2 shows the residuals, adaptive thresholds and the detection signals of the monitoring agents M (1) , M (7) , while the rest of the monitoring agents (i.e., M s ,M (2) ,...,M (6) ) are not presented since their ARRs are not violated for all t. At the time instant t (7) D = 20 h, a fault is detected by M (7) which initiates the isolation procedure in the monitoring agent M (7) and its neighbouring agents M s , M (4) and M (6) . In the case of M s , the observed pattern Φ s equal to Φ s = [0,0,0,0,0,0,0,1], which is compared to the theoretical patterns F s k for all k ∈ {1,...,255}, leading to ϒ s = { f (7) } and I s = I s,1 = ⋅⋅⋅ = I s,6 = 0 and I s,7 = 1.…”

“…is detected by M (1) which initiates the isolation procedure locally and its neighboring monitoring agents M s , M (2) and M (3) . By applying the distributed isolation process shown in Section III-B, the new non-zero isolation signals are generated: I s,1 , I (2,1) , and I (3,1) .…”

“…By applying the distributed isolation process shown in Section III-B, the new non-zero isolation signals are generated: I s,1 , I (2,1) , and I (3,1) . The dead-zone operator D (1) [.]…”

“…The dead-zone operator D (1) [.] enables the estimation for the sensor faultf (1) in M (1) , followed by the accommodation of control agents C s , C (2) , C (3) , and C (1) to the new isolated sensor faults. Note that C s has been accommodated twice.…”

“…Fig.3 presents the response of the temperatures, controlled either by the nominal control scheme or by the distributed FTC scheme. The faults occur in S (1) , S (7) , significantly effect the temperature dynamics of the local subsystems Σ (1) , Σ (7) , respectively, as well as the dynamics of their associated neighboring subsystems {Σ (2) ,Σ (3) }, {Σ (4) ,Σ (6) }, while Σ s is less affected by these faults. The use of the proposed distributed adaptive FTC scheme, contributed to the successful compensation of the sensor faults effects on the local and neighboring dynamics of subsystems Σ (1) , Σ (7) .…”