Global Optimisation of Chiller Sequencing and Load Balancing Using Shuffled Complex Evolution

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2 Global Optimisation of Chiller Sequencing and Load Balancing Using Shuffled Complex Evolution IAIN STEWART A (MENG) LU AYE A (FAIRAH, FAIE, PHD) TIM PETERSON B (PHD) A Renewable Energy and Energy Efficiency Group, Department of Infrastructure Engineering, The University of Melbourne, Vic 3010, Australia B Environmental Hydrology and Water Resources Group, Department of Infrastructure Engineering, The University of Melbourne, Vic 3010, Australia

3 Evolutionary Chiller Optimisation (ECO) The why? A brief history of building controls Chillers and VSDs The how? Improving system efficiency Aims- Optimising equipment loading Model- Structure, inputs & outputs Results- Modelled savings

4 Why? Energy costs Carbon emissions Peak demand reduction

5 Typical Commercial Building End Use Energy HVAC 20% 92PJ Lighting 15% 40% 22MtCO 2 Equipment Other 35%

6 A Brief History of Building Controls Pneumatic controls.

7 A Brief History of Building Controls Direct Digital Control (DDC)

8 A Brief History of Building Controls Building Management System (BMS)

9 Reciprocating vs Centrifugal Chillers

10 Chiller COP Chiller COP vs Loading New centrifugal variable speed chiller % 100% Old reciprocating chiller 2 0 0% 25% 50% 75% 100% Chiller Load %

11 Variable Speed Drives (VSDs)

12 Energy Consumption Variable Speed Drives (VSDs) 100% 90% Pump Speed vs Energy Consumption 80% 70% 60% 50% 10% 80% 40% 30% <50% 20% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Pump Speed

13 Technological improvements Capabilities of Building Management Systems Improved chiller part load efficiency Affordability of Variable Speed Drives

14 Current Control Technique Return water temperature staging Switch-on thresholds Switch-off thresholds From 1 chiller to 2 chillers: T rtn > C From 2 chillers to 1 chiller: T rtn < 9.28 C From 2 chillers to 3 chillers: T rtn > C From 3 chillers to 2 chillers: T rtn < C From 3 chillers to 4 chillers: T rtn > C From 4 chillers to 3 chillers: T rtn < C T rtn = Temperature of return chilled water from the building

15 The Opportunity Maximize cooling system efficiency [COP] Non linear efficiency curves Multiple pieces of equipment Inherently difficult for humans

16 Aims Accurately simulate waterside plant equipment Optimise equipment loading for cooling loads Maximise plant COP for all cooling loads

17 All models are wrong but some are useful George Box

18 ECO 1. Simulate waterside plant Energy inputs Flow rates Mechanical work Efficiencies Physical limits Refrigeration delivered 2. Optimise equipment staging and loading using genetic algorithm for a range of cooling loads

19 Modelling assumptions Chiller turn down ratio = 10:1 Condenser and chilled water pumps operate at minimum flow rate when chillers are at minimum output (eg. 50% pump speed when chiller is at 10% capacity) Pumps speed increases linearly with chiller energy Maximum temperature difference across chillers = 7 C Fans excluded from simulation

20 Plant layout

21 Plant Simulation Inputs Simulation Output Low load chiller energy [kw] High load chiller 1 energy [kw] High load chiller 2 energy [kw] Simulation of chilled water plant (Chillers & Pumps) *Fans excluded Cooling Load [kwr] Input energy [kwe] System COP

22 Plant simulation ǗQ r = Ǘmcwc w (T i T o ) where, Ǘmcw is the mass flow rate of the chilled water [kg s -1 ] c w is the specific heat of chilled water [kj kg -1 K -1 ] T i and T o are the inlet and outlet chilled water temperatures [ C] across the chiller.

23 Regression analysis ǗQ r = ap + bp 2 where, ǗQ r is the refriegeration energy [kw r ], P is the chiller input power [kw e ], and a & b are scalars to be calculated in the regression analysis

24 Cooling capacity [kw r ] 1400 Regression of Chiller [kw r vs kw e ] y = x x R² = Input Energy [kw e ]

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26 Results of chiller regression analysis Chiller # Input Power [kw e ] Nominal Capacity [kw r ] a b R

27 Modelling Structure Input Simulation & Optimisation Outputs Cooling Load [kwr] Simulation of chilled water plant (Chillers & Pumps) *Fans excluded Low load chiller energy [kw] High load chiller 1 energy [kw] Shuffled complex evolution algorithm Objective function: Plant equipment energy High load chiller 2 energy [kw]

28 Shuffled Complex Evolution Deterministic & probabilistic Competitive evolution Clustering Steepest decent

29 Chiller Input Power [kwr] Chiller Input Power [%] vs Cooling Load [kwr] 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Low Load Chiller High Load Chiller 1 High Load Chiller 2 0% Cooling Load [kwr]

30 System COP 6.35 System COP vs Cooling Load [kwr] Cooling Load [kwr]

31 System COP 6.35 System COP vs Cooling Load [kwr] Cooling Load [kwr]

32 Response surface, COP vs Chiller Loading [%]

33 Energy Savings [kwe] 140 Energy Savings [kwe] vs Cooling Load [kwr], aggregated into bins of 100kWr Cooling Load [kwr]

34 Energy savings [kwe] Energy savings [kwe] vs Cooling load [kwr] Cooling Load [kwr]

35 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Modelled Energy Savings, 2016 [kwh] 21,579 33,173 11, , ,952 18,683 Chillers Pumps Total Modelled energy consumption 2016 Energy savings from observed consumption

36 Results Chiller 1 Chiller 2 Chiller 3 Pumps Total 2016 actual [kwh] 95,254 71,480 3,114 30, ,125 Modelled control strategy [kwh] 82,014 50,877 15,378 18, ,952 Energy savings [kwh] 13,240 20,603 (12,264) 11,594 33,173 % Savings 13.9% 28.8% % 38.3% 16.6%

37 Typical Commercial Building End Use Electricity 92PJ HVAC Lighting Equipment Other 15% 20% 35% 40% 22MtCO 2 Savings 15PJ 4MtCO % Peak demand reduction

38 Next steps Test ECO in real world Represent chillers and fans using neural networks to increase optimisation opportunities (adaptive control) Incorporate ambient conditions and fan/condenser pump speed modulation (dynamic control)