District Energy - 2009 4th Quarter

Tradeoffs among reliability, com-
plexity and efficiency no longer have to
be made with this new optimization
approach. The fourth important piece of
the paradigm shift enabling ultra-high
performance is the introduction of
chiller plant optimization software to
mitigate the challenges above. Optimum

Energy has now packaged complex optimization logic using relational controls in software that is integrated into the existing plant control system ( programmable logic control or direct digital control).

The benefits of this optimization software include the following:

; Reliability – Software algorithms are prefabricated and can be pretested prior to implementation.

Ultra-High Performance Examples

Optimization Software

In district energy plants, the priority has always been 100 percent reliability/ availability of services. In the past, complex energy efficiency improvement projects were difficult to implement because of the months and months they required of custom programming, commissioning and continuous tweaking of the control algorithms. This process could sometimes take more than one year to complete – with finger pointing, manual overrides and customer complaints weakening the importance and intent of the original goal. Unfortunately, the project still is often at the mercy of the expertise of the local control contractor. While there are many skilled control programmers, many of us never seem to get these guys on our jobs!

The software model allows the complex algorithms to be turned on or off manually (using the existing control system front end) or automatically if there is a communication failure. The ability to ‘turn optimization off’ offers huge flexibility in troubleshooting and fundamental control sequence redundancy.

; Scalability – In the old paradigm, highly custom optimization programming is implemented on nearly every central plant job. Though the relational control algorithms are customized for the specific plant, the patented demand-based software modules allow operators to eliminate PID control and associated loop tuning and not to reinvent the high-performance modules each and every time.

Ultra-high performance plant k W/ton

Implementing high-performance solutions in large district and campus cooling plants can save money and help reduce carbon footprints. Figure 2 provides examples of two ultra-high performance district cooling plants – one plant in the United Arab Emirates and the other on a college campus in the desert of Southern California. These facilities present two distinctly different examples of all-electric central plant profiles, average wet-bulb temperatures and total plant kilowatt-per-ton performance, including all chillers, chilled-water pumps, condenser pumps and cooling tower fans. The annual average ultra-high efficiency goal in the United Arab Emirates would be less than 0.7 kW/ton and in California less than 0.52 k W/ton.

; Decreased implementation time schedule – Because of software scalability and the ability to operate the plant without the optimization

When originally designed and constructed, both the UAE and California plants used conventional designs and control methodologies. Based on the one year of trend data, the UAE plant

Figure 2. Ultra-High Performance Profile for District Cooling Plants in the United Arab Emirates and on a Southern California College Campus (Central Plant Load Profile, Kilowatts Per Ton and Average Outdoor Wet-Bulb Temperature).

Percent hours spent operating at each point

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Avg. WB

Avg. WB 55º F

63º F Avg. WB

Avg. WB

Avg. WB 78º F

68º F

73º F

Avg.

Avg. WB

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

WB

Avg.

WB

Avg.

58º F

82º F

58º F

61º F

WB

Avg. WB 67º F

9% 89% Avg. WB

Avg. WB

64º F

52º F

87º F

The most important difference between relational controls and PID control is energy optimization. Energy optimization with PID control is accomplished by continually adjusting the various controlled setpoints. But continually changing setpoints leads to additional stability issues and is a reason that operators often discontinue setpoint reset functions. Relational controls are far more effective, providing simpler and more direct energy optimization relationships than what are required when attempting to optimize for energy efficiency by adjusting temperature and pressure setpoints.

software, troubleshooting, commissioning and cut-over are much quicker.

; Performance verification – Measurement and verification (M&V) of overall plant performance is crucial to ensuring desired efficiency levels have been achieved, and just as importantly, are maintained. The new optimization software offers M&V ‘dashboards’ for monitoring, trending and documenting savings over time.