Electricity + Control July 2016

CONTROL SYSTEMS + AUTOMATION

EMIS – Energy Management Information System IEC – International Electrotechnical Commission ODVA – Open Device Vendors Association OECD – Organisation of Economic Cooperation and Development PAS – Process Automation System

Abbreviations/Acronyms

NPS to 450

How am I performing?

What is my strategy?

19 000

CPS to NPS

Current Policies Scenario New Policies Scenario 450 Scenario

Energy savings in 2035

18 000

Mtoe

Efficiency in end-uses 67% 66% Efficiency in energy supply 5% 8%

17 000

Energy Management Life Cyle

16 000

Fuel and technology 12% 12% switching Activity 16% 14% Total (Mtoe) 1 479 2404

How do I optimise?

How do I buy?

15 000

14 000

Note: CPS = Current Policies Scenario NPS = New Policies Scenario 450 = 450 Scenario

How do I control?

13 000

12 000

2010 2015 2020 2025 2030 2035

Figure 2: Change in global primary energy demand by measure and by scenario.

Figure 3: The energy management life cycle.

Managing energy In order to help customers meet this challenge and generate large energy savings, it is necessary to take a more holistic approach to energy management. The following energy management life cycle model illustrates an effective guide. It shows five distinct areas of focus for improving energy management: strategy, supply, demand (our focus), analysis, and performancemonitoring. Themost common starting point for energy management is to measure performance. It is hard to develop a strategy without first understanding the current position, and most energy management processes will start with an audit or measurement. This stage of ‘Energy Awareness’ often looks at benchmarking plants and production against target energy consumption levels. In Europe, this Energy Efficiency Audit, or Energy Management Informa- tion System, is required as part of the European Parliament’s Energy Efficiency directive (published October 25, 2012). The information from the performance phase is typically dis- played on a dashboard. The data can be shown on large screens so it is visible across an enterprise. In industries where there are lots of repetitive systems or existing benchmarks, this information provides businesses with a clear picture of their performance. Whenmeasuring building efficiencies, there are clear benchmarks for energy consump- tion based on the building’s floor area and the external temperature. Based on these values, energy consumption models can be used for generic buildings. This approach can also be applied to the industrial sector where there are benchmarks for some processes, but where it is rare that we get a clear benchmark on energy consumption. The issue with benchmarking for industrial companies is twofold. First is the complexity of the process. Take, for example, a simple process such as a water pumping station. Its energy consumption will change on a daily basis; it will also be impacted by the distance and height pumped, as well as local rainfall. All these factors increase the complexity of our model. The second is that while a benchmark offers a point of comparison, it does not provide guidance on what to change within the system.

Key to delivering energy savings in a manufacturing process is the ability to convert the information into an action or a change within the plant. To create actionable change in our plant, we must stop focusing on energy consumption against time and instead focus on what the energy is actually doing (i.e. the production). To get an ac- curate link between production and the energy consumed, we need to collect energy information in alignment with process data. The cleaner the relationship is between the action and the data collected, the more accurate we can be in our analysis and the better our results. A typical control system includes a large number of energy consum- ing elements. Each of these elements contains one or more of our energy sources (water, air, gas, electricity, and steam). Some pieces of process equipment may actually change energy source based on the customer’s strategy for managing their energy supply.

Visualisation

Continuous improvement

Improved awareness, identification of O&M improvements

15% to 30%

Improved awareness

Increased employee awareness

10%

Benchmarking Project improvements Continuous attention

3%

2%

Installation of meters

Bill allocation only

Facility tune-up

Continuous Improvement Action plans

Figure 4: The potential energy savings difference between visualisation and continuous improvement.

• The global expanding population is driving the demand for energy. • Energy usage will double by 2050 and electrical consumption by 2030. • This increase in demand can only be supported by new power generation and infrastructure… resulting in higher prices.

take note

July ‘16 Electricity+Control

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