Nasr Alkadi

Assessment of Industrial Load for Demand Response across U.S. Regions of the Western Interconnect

....................................................................................................................... 5 FORWARD ........................................................................................................................ 6 EXECUTIVE SUMMARY .............................................................................................. 12 Summary of Results ........................................................................................................... 15 Introduction ................................................................................................... 18 Chapter 1 1.1 Background ............................................................................................................ 18 1.2 Overall Approach ................................................................................................... 18 1.3 Report Organization ............................................................................................... 21 Methodology ................................................................................................. 22 Chapter 2 2.1 Electrical Energy Consumption Estimation by Manufacturing Plant .................... 22 2.2 Daily Load Curve Development for Industrial Processes ...................................... 25 2.3 Selection of Top Industrial Subsectors for DR Analysis ....................................... 28 2.4 Flexibility Factors .................................................................................................. 30 2.4.1 Industrial Load Types ........................................................................................ 31 2.4.2 Derivation of the Industrial Demand Response Flexibility-Factor (IDRFF) ..... 31 2.5 Assumptions ........................................................................................................... 33 Results ........................................................................................................... 36 Chapter 3 3.1 Industrial DR Profiles Aggregation in Western Interconnect (WI) ....................... 36 3.2 Industrial Plants in WI Region ............................................................................... 37 3.2.1 BAA Summaries ................................................................................................ 38 CONCLUSIONS, AND FUTURE RESEARCH ......................................... 49 Chapter 4 4.1 Conclusions ............................................................................................................ 49 4.2 Future Research ..................................................................................................... 49 References ......................................................................................................................... 50 APPENDIX A Industrial Energy Estimation Approach ................................................ 54 A.1 Abstract .................................................................................................................. 54 A.2 Introduction ............................................................................................................ 54 A.3 Tool Development Methodology ........................................................................... 55 A.4 Database Querying ................................................................................................. 55 A.5 Data Filtering Process ............................................................................................ 57 8 A.6 Statistical Model Development .............................................................................. 59 A.7 Electricity Intensity (ELI) ...................................................................................... 60 A.8 User Interface ......................................................................................................... 61 A.9 Case Study ............................................................................................................. 64 A.10 Conclusion ............................................................................................................. 66 APPENDIX B Load Curve Development ..................................................................... 68 B.1 Genetic Algorithms (GA) Method to Create the LOAD Curve ............................. 68 B.2 Scaling Per Unitized Load Curve .......................................................................... 70 B.3 Breakdown of Load Curve Based on Process Steps .............................................. 70 B.4 Graphical Representation of Manufacturing Plants ............................................... 72 APPENDIX C Demand Response Potential in Industrial Sector .................................. 74

Product design for energy reduction in concurrent engineering: an inverted pyramid approach

The consideration of energy aspects as X in the design for X (DFX) paradigm in concurrent engineering (CE) is important for enabling reductions in operating costs, reducing greenhouse gas (GHG) emissions and enhancing sustainability. This research presents a CE methodology for the consideration of energy use in product development and manufacturing, specifically addressing the machining process. The research reported in this paper showcases the effectiveness of using a systematic integrated approach towards consideration of energy in product development, addressed through product, process, and system level parameters. It adds an important tool to the DFX toolbox for evaluation of the impact of design decisions on the product manufacturing energy requirement early during the design phases. The use of the inverted pyramid approach (IPA) has been described as an effective methodology for incorporation in expansion of the DFX toolbox.

Improving Compressed Air Energy Efficiency in Automotive Plants - Practical Examples and Implementation

Automotive stamping and assembly plants are typically large users of compressed air with annual compressed air electricity bills over

Energy consumption modelling and benchmarking in continuous galvanising lines

500,000 per year. This paper describes typical compressed air systems in automotive stamping and assembly plants, and compares these systems to best practices. The paper then presents a series of case studies, organized using the inside-out approach, that identify significant energy savings in automotive plants. Case studies include ways to reduce end use compressed air by replacing pneumatic motors with electric motors and replacing pneumatic suction cups with magnets, reduce distribution losses by replacing braided with rubber hoses, reduce drying losses by employing demand-based desiccant regeneration, and control losses by increasing throttling capability and operating centrifugal air compressors in auto-dual control mode. PAPER OUTLINE This paper presents a series of case studies that identified significant energy savings in automotive plants. The case studies are presented using the inside-out approach of first minimizing end use demand, then minimizing distribution losses, and finally making improvements to primary energy conversion equipment, the air compressor plant (Kissock, XXXX). Case studies include ways to reduce end use compressed air by replacing air-powered tools with electric tools and replacing pneumatic suction cups with magnets, reduce distribution losses by replacing braided with rubber hoses, reduce drying losses by employing demand-based desiccant regeneration, and reduce control losses by increase throttling range and operating centrifugal air compressors in auto-dual control mode.

Assessment of Industrial Load for Demand Response across Western Interconnect

The pot hardware in continuous galvanising lines is prone to failure and needs to be replaced quite frequently which results in considerable loss of production time and high energy consumption. Galvanizing Energy Profiler and Decision Support Systems (GEPDSS) was developed to consider major energy consuming equipment in a typical hot dip continuous line which tracks the current production and energy consumption for up to three different processes. It can simulate a scenario to identify energy, cost, and productivity benefits obtained from energy savings measures and can evaluate the economic desirability of investment in new and improved pot hardware.

Wireless sensors and energy efficiency at Alcoa

Demand response (DR) has the ability to both increase power grid reliability and potentially reduce operating system costs. Understanding the role of demand response in grid modeling has been difficult due to complex nature of the load characteristics compared to the modeled generation and the variation in load types. This is particularly true of industrial loads, where hundreds of different industries exist with varying availability for demand response. We present a framework considering industrial loads for the development of availability profiles that can provide more regional understanding and can be inserted into analysis software for further study. The developed framework utilizes a number of different informational resources, algorithms, and real-world measurements to perform a bottom-up approach in the development of a new database with representation of the potential demand response resource in the industrial sector across the U.S. This tool houses statistical values of energy and demand response (DR) potential by industrial plant and geospatially locates the information for aggregation for different territories without proprietary information. This report will discuss this framework and the analyzed quantities of demand response for Western Interconnect (WI) in support of evaluation of the cost production modeling with power grid modeling efforts of demand response.

Demand Response for Ancillary Services

Inexpensive wireless sensors developed under a DOE Building Technology Program (BTP) project have proven to be robust enough for harsher environmental and RF interference-laden locations found in industrial settings. Coupling with Alcoa-ORNL activities pertaining to optimization of energy utilization at Alcoa facilities these sensors, and an associated ORNL-architected instrumentation network fabric, were selected for deployment at an Alcoa facility in Warrick Indiana - on the banks of the Ohio River. The Warrick facility was chosen as the demonstration site principally for the fact that a 750MW power plant, a rolling mill and a smelter system are all coresident at this location. This paper presents a description of that deployment at Alcoas Warrick IN facility as well as Alcoas vision for implementing inexpensive, standards-based wireless devices for reducing energy consumption.