Ookie Ma

Grid Integration of Aggregated Demand Response, Part 2: Modeling Demand Response in a Production Cost Model

NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Foreword This report is one of a series stemming from the U.S. Department of Energy (DOE) Demand Response and Energy Storage Integration Study. This study is a multinational laboratory effort to assess the potential value of demand response (DR) and energy storage to electricity systems with different penetration levels of variable renewable resources and to improve our understanding of associated markets and institutions. This study was originated, sponsored, and managed jointly by the Office of Energy Efficiency and Renewable Energy and the Office of Electricity Delivery and Energy Reliability. Grid modernization and technological advances are enabling resources, such as DR and energy storage, to support a wider array of electric power system operations. Historically, thermal generators and hydropower in combination with transmission and distribution assets have been adequate to serve customer loads reliably and with sufficient power quality, even as variable renewable generation like wind and solar power become a larger part of the national energy supply. While DR and energy storage can serve as alternatives or complements to traditional power system assets in some applications, their values are not entirely clear. This study seeks to address the extent to which DR and energy storage can provide cost-effective benefits to the grid and to highlight institutions and market rules that facilitate their use. The project was initiated and informed by the results of two DOE workshops; one on energy storage and the other on DR. The workshops were attended by members of the electric power industry, researchers, and policymakers, and the study design and goals reflect their contributions to the collective thinking of the project team. Additional information …

Fundamental Drivers of the Cost and Price of Operating Reserves

Operating reserves impose a cost on the electric power system by forcing system operators to keep partially loaded spinning generators available for responding to system contingencies variable demand. In many regions of the United States, thermal power plants provide a large fraction of the operating reserve requirement. Alternative sources of operating reserves, such as demand response and energy storage, may provide more efficient sources of these reserves. However, to estimate the potential value of these services, the cost of reserve services under various grid conditions must first be established. This analysis used a commercial grid simulation tool to evaluate the cost and price of several operating reserve services, including spinning contingency reserves and upward regulation reserves. These reserve products were evaluated in a utility system in the western United States, considering different system flexibilities, renewable energy penetration, and other sensitivities. The analysis demonstrates that the price of operating reserves depend highly on many assumptions regarding the operational flexibility of the generation fleet, including ramp rates and the fraction of fleet available to provide reserves.

Load Participation in Ancillary Services Workshop Report

Developing load participation in ancillary services to the electric grid. Challenges: coordination among multiple entities, targeted R&D for market conditions and regulatory and policy environments.

Impact of Wind and Solar on the Value of Energy Storage

This analysis evaluates how the value of energy storage changes when adding variable generation (VG) renewable energy resources to the grid. A series of VG energy penetration scenarios from 16% to 55% were generated for a utility system in the western United States. This operational value of storage (measured by its ability to reduce system production costs) was estimated in each VG scenario, considering provision of different services and with several sensitivities to fuel price and generation mix. Overall, the results found that the presence of VG increases the value of energy storage by lowering off-peak energy prices more than on-peak prices, leading to a greater opportunity to arbitrage this price difference. However, significant charging from renewables, and consequently a net reduction in carbon emissions, did not occur until VG penetration was in the range of 40%-50%. Increased penetration of VG also increases the potential value of storage when providing reserves, mainly by increasing the amount of reserves required by the system. Despite this increase in value, storage may face challenges in capturing the full benefits it provides. Due to suppression of on-/off-peak price differentials, reserve prices, and incomplete capture of certain system benefits (such as the cost of power plant starts), the revenue obtained by storage in a market setting appears to be substantially less than the net benefit (reduction in production costs) provided to the system. Furthermore, it is unclear how storage will actually incentivize large-scale deployment of renewables needed to substantially increase VG penetration. This demonstrates some of the additional challenges for storage deployed in restructured energy markets.

A methodology for estimating the capacity value of demand response

An understanding of the capacity value of demand response, which represents the contribution it could make to power system adequacy, could provide an indication of its potential economic value and allow for comparison with other resources. This paper presents a preliminary methodology for estimating the capacity value of demand response utilizing demand response availability profiles and applying a response duration constraint. The results highlight the sensitivity of the capacity value of demand response to the energy limitation of the resource, the need to target different load types in different systems and the relatively small size of the demand response resources examined in relation to overall system size.

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

Summary of Market Opportunities for Electric Vehicles and Dispatchable Load in Electrolyzers

Electric vehicles (EVs) and electrolyzers are potentially significant sources of new electric loads. Both are flexible in that the amount of electricity consumed can be varied in response to a variety of factors including the cost of electricity. Because both EVs and electrolyzers can control the timing of electricity purchases, they can minimize energy costs by timing the purchases of energy to periods of lowest costs.

Analytic Challenges to Valuing Energy Storage

Electric grid energy storage value. System-level asset focus for mechanical and electrochemical energy storage. Analysis questions for power system planning, operations, and customer-side solutions.

Assessment of Industrial Load for Demand Response across Western Interconnect

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.