Demand Response vs. Demand Optimization–How to Optimize Demand for a More Efficient Grid

by Jay Shaver and John McDonald, GE’s Digital Energy Business

The smart grid industry is evolving rapidly and becoming increasingly complex. In a few years, the electric grid has shifted from an operations-based dinosaur into an information-producing and intelligent entity. For utilities to sustain their operations, they must make smart investments that will make their electric grids reliable, efficient, sustainable and resilient.

Demand, or the amount of load customers need from the electric grid, consistently fluctuates and sometimes stresses the grid. Besides adjusting generation resources, utilities have managed this issue with demand response strategies that change the load conditions and manage consumer consumption. This makes energy more cost-effective, reliable and environmentally sustainable. Utilities that incorporate demand response into their planning and operations will yield an optimal grid, save consumers’ money and reduce stress on the grid.

Although demand response is effective, if done traditionally it can be isolating. In the 30-plus years utilities have practiced demand response, they have focused on peak reduction and controlling singular devices, products and systems. New business models, use cases and technology can expand the value of demand response. Today’s smart grid merges devices and systems to form integrated solutions, making it critical for utilities to look at greater opportunities for value propositions across more interoperable technology. GE calls this approach demand optimization, a strategy about creating operational and economic efficiencies across the value chain while recognizing how demand response can be leveraged in multiple ways. The focus is placed on a two-way conversation with customers about electricity costs, how to make the grid more reliable and secure, etc. Companies should work with their customers to change the conversation into a consumer-facing strategy vs. the historical engineering model.

As an example, let’s think about how traditional demand response strategies have addressed short-time supply-side gaps or improved reliability margins. Direct or interruptible load control programs have been used to reduce load during peak demand in summer and winter. Similarly, time-of-use and peak-pricing programs charge higher rates to curb energy use and accommodate the higher cost of peak generation. As this happens, consumers continue to be more aware of energy efficiency and the increased costs of energy use. Demand optimization accounts for the effects of reduced demand across the value chain, from supply and delivery all the way to individual energy consumption. The traditional way the industry has planned and managed its transmission and distribution assets will continue to change, as will the technology required for energy delivery.

This shift already is happening. Take advanced metering infrastructure (AMI) as an example. Current practices are blending into a comprehensive approach that combines separate responsibilities and tasks into a single business goal. Another example is the performance analytics used to mine the massive amounts of available meter data providing insight into regional and segmented growth trends. These analytics point to long-term capital or infrastructure investment needs. Demand optimization also provides the necessary aspects of a control system as it will incorporate the ability for fast load shedding or send dynamic pricing asks in real time. Effective demand optimization is meant to be an integration of existing systems with several advanced capabilities layered on top, thus demonstrating grid operations value. Examples of the required capabilities are as follows:

  • Network awareness. The ability to synchronize the type and location of assets on the normal state of the electrical network is a necessary first step. Utilities must control load at different locations around the grid to expand operations beyond traditional bulk load shed.
  • Customer awareness. Utilities must integrate program and contractual arrangements of residential, commercial and industrial consumers before any decisions can be made. Operations departments require confirmation that a system is only releasing load that can be reduced at necessary times.
  • Forecasting. Utilities should include consumer participation rates and policy factors in calculations of consumer baseline and reduction forecasts from meter data. This method facilitates market and consumer settlements and shows how behavior-based analytics are a strong indicator of future performance more so than calculations for specific devices.
  • Visualization. Companies must equip grid operators with a view that provides contextual awareness of available load resources and present reduction scenarios based on information feeding in from several discrete sources.

Integrating these capabilities into a single demand response platform will show value beyond what traditional demand response models could anticipate. One of GE’s product suites, PowerOn Precision, is an example of this integration. It uses the aforementioned capabilities in a single platform as part of the asset control suite for grid management solutions. GE first looks at a customer’s business needs and the associated challenges. Usually, customers are focusing on one or two aspects of their grid, so GE pieces together a comprehensive solution that accounts for parts of the grid the customer might not be thinking about. Taking a holistic look at how customers can accomplish their goals is critical. This strategy provides a way for utilities to seamlessly merge demand response solutions into a comprehensive offering that will improve performance, reliability and efficiency of a utility’s grid.

The benefits that a comprehensive solution provides can build an attractive business case. For a utility to build out such an offering, it should take one of three approaches that won’t affect investment or operational strategies:

  • Reduce Capex. Utilities must use careful planning to adjust capital infrastructure investments in a way that will align with budgets and delay the costs associated with building more assets or generation.
  • Integrated volt/VAR control (IVVC). Accounting for volt/VAR control in coordination with consumer-controlled demand response actions will present grid operators with new options.
  • Improved reliability. Utilities should evaluate demand response as load shed options instead of bulk load shed, enabling load reduction measurements to be taken by location in real time to adjust current load flow.

A key to making demand optimization work is educating and engaging consumers about the benefits that can be achieved through their participation in demand response strategies. The industry is moving in the right direction. December’s “FERC Assessment of Demand Response & Smart Metering Staff Report” states that the potential demand response contribution has grown 18 percent during the past two years and represents 9.2 percent of U.S. peak demand. If utilities will evaluate and execute demand optimization strategies, their grid operations will be in better positions to manage energy delivery as it evolves from dynamic consumer demand.

Further, when a company understands its customer’s point of view, the collaboration and partnership is seamless. GE employees sit on the boards of relevant organizations such as the Association for Demand Response and Smart Grid, the Smart Grid Consumer Collaborative and the Smart Grid Interoperability Panel. These positions enable the company to gain insight across numerous areas of the industry and provide more comprehensive solutions to its customers.

Utilities must start moving away from demand response’s old model and look at the future of demand optimization, a more holistic and comprehensive way of operating. The industry is beginning to shift to this mentality, shown through summits such as the National Summit on Integrating Energy Efficiency & Smart Grid. Events such as this bring together utilities, government, technology companies, performance contractors, service companies and other stakeholders to make the grid more efficient and reliable through demand response strategies.


Jay Shaver is senior product manager of software solutions at GE’s Digital Energy Business.
John McDonald is director of technical policy and strategy development at GE’s Digital Energy Business.

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