Load Shifting vs. Load Shaping: Customer-Centric Strategies for Demand Management

Read More

The traditional energy paradigm is shifting dramatically as consumers evolve from passive recipients to active participants. This change, driven by factors like solar generation, EV charging, and home electrification, is necessitating utilities to develop new, customer-centric approaches to demand-side  management.

Efforts to manage load through demand response can take multiple forms: shimmying, shedding, shifting, and shaping. Each of these help address grid instabilities at different timescales — shimmying on the order of seconds to hours, shedding on the order of seconds to days, shifting on the order hours to days, and shaping on the order of hours to years. Utilities typically use a combination of these different strategies depending on a region’s specific grid needs (i.e. level of renewable penetration, rate prices, etc.).

But, when it comes to distributed energy resources (DERs) and addressing long-term stability of the grid, load shifting and shaping are the most critical. While load shifting and load shaping may both be a means to the same end, they are often mistakenly used in conversation interchangeably despite serving different purposes.

What is Load Shifting?

Load shifting aims to move energy consumption from peak demand periods to off-peak periods. This is about changing when energy is used, not necessarily how much is used.  

To reduce the strain from peak hours, load shifting utilizes a range of behavioral demand response (BDR) strategies, such as adjusting set points on smart thermostats or electric vehicle (EV) charger control. The goal here is incentivizing customers to move energy-intensive household activities, like HVAC or EV charging, to off-peak hours when electricity prices are cheaper.   

Load shifting is also used to manage specific events, like extreme weather, that place additional strain on the grid. A summer heat wave in Texas, for example, may prompt the utility to encourage reduced air conditioner use on a specific day and time. 

What is Load Shaping?

Unlike load shifting, which focuses on when energy is consumed over time, load shaping intends to modify the electricity demand profile through various strategies, including both shifting and reducing consumption.

Specifically, the goal of load shaping is to adjust the overall pattern of energy consumption and even out grid fluctuations with smoother, more predictable consumption patterns. This could involve reducing peak demand, filling in valley demand, or both. By avoiding large spikes in demand, DSM teams and grid operators can create a more consistent demand curve.

Time-of-use (TOU) rate plans are a typical strategy used to achieve load shaping, offering a near permanent change in energy use patterns by encouraging people to stagger the use of appliances. Implementing energy efficient appliance programs are also useful. 

Maximizing the Value of Load Management

Regardless of the approach — load shifting or shaping — utilities can no longer take a blanketed approach to recruiting customers. Thanks to the rapid growth in DERs, which are non-uniform in both geographical adoption and load consumption patterns, progressive utilities are taking a nuanced approach to load shaping and shifting that involves three major steps:

1. Identifying the most suitable customers

This starts by first understanding the appliance-level energy consumption patterns for every individual customer on an hourly, daily, and monthly level to identify which customers are most likely to have the biggest impact on load shaping or shifting.

A growing number of utilities are now employing artificial intelligence (AI) to analyze individual household meter data, unlocking a level of granular insight that significantly exceeds traditional feeder-level analysis. Not only do utilities gain access to energy usage by appliance, they can also identify specific appliance types — including EVs — and gas vs. electric appliances, as well as consumption patterns that reveal whether a home consistently uses energy during peak hours or exhibits off-peak, low-load tendencies.

2. Engaging the target population

While identifying the correct customer segment opens the door for more targeted recruitment, utility marketers are still tasked with fostering positive engagement in ways that lead people to take action voluntarily.

For the majority of customers, utility providers remain largely out of sight, out of mind with the exception of a monthly bill payment. Motivating these customers to help balance the grid is challenging to say the least. While utilities have tried their best to educate customers on the financial and environmental benefits of modifying their energy use, these traditional home energy reports lack the personalized insights and recommendations needed to incite real change. 

AI, on the other hand, allows for precise, tailored recommendations that takes into account each customer’s specific appliance usage, EV/solar assets, and lifestyle preferences. These messages resonate much better with most people, as it reflects their real-time home and energy habits.

3. Recruiting target population

Now that the attention of the right customers has been captured, utilities have a much easier time recruiting them for load shaping and shifting programs.

Again, this AI-powered personalization can be a valuable tool. For example, messaging can be customized depending on whether a customer owns an EV or not, or is on a TOU rate or not. AI can also provide personalized insights into how much a customer could save by signing up for a TOU rate and continuing to stay on one. When customers see how these programs directly benefit their own energy costs and usage, participation dramatically increases.

Take EVs, for instance. To effectively manage EV charging, utilities can target EV customers with a highly personalized breakdown of their charging times coupled with corresponding energy costs. For many customers, this financial side-by-side is highly effective in motivating load shift behaviors.

Additionally, because some EV drivers already charge off-peak, utilities can focus only on the customers with the greatest load shift potential to boost program enrollment and rate value.

Driving Grid Resilience Through AI-Powered Engagement

The shift from passive consumer to active grid participant isn’t just a trend; it’s a fundamental recalibration of the energy relationship. Demand is no longer simply managed but rather dynamic partnerships are cultivated with every home, appliance, and EV.

Because the future grid can’t be built on monolithic infrastructure, a network of personalized energy ecosystems, where individual choices ripple outwards, will shape collective resilience. Leveraging meter data and AI analytics, utilities can help customers foster a deeper understanding of their home usage so that every individual feels a direct stake in the energy that powers their life.