According to a study by Jackson Associates, many utilities and state agencies are finding it difficult to evaluate the cost effectiveness of smart grid initiatives with unreliable elasticity-based models and uncertainty surrounding estimated financial benefits can be removed by applying agent-based models.

 

“Most analyses apply elasticity or percentage-change models that are notoriously unreliable when the underlying economic/technology structure is in a period of flux. The fact that total smart grid investments may exceed $1 trillion while elasticities are being used to estimate customer impacts for the next 20 years is disturbing. An agent-based modeling approach is required for reliable analysis,” said Jerry Jackson, president of Jackson Associates and author of the study.

 

Agent-based models provide a better analysis of smart grid impacts because they reflect electricity use of individual utility customers or agents, company officials said. The models also recognize and account for all important factors that determine electricity use of individual customers including income, demographics and other factors. Agent-based models are used in modeling applications outside the utility industry.

 

Jackson Associate’s MAISY agent-based end-use model was applied to analyze peak hour electricity savings for a Duke Energy Indiana smart grid implementation. MAISY Utility Customer Databases are the source of the utility customer hourly load and other information applied in the independent study.

 

The report also says that Duke Indiana smart grid programs can expect to achieve between 4 and 8 percent reductions in residential summer peak loads over a 15-year period. MAISY’s capabilities include simultaneous evaluation of smart grid programs; electricity price increases, utility efficiency programs, and so on; detailed program development and analysis; and customer-detailed information for marketing strategy and analysis

 

“Utility customer load reductions reflect half or more of the benefits of smart grid programs with much of these benefits coming in future years; consequently, more reliable medium and long term analysis is critical,” added Jackson, who is also a professor at Texas A&M University. “The lag in analysis capabilities is typical with new technology evaluations; however, given the increased level of investment, a more detailed approach is clearly needed now.”

 

This study is the first to apply individual utility customer end-use hourly electric loads to evaluate smart grid costs and benefits. Data for more than 800,000 residential and commercial utility customers in the 200 largest U.S. utilities were applied in the study.

 

Before this analysis, studies, including a recently released FERC analysis, have relied on assumptions about elasticities and electricity pricing to estimate changes in broad customer-class aggregate hourly loads. Instead, this new study applies load control and pricing program impacts directly to individual customer end-use loads such as air conditioning, water heating and so on to determine utility-level impacts.