Business models for distributed energy resources

April 2016
Business models for distributed energy resources

More information:

This paper presents a novel, empirical analysis of the most common business models for the deployment of distributed energy resources. Specifically, this research focuses on demand response and energy management systems, electricity and thermal storage, and solar PV business models. We classify the revenue streams, customer segments, electricity services provided, and distributed energy resources leveraged for 144 business models. We use this empirical assessment to identify a limited set of business model archetypes in each distributed energy resource category. Within each archetype, concrete examples of individual business models are presented, along with notable exceptions or extensions of these business models. Our review leads us to five key takeaways regarding the structure of distributed energy resource business models. First, business models can be classified into a discrete number of archetypes based on common characteristics. This clustering indicates that there are factors that contribute to the success or failure of a business model that cannot be captured in reviews of business model structures (for example, company culture, or factors linked to execution). Second, as anticipated, regulatory and policy environment is a significant, if not the most significant driver of business model structure. Third, business models are not static with time – technological, policy, and regulatory developments all drive changes in a company’s business model. Finally, business models compete for the provision of a limited set of commodity electricity services. This observation leads two final conclusions. Structures (such as markets) should be encouraged to allow competition among service providers and efficient solutions to emerge; additionally given that these business models are fundamentally competing for the provision of commodity services, differentiation beyond price will be difficult to realize.

MITEI Authors

Scott Burger PhD

Institute for Data, Systems, and Society

Related Research

We're hiring! Learn more and apply