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DER Valuation for Regulated Utilities


Utilities are receiving continued pressure from regulators and consumers to integrate power from low-carbon, renewable sources. In the past, renewable growth has largely been fuelled by tax credits and net metering. However, the economic benefits of these programs have been enjoyed by a minority of consumers who have the necessary physical and financial assets to build small-scale solar or wind systems. Too often, the additional infrastructure required to support these Distributed Energy Resources (DERs) are an economic burden on the utility that is ultimately shared among the rest of its rate payers.


Although the political will to support DERs with tax subsidies and other incentives continues to wane, the pressure for utilities to expand their renewable energy portfolios remains. As a result, Utilities are going to have to determine the net impact, cost, and value that specific DER assets contribute to grid operations and services, as well as what expanded DER penetration means in due course to the Utility business model. These include everything from distribution system capacity, voltage control, to price arbitrage. A number of state PUCs are already demanding that Utilities perform DER studies to determine their value to the consumer and grid as a whole and ultimately the rate cases to support DER investment.

DER valuation is not a straightforward process and requires the adoption of new methodologies for system planning that have yet to become industry accepted practices and are contrary to decades of rate based revenue recovery models for capital investments. Utilities will need to develop highly granular temporal and spatial models that forecast load and generation at and below the circuit level and compare these forecasts to near-term and long-term asset constraints. They will also need a new approach to system planning and revenue recovery that will involve the detailed analysis of service point level net load (Load–DER) and revenue, versus long-term system investment requirements and forecasted revenues.


Transitioning from Long-Term Interconnection Studies to Dynamic Modelling

As DERs continue to proliferate, energy utilities have started to conduct interconnection studies to understand the effect these dynamic assets on the grid. Going forward, these types of studies will become increasingly difficult to near impossible to manage for hundreds of thousands of interconnected resources. Traditional interconnection agreements and models will strain under these assets. Expanding use of EVs and other on-premise energy storage devices will make these processes impractical. PowerRunner has the ability and experience to be able to deliver dynamic models that can deliver detailed cost & benefit valuations to support rate cases based on additional DER integration. Our dynamic modelling tools can deliver answers to the following questions:

  • The net value that a solar array or wind turbine contributes to the grid

  • When is the power used or not used?

  • When does it improve efficiency?

  • When and where is it ineffective and increases system congestion?

  • What is the actual price the asset owner should receive for that electricity at any time in a given day?

  • What is the actual cost to serve at each consumer endpoint and how does the DER affect the cost?

The Convergence of IT and OT Data Models

When customers add thousands of generation points to the grid, power flow analysis and resource planning will require the convergence of both operational and commercial data models to assess the value and future siting of these assets. This is no easy task. The commercial and operational data are quite disparate and often stored in applications that are not easily accessible to one another. IT applications deal with the relationship of each individual customer to the lower-level assets and grid end points that serve the customer. Data from applications, such as CIS, MDM, and OMS are monitored by the commercial side of the utility to handle customer care, billing and localized service disruptions. Operational data is derived from higher level assets such as feeders, sensors, switches, transformers and substations that can be spread out across hundreds or thousands of square miles of the distribution grid. This data is largely used to model voltage regulation, load balancing, grid reliability, and broader outage management. The data is collected and modelled in OT systems such as SCADA and ADMS.

Consumer Driven Transformation – This time it’s different!

Consumers are not only changing the way energy is consumed, they’re changing the way it is produced. Although the change is rapid in some areas while gradual in others, it will continue to grow. New homes are being offered with integrated solar systems. Advances in battery storage are changing the economics of residential, behind-the-meter storage. Electric vehicles could have more than 30% market share over the next decade. Regardless of the whether utilities are vertically integrated, deregulated, a municipality or co-operative, this diversity of on-system generation and storage devices will demand all utilities to dynamically model the commercial and operational performance of every asset on the system — hour-by-hour and beyond.

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