Fit and Transactive

Utility Survival Skills in the 21st Century Grid

While safety and reliability will always remain the chief charge of electric utilities, the proliferation of distributed energy resources (DER) in all its forms is changing the utility model to distribution system operator (DSO), closely resembling the RTO/ISO model at the wholesale transmission level.

In this “transactive” revenue model, the utility acts not only as a fidelity traffic controller, but as a financial clearing house for energy transactions on its distribution grid. Going forward, this means utilities’ operations can no longer be compartmentalized from the revenue side of the business.

Operational decisions will now be made not only based on physical constraints, but also on financial considerations that can change on a minute by minute basis, much like in wholesale markets. Nonutility DER owners and operators also will make decisions based on financial constraints or incentives. This is a trend that will accelerate as storage technology improves, giving DER asset owners more options on when to offer their generation to the grid.

Figure 1 : convergence of commercial and operational data in 21st Century Utility Corporate Model

Ending the Traditional ROI Model

This new transactive model adds increased complexity to grid operations and infrastructure planning. In addition, it alters the traditional utility return on capital financial model. Revenues generated from volumetric rates that are typically set by a rate of return indexed to rate base capital investments are marginalized in a declining growth environment.

Public utility commissions (PUCs) are encouraging utilities to define structures based upon alternative capital investment strategies. The utility is no longer simply a one-way delivery and infrastructure service entity. It is now a physical and financial transaction management and settlement service. Its value to consumers as a facilitator and enabler must be reassessed on that basis.

Until recently, the core driver of the North American utility business was the assumption that load will increase over time. Population grows, businesses grow and new technology demands more electrification. As utilities continue to build new infrastructure to support long-term growth, regulators ensure a reasonable return on investment (ROI).

In many parts of North America, this is changing. Slow economic growth, continued transition from a production to a service-based economy, lower rates based on lower fuel prices, and progress in energy efficiency have tossed this model on its head.

In the 21st century model, utilities, as grid owners, become more of a combined electron traffic controller and clearing house. They must assure that all paths are operating safely and efficiently and record each physical transaction at every point where power is added to the grid or delivered to the end user. Utilities also must place a value on that type of transaction based on the value of electricity to the consumer, the efficiency or inefficiency it contributes to the grid, and the cost to serve at each endpoint-all at any given time of the day.

This is a way in which utilities can determine the relative value of all grid transactions and the overall value of all grid assets, whether DER or conventional. In the near term, and in more frequent cycles, this will facilitate better DER valuation and infrastructure planning while providing cost-efficiency studies to support new rate cases. In the longer term, it is the pathway to what might become distribution locational pricing similar to wholesale locational marginal pricing markets. All electricity price risk is borne by the energy supplier. Utilities make money on the transaction, not the commodity. The number and size of the physical transaction determines utility revenues and margins. Utilities, therefore, are incentivized to build infrastructure and support DER assets that provide optimal utilization. They will pay less attention to DER that may contribute marginal value. Ultimately, utilities will calculate temporal profit margins for transactions on all interconnections and delivery points and even create an individual profit and loss for serving every rate payer. This is possible only if the silos between commercial and operational data can be bridged.

AMI and Commercial and Operational Management

Today’s electric distribution company is basically built on two distinct data models. One model encompasses the relationship of each individual customer to the lower-level assets and grid end points that serve the customer. Data from these service points are typically collected in IT systems such as meter data management, customer information systems and outage management systems. These applications are monitored by the commercial side of the utility to handle customer care, billing and localized service disruptions. The second model involves the relationship of operational data 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. It is also used to analyze power flows and for operations and infrastructure planning. The data is collected and modeled in OT systems such as SCADA and advanced distribution management systems (ADMS). It is the convergence of these commercial and operations models that will drive the efficiency and revenue models of the 21st century utility.

This convergence began recently when distribution companies started to recognize the additional value of advanced metering infrastructure (AMI) in operations. Historically used to support commercial operations, AMI is increasingly used by utility operations to augment input from their traditional sensors. Millions of new AMI data points are used to model more efficient voltage control, load management and even long-term planning. Going forward, the key mission is the integration of AMI data with data from higher-level assets such as the transmission networks. This allows customers to continue adding DER devices like solar panels, wind turbines or electric vehicles, to a network that is smart enough to model these assets.

DER-The True Game Changer

DER is a profound game changer. When customers add thousands of pieces to the grid puzzle, power flow and resource planning will be driven by end-use consumption patterns that must be dynamically modeled. In addition to distributed generation, home area networks with smart appliances, wall mounted batteries and electric vehicles (EVs) that change location, will all require utilities to create hourly load and generation models that reflect spatial and temporal grid conditions to feed next-hour power flow analyses. This bottom-up, consumer-driven disruption will accelerate utilities transformation and their fiduciary responsibility will change from energy supplier to energy traffic controller and financial clearing house. Utilities must adapt to the complexities of bidirectional power flow. This requires a comprehensive understanding of how DER impacts other assets on the grid at any location at any time of the day.

A major paradigm shift in utility systems operations and planning is being driven by consumers who are adding thousands of new generation assets to a grid that was once supported by a handful of baseload generators. The new asset and revenue optimization models necessary to support this transition will come from one major change-the integration of AMI and operational systems in real time. Commercial and operational data silos are adequate for a business with revenue from rates that are based simply on customer class or time of use. The continued proliferation of DER, however, will make these silos untenable.

The 21st century transactive model will require convergence of commercial data and operational data to provide granular spatial and temporal analysis, which is needed for asset valuation, resource planning, increased efficiency and, ultimately, retail locational pricing.

This is no easy task. Commercial and operational data are quite disparate and often stored in applications that are not easily accessible to one another. As DER and other factors drive standard utility ROI formulas to increasing irrelevancy, utilities will need a way to calculate the pricing at every delivery point, or risk rate cases that are unsupportable and revenue projections that are unachievable.

Utilities will require a much more sophisticated, 360-degree holistic view of all grid assets to forecast their economic viability in near real time. Compartmentalized systems such as distributed energy resource management systems (DERMS) are helpful, but alone will not provide the highly granular, disparate data from every asset necessary to incentivize dispatchable load through the creation of price signals. DERMS provide a new layer of insight, but in some new ways they are simply another silo.

New Software Systems Needed

As the industry moves toward a transactive revenue model, settlement of micro transactions at more locations within the distribution network will come increasingly to the fore. To dynamically assess evolving pricing models and ensure a sufficient rate of return, utilities will need a solution that can analyze the cost of service at every delivery point in relation to the revenue from each individual rate payer at any time of the day.

Despite all the buzz, this is not the province of the Internet of Everything, machine learning or artificial intelligence. The solution lies instead in a software platform comprised of business-centric applications that join disparate internal and external, time-series data sources in existing utility operational and commercial systems.

This platform must provide load and generation forecast data for every asset on the distribution grid, with the spatial and temporal granularity required to support operational decision management. This bottom-up approach is the analytical way to aggregate asset forecasts by circuit, substation or other spatial attributes to analyze the impacts of DER assets on other grid assets. The platform must be able to dynamically model local resource requirements based upon actual historical and forecasted load, as well as any DER data for each local circuit. These forecasted localized resource requirement plans are necessary to determine the need for system investments, DER deployments, or a combination of resources to meet local requirements.

Jason Iacobucci is founder and president of PowerRunner LLC. In 2007, he founded the high-value software and consulting company that serves the energy industry. Mr. Iacobucci has more than 15 years of experience in the energy industry, successfully designing and implementing software systems for energy market operations, energy settlements, risk management/deal capture, billing, forecasting and transaction management systems for North American ISOs/RTOs and European central markets, as well as for regulated and deregulated energy industry companies.

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