Advanced Distribution Planning: “A Grid Modernization Solution for Electric Utility Planners”

Gene Shlatz's picture
DIRECTOR | Energy, Sustainability, and Infrastructure Guidehouse

Gene Shlatz is a director in Guidehouse's Energy practice. He has over 25 years' experience in electric utility engineering, operations, and technology assessment. Gene has managed several recent...

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  • Jun 15, 2020

This item is part of the Grid Modernization - Pushing Boundaries - Summer 2020 SPECIAL ISSUE, click here for more

Until recently, most electric utility planners have relied on well-defined parameters for their distribution systems, making planning for the future straightforward and manageable. Traditional planning processes include reasonably predictable load growth and patterns, conventional solutions to address capacity and reliability performance needs, and models based on one-way power flows. With the advent of distributed generation, demand management, and highly variable power flows from distributed energy resources (DER), there is now a need for more comprehensive data, methods, and tools. The arrival of these technologies results in a major transformation of the function of electricity utility planners in the utility organization. Planners view solutions like legislative mandates and public policy initiatives as challenges that can also create additional demands and uncertainty.

Advanced distribution planning (ADP) has garnered a lot of media attention lately. ADP, also referred to as advanced integrated planning or AIP, can roughly be described as a set of processes and systems designed to enable electric utility planners to transform how they plan and design their power delivery system in times of uncertainty, prompted by technological advances, higher customer expectations, and financial constraints. Regulatory uncertainty further clouds the issue for many utilities. These challenges have raised the bar for planners, given the complexity of the power delivery system and expanding mix of competing options that must be addressed in the decision-making process.

The Energy Cloud and Transformation of Traditional Planning

Guidehouse has long promoted its Energy Cloud vision, loosely defined as a mix of technologies, systems, and processes representing the transformation of utility planning from more predictable, one-way flows with centralized resources to a highly complex mesh of traditional and non-traditional sources and two-way power flows. The following figure illustrates this transformation.

The transformation of the utility industry as envisioned in the Energy Cloud underscores the need to transform distribution planning processes, tools, and work practices to incorporate a wider mix of resources and options in an era of increasing uncertainty. Utility planners should consider the following:

  • Modeling load forecasts under different DER adoption scenarios on a more detailed from the ground up basis rather than a top-down basis
  • Stacking bulk system, transmission, and distribution costs and benefits of DER scenarios
  • Considering a broader array of benefits outside of traditional system operations and reliability
  • Modeling and optimization based on collaborative, ongoing processes, leading to continuous improvement of distribution planning
  • Moving from annual or biannual planning to increase the frequency of planning to near-real time to account for rapid changes in the DER landscape

Advanced Distribution Planning Architecture

Most utilities use a broad array of sophisticated tools and databases to support distribution planning and grid modernization activities. These include highly detailed circuit models that accurately predict the effects of alternative approaches and forecasts, including time series analysis of intermittent resources for a wide range of technologies and configurations. Scripting alternatives allows planners to evaluate a vast array of scenarios without the burden of manual repetitive analyses. Revisions to industry standards like IEEE 1547 enable planners to apply formerly novel approaches—such as low voltage ride-through and active inverter management—to the options available to manage the grid. The ability to accurately model these changes on a granular, locational basis is paramount to avoiding unanticipated and undesirable outcomes.

Despite these advances, the primary shortcoming of the planning process is the need to fully account for the composite impacts of planning decisions at the local distribution level over the entire power generation and delivery system. The need to consider impacts at all levels is particularly important when DER are part of the planning process. Because DER forecasts are highly unpredictable, it is critical that the processes and tools planners use to analyze DER scenarios are rigorous yet efficient. The management and oversight of planning is equally critical, as organizations traditionally outside of distribution planning (for example, customer service) are now integral to the successful implementation of ADP. How will distribution planners and managers address these challenges in the landscape of distribution planning?

The following figure presents a high level architecture that integrates the processes, tools, data, and systems needed to successfully implement ADP. Some view the development and offering of a fully integrated set of models with data linkages, coupled with the ability to produce optimized solutions, as the holy grail of simulation modeling. While progress has been made to create linkages among ADP components, a black box system that produces results absent user intervention and assessment does not exist today (despite possible claims to the contrary).

The Evolution of ADP and Success Factors

There must be a focus on planning processes; namely, the idea that effective planning relies on the use of sophisticated tools and the management of these components and associated objectives of planning strategies. ADP helps organize resources across the utility organization to achieve competing objectives, including the following:

  • Enabling integration of DER
  • Enhancing reliability and resiliency
  • Assisting budget management
  • Offering new services and rate incentives to its customers

There are tradeoffs associated with these goals, not the least of which is financial; balancing these disparate objectives may not be the lowest cost solution. There is also a critical data management element as extensive amounts of granular information (if accurately and consistently applied) supports the ADP architecture.

Sound management practices are essential to ensure ADP captures the full range of costs and benefits associated with planning scenarios. The processes outlined in the figure above rely on numerous assumptions and data streams for each modeling component. Absent efficient control and oversight, with proper vetting and confirmation of assumptions and outputs from internal (and in some instances, external) stakeholders, processes can bog down with sub-optimal results and organizational frustration. Senior management buy-in and support of ADP is essential for its successful implementation.

As continuous oversight is required for inputs and outputs, the visualization and management layer within ADP’s architecture is critical to ensuring consistent and accurate outcomes. A standardized, dynamic data exchange for distinct models manages the vast amount of data and results associated with ADP models. Each planning scenario under consideration can produce a stream of data that are essential inputs to several ADP modules. For example, a DER scenario that considers an array of sources such as solar PV, energy storage, and EVs located on distribution circuits throughout an electric utility’s service territory. To accurately assess alternatives, detailed forecasts of electric demand, solar adoption, and EV purchases are required for each circuit. ADP staff will need to be devoted to monitoring these activities, including oversight and vetting via visualization and management systems.

Conclusion and Recommendations

ADP provides utility planners with a set of tools and processes to effectively manage the increasingly complex landscape of alternatives and strategies and transform their way of supporting utility organizations. Models will continue to evolve along with the integration of discrete components such as localized forecasting and automated data transfer among ADP components. A fully optimized solution that incorporates and evaluates numerous scenarios at all levels, from behind-the-meter to grid-connected resources via probabilistic modeling, eventually may come to fruition.

Regardless of the state of advanced modeling, effective and coordinated management controls and support systems are critical components of ADP. To meet the challenges confronted by utility planners, utilities must address the organizational structure needed to successfully maintain ADP.

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Howard Smith's picture
Howard Smith on Jun 26, 2020

I would add this comment which I think is lacking and is very important in your discussion.  I made the following comment in another article.

The forecasting for distribution planning will need to be a 8760 based forecast that looks behind the meter and determines individual firm loadshapes, any "controllable resources" (solar, storage, DSM, dispatchable loads, etc.) and not the net of these.  Also, for the "controllable resources" their will be a need to know who has the control, when they are likely to be controlled and in what time periods, and are they dedicated for customer, retail or wholesale use.  In other words, they will have to be scenarios of load forecasts instead of the traditional single point or in some cases seasonal single point net load forecasts that have been used in the models.

Matt Chester's picture
Matt Chester on Jun 26, 2020

In other words, they will have to be scenarios of load forecasts instead of the traditional single point or in some cases seasonal single point net load forecasts that have been used in the models.

What has been preventing the use of such models in the past-- lack of resources or ability to capture that type of granularity?

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