The Long Island Power Authority has observed that each step of classic asset management methodologies seems to lead to a need for increased and improved data management, data mining, performance analysis, work prioritization or data process automation tools. This need, combined with the evolution of more data streams scattered in various systems, the growing demands for real-time analysis and more thorough forecasting, makes a next step inevitable.

That next step will combine, orchestrate and automate the various classic tools along with significant real-time data streams to provide powerful operational recommendations while helping the utility manage risk, improve forecasts and optimize decisions. The Long Island Power Authority (LIPA) calls this overarching new process dynamic asset risk management (DARM).


The Viewpoints

Ultimately, DARM will prove to be a helpful decision-enhancing tool at all levels of the organization by providing timely and appropriate information to operators, asset managers, reliability engineers, financial decision makers and executives. Utility data is inherently abundant and complex; therefore, it must be selectively considered from different viewpoints and levels of detail, depending on user needs.

LIPA has chosen the following general viewpoints for risks and goals in its transmission and distribution business:

  • Technical performance
  • Financial performance
  • Regulatory compliance
  • Customer satisfaction.

Putting Risk into Play

An example of how LIPA uses risk assessment can be found in the capital investment program, where risk is measured in six to eight risk categories under each viewpoint. For example, through that process, a reliability improvement or an end-of-life asset replacement project (technical performance) can compete with a billing system improvement (customer satisfaction) or a project with a quick payback (financial performance) for funding. Clearly, the viewpoints used by implementing utilities and the more detailed categorizations can be utility specific.

There are several illustrative examples of where DARM might be applied to a utility’s normal operating business risks:

  • Vegetation management on a line is at normal cycle end
  • A substation battery is near end of life
  • A distribution breaker does not always reclose properly
  • There is a highly loaded circuit
  • Protection relays have exhibited problems and are being phased out
  • A thunderstorm is on the way.

These items are examples of real business issues concerning programs such as storm hardening, maintenance, planning, capital spending, customer satisfaction and the timing of any of those programs. While each of these risks can exist at any time relative to a particular asset, what if three, four or five of these exist in the same place with the same set of assets? Importantly, although a real-time coincidence of these risks would be a concern, it is perhaps more important to forecast these coincidences so the risk can be avoided altogether. Utilities often miss the opportunity to get ahead of these types of cascading or increasing risk problems.