IEEE Power & Energy Magazine - July/August 2019 - 83

resilience concept and the specific requirements that implement mitigation measures.
To summarize, as the electric power system evolves into
an increasingly complex and dynamic hybrid energy system, it is vital to adequately prioritize and address existing
and emerging challenges and associated risks, including
✔ changing the generation resource mix
✔ BPS planning, adequacy, and performance
✔ joint T&D systems planning and operation
✔ monitoring and controlling DERs in T&D grids
✔ extreme natural events such as earthquakes, hurricanes, floods, fires, and GMDs

✔ IEEE 1547-2018 defines interoperability require-

Summary
A modern hybrid energy system,
mixed with traditional synchronous generation, high-penetration DERs, storage and smart
grid technology-enabled T&D
networks with conventional and
smart energy-efficient loads,
offers a method of delivering electrical energy in a sustainable and
environmentally friendly manner but faces challenges. Those
challenges can be addressed with
advanced technology and interoperability standards, accompanied
by appropriate regulatory frameworks and infrastructure investments. IBRs have the capability
of providing ERSs, if properly
addressed and supp or t e d by
market mechanisms, which, in
turn, would require reliability
standards to price the ERSs. An
adequate mix of fuel-secure and
fuel-independent technologies
helps with maintaining ERSs
and assuring resilient and reliable power system operation.
Incorporating resilience into the
T&D planning process is recommended, including defining the
july/august 2019

Skill Levels

ments and capabilities for DERs regardless of their
size and type. These requirements enable utilities to
monitor and control DERs at their discretion. The
new IEEE Standard 1547 also mandates DERs to
comply with one of three "local" communication
protocols: IEEE Standard P2030.5 (SEP2), IEEE
Standard 1815 (DNP3), or SunSpec Modbus. These
protocols enable utilities to integrate DERs with
their DMSs or DERMSs. Other communication protocols (e.g., IEC 61850) are also allowed as long as the
DER complies with one of the three protocols. IEEE
Standard 1547.1 will provide guidance on the implementation and commissioning of interoperability from
DERs to local electric power
systems and BPSs.
Standards organizations, both
industry and government, should
continue addressing gaps and
develop required standards, e.g., the
standard mentioned in this article.

Types of Analytics
Learning
Optimization

Cognitive

Prescriptive
Predictive

Information

Discovery
Foresight

Diagnostic
Descriptive

Insight

Hindsight
Value
* Descriptive Analytics: Describe past performance of distribution grid by analyzing
historical data, e.g., use service interruption records to calculate reliability indices
(SAIFI, CAIDI, SAIDI, etc.).
* Diagnostic Analytics: Diagnose root cause of distribution system performance,
e.g., to identify the root cause of service interruptions and equipment outages.
* Discovery Analytics: Provide additional insights about distribution grid performance
to identify unknown issues, particularly in areas of the grid that traditionally have had
limited real-time visibility and awareness, e.g., assess grid edge performance.
* Predictive Analytics: Estimate expected distribution grid performance based on
historical and real-time data, e.g., estimate potential equipment overloads that might
occur as a consequence of extreme weather patterns.
* Prescriptive Analytics: Use historical and real-time data along with system analysis
capabilities to provide recommendations regarding preventive measures that would
allow precluding or minimizing performance disruptions, e.g., advice on most resilient
system configuration to withstand major weather events.
* Cognitive Analytics: Use computational intelligence technologies inspired by human
learning (e.g., artificial intelligence techniques such as machine learning, deep learning,
etc.) to collect, process, analyze, and manage qualitative (e.g., natural language) and
quantitative data from diverse sources. Cognitive analytics may be used to develop
adaptive self-learning solutions whose accuracy improves over time.

figure 6. The different types of grid analytics. (Source: ICFAI Business School.)
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IEEE Power & Energy Magazine - July/August 2019

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