IEEE Power & Energy Magazine - March/April 2020 - 72

The knowledge gained on use-case specifics and relative
economic benefits could provide useful information for
binding multiple services together.
of the utility's asset pool for minimizing the global, day-today system operation cost. For the first case, the identification and evaluation of a set of economic opportunities, or
use cases, in relation to the local portion of the network are
performed, and the use cases could be implemented through
a site controller. For the second case, the ESS will need
to be integrated within a central optimization framework
(e.g., an economic dispatch model) of the utility, and ESS
control commands would be derived from dispatch instructions issued by the utility's system operation platform. This
platform is essentially a suite of software tools used for dispatching resources economically while satisfying system
energy, capacity, and ancillary service needs and resource
cost and capability constraints. The module that performs
the dispatch task is often referred to as an energy management system (EMS). The following are some key reasons
why the direct implementation of the second approach could
be difficult for a utility without prior experience on ESS
integration and operation:
✔✔ The ESS must be modeled using its performance
characteristics, and integrating constraints into the
EMS platform and dispatch signals will be created
based on system-level economic dispatch requirements, as shown in Figure 3. However, this process
requires significant time and effort due to modeling
complexities, system integration, testing, and validation requirements.
✔✔ The modeling of the ESS for integration with the system operation platform requires information that may
be difficult to obtain without analyzing operational
data; local economic operation could provide such
information.
Because of these reasons, it is not uncommon for utilities to start the operation of an ESS using a local controller
without its full integration with a system operation platform.
A similar approach was used for the SSPC ESS. When first
developed, the utility tested the ESS capabilities to perform
various use cases in an individual fashion. However, those
use cases were not in regular operation. During the initial
phase of the control capability expansion project, control
strategies were built to perform multiple use cases through
a rule-based coordination strategy. Currently, the utility is
working to integrate the SSPC ESS with its system operation platform. A detailed discussion on this aspect is presented in the "Integration of ESS with System Operation
Platform" section. A site controller could be designed to
switch between operations guided by a system operation
72	

ieee power & energy magazine	

platform and automatic or operator-fed dispatch schedules
for local asset benefit maximization. The rest of this section
is focused on control expansion for benefit maximization of
the ESS as a local asset. A summary of the SSPC ESS economic evaluation findings is presented in the following section,
followed by the approach used for control strategy development and implementation.

Economic Evaluation of Use Cases-The Basis
of Control System Expansion
Following discussions with PGE personnel and assessments performed by Pacific Northwest National Laboratory (PNNL), nine use cases were considered for economic
evaluation. PNNL's battery storage evaluation tool was used
to perform an hourly look-ahead optimization to determine
the ESS power schedules with tradeoffs among different
services while taking into account battery performance
parameters (e.g., round-trip efficiency) and operational limits (e.g., power/state of charge limits and annual usage obligations) and technical and financial (e.g., energy/capacity/
ancillary service price) aspects of each use case. The tool
was then used to simulate battery operation and estimate
the co-optimized value of the modeled services. The economic evaluation also provides information on various use
cases that would be beneficial for implementation of the
use-case control strategies, i.e., ranking the benefits from
use cases, ranges of state-of-charge (SoC) variation while
performing the use cases, typical conditions that cause transition between use cases or causes an interruption (e.g., the
SoC out of limit). (The detailed modeling and formulation
of this method can be found in Balducci et al. in the "For
Further Reading" section.) Figure 4 presents a schematic
of the economic evaluation process including the estimated
net present value benefit of the use cases considered over
a lifecycle of 20  years, with an ESS replacement after
10 years of operation.

Development of Control Strategies
One key aspect that differentiates between the economic
evaluation for determining maximum benefit and the
implementation of control strategies to maximize the benefit is that the evaluation works on historical or projected
data and estimates the benefits over a given time horizon
assuming perfect foresight while during actual operation, a
control system needs to make a decision in real time based
on the prevailing situation. Therefore, a co-optimization
process used for economic evaluation cannot be directly
march/april 2020



IEEE Power & Energy Magazine - March/April 2020

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - March/April 2020

Contents
IEEE Power & Energy Magazine - March/April 2020 - Contents
IEEE Power & Energy Magazine - March/April 2020 - Cover2
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