DMC490 Overview
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Figure 12 Prediction horizon
Different routines within the optimization framework are formulated and solved at each time step (for example,
12 minutes) over the prediction horizon (24 hours) based on load, renewable resources, and price forecasts. In
this framework, a variety of operational considerations are factored in. These include and are not limited to the
support of a hydro unit in isochronous mode, minimum up/down times required for storage charging,
interaction with the grid, and support of manual-start dispatchable generators.
The objective function of the optimization problem can, in general, include the following terms:
•
Fuel/operation costs of all power generation devices in the microgrid
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Cost/incentive terms for storage device charging/discharging. These are more subjectively
determined, being driven by the requirement to prevent simultaneous charging and discharging and
the need to limit storage cycling.
•
Penalty terms mainly related to those generation/storage devices allowed to have their limits on
minimum powers violated (that is, having soft constraints)
•
Power importing/exporting costs/revenues when the microgrid is grid-connected
Also, the constraints of the optimization problem capture the following limitations for both electrical and
thermal systems:
•
Minimum and maximum values of generated power with the consideration of their isochronous or
non-isochronous operation, and the microgrid reserve margin requirements
•
Limits on importing/exporting powers considering the microgrid reserve power requirements (for grid-
connected microgrids)
•
Power balance in the microgrid considering the contribution of power generation/storage devices,
load, and any grid
•
Minimum, maximum, and initial values of storage devices state of charge
•
Limits on storage input and output powers
•
The energy balance equation of storage devices representing the storage state of charge in each time
step based on its value in the previous time step as well as charging and discharging powers with the
consideration of the related efficiencies and standby losses
When the prediction horizon is long enough, the algorithm can determine when to charge storage, because it
can anticipate times when the loads are large and when the stored power can be utilized. The optimal dispatch
algorithm implemented within the microgrid controller can be configured for up to 32 resources. Assuming a
worst-case scenario where all resources are committed, the optimization problem can have on the order of
20,000 variables and 40,000 constraints.
Incorporation of CHP Plants
Combined heat and power (CHP) is the generation of electricity and heat in a single process. Inclusion of CHP
plants in the microgrid significantly increases the overall efficiency of the system by using the hot exhaust
gases from the gas turbine to heat water. Recovered heat can also be used for district heating or covering the
heat demand requirement of the system. The incorporation of CHP plants introduces another energy source
(natural gas) and another energy carrier (heat) into the system.
A CHP plant can be modeled by constant conversion ratios. The conversion ratios indicate efficiency. For
example, a gas-to-electricity conversion ratio of 0.35 means that 35% of the energy content of natural gas is
converted to electricity and the remainder is in the form of heat. Two dimensionless conversion ratios for gas-
to-electricity (rge) and gas-to-heat (rgh) are defined as shown in the following figure. Typical values of rge and
rgh are 0.35 and 0.45, respectively, considering a 20% parasitic loss.
Summary of Contents for DMC490
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