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Numerous options allow the user to simplify the detailed commitment and dispatch problem needed to perform simulations with reasonable runtime. This may include aggregating hours within a day and using typical days-of-the-week or typical on-peak/off-peak days for each month but still maintaining the chronology needed to enforce ramp rates, storage limits, and commitment requirements.

EnCompass allows runs to be segmented with overlapping periods so that a 1-year simulation is a succession of 7-day simulations with an extra day added on to reduce end effects and initial conditions of subsequent weeks set accordingly. The unit commitment logic level of precision may also be adjusted to produce faster simulations but still account for commitment costs and ancillary service requirements. A run can be split into segments based on some number of months and processed in parallel either on a local machine or grid of connected computers.

EnCompass_Longterm Optimization


All costs, volumes, and constraints in EnCompass are input using a Time Series, which can be just a single entry or multiple future entries that repeat or escalate throughout time. Certain inputs such as Demand and Energy Prices that have daily shapes defined may be escalated annually (maintaining the proper days of the week) while being scaled up or down to match inputs for annual peak and energy (for Demand) or monthly on-peak and off-peak (for Energy Prices). 

Planned maintenance for resources may be entered with historical or planned dates that repeat in the future every 12 or 18 months, for example. Each potential outage may also have an allowable shift allowing EnCompass to determine the optimal timing of planned outages within a region. Unplanned outages can then be randomly determined for each resource based on input forced outage rates and lengths. The structured scenarios within EnCompass make it easy to use the same outage schedule from a long-term parent scenario to multiple child scenarios.

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