Abstract
Electrical power systems with a high share of hydro power in their generation portfolio tend to display distinct behavior. Low generation cost and the possibility of peak shaving create a high amount of flexibility. However, stochastic influences such as precipitation and external market effects create uncertainty and thus establish a wide range of potential outcomes. Therefore, optimal generation scheduling is a key factor to successful operation of hydro power dominated systems. This paper aims to bridge the gap between scheduling on large-scale (e.g., national) and small scale (e.g., a single river basin) levels, by applying a multi-objective master/sub-problem framework supported by genetic algorithms. A real-life case study from southern Norway is used to assess the validity of the method and give a proof of concept. The introduced method can be applied to efficiently integrate complex stochastic sub-models into Virtual Power Plants and thus reduce the computational complexity of large-scale models whilst minimizing the loss of information.
Highlights
Producing electrical energy from hydro power shows two unique characteristics that no other alternative form of electricity generation offers
This paper aims to bridge the gap between scheduling on large-scale and small scale levels, by applying a multi-objective master/sub-problem framework supported by genetic algorithms
As mentioned above, scheduling of hydro power is subjected to various stochastic factors—all originated in both market circumstances and external causes
Summary
Producing electrical energy from hydro power shows two unique characteristics that no other alternative form of electricity generation offers. The renewable, free inflow and the long plant lifespan allow for nearly expense-free production; for another thing, generation units can be interdependent due to physical interconnection of their waterways Such serial correlation of hydro power reservoirs is the main focus point for efficient scheduling of hydro power dominated systems. Considering prices as external uncertain factors allows for them to have similar methods applied on [2] This price taker decision results from a separation in bidding and scheduling models that are state of the art in hydro power dominated systems [3]. After the planning is conducted for this setup, the river basin is calculated in detail [3] to determine specific schedules of the plants Techniques to derive those aggregates are manifold: Ref. For newer systems, this might not be the case and might limit the quality of the results
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