Day-Ahead Energy Market as Adjustable Robust Optimization: Spatio-Temporal Pricing of Dispatchable Generators, Storage Batteries, and Uncertain Renewable Resources
Takayuki Ishizaki, Masakazu Koike, Nobuyuki Yamaguchi, Yuzuru Ueda,, Jun-ichi Imura

TL;DR
This paper models day-ahead energy markets using adjustable robust convex programming, incorporating renewable uncertainty and dispatchable resources, to analyze market behavior and optimal resource management.
Contribution
It introduces a novel market model with a multi-variable prosumption cost function based on a parameterized max-min program, enabling unified evaluation of dispatchability and renewable uncertainty.
Findings
Renewable generators do not always have priority over conventional generators due to uncertainty.
Market merit order can reverse depending on renewable penetration levels.
Optimal battery penetration maximizes individual profits in long-term market evolution.
Abstract
We present modeling and analysis of day-ahead spatio-temporal energy markets in which each competitive aggregator aims at making the highest profit by managing a complex mixture of different energy resources, such as conventional generators, storage batteries, and uncertain renewable resources. First, we develop an energy market model in terms of an adjustable robust convex program. This market modeling is novel in the sense that the prosumption cost function of each aggregator, which evaluates the cost to realize an amount of spatio-temporal energy prosumption, is a multi-variable function resulting from a "parameterized" max-min program, in which the variable of the prosumption cost function is involved as a continuous parameter and the variable of dispatchable resources is involved as an adjustable variable for energy balance. This formulation enables to reasonably evaluate a reward…
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