# A Decentralized Trading Algorithm for an Electricity Market with Generation Uncertainty

## TL;DR

This paper introduces a decentralized day-ahead energy trading algorithm that manages renewable generation uncertainty and privacy concerns, optimizing costs and profits while minimizing renewable shortage risks in power systems.

## Contribution

It proposes a novel decentralized trading method using Lagrange relaxation and CVaR to handle generation uncertainty and privacy, outperforming centralized solutions.

## Key findings

- Converges in 45 iterations on IEEE 30-bus system.
- Reduces load aggregator costs by 18%.
- Increases generator profits by 17.1%.

## Abstract

The uncertainties of the renewable generation units and the proliferation of price-responsive loads make it a challenge for independent system operators (ISOs) to manage the energy trading market in the future power systems. A centralized energy market is not practical for the ISOs due to the high computational burden and violating the privacy of different entities, i.e., load aggregators and generators. In this paper, we propose a day-ahead decentralized energy trading algorithm for a grid with generation uncertainty. To address the privacy issues, the ISO determines some control signals using the Lagrange relaxation technique to motivate the entities towards an operating point that jointly optimize the cost of load aggregators and profit of the generators, as well as the risk of the generation shortage of the renewable resources. More, specifically, we deploy the concept of conditional-value-at-risk (CVaR) to minimize the risk of renewable generation shortage. The performance of the proposed algorithm is evaluated on an IEEE 30-bus test system. Results show that the proposed decentralized algorithm converges to the solution of the ISO's centralized problem in 45 iterations. It also benefits both the load aggregators by reducing their cost by 18% and the generators by increasing their profit by 17.1%.