Abstract

The stochastic nature of renewable energy sources has increased the need for intraday trading in electricity markets. Intradaymarkets provide the possibility to the market participants to modify their market positions based on their updated forecasts. In this paper, we propose a multistage stochastic programming approach to model the trading of a Virtual Power Plant (VPP), comprising thermal, wind and hydro power plants, in the Continuous Intraday (CID) electricity market. The order clearing in the CID market is enabled by the two presented models, namely the Immediate Order Clearing (IOC) and the Partial Order Clearing (POC). We tackle the proposed problem with a modified version of Stochastic Dual Dynamic Programming (SDDP) algorithm. The functionality of our model is demonstrated by performing illustrative and large scale case studies and comparing the performance with a benchmark model.

Highlights

  • D UE to the inherent stochasticity of Variable Renewable Energy Sources (VRES) the traded volumes in the short-term electricity markets are on the rise [1]

  • We consider the gate-opening of the Continuous intraday (CID) market to be at 15:00 CET of the D-1 while the gate-closure is at 23:00 CET for Delivery Product (DP), d = 1

  • We have modeled the problem of a Virtual Power Plant (VPP), with wind, hydro and thermal power generation portfolio, participation in the continuous intraday market as a multistage stochastic integer programming problem (MSSiP)

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Summary

INTRODUCTION

D UE to the inherent stochasticity of Variable Renewable Energy Sources (VRES) the traded volumes in the short-term electricity markets are on the rise [1]. As per December 2021, 23 European countries are coupled to trade in the intraday market through a single trading platform called Single Intraday Coupling (SIDC) [3] The trade in this SIDC platform is organized using a CID mechanism, making this mechanism relevant to the market participants across Europe [3]. SDDP algorithm is a multi-stage Benders decomposition algorithm, which utilizes sampling approaches to counter the curse of dimensionality, considering the stage coupling and uncertainties. It was proposed in the seminal work of [7]

Literature Review
BACKGROUND
PROBLEM FORMULATION
Technical Constraints of the VPP’s Portfolio
Order Clearing Models
Solution Methodology
STOCHASTIC PROCESS MODELING
RESULTS AND DISCUSSION
Illustrative Example
Comparison with the Deterministic Solution
CONCLUSIONS
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