DRL-Based Medium-Term Planning of Renewable-Integrated Self-Scheduling Cascaded Hydropower to Guide Wholesale Market Participation

TL;DR

This paper introduces a deep reinforcement learning framework for medium-term planning of renewable-integrated cascaded hydropower, improving water management and profitability in wholesale market participation.

Contribution

It develops a DRL-based method that considers seasonal storage needs and short-term profits, enhancing planning accuracy over traditional optimization or rule-based approaches.

Findings

Outperforms traditional methods in real-world tests.

Balances reservoir storage and profit objectives effectively.

Accelerates training using multi-parametric programming.

Abstract

For self-scheduling cascaded hydropower (S-CHP) facilities, medium-term planning is a critical step that coordinates water availability over the medium-term horizon, providing water usage guidance for their short-term operations in wholesale market participation. Typically, medium-term planning strategies (e.g., reservoir storage targets at the end of each short-term period) are determined by either optimization methods or rules of thumb. However, with the integration of variable renewable energy sources (VRESs), optimization-based methods suffer from deviations between the anticipated and actual reservoir storage, while rules of thumb could be financially conservative, thereby compromising short-term operating profitability in wholesale market participation. This paper presents a deep reinforcement learning (DRL)-based framework to derive medium-term planning policies for VRES-integrated S-CHPs (VS-CHPs), which can leverage contextual information underneath individual short-term periods and train planning policies by their induced short-term operating profits in wholesale market participation. The proposed DRL-based framework offers two practical merits. First, its planning strategies consider both seasonal requirements of reservoir storage and needs for short-term operating profits. Second, it adopts a multi-parametric programming-based strategy to accelerate the expensive training process associated with multi-step short-term operations. Finally, the DRL-based framework is evaluated on a real-world VS-CHP, demonstrating its advantages over current practice.