[Objective] In support of China's objectives of “Carbon Peaking and Carbon Neutrality,” there is a significant expansion of clean-energy initiatives, accompanied by concurrent advancements in electricity market reforms. In reality, numerous large-scale clean-energy facilities still face a fundamental decision-making challenge: how to synchronize multi-market bidding with the operational constraints of cascade and pumped-storage hydropower (PSH) systems to maximize the total revenue of a hydro-photovoltaic (PV)-PSH portfolio under realistic settlement rules. Existing studies have offered valuable insights into renewable energy involvement and hybrid dispatch strategies; however, they often (i) focus on a single type of renewable technology, (ii) have a narrow focus on generation-side strategic optimization, or (iii) weakly integrate physical constraints with market participation. This study develops a practical, revenue optimization framework for a clean-energy base that explicitly benefits from multi-functional complementarity among cascade hydropower, PV generation, and PSH. [Methods] A two-level optimization framework is defined to maximize the total expected trading revenue for mid- to long-term contracting, day-ahead market, and real-time market. PV is treated as a priority injection, whereas cascade hydropower and PSH offer balancing and arbitrage functions to adjust the net delivery profile. The objective function combines multi-market revenues and includes settlement elements that adhere to practical guidelines, such as deviation settlement, benchmark settlement, mid-and long-term contract congestion charge, and penalties. From the physical aspects, comprehensive hydropower and PSH restrictions are imposed, including generator output bounds, ramping limits, water balance, reservoir level bounds, release constraints, and hydraulic coupling along the cascade. PSH is represented by mutually exclusive pumping and generating modes with power-flow conversion and head-related constraints. To prevent short-termism in intraday operations, an intraday balancing assumption is introduced to ensure that the daily outflow does not exceed the inflow. Uncertainty in PV output and market prices was captured by developing representative scenarios: historical PV and price time series were reduced using principal component analysis and a clustered K-means approach to produce typical-day scenarios, which were then combined through a Cartesian product with related probabilities. The outer layer optimizes bidding allocation across markets using Gaussian Process Bayesian Optimization, while the inner layer calculates the scenario-wise optimal cascade of hydropower and PSH dispatch through a multi-layer nested dynamic programming algorithm that breaks the cascade into subsystems to reduce complexity and memory usage. [Results] An upstream clean-energy base of the L River, with transactions settled in Guangdong's electricity market, is taken as a case study. This portfolio comprises multiple cascade hydropower stations, a large PV installation, and planned PSH capacity. Results show that, under the pricing structure observed during the studied period, allocating all energy to medium- and long-term contracting maximizes revenue. This suggests that the generator has limited incentives to shift volumes into day-ahead or real-time markets. In this common scenario, hydropower and PSH are highly complementary to PV. During peak PV output hours, cascade hydropower output is reduced, and PSH tends to pump, while during high-price periods and low PV output, hydropower and PSH are generated. Accordingly, the model identifies economically optimal pumping and generating intervals for PSH. Monte Carlo simulations also show that volatility-type forecast noise in PV output and in daily-ahead and real-time prices has a negligible influence on long-term expected revenue, whereas biases in mean PV output and mean price forecasts can significantly change revenue. This highlights that the importance of improving forecast means, particularly for expected PV production and price levels, is more critical for revenue-focused bidding and operation than fine-tuning short-term volatility patterns. [Conclusions] This study provides a consistent and detailed physical methodology for jointly optimizing market participation and hydro-PSH dispatch for clean-energy bases under uncertainty, thereby effectively narrowing the gap between market bidding and constrained multi-reservoir operations. The findings also suggest policy-relevant implications: if medium- to long-term prices remain structurally more attractive than spot prices, the real-time price discovery and balancing value of spot trading may not be fully realized. The limitations include the day-scale focus and the lack of endogenous price impacts from large strategic participants. Future research can broaden the framework to cover multi-day or monthly periods by incorporating intertemporal water value and strategic interaction by game-theoretic market models.
Key words
multi-energy complementarity /
clean energy /
electricity market /
revenue optimization
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