Internet data centers are booming in the last few years, leading to gigantic amount of energy consumption and greenhouse gas emission. In this paper, a data center microgrid including conventional units, energy storage, and renewable generation is modeled to optimize its electricity bill and carbon footprint. Considering the randomness of electricity price, renewable output and workload distribution, the volatility risk is incorporated to measure the associated operation risk. Then, a day-ahead emission-aware stochastic resource planning scheme is formulated to decide the strategy on power procurement, energy storage operation, batch workload allocation, and unit commitment of conventional units. Simulation results show the effectiveness of the proposed approach.

With the deepening of power market reform and the large-scale integration of bidirectional systems such as energy storage and electric vehicles, achieving sustainable carbon management has become increasingly urgent. Traditional carbon emission accounting methods face challenges, including insufficient dynamics and unclear responsibility boundaries. To address these issues, this paper proposes a sustainability-oriented accounting method for indirect carbon emissions from electricity in the context of bidirectional system integration in the power market environment. First, the dynamic carbon emission characteristics of bidirectional systems such as energy storage and vehicle-to-grid (V2G) systems are analyzed, and a carbon emission accounting model is constructed to address the fairness issue of emission responsibility allocation during charging and discharging. Second, on the basis of the theory of carbon emission flows and incorporating electricity trading contract data, an accounting method for indirect carbon emissions from electricity in green electricity trading, coal-fired electricity trading, and hybrid scenarios under bidirectional system integration is developed. Finally, the case study demonstrates that the proposed method accurately captures the temporal variation of carbon emission factors, ensures conservation of total emissions, and fairly redistributes carbon responsibility among users under different market scenarios, while revealing how bidirectional systems and green electricity trading reshape nodal carbon intensities and spatial emission distributions without causing double counting.

The rapid rise of renewable energy penetration in electricity markets is fundamentally altering market dynamics and the clearing process. Traditional bidding strategies struggle to accommodate the variability and uncertainty introduced by high shares of wind and solar generation. This paper proposes a non-cooperative bidding strategy optimization framework based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that explicitly addresses the challenges of market clearing under a high proportion of renewable energy. By modeling multiple heterogeneous market participants—including conventional thermal units, renewable energy units, and energy storage devices—in a non-cooperative game setting, we employ a MADDPG-based deep reinforcement learning approach to simulate and optimize their bidding decision-making processes. Renewable output uncertainty is incorporated into the environment model, allowing agents to learn robust strategies that anticipate and respond to fluctuations in wind and solar generation. Experimental simulations under a high-renewable penetration scenario demonstrate that the proposed MADDPG approach not only enhances the profitability of individual market participants but also improves overall market stability and clearing efficiency.

The accelerating transition toward carbon neutrality necessitates the synergistic integration of power and hydrogen systems to mitigate renewable intermittency; however, coordinating regulatory policies with the operational flexibility of these coupled systems remains a critical challenge under deep uncertainty. Motivated by this gap, this study develops a bilevel carbon price-coupled optimization framework for integrated power–hydrogen systems, aiming to coordinate environmental policy design with operational scheduling under deep uncertainty. The upper-level model represents the decision-making of a market regulator that determines the optimal carbon price and emission allowances to maximize overall social welfare, while the lower-level model captures the coordinated operation of electricity and hydrogen subsystems that minimize total dispatch cost, including renewable utilization, electrolyzer conversion, and fuel-cell recovery.To address stochastic variations in renewable generation and load demand, a Distributionally Robust Optimization (DRO) formulation is introduced using Wasserstein ambiguity sets, ensuring decision feasibility against worst-case probability distributions. The bilevel structure is efficiently solved via a Benders–Column-and-Constraint Generation (CCG) algorithm, which decomposes policy and operation layers into tractable subproblems with provable convergence. Case studies on a 33-bus integrated power–hydrogen network demonstrate that the proposed framework effectively balances economic efficiency and carbon reduction. Results show that the optimal carbon price of approximately 45 $/tCO₂ achieves a 27% emission reduction with only a 9% cost increase, revealing a near-optimal social welfare equilibrium. Hydrogen subsystems operate flexibly, with electrolyzer utilization increasing by 30% and storage cycling deepening by 15%, enabling enhanced renewable absorption. Sensitivity analyses confirm that the DRO layer reduces operational risk by 4% compared with stochastic optimization, validating robustness against distributional shifts. The study provides a rigorous and computationally efficient paradigm for policy-coordinated decarbonization, highlighting the synergistic role of carbon pricing and cross-energy scheduling in the next generation of resilient low-carbon energy systems.