With the rapid development of urban industry, park micro-energy grids (PMEG) have been widely promoted and applied. In order to balance the supply and demand of multiple energy types within the micro-energy grids, how to reasonably allocate its energy storage capacity has become a key issue to be solved urgently. To this end, the paper considers integrating hydrogen production, storage and utilization within the PMEG, and proposing a PMEG system framework incorporating hybrid energy storage system (PMEG-HESS). Secondly, it introduces a method for quantifying flexible ramping capacity (FRC) demand based on Fisher optimal segmentation and conditional generative adversarial networks (CGAN), combining this with the FRC supply of the PMEG-HESS to quantify the flexibility sufficiency. Subsequently, a two-level capacity optimization allocation model for PMEGHESS is constructed, accounting for flexibility sufficiency and stepped carbon emission trading. Finally, the results of the case study show that: (1) The FRC demand quantification method proposed in this paper effectively preserves the temporal and fluctuating characteristics of renewable energy. As the number of sample days increases, the CGAN model more accurately captures renewable generation features, thereby enhancing the precision of FRC demand calculations. (2) Compared to conventional low-carbon economic dispatch, the proposed capacity allocation method reduces annual upward and downward FRC deficits by 44.17 MW and 96.55 MW respectively, at an additional total cost of only 2 %. This achieves a balance between the economic, low-carbon, and flexible trade-off for PMEG-HESS. (3) stepped carbon emission trading can stimulate PMEG's production and utilization of green hydrogen, reduce its reliance on high-carbon electricity, and indirectly enhance the upward FRC supply of the PMEG-HESS.

The transition toward deeply decarbonized energy systems requires optimization frameworks that can simultaneously capture long-term dynamics, operational reliability, and contractual stability while managing multiple forms of uncertainty. This paper introduces a comprehensive modeling and solution framework for long-term welfare optimization of virtual power plants, where seasonal, annual, and rolling horizons are jointly considered under constraints of network feasibility, renewable integration, reliability assurance, and carbon accountability. A unified welfare objective is formulated to internalize operating cost, curtailment penalties, reliability risk, and carbon charges, with constraints codifying the technical physics of dispatch, reserve adequacy, and contract coverage. The methodology employs a distributionally robust optimization layer combined with scenario reduction, stability metrics, and fairness tracking to ensure computational tractability and resilience to stochastic variations in renewable output and demand. A case study on a 33-bus system with heterogeneous virtual power plants demonstrates the effectiveness of the approach. Results show that the proposed optimization reduces total seasonal welfare costs by 8-13%, cuts curtailment by up to 45%, and lowers overload probabilities on critical lines by 20-30%. Attribution analysis reveals that 55% of carbon abatement arises from curtailment relief, 25% from redispatch optimization, 12% from loss reduction, and 8% from contract rebalancing, underscoring the multi-mechanistic nature of emission savings. The contributions of this paper are fourfold: the design of a multi-layered welfare optimization model for long-term horizons, the integration of distributionally robust techniques with fairness and stability considerations, the demonstration of quantitative improvements in both welfare and reliability, and the attribution of carbon reduction across complementary drivers. Together, these elements provide a rigorous and adaptable blueprint for optimizing future low-carbon virtual power plant systems under uncertainty.

Facing the demand of new power system construction and dual-carbon target landing, aiming at the problem that the coupling of electricity and carbon trading is generally ignored in the operation optimization of the existing integrated source-grid-load-storage park and only a single time scale is considered. This paper constructs a multi-time scale operation optimization model of source-grid-load-storage considering the coupling of electricity and carbon trading. First, we analyze the coupling mechanism of electricity and carbon trading and the flexible operation characteristics of various resources, and construct the trading structure of the source-grid-load-storage integrated park in the electricity and carbon market. Next, based on the coordination mechanisms across multiple time scales, a multi-time-scale trading and operation framework is developed. Then, integrating the carbon market, ancillary service market, and day-ahead, intra-day, and real-time electricity markets, a hierarchical multi-time-scale optimization model is constructed using model predictive control theory, solved layer by layer. Finally, an example simulation is carried out. The research results show that participating in multi-market collaborative transactions including auxiliary service market, carbon market and day-ahead, intra-day and real-time electric energy market is the key to ensure the low-cost and low-carbon operation of the source-grid-load-storage integrated park. The multi-time scale transaction operation framework of "day-ahead direction, intra-day repair deviation and real-time balance" ensures the fine operation needs of the source-grid-load-storage integrated park. The single-day operation cost is reduced by about 21.82% and significantly improving economic efficiency, low-carbon performance, and operational flexibility.

The carbon emission trading policy is a key policy for China to achieve its dual carbon goals. This paper aims to examine the emission-reduction effects, transmission mechanisms, and carbon-market efficiency of China's carbon-emission trading policy from 2012 to 2023. We adopt the difference-in-differences (DID) model to analyze the effects of policy on emissions, and the empirical results from the DID model confirm that the pilot carbon emission trading policy has significantly reduced carbon emission intensity in pilot areas. Then we use the mediation model to study the transmission mechanism of the pilot carbon emission trading policy, and the mediation analysis demonstrates that the pilot carbon emission trading policy achieves emission abatement through four parallel transmission channels: scientific innovation, energy conservation, clean energy substitution, and industrial structure upgrading. Data envelopment analysis evaluates the carbon market efficiency of China. The result shows that the average carbon market efficiency of pilot areas has improved steadily, particularly from 2012 to 2023, especially in Beijing, Hubei, and Guangdong. Moreover, the efficiency of the national carbon market has shown an upward trend since its 2021 launch; it remains lower than the pilot average, constrained primarily by limited sectoral coverage, which impacts scale efficiency.

The rapid proliferation of renewable energy sources has introduced multi-layered uncertainty into modern power system operation, challenging conventional deterministic and stochastic optimization frameworks. To address this complexity, this study proposes a deep learning-assisted distributionally robust optimization (Deep-DRO) framework designed to enhance both economic efficiency and operational reliability under uncertainty. The model integrates a hierarchical coordination architecture, wherein deep learning modules infer the probabilistic structure of uncertain variables-such as solar irradiance, wind availability, and load fluctuation-while the DRO layer enforces system-wide robustness through an adaptively reshaped ambiguity set. The learning-assisted ambiguity reconstruction enables the optimization to dynamically adjust conservativeness, improving the tradeoff between cost, reliability, and renewable utilization. Methodologically, the proposed framework employs a multi-agent dispatch structure consisting of three decision layers-county, feeder, and distributed energy resource (DER)-each learning distinct policy mappings through reinforcement-guided coordination. Deep networks trained on high-resolution meteorological and operational data estimate scenario distributions, while the robust optimization core minimizes expected cost and reliability penalties under distributional ambiguity. The resulting hybrid system seamlessly couples data-driven forecasting and model-based optimization, bridging the gap between predictive intelligence and operational robustness. To ensure scalability and interpretability, convergence diagnostics, sensitivity analyses, and cost decomposition studies are performed across multiple test systems and uncertainty scenarios. Simulation results on a benchmark multi-region distribution network demonstrate substantial performance gains. Compared to conventional DRO, the Deep-DRO model reduces total operational cost by 11.0-13.5%, improves reliability indices from 0.864 to 0.911, and raises renewable utilization from 85.6% to 89.7%. The integrated deep learning mechanism effectively captures latent correlations among stochastic parameters, enabling the system to maintain resilience even under 30% higher uncertainty variance. Furthermore, carbon emissions decline by 28.6% relative to baseline, confirming that the proposed method achieves an intrinsic balance between economic optimization and environmental sustainability. The analysis reveals that hierarchical learning fosters adaptive coordination among agents, while the robust layer guarantees performance consistency across uncertain conditions. The study thus advances a generalizable paradigm for intelligent, risk-aware energy management, offering theoretical and practical implications for future power system restoration, smart grid autonomy, and sustainable dispatch design.

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN-PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs.

This study presents a hybrid reinforcement learning-assisted distributionally robust optimization (RL-DRO) framework for resilient and low-carbon energy system operation under uncertainty. The proposed model integrates a multi-agent reinforcement learning structure with a Wasserstein-metric distributionally robust formulation to capture both adaptive decision-making and conservative risk management. Reinforcement learning agents, representing distributed subsystems such as renewable generators, storage units, and flexible loads, are trained to minimize a composite objective combining expected cost and risk, while the DRO layer ensures robustness against distributional ambiguity. A case study on a renewable-dominated microgrid demonstrates that the RL-DRO framework converges smoothly within 4000 training iterations, achieving a 9.7 % reduction in expected cost and a 28 % improvement in robustness compared with stochastic optimization. The optimal ambiguity radius balances efficiency and resilience, while renewable curtailment and storage utilization exhibit clear compensatory dynamics across uncertainty scenarios. Emission trajectories show an exponential decay from 200 to 140 tCO[Formula: see text] across learning epochs, confirming the model's ability to internalize environmental objectives. Overall, the RL-DRO architecture unifies data-driven learning and mathematical robustness, enabling distributed agents to achieve stable coordination and sustainable operation under high renewable penetration. The framework establishes a practical foundation for intelligent, risk-aware, and carbon-efficient decision-making in modern power systems.

High renewable penetration makes imbalance settlement inseparable from the physical decisions governing reserve procurement and flexibility activation. This paper develops a decision-focused learning-based optimization framework that trains renewable-deviation and flexible-resource deliverability representations through downstream dispatch, reliability, and settlement consequences. The mathematical contribution is a settlement-aware learning objective that couples learned uncertainty, resource-time credible-capacity certification, network-constrained multi-stage dispatch, and counterfactual marginal-contribution allocation while retaining an exact revenue-adequacy identity. The 33-node Zhangjiakou-type regional case uses 15 min intervals and comprises five resource classes: independent storage, data-center flexibility, industrial adjustable load, commercial demand response, and electric-vehicle aggregation. Relative to a fixed-ratio reserve rule, the proposed method lowers the regional balancing cost from 950 to 618 thousand USD (34.9%), achieves 97.8% renewable accommodation, limits the shortage probability to 0.7%, and attains a settlement-fairness index of 0.92. The framework solves a 500-asset instance in 118 s. External validation uses 4027 half-hour observations from the 2025 Elexon/BMRS market, including measured wind and solar output, day-ahead forecasts, load, imbalance prices, and procured-reserve prices. On the 1487-interval December test set, the proposed model reduces the replay cost from 2953.3 to 2598.2 thousand GBP (12.0%), decreases the shortage-interval frequency from 4.64% to 1.28%, and reaches 99.74% renewable accommodation. Comparisons with forecast-then-optimize, Wasserstein distributionally robust optimization, off-policy reinforcement learning, and graph-based behavioral cloning establish that the improvement comes from jointly learning which uncertainty matters for dispatch and which flexible capacity is deliverable.

High renewable penetration and large-scale green hydrogen production are accelerating the formation of the new-type power system (NTPS), in which electrical dispatch, electrolysis, hydrogen storage, fuel-cell reconversion, and flexible demand must be coordinated under nonlinear network physics and uncertain renewable, load, and hydrogen-demand trajectories. This study develops a physics-informed distributionally robust multi-agent reinforcement learning (PI-DRO-MARL) framework for coordinated NTPS operation with integrated electricity–hydrogen coupling. The operational objective is to minimize worst-case expected operating cost, including generation and grid-exchange cost, electrolysis and hydrogen-delivery cost, storage degradation, renewable curtailment, and load- or hydrogen-shedding penalties, while satisfying AC power-flow balance, voltage limits, line-loading limits, ramping limits, battery state-of-charge constraints, hydrogen-storage dynamics, and electrolysis/fuel-cell conversion constraints. The framework embeds physics-informed residuals and projection operators into a centralized-training decentralized-execution architecture; represents renewable, electrical-load, hydrogen-demand, and price uncertainty through statistically calibrated Wasserstein ambiguity sets; and trains agents with robust value estimation and feasibility-aware action correction. Validation is conducted on a modified IEEE 33-bus distribution network coupled with a 12-node hydrogen system, with additional scalability checks on modified IEEE 69-bus and IEEE 123-node reference systems. Across ten random seeds, the primary case shows an operating cost of USD 8850 with a 95% confidence interval of USD 8770–8940, a mean constraint-violation rate of 0.37%, and a shifted-scenario cost increase of 12.6%, outperforming deterministic optimization, stochastic programming, standard reinforcement learning (RL), proximal policy optimization (PPO), soft actor–critic (SAC), multi-agent deep deterministic policy gradient (MADDPG), constrained RL, safe RL, and robust RL baselines. Ablation, Wasserstein-radius, time-step, and stress-test analyses further show that distributional robustness, physics-informed projection, and multi-agent coordination provide distinct and complementary benefits. The results support PI-DRO-MARL as a simulation-validated architecture for real-time, uncertainty-aware NTPS dispatch, while field deployment still requires digital-twin calibration, hardware-in-the-loop testing, and site-specific operational validation.

This paper proposes a hybrid reinforcement learning-assisted distributionally robust optimization (RL-DRO) framework for robust and economically efficient energy management in interconnected multi-microgrid systems under renewable, demand, and price uncertainty. The framework integrates deep reinforcement learning to generate adaptive scheduling policies with a Wasserstein-metric distributionally robust optimization formulation that enhances robustness against probability distribution shifts and non-stationary uncertainty. The upper level maximizes cumulative rewards of reinforcement learning agents representing individual microgrids, while the lower level optimizes power dispatch and energy exchange decisions subject to operational and network constraints. A five-microgrid test system equipped with photovoltaic generation, battery storage, and flexible loads is evaluated using 300 stochastic scenarios derived from historical data. Simulation results demonstrate that the proposed RL-DRO framework achieves a superior trade-off between cost efficiency and operational robustness when compared with deterministic, stochastic, and standalone reinforcement learning benchmarks. Specifically, the framework reduces expected operational cost by 14.8%, improves operational feasibility and service continuity as reflected by a proxy-based resilience indicator from 84.5% to 96.1%, and decreases the loss-of-load probability from 4.8% to 2.1%. Furthermore, the proposed approach maintains near-optimal performance as the Wasserstein ambiguity radius increases to 0.25, highlighting its robustness to distributional shifts and adverse uncertainty realizations. Rather than modeling explicit physical disturbances or fault-driven contingencies, the proposed framework focuses on sustaining feasible, adaptive, and cost-effective operation under severe uncertainty and stressed operating conditions. The hybrid learning-optimization paradigm thus unifies data-driven adaptability with theoretical robustness, providing a scalable and uncertainty-aware pathway for autonomous operation of future distribution networks.

This paper develops a bilevel multi-market coupling optimization framework to analyze the strategic participation of nuclear power plants in modern electricity systems where energy, reserve, and capacity markets are simultaneously cleared. The upper-level problem represents the Independent System Operator's objective of maximizing system-wide social welfare under network, reserve, and carbon-cap constraints, while the lower-level problem captures the nuclear operator's profit maximization subject to ramping limits, minimum uptime requirements, fuel-cycle depletion, and deliverability restrictions. By embedding these technical constraints into a bilevel structure reformulated through tractable complementarity conditions, the model captures the interdependence of nuclear scheduling, reserve deployment, capacity commitments, and carbon compliance in a single optimization environment. The proposed framework is applied to a stylized but realistic case study with 96-h time resolution, 12-bus network topology, and detailed representations of wind variability, demand elasticity, and emission caps. The model quantifies how nuclear participation displaces 40,000 tCO2 over the horizon, raises producer surplus by 12 percent, and increases total social welfare by nearly 18 percent when all three markets are coupled, relative to an energy-only benchmark. Nuclear profitability is shown to be highly sensitive to renewable volatility, with +/- 20 percent swings in wind uncertainty altering profits by 0.24 million USD. Reserve deliverability emerges as the second most influential driver, while policy variables such as carbon price shifts play a smaller role. Reliability analysis based on the complementary cumulative distribution of unserved energy demonstrates that joint market clearing reduces the probability of load shedding at the 0.5 percent threshold from 8 percent in energy-only markets to 2 percent under full coupling. Overall, the study provides the first integrated modeling treatment of nuclear bidding across energy, reserve, and capacity markets within a bilevel optimization framework. By jointly considering operational constraints and policy targets, the framework reveals how nuclear power can simultaneously improve economic efficiency, enhance system reliability, and support carbon mitigation. The results highlight that nuclear's value extends well beyond baseload energy provision, with multi-market strategies offering measurable gains for both individual operators and social welfare under conditions of uncertainty and constraint.

Against the backdrop of the increasing penetration of renewable energy and the rising frequency of compound extreme weather events, power systems are facing dual challenges associated with declining reliable capacity and increasingly stringent flexibility constraints. To address the coupled inadequacy of capacity and flexibility, this paper proposes a two-stage robust planning model for the coordinated optimization of capacity and flexibility under extreme weather conditions. First, a compound extreme weather identification and probability assessment model considering the coupling effects of multiple meteorological factors is developed. Based on this model, a method for quantifying the incremental capacity and flexibility requirements under extreme conditions is proposed. Subsequently, a two-stage adaptive robust planning framework is established for capacity-flexibility co-optimization. A regional distribution network is employed as a case study, and multiple scenarios are designed for comparative evaluation. The results demonstrate that: (1) incorporating compound extreme weather conditions improves the accuracy of characterizing the capacity and flexibility regulation boundaries of source-grid-load-storage (SGLS) resources; (2) through the quantification of incremental capacity and flexibility requirements, the expected energy not supplied (EENS) is reduced by 41%, while the ramping reserve ratio increases by 9.2%; and (3) the proposed improved two-stage robust planning model decreases the total system cost by 14.7%. Overall, the proposed SGLS integrated two-stage robust planning model effectively reduces electricity supply costs, enhances system reliability and ramping capability, and provides practical technical support for the planning and operation of new power systems under extreme weather conditions.

The increasing penetration of renewable energy resources and the rising volatility of wholesale electricity prices introduce significant uncertainty into tariff design and retailer procurement decisions. Conventional tariff-setting approaches typically rely on deterministic forecasts or limited scenario analyses, which may underestimate tail risks and fail to ensure equitable cost allocation among consumers and retailers. Moreover, existing regulatory frameworks often lack an integrated mechanism for jointly considering consumer welfare, retailer profitability, and system-level financial risk exposure, particularly under distributional uncertainty. To address these tensions, we propose a two-level model in which the upper-level regulator maximizes a risk-adjusted social welfare metric that incorporates consumer surplus, retailer surplus, and penalties for variance and tail risk, while the lower-level retailers optimize their own profit given retail tariffs, wholesale procurement, and imbalance penalties. The framework embeds stochastic demand elasticity across heterogeneous consumer segments and introduces hedging portfolios composed of forwards and call options to capture realistic financial risk management strategies. Distributional robustness is incorporated through a Wasserstein ambiguity set that models uncertainty in wholesale prices, renewable availability, and demand response distributions. Methodologically, the model is reformulated as a tractable single-level mixed-integer program using Karush–Kuhn–Tucker conditions for the lower-level retailer problem, Big-M linearizations for complementarity constraints, and epigraph-based linearizations for variance and conditional value-at-risk terms. This reformulation enables efficient solution by state-of-the-art solvers and provides convergence guarantees. To enhance scalability, the model is equipped with a Benders decomposition procedure that separates scenario-based risk evaluation from tariff and hedging decisions. Computational experiments demonstrate convergence within forty iterations to sub-percent optimality, confirming tractability for realistic day-ahead instances with 24 hourly blocks and 100 stochastic scenarios. A case study based on a stylized 1,100 MW wholesale system, with ten thermal generators and five renewable sites, illustrates the economic and operational implications of alternative tariff designs. Results show that real-time pricing yields the highest expected welfare, exceeding time-of-use by nearly one million dollars per day, but also exposes consumers to bill variability of up to 25–30 percent in high-risk scenarios. Time-of-use tariffs achieve a balanced compromise, improving expected welfare by 15 percent relative to flat pricing while maintaining tail risks within manageable bounds. Flat tariffs, while stable, impose high hidden welfare losses due to inefficient resource allocation. Hedging portfolios shift markedly across regimes: forwards dominate under flat tariffs, mixed portfolios emerge under time-of-use, and option-heavy strategies prevail under real-time pricing. Shadow price analysis further reveals that affordability constraints bind most strongly in evening hours under real-time pricing, underscoring the tension between efficiency and equity. Sensitivity tests on the ambiguity radius show that welfare losses under distributional robustness are modest for flat and time-of-use tariffs but pronounced for real-time pricing, reflecting its direct exposure to tail distributions.

To adapt to the energy development situation and address the two challenges of multi-time scale changes and multi-entity games after the virtual power plant (VPP) coupled with hydrogen energy, this paper innovatively designs a cooperative operation mode between the VPP and the virtual hydrogen plant (VHP). It also constructs a multi-time scale operation optimization and bargaining model of the two, applies the alternating direction method of multipliers to solve the problem, and simulates the case. The following conclusions are obtained: 1) The synergistic operation of VPP and VHP has been demonstrated to yield substantial economic advantages. 2) The symbiotic operation of VPP and VHP has the potential to yield enhanced environmental benefits. 3) The collaborative operation of VPP and VHP exerts a negligible influence on users and does not modify their energy consumption patterns. 4) The ADMM algorithm can facilitate the efficient resolution of two-stage negotiation problems while ensuring the confidentiality of subjects in VPP and VHP. The paper also provides recommendations to VPP and VHP operators, market regulators, and technology developers, as well as identifies future research directions.

Within the background of carbon emission trading (CET) and green certificate trading (GCT) mechanisms, the study establishes a two-stage stochastic optimal (TSO) dispatching model for the integrated wind power-energy storage-carbon capture power plant (WP-ES-CCPP) system, designed to mitigate operational costs and address wind power uncertainty. First, a coordinated operational strategy is developed through topological analysis and mathematical modeling of CCPP, enabling synergistic interactions between WP, ES, and CCPP across day-ahead and real-time scheduling horizons. Second, a TSO model is formulated, wherein the day-ahead stage optimizes unit commitment and power allocation based on wind power forecasts to minimize operational expenditures, while the real-time stage employs stochastic optimization to resolve wind power forecast errors through ES dispatch and CCPP power adjustments, thereby reducing redispatch costs. Finally, simulation results demonstrate: 1) Compared with low-carbon economic dispatch, the addition of the GCT mechanism reduces the integrated system costs by 18.7 % and increases wind power consumption by 44.85 MWh, which is conducive to lowering the marginal peak-shaving cost of CCPP. 2) Configuring the solvent storage tanks reduces the total system cost by 630,000 RMB and reduces carbon emission by 2767t. which significantly improves the performance of CCPP. 3) The average cost of the TSO model proposed in this paper decreases by 19.18 %, 5.91 %, 8.89 %, and 3.65 %, respectively, in comparison with the deterministic dispatch, SO, RO, and TRO; and the average carbon emission decreases by 6.23 %, 2.92 %, 3.53 %, and 2.64 %, respectively. It shows that the TSO model has excellent performance in terms of economy and decarbonization.

Inter-provincial electricity transactions within China’s unified power market are complicated by spatial heterogeneity, asynchronous dispatch timelines, and strategic deviations in bilateral commitments. Existing mechanisms often struggle with ex-post contestability, temporal inconsistencies, and poor alignment between real-time system conditions and deviation pricing, undermining the market’s fairness and reliability. To address these challenges, this paper proposes a novel Tri-Ledger Coordinated Settlement (TCS) framework with built-in temporal consistency. The tri-ledger design consists of (1) a Contract Ledger capturing day-ahead bilateral schedules, (2) a Dispatch Ledger reflecting system-level nodal redispatch outcomes, and (3) a Deviation Ledger reconciling discrepancies across provinces through an enforceable and tamper-resistant protocol. Central to this framework is a Distributionally Robust Deviation Pricing (DRDP) model, which penalizes deviation behaviors not based on deterministic thresholds but through ambiguity-aware dual pricing anchored in Wasserstein-ball uncertainty sets. This allows the pricing system to anticipate manipulative strategies while offering probabilistic fairness to genuine imbalances caused by renewables or congestion. Furthermore, a Non-Contestable Decoupled Execution Mechanism (NCDEM) is developed to isolate provincial profit zones during redispatch operations, ensuring that a province cannot benefit by manipulating its declared bilateral trades or influencing others’ deviation compensations. The proposed approach guarantees strategy-proofness under minimal information assumptions and supports distributed execution by provincial grid companies without centralized re-optimization. The effectiveness of the framework is demonstrated on a stylized multi-province testbed derived from China’s Eastern and Central grid clusters. Numerical experiments show that the DRDP-based settlement leads to over 18.4% improvement in fairness-adjusted social welfare and reduces strategic deviation incentives by up to 73% compared to deterministic baseline models. Sensitivity analyses validate robustness under multiple load and RES penetration scenarios. The proposed TCS framework offers policy-relevant insights for implementing transparent and resilient provincial electricity market settlements under China’s “dual-track” trading architecture.

Addressing the urgent need for sustainable energy transitions in rural development while achieving the dual carbon goals, this study focuses on resolving critical challenges in agricultural photovoltaic (PV) applications, including land-use conflicts, compound energy demands (electricity, heating, cooling), and financial constraints among farmers. To tackle these issues, a dual-mode cost–benefit analysis framework was developed, integrating two distinct investment models: self-invested construction (SIC), where farmers independently finance and manage the system, and energy performance contracting (EPC), where third-party investors fund infrastructure through shared energy-saving or revenue agreements. Then, an integrated photovoltaic-storage agricultural greenhouse (PSAG) microgrid optimization model is established, synergizing renewable energy generation, battery storage, and demand-side management while incorporating operational mode selection. The proposed model is validated through a real-world case study of a village agricultural greenhouse in Gannan, China, characterized by typical rural energy profiles and climatic conditions. Simulation results demonstrate that the optimal system configuration requires 27.91 kWh energy storage capacity and 18.67 kW peak output, with annualized post-depreciation costs of 81,083.69 yuan (SIC) and 74,216.22 yuan (EPC). The key findings reveal that energy storage integration reduces operational costs by 8.5% compared to non-storage scenarios, with the EPC model achieving 9.3% greater cost-effectiveness than SIC through shared-investment mechanisms. The findings suggest that incorporating an energy storage system reduces costs for farmers, with the EPC model offering greater cost savings.

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms.

Under energy structure transformation and multi-energy complementary development, there is an urgent need to explore more efficient, clean and low-carbon integrated energy utilization. The micro-energy grid can realize the synergistic and complementary operation of electricity, thermal and cooling multi-energy systems through energy conversion and storage devices, thereby mitigating the intermittency of renewable generation and spatiotemporal imbalances in energy supply-demand. This paper develops a robust optimization model for micro-energy grids that accounts for demand response and carbon green certificate market participation. The study initially establishes demand response and carbon green certificate trading models to systematically evaluate their economic impacts on both microgrid and external energy systems. Subsequently, a deterministic operational optimization model is established for the micro-energy grid, aiming at net profit maximization. Finally, the study establishes a robust coefficient-based uncertainty set for WT and PV output fluctuations, facilitating the derivation of a robust optimization model for micro-energy grid. Case studies verify the model’s capability in addressing uncertainty-related operational challenges, maintaining reliable and economical operation of energy systems with enhanced robustness and economy.