Evaluating the performance and economic feasibility of fa & ccedil;ade-integrated asymmetric compound parabolic concentrating photovoltaic (ACPC-PV) systems across different climatic regions is important for their practical deployment. In this study, a three-dimensional optical model is established to evaluate the optical performance of ACPC-PV. Based on long-term meteorological data, the annual energy yield and economic performance of fa & ccedil;ade-integrated ACPC-PV are compared with those of fa & ccedil;ade flat PV across different climatic regions. Key parameters considered in this study include installation angles, solar altitude and azimuth angles, climatic conditions, and reference economic parameters. The results indicate that a reference installation angle for fa & ccedil;ade ACPC-PV is determined to be 30 degrees in regions between 20 degrees and 45 degrees N. The annual solar energy harvested by ACPCPV at 30 degrees can be up to 115% higher than that at 0 degrees . ACPC-PV installed in cities between 30 degrees N and 40 degrees N demonstrates superior annual performance compared to flat PV, with a maximum annual energy gain of 52%. Based on the reference energy demand and electricity price, the shortest and longest payback periods of fa & ccedil;ade ACPC-PV are 7.5 and 11.8 years in Lhasa and Chongqing, respectively. In terms of cost-effectiveness, fa & ccedil;ade ACPC-PV installed in regions with scarce solar resources offers the greatest advantage compared to flat PV, and the maximum reduction of payback period is 21%. These findings provide quantitative guidance for identifying suitable installation configurations and climatic regions for early-stage assessment of fa & ccedil;ade-integrated ACPC-PV systems.

Increased penetration of renewables has posed a significant influence on the wholesale electricity market. For markets with a limited number of large generation companies, such as China, the manner in which bidding strategies for these companies could impact system operation and pricing has not been investigated. This paper proposes a market gaming model to explore such impact. The model is based on a bi-level optimization problem to simulate the interaction between bidding strategies for generation companies and market clearing processes for system operators. A dynamic-gradient, active-set algorithm is developed to obtain an analytical solution for the proposed model. The model is applied to several regional power systems using real-world data. Results indicate that generation companies, under higher market concentration (less generation companies), have the incentive to withhold their available renewable generation in the bidding process to maximize revenue: renewables can be effectively curtailed with no physical constraint binding, at a high degree of market concentration under elevated renewable penetrations. With one generation company owning renewables, saturation occurs at 19% with 2 generation companies and curtailment starts at 50% with 12. When all have renewables, no saturation occurs even at 80% penetration. The issue would be mitigated by increasing the number of generation companies (lowering market concentration) and distributing the renewable capacities more evenly among these generation companies. The results could provide important input for the ongoing, trillion-dollar scale, power market reform in China.

Rapid data center expansion intensifies the tension between deep power-sector decarbonisation and the need for highly reliable, continuous electricity supply. Under grid congestion and limited interconnection capacity, annual renewable claims cannot guarantee hour-by-hour deliverable clean power. We develop an integrated planning framework for private-wire-supplied data centers. This framework determines the optimal within-city siting of the data center hub and its matched wind, solar photovoltaic, and battery resources, subject to point-ofcommon-coupling exchange limits and dedicated-line deliverability constraints. Candidate renewable sites are identified through spatial screening; hourly generation is simulated using ERA5 reanalysis; and an 8760-h load profile is reconstructed for a standardised 25,000-rack campus using a climate-driven power usage effectiveness model. A mixed-integer linear programming model then co-optimises siting, capacities, line sizing, and grid transactions under renewable supply targets of 30%, 50%, 70%, and 95%. Using Qinghai, China, as an illustrative case, we find that hub siting depends mainly on interconnection access and coupling conditions, whereas renewable siting increasingly concentrates in the strongest resource corridors as the target tightens. Solar photovoltaics form the main scalable backbone, while wind is used selectively only in the most advantageous zones. Across candidate-city cases, total annual cost rises from 1.76 to 1.96 billion USD at 30% to 8.56-9.56 billion USD at 95%. Annual displacement of grid electricity purchases increases from about 0.76 TWh to 2.17-2.26 TWh per city, and implied emissions reductions rise from about 0.14 to 0.39-0.41 Mt. COQ per city. The key finding is that, at high clean-electricity shares, the main constraint is not annual renewable availability but hour-by-hour deliverability within the project boundary.

Rapid expansion of offshore wind capacity is giving rise to large-scale wind farm clusters. Their availability is challenged by increased turbine number, extended port-turbine distances and elevated wave heights. Existing maintenance optimization models are computationally intensive, while an analytical model directly estimating cluster availability from cluster, logistical and ambient characteristics remains lacking. This study proposes an econometric framework that adopts a full-year, hourly, component-level failure-repair model integrating real-world oceanographic, wind farm, port, vessel, failure rate and repair time data. 22,500 scenarios are built based on China’s largest planned cluster (54 GW), varying turbine numbers, port-turbine distances, 30-year wave conditions, and vessel wave-height limits. Results show that approximately one CTV is required per 40 turbines and one HLV per 130 turbines. Far-shore clusters additionally require a 0.5 m increase in vessel wave-height limit to maintain availability. A 1 m increase in median wave height could decrease cluster availability by up to over 30%. Challenges from climate change could be addressed by increasing vessel wave-height limit by less than half the sum of increases in wave-height characteristics. Our work provides a scalable framework of resilience assessment and optimization for large-scale offshore wind farm clusters and offers actionable strategies for maintaining cluster availability.

Frequent trading and increasingly complex rules in electricity markets make abnormal bid submissions more covert. Regulators therefore need a practical way to convert indicator exceedances into verifiable behavioral clues and evidentiary chains. This paper proposes a regulatory-oriented categorization and labeling framework for abnormal bidding behaviors. Abnormalities are partitioned into four mutually exclusive categories according to operating mechanisms and payoff paths: single-product price formation impact (SPFI), single-product process interference (SPI), rule/settlement exploitation (RSE), and multi-product linkage manipulation (MLM). A three-dimensional labeling scheme—product scope (S/M), participant relationship (U/A/N), and strategy dependency (P/X/R/I)—is used for preliminary annotation to support verification workflows and the allocation of enforcement responsibilities. We further construct an indicator system covering structural shape, process timing, rule boundaries, and cross-market consistency to trigger clues and generate early warnings. A case study based on a representative forward power market shows that, during event days, abnormal entities significantly exceed the upper envelope of normal distributions in key indicators, enabling category identification and label annotation and thereby providing regulators with actionable verification checklists and investigative directions.

High-density tropical island cities depend heavily on fossil fuels, and building-integrated photovoltaics (BIPVs) are an ideal solution given their land scarcity. Taking Singapore as a case, we develop an integrated energy-economic-environmental assessment framework, calculating the hourly BIPV potential of building rooftops/facades with load curves and battery storage and conducting multi-objective optimization for minimal life-cycle costs and maximal supply-demand matching. Singapore’s theoretical annual BIPV potential is 28,161 GWh (50.8% of electricity demand), with north-facing facades showing notable potential (different from higher latitudes). Optimized feasible generation is 18,328 GWh (33% of demand), reducing 6.58 Mt of CO2. Bedok is the top BIPV development priority. The optimal BIPV path is tied to urban functions: commercial and industrial zones offer high economic benefits due to better load synchronization, while residential zones are less cost-effective due to time misalignment and greater reliance on expensive storage systems. This study provides insights for BIPV deployment in dense tropical cities, boosting global carbon neutrality.

Integrated assessment models serve as key tools for exploring climate mitigation pathways under global temperature targets. This paper introduces C(3)IAM v3.0, an upgraded version of China's Climate Change Integrated Assessment Model with expanded economic, technological, and climate systems, and strengthened cross-system linkages. Compared to previous versions, it incorporates seven major improvements: updated base-year data; a global carbon source-sink optimization module; an updated climate system model with explicit physical mechanisms; refined climate impact assessment, enhanced energy technology details in the China multi-regional economic module; improved non-CO2 and health modeling; and static coupling between the global and China economic modules. Utilizing the upgraded model for climate policy scenario simulations under 1.5 degrees C and 2 degrees C targets, this study systematically presents projected pathways for global energy consumption, land use, emissions, and climate effects, alongside associated assessments of the economic costs and the resulting health co-benefits.

Offshore wind power is crucial for achieving carbon neutrality in East Asia and North America. However, these regions are prone to tropical cyclones, raising concerns about their resilience against such persistent threats. While previous work has focused on turbine fragility and single-event resilience, system-level wind farm resilience under multi-decadal climate considering cyclone-induced damage and maintenance interruption, stochastic component degradation and actual repair constraints remains largely unexplored. Here, we investigate multi-decadal offshore wind farm resilience under these interrelated factors using a proposed component-level, full-year, hourly failure-and-repair simulation model, and adopting reliable tropical cyclone, oceanographic, wind farm, maintenance parameterization, failure rate and repair time data. Wind farm-specific historical and expected cyclone conditions are constructed using a real-world data-calibrated Holland model. A turbine blade fragility curve is compiled based on previous studies. A two-parameter Weibull distribution component degradation model is adapted to directly adopt real-world failure rate data. We take the 54-GW planned offshore wind farms in South China as a case study given its substantial risk due to large capacity and frequent tropical cyclones. Results show that historical tropical cyclones could reduce annual wind farm availability by up to similar to 14 percentage points and induce a long-term declining trend. Excluding structural damage, maintenance interruptions alone under expected tropical cyclones with return periods exceeding the turbine design lifetime still lead to substantial availability losses. Our work provides a holistic framework for quantifying tropical cyclone risks, reveals their substantial and intensifying impacts, and brings attention to the considerable but often overlooked threat from maintenance interruptions.

Urban water supply systems are increasingly challenged by water scarcity, rising energy consumption, carbon emissions, and water pollution under rapid urbanization and climate change. Existing studies have mainly focused on optimizing individual objectives such as water allocation or energy efficiency, while limited attention has been paid to the coordinated optimization of the Water-Energy-Carbon-Pollution (WECP) nexus under uncertainty. This study develops an integrated multi-objective optimization framework for the WECP nexus using Zhengzhou, China, as a representative case. First, a coupling coordination degree model is established to evaluate the interactions among water conservation, energy saving, carbon reduction, and pollution control. Subsequently, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), integrated with interval parameters and dynamic constraints, is employed to optimize multi-source water allocation while balancing economic, social, and environmental objectives. Furthermore, an elastic storage mechanism is introduced to improve system adaptability under uncertain water demand and climate variability.The results indicated that the optimized allocation significantly enhanced system coordination, with the coupling coordination degree increasing from 0.67 to 0.87. Groundwater extraction decreased by 18.5%, reclaimed water utilization increased to 14.7%, and annual carbon emissions were reduced by approximately 57,000 t CO2, while maintaining economic benefits and reducing water shortages. Compared with conventional optimization methods, the proposed framework provided a more balanced trade-off among competing objectives and improved decision-making flexibility through Pareto-optimal solutions. This study demonstrated the potential of integrated WECP optimization for promoting sustainable urban water management and provided practical guidance for low-carbon transformation of water supply systems in Zhengzhou and other water-scarce cities.

In order to improve the low-carbon economy and flexibility of electricity supply and demand of the electric-thermal integrated energy system, this paper proposes a collaborative optimization scheduling method for multiple integrated energy systems considering the carbon-green interaction mechanism. First, the carbon-green equivalent interaction mechanism is described to construct a carbon-green certificate joint transaction cost model. Secondly, the integrated demand response is introduced, and on this basis, a multi-integrated energy system cooperative operation model is constructed with the goal of minimizing the total system cost. Finally, a case simulation is carried out, and the superiority of the mechanism proposed in this paper is verified by setting different scenarios. The case results show that the strategy can effectively reduce carbon emissions and improve the economy of system operation.

Driven by the rapid transition towards high-proportion renewable energy and market-oriented reforms, China's real-time electricity markets face unprecedented price volatility. Accurate forecasting of these critical prices remains challenging as existing models often neglect physical grid constraints and robust anomaly handling, exposing market participants to substantial financial risks. To address these gaps, we propose a novel grid physics-informed time-adaptive stacked learning model for short- to mid-term real-time electricity price forecasting. Considering physical constraints, it incorporates a security-constrained unit commitment simulation to emulate the real-time market clearing process, enhancing spike prediction by capturing real-time supply-demand balance through accurate commitment unit capacity forecasting. A dual-stage time-segment robust outlier correction factor with outlier labeling is introduced to accurately distinguish and label true price anomalies, which promotes precise trend extraction by variational mode decomposition and substantially improves both stable and extreme-period forecasts. Furthermore, a time-adaptive stacked learning framework accounting for distinct fluctuation patterns across different time periods is developed, dynamically weighting base models via feedback mechanisms to generate targeted predictions, ensuring robust performance from 24-h to 168-h horizons. Validated by Hubei provincial data, the proposed model achieves mean absolute errors of 37.42 CNY/MWh (5.24 USD/MWh) and 92.08 CNY/MWh (12.89 USD/MWh) for 24-h and 168-h predictions, respectively, outperforming all state-of-the-art benchmarks by at least 34.25% and 9.24%. For extreme price forecasting, the model achieves root mean square error of 43.09 CNY/MWh (6.03 USD/MWh) for spikes and 53.21 CNY/MWh (7.45 USD/MWh) for troughs, reducing errors by up to 71.91% and 57.45%. Overall, the proposed model provides a robust and accurate solution for multi-horizon real-time price forecasting, offering valuable insights for highly volatile electricity markets and practical guidance for market participants in risk management and bidding strategy optimization.

Power flow calculation and static voltage stability (SVS) analysis of active distribution networks (ADNs) are facing convergence and applicability issues caused by extreme conditions and control strategies of grid-forming (GFM) and grid-following (GFL) distributed generations (DGs). To solve these challenges, an SVS and DG integration capacity quantification method based on extended holomorphic embedding power flow (EHEPF) is proposed which includes four aspects: 1) an EHEPF algorithm is developed that accommodates both convergence and converter’s internal primary and secondary control characteristics of DGs; 2) the unsolvable mathematical expression of EHEPF is derived using Padé approximant to clearly distinguish between no solution and lower-branch (inoperable) solution; 3) the voltage sensitivity and SVS indexes of EHEPF combining different control strategies of DGs are formulated to analyze the weak bus and SVS of ADNs; 4) the DGs integration capacity is quantified by balancing SVS and network loss under voltage distribution calculated in EHEPF. Case studies are carried out on 12 bus, IEEE 33 and IEEE 123 bus systems. Numerous test results are analyzed to verify the applicability, convergence and effectiveness of proposed EHEPF for SVS and DGs integration capacity quantification in ADNs.

With the rapid expansion of renewable energy (RE) sources such as photovoltaic (PV) and wind power (WP), managing surplus energy and mitigating renewable energy curtailment have become critical challenges. This study investigates the synergistic integration of electric heat pumps (EHPs) and pit thermal energy storage (PTES) as a power-to-heat pathway to enhance renewable energy utilization and district heating (DH) performance.. A theoretical framework and a PTES heat transfer model are developed to evaluate the impacts of EHP-PTES integration on curtailment mitigation, auxiliary heating demand, and system investment costs. Integrating EHPs with PTES increases storage temperatures, reduces reliance on backup heating, and decreases solar collector investment by up to 56%. In high-penetration RE systems, a 94,985 m3 PTES can lower curtailment rates by 32.43%, saving approximately CNY 433.61 million in curtailed energy costs, CNY 30.27 million in collector investment, and reducing auxiliary heating electricity consumption by 94.5%. Compared with electrochemical storage, power-to-gas technologies, and conventional solar district heating systems, the EHP-PTES pathway enables large-capacity, low-cost, and long-duration utilization of curtailed electricity while directly interfacing with existing district heating networks. These findings highlight the significant potential of EHP-PTES integration for optimizing RE utilization, promoting heating decarbonization, and reducing overall system costs.

With the routine operation of China’s electricity spot markets, covert coordination and abnormal fluctuations in generators’ bids increasingly threaten clearing fairness and the integrity of price signals. Traditional indicator systems built on market-power classification often fail to characterize collusion effectively and to deliver stable screening performance. This paper proposes an unsupervised framework for abnormal behavior identification in electricity markets, with bid collusion detection as the central task. Specifically, we design feature representations that capture both the consistency and the heterogeneity of generators’ bidding behaviors, and employ an Isolation Forest to isolate anomalous samples and generate a collusion suspicion score for identifying potential collusive groups and abnormal entities. In addition, a price-impact score derived from an improved CRITIC method is introduced for cross-validation, reducing false alarms while enhancing interpretability. Simulation results demonstrate that the proposed approach can reliably detect representative collusive behaviors and distinguish them from ordinary strategic fluctuations, providing practical support for abnormality screening and regulatory investigation in electricity markets.

As the rapid growth of renewable energy sources (RESs) with fluctuating and uncertain power generation, reserve shortages become increasingly severe, limiting the further integration of RESs and making it essential to exploit additional reserve sources. The fast-growing data centers offer significant potential to provide reserves due to their spatial and temporal dispatch flexibility. However, large quantities of jobs in the data centers have different computing requirements, making it challenging to estimate the availability of data centers for providing reserves. Meanwhile, the regional resource mix leads to uneven reserve distribution and mismatches between reserve supply and demand. The cross-region reserve supply has the capability to mitigate such mismatches as it allows different regions to provide reserve to each other. This paper enriches the reserve sources in multi-region system dispatch to enhance renewable energy integration by exploiting the reserve supply capabilities of data centers and cross-region systems. The model for available reserves of data centers is proposed via analyzing the envelope of computing constraints of aggregated jobs. In addition, a comprehensive cross-region reserve supply model is presented by considering reserve supply and demand limitations and reserve transmission capability. Case studiesare performed and the results show that the data centers can provide a substantial amount of reserves. Both the migration of jobs among data centers and the cross-region reserve supply transfer reserve resources among multiple regions. Simulations on a real-world power grid system further demonstrate that unlocking the reserve supply potential of data centers can reduce operating costs by 0.4% and the curtailment of RESs by 1.7%.

With the in-depth development of decarbonization in the power system, renewables will gradually occupy a dominant position in the electricity spot market. The significant impact of renewable energy market power on the electricity market under high renewable penetration has emerged as a critical concern, particularly in countries with high market concentration like China. This paper explores how the market power and the market concentration of renewables impact the operation and pricing of the electricity market at elevated renewable penetration. The game-theoretic interactions between strategic bidding behaviors and electricity markets under different market concentration levels are modeled through a bi-level optimization framework. A novel algorithm based on multi-agent reinforcement learning is put forward to efficiently solve the model with multiple generation companies in a provincial power system. The proposed model and algorithm are applied to the West Inner Mongolia of China. The results show that strategic bidding by renewables under high market concentration induces severe curtailment and high prices through physical and financial withholding. Although reduced concentration drives rapid price declines, converged levels remain substantially higher than those in marginal cost bidding scenarios, demonstrating that strategic bidding effectively mitigates the price suppression effect caused by renewable energy expansion.

With the rapid development of information technology, energy consumption in data centers has become increasingly prominent. As a core component, cooling systems account for substantial energy use while offering significant energy-saving potential, making them crucial for energy efficiency optimization. To address energy conservation in cooling systems, a free cooling system integrated with cold thermal energy storage is investigated in this study. Using typical meteorological parameters of Wuhan as a case study, a genetic algorithm (GA)-based model predictive control (MPC) strategy is employed to optimize system performance, and its adaptability across different climatic zones in China is evaluated. The results demonstrate that optimizing with power usage effectiveness (PUE) minimization as the objective function reduces the PUE value by 0.018 compared to the baseline system. When applied nationwide, lower PUE values are observed in regions with more abundant free cooling resources. After MPC optimization, the most significant improvements are exhibited in the mild climate zone, where a maximum PUE reduction of 0.0185 is achieved compared to pre-optimized systems.