To ensure the long-term security and reliability of the power system, the design of a capacity adequacy mechanism has become a critical component in electricity market development. This paper systematically reviews mainstream international capacity adequacy mechanisms, and proposes a combined capacity mechanism framework alongside its core mechanism—the revenue-based reliability option，which is the revenue-option based combined capacity mechanism. The combined capacity mechanism adopts a dual-layer structure integrating centralized and decentralized approaches. The centralized segment provides stable revenue guarantees for new generation units through long-term revenue-based reliability option auctions, while the decentralized segment ensures short-term resource allocation efficiency via capacity obligations and certificate trading. In view of the immaturity of China electricity market, this paper further proposes a four-stage revenue-option based combined capacity mechanism development path, and designs in detail the revenue-based long-term contract spanning the first three stages, laying the foundation for the future transition to the fourth stage of the mature revenue-option based combined capacity mechanism.

Against the backdrop of building a unified national electricity market and integrating high shares of renewable energy, China is forming a multi-level, cross-regional market structure. Transactions concluded at different levels, regions, and time points ultimately act on the same interconnected grid during real-time delivery, making the efficient and accurate calculation of available transfer capability (ATC) and the design of its allocation mechanisms critical. This paper first clarifies the concept and theoretical framework of ATC, and summarizes the main technical approaches and limitations of existing calculation methods. It then reviews research progress in typical markets that simultaneously address ATC calculation and allocation, highlighting their characteristics and shortcomings. Building on this, the paper compares ATC calculation and allocation mechanisms in PJM, Europe, and the China State Grid and China Southern Grid regions, revealing the institutional logic and market suitability underlying different designs. Finally, it outlines future research and practical development directions and proposes optimization recommendations for ATC calculation and allocation mechanisms in light of China’s market development needs.

A joint trading mechanism for electricity energy and flexible ramping products （FRP） with the collaborative participation of energy storage systems （ESS） and demand-side resources （DSR） is proposed to address the system ramping pressure caused by the uncertainty of new energy output in power systems with a high proportion of new energy. An optimized dispatching model for the electricity energy-FRP market is constructed, considering the participation of energy storage and demand-side resources. A multi-segment pricing approach is adopted for FRP to incentivize adjustable resources in the system to participate in FRP services. Through demand response, energy storage and flexible demand-side resources collaborate to provide ramping capacity for the system, meeting the system's ramp requirements. Simulation results based on the IEEE 30-bus system demonstrate that ESS participation in FRP services significantly reduces FRU clearing frequency and prices during sharp net load increase periods, particularly under high renewable penetration scenarios. The high responsiveness of ESS substantially enhances system flexibility and stability. Furthermore, while ESS participation marginally increases electricity market prices, it synergistically improves regulatory capacity of the system.

Under the backdrop of the new power system, traditional generation companies may leverage market mechanisms to exercise market power and gain additional profits. To prevent potential market manipulation strategies by generators, from a regulatory perspective, a comprehensive strategy simulation model considering multiple market behaviors is proposed to simulate the behavior of power generators.. Firstly, the coupling relationships of multi-side markets in the carbon context are analyzed, and factors potentially influencing market clearing are examined based on market mechanisms. Secondly, recognizing the limitations of single behaviors, the model holistically considers various manipulation which tactics generators might employ, including capacity withholding, price bidding, and collusion. A dual-layer bidding model is constructed, consisting of the generator decision-making layer and the market clearing layer.Then, given that decision variables include both discrete and continuous types, a hybrid approach combining DDQN and TD3 algorithms is adopted to solve the two-stage action decision problem involving collusion intentions and information bidding, simulating the evolution of each generator′s strategy. Simulation results reveal that in a multi-side coupled market environment, generators tend to form strong alliances with competitors possessing bidding advantages, enabling tacit collusion to manipulate electricity prices and capacity, thereby reducing market efficiency. Additionally, it is found that reasonable market mechanisms can partially mitigate capacity withholding and alleviate market power, offering valuable insights for market regulation.

Currently, China′s electricity market is in a phase of deepening reforms, where collusion between the generation side and the retail side intensifies the risk of market power abuse, severely hindering the market-oriented development of the power sector. To enhance the prevention of collusion between the generation and retail sides in the electricity marketization process, a progressive bilateral matching model for market power risk identification is proposed. This model is applicable to medium- and long-term trading scenarios in the electricity market and aims to identify market power risks among power generation companies and retail suppliers. By integrating the analysis of subjective and objective behavioral characteristics of market entities, the method establishes a comprehensive identification framework of "risk feature screening - abnormal transaction locking." It employs the Borda counting method to quantify subjective preference features of the generation and retail sides across three dimensions—pricing, volume bidding, and cooperative stability—while introducing an entropy-balanced matching algorithm to objectively quantify the concentration of clearing results. This ultimately enables precise identification of at-risk entities. Case study results demonstrate that the method not only accurately identifies high-risk trading entities but also effectively enhances market supervision efficiency.

In order to effectively alleviate the problem of "abandoning wind and light" of new energy, a market bidding method for hydrogen chain of rivers and waterways based on a two-layer decision-making model is proposed. Firstly, the operation mechanism of the hydrogen chain of rivers and waterways is explained, and the decision-making structure for its participation in the hydrogen energy market is designed. On this basis, a bidding mechanism for hydrogen chain market in rivers and waterways based on a two-layer decision-making model is proposed. Among them, the upper level aims to maximize the daily operating income, and establishes a hydrogen chain operation model considering the fine power adjustment of hydrogen compressors. With the goal of maximizing social well-being, the lower level establishes a hydrogen energy market clearance model considering multi-agent bidding. Then, the model is reconstructed based on the KKT condition, and the model is transformed into a classic mix-integer linearization programming (MILP) problem by using duality theory and large M method to achieve efficient solution of the model. The results of case analysis show that compared with the traditional method, the proposed strategy can improve the operation economy of the system by no less than 5.4%. The proposed strategy provides a new perspective for hydrogen chain operation and hydrogen energy market trading.

To achieve effective carbon reduction in the source-grid-load system and explore the economic value of new energy vehicles in a multi-energy coupling system, an optimal scheduling strategy for an integrated energy system (IES) considering vehicle-to-grid (V2G) and carbon trading under the combined application mode of carbon capture systems and power-to-gas is proposed. Firstly, a system coupling framework is constructed, integrating a flexible operation carbon capture system with power-to-gas, hydrogen storage tanks, and hydrogen-blended gas turbines and other hydrogen energy components. Secondly, the Monte Carlo method is used to simulate the disordered charging behavior of vehicles, and by combining battery degradation costs and incentive costs, the electric vehicle - hydrogen fuel cell vehicle (EV-HFCV) cluster is guided to participate in the scheduling. Then, a stepwise carbon trading mechanism and a carbon quota trading mechanism for the EV-HFCV cluster are introduced, and a risk-averse model based on information gap decision theory is constructed. Finally, case simulation analysis results show that the proposed strategy can effectively reduce carbon emissions and increase the consumption of renewable energy, and further reduce the overall operating cost of the system through the coordinated scheduling of the EV-HFCV cluster.

To address the uncertainty of renewable energy output and the fragmentation between green certificate and carbon trading markets, a distributed robust optimization scheduling method for multi-regional integrated energy systems based on a green certificate-carbon trading joint mechanism is proposed. Firstly, an interconnected architecture for multi-regional integrated energy systems encompassing electricity, heat, gas, and cooling multi-energy flows is constructed. Secondly, a price-linked green certificate-carbon trading joint interaction mechanism is designed. By introducing a dynamic conversion coefficient, the transformation relationship between the environmental rights of green certificates and carbon quotas is quantified, capturing the value transmission process between the green certificate-carbon trading markets, thereby achieving synergistic incentives for the environmental value of both markets. Finally, a two-stage distributionally robust optimization scheduling model is established, aiming to minimize the system's comprehensive operating costs, based on constructing a fuzzy set for the probability distribution of wind and solar power output under comprehensive norm constraints. Case study results demonstrate that the green certificate-carbon trading joint mechanism can effectively reduce system operating costs and carbon emission levels.

To promote the efficient mobilization of flexibility resources on the user side in integrated energy systems, a dual incentive mechanism integrating economic compensation and carbon offset is constructed, and the research is conducted in combination with a demand response optimization model. Firstly, based on users′ response characteristics to multiple energy sources such as electricity, heat, and gas, flexible loads are categorized into three types of flexibility loads for scheduling, and a joint incentive strategy incorporating economic and carbon compensation is introduced. Secondly, a bi-level game model between integrated energy operators and load aggregators is established, combining multi-dimensional demand response constraints and Stackelberg game theory to solve for optimal benefit allocation. Additionally, to enhance the optimization capability of the algorithm, an adaptive multi-mutation differential evolution algorithm is proposed. Finally, simulations under different scenarios demonstrate that the model significantly reduces system operational costs and carbon emissions while also prove that the proposed algorithm exhibits superior optimization capability.

The application of soft open points with energy storage (E-SOP) in flexible interconnected distribution networks (FIDN) poses new challenges for reliability assessment, and the "dual-carbon" goals urgently require electricity-carbon synergistic evaluation. Addressing the complex fault characteristics of E-SOP, difficulties in handling wind-solar uncertainties, and the lack of carbon emission considerations in traditional assessment methods, an electricity-carbon synergistic reliability assessment method for FIDN is proposed based on Kantorovich distance scenario reduction and mixed integer linear programming (MILP). Firstly, the basic structure and mathematical model of E-SOP are elaborated. Secondly, wind-solar uncertainty models are constructed, and Copula function models combined with Kantorovich distance-based scenario reduction methods are introduced for processing. Then, electricity-carbon synergistic objective functions are constructed to establish a MILP reliability assessment model with carbon emission constraints. Finally, experimental analysis is conducted on the BUS6 F4 feeder system. Results demonstrate that the proposed method achieves synergistic optimization of reliability and carbon emissions, with SAIFI reduced by 76.2 % and carbon emission saving factor reaching 6.81 %, providing an effective approach for low-carbon reliable operation of flexible interconnected distribution networks.

Commercial building loads account for approximately 25 % of the total end-use load in urban areas, with a substantial portion comprising flexible loads such as air conditioning systems. This endows commercial buildings with significant potential for demand response （DR） regulation. Load disaggregation serves as a means of identifying flexible resources to facilitate the formulation of demand response strategies within the power grid. Nevertheless, existing non-intrusive disaggregation approaches are constrained by variations in the operating frequencies of building systems, whereby single time-scale models exhibit limited capability in effectively capturing the asynchronous characteristics of end-use devices. To overcome these limitations, a non-intrusive load disaggregation method based on multi-timescale deep learning is proposed in this paper. Specifically, a hybrid model integrating convolutional neural networks and long short-term memory networks is constructed to enable the collaborative extraction of spatiotemporal features from dynamic load data. Furthermore, a multi-timescale sampling mechanism is employed to conduct temporal sensitivity analysis and to determine the optimal sampling interval at the edge. Finally, experimental results using real-world commercial building load data demonstrate that the proposed model achieves superior comprehensive performance. When the sampling interval is increased from 30 seconds to 60 seconds, the Matthews correlation coefficient of the proposed model is improved by 7.35 %.

Commercial buildings account for over 25 % of the total building area nationwide and exhibit higher carbon emission intensity than other building types. Retrofitting these buildings into virtual power plants (VPPs) offers a promising pathway to mitigate their carbon footprint. A technical and economic assessment method of the virtual power plants renovation in commercial buildings based on commercial buildings participating in carbon trading is proposed, exploring the transformation of virtual power plants in commercial buildings from a full lifecycle perspective. From an economic perspective, the analysis incorporates initial retrofit investment, operating costs, and revenue from carbon trading to evaluate lifecycle economic viability of virtual power plant renovation for existing commercial buildings. From a technical perspective, the characteristics of carbon emissions behavior in commercial buildings are analyzed, and the carbon reduction potential of existing commercial buildings throughout their entire lifecycle based on three types of transformation behaviors are evaluated: energy consumption, energy storage, and power generation in virtual power plants.The simulation results show that as the total cost of virtual power plant renovation in commercial buildings increases, the carbon emissions in the lifecycle of commercial buildings will also relatively decrease. It is necessary to balance the relationship between the two and ultimately achieve a win-win situation between the cost of virtual power plant renovation and carbon reduction in commercial buildings.The technical and economic assessment shows that the static investment payback period for the transformation of commercial building virtual power plants is about 4 years, which is feasible in terms of economy, and the transformation of commercial building virtual power plants has significant carbon reduction potential.Therefore, although a certain initial investment is required, the transformation of existing commercial buildings into virtual power plants is technically and economically feasible.

In hybrid electricity markets, the coupling of transactions complicates the tracing of carbon emission paths between sources and loads, while the mechanism for allocating carbon responsibilities associated with network losses remains unclear. These challenges hinder the realization of accurate carbon accounting in new electricity system. To address this challenge and align with low-carbon-oriented carbon management objectives, a framework and methodology for carbon emission path tracing and bidirectional allocation of network losses is proposed, integrating directed graph modeling with path optimization. A virtual lossless network is constructed to decouple bilateral trading from pool trading. For the single-source-single-sink structure of bilateral trading, a low-carbon path priority maximum flow model is developed by introducing a carbon emission factor-based prioritization mechanism. For the multi-source-multi-sink structure of pool trading, a “BFS ordering followed by DFS tracing” mechanism is designed to achieve bidirectional allocation, thereby a recursive sequence for virtual node carbon flow distribution is established. Case studies demonstrate that the proposed framework significantly enhances the accuracy and fairness of carbon emission tracing in hybrid markets, highlights the value of low-carbon-oriented path tracing, and provides technical support for the development of low-carbon power systems under the “dual-carbon” goals.

To address the issue of low prediction accuracy in provincial-level area carbon emission forecasting caused by insufficient historical data, a novel prediction method based on electricity data is proposed. Firstly, the correlation between provincial-level area power load data and total carbon emissions is analyzed. Subsequently, a hybrid long short-term memory⁃gradient boosting regression tree (LSTM-GBRT) model is constructed to perform carbon emission forecasting, based on the electricity-carbon correlation. To further enhance prediction accuracy, a K-means clustering algorithm is employed to classify provincial-level area energy consumption structures, enabling the identification of provinces with similar carbon emission characteristics. This clustering aids in enriching both the dataset and feature set. Finally, based on historical electricity and carbon emission data across multiple provincial-level area in China, the model is applied to forecast emissions in a target province. The results demonstrate that the proposed model can effectively capture the carbon emission trend. It consistently outperforms benchmark models-including long short-term memory (LSTM), convolutional neural network (CNN), and gated recurrent unit (GRU)-in terms of mean absolute percentage error, mean absolute error, and root mean square error. These findings highlight the model’s superior generalization performance and robustness across varying data scenarios.