The rapid growth of renewable energy increases uncertainty in power system operation. The locational marginal pricing (LMP) mechanism, though reflecting network constraints, is prone to strategic bidding that induces ‘pseudo-congestion,’ raising costs and reducing efficiency. To address this, a trilevel optimization model with a zonal settlement mechanism is proposed. The upper-level designs partition schemes, the middle-level captures generator bidding strategies via Nash equilibrium iteration, and the lower-level applies a DC optimal power flow model to ensure balance and minimize costs. Case studies show that zonal settlement reduces incentives for price manipulation, alleviates pseudo-congestion, and yields more balanced outputs and power flows. Compared with nodal settlement, it lowers total operating costs and enhances fairness and efficiency in market operations.

The rapid advancement of China's carbon market, initially focuses on restricting emissions from power generators, often neglecting indirect user-side emissions. This study proposes an annual production constrained daily optimization model for energy-intensive users (e.g., steel companies), incorporating hourly carbon responsibility allocation on the user side and daily settlement based on dynamic carbon pricing. The carbon responsibility allocation method is based on the Regional Marginal Carbon Emission Factors (RMCEF). The method establishes a link between electricity consumption behavior and carbon responsibility. This connection incentivizes users to voluntarily reduce emissions by shifting their electricity usage patterns. Then A monthly rolling annual optimization model based on the Long-Period Variable Intervals (LPVI) method is then employed. The annual optimization provides monthly decomposition results, which serve as boundary conditions for monthly rolling optimization. Each month, a bi-level model is established. At the upper level, the ISO clears the market with the objective of minimizing both generator energy and carbon costs for power supplier companies operating wind and thermal power units. At the lower level, users adjust their electricity consumption behavior by considering market product prices and electricity costs, while also incorporating their carbon responsibility into their decisions. Additionally, the impact of Carbon Penalty Coefficients (CPC) and Regionally Permitted Carbon Emission Cap (RPCEC) on user benefits and system carbon emissions is analyzed. Numerical experiments demonstrate that introducing user-side carbon responsibility increases user benefits by 32.82 million dollars and leads to a reduction in generator costs by 1.68 million dollars, while maintaining comparable carbon reduction levels. These results indicate that incorporating carbon responsibility on the user side effectively balances emission reductions with economic gains, contributing to a more efficient and sustainable low-carbon energy system.

With the advancement of dual-carbon goals, the high penetration of renewable energy has significantly increased the demand for regulating capacity in power systems. However, traditional capacity markets, focused solely on ensuring resource adequacy during peak load, struggle to address the surging regulating capacity demand caused by renewable intermittency. This paper analyzes the net load ramping characteristics of power systems with high renewable energy penetration rates in regions with different renewable energy penetration rates and determines the system's regulating capacity demand using a three-hour timescale. Based on this, a capacity market clearing model is constructed to maximize social welfare, achieving the joint clearing of trusted capacity and various types of regulating capacity, and designing a corresponding demand price curve. Case analysis demonstrates that compared to traditional capacity markets that only consider trusted capacity demand, this model can incentivize the investment and construction of regulating resources, achieve investment cost recovery for flexible units, and offer better economic and reliability at the dispatch and operation level. It can effectively address extreme scenarios of both high and low renewable energy generation, achieving the goals of ensuring supply and promoting consumption.

With the continuous expansion of data centers, their potential to serve as new flexible loads in power system demand response services has become increasingly significant. In response to the problem of low internal resource utilization efficiency in single-site data center demand response under a multi-loop power supply structure, this paper first proposes a coordinated response strategy. The strategy improves overall response capability by jointly scheduling the UPS backup battery and flexibly adjusting the topological connections between UPS systems, photovoltaic and energy storage systems. Furthermore, a block order mechanism based on integrated learning is developed to address the multi-period non-convex optimization problems and user privacy challenges that arise during the bidding process in the electricity market. A neural network is used to fit user bidding behavior, which is then embedded into the Independent System Operator's market-clearing optimization model as a Mixed-Integer Linear Programming formulation. This integration enables scheduling decisions to be made under privacy-preserving conditions. Finally, case studies are conducted on the modified PJM 5-bus system and the IEEE 118-bus system. The results show that the proposed strategy increases the regulation potential of data centers by 26%, while improving their revenue compared with the traditional block orders approach.

In power systems with high penetration of new energy, the shortage of inertia and frequency regulation resources seriously threatens frequency stability. Traditional joint clearing models suffer from low computational efficiency and poor engineering applicability due to the direct introduction of nonlinear constraints from frequency dynamic equations (such as minimum frequency and quasi-steady-state deviation). This paper proposes a joint market clearing model based on demand mapping, which simplifies model complexity through a hierarchical constraint mechanism. First, frequency security requirements are decomposed into basic inertia demands (ensuring limits on rate of frequency change) and enhanced demands (via primary frequency regulation constraints), avoiding direct modeling of dynamic frequency indices. Second, considering the limited capacity of new energy to provide virtual inertia in the current power grid (accounting for only 5–10

With the increasing penetration of renewable energy, the coupling between energy markets and frequency regulation markets has become increasingly significant. To address the growing coupling and the complexity of multi-agent competition, this paper proposes a bidding strategy optimization framework for the joint energy–frequency regulation market based on multi-agent deep reinforcement learning. The proposed method employs the Soft Actor-Critic (SAC) algorithm to construct revenue models for thermal units and wind–storage systems, and enables adaptive learning of optimal bidding strategies through multi-agent interactive training. Simulation results based on a modified PJM 5-bus system demonstrate that the proposed approach achieves stable convergence and enhances the revenues of all market participants. This study provides an effective reference for the coordinated participation of renewable energy and energy storage systems in joint electricity markets.

Free-floating bike sharing (FFBS) is a pivotal component of low-carbon transportation systems, providing short-distance travel services and connections to public transportation. Nonetheless, its development is often accompanied by overinvestment and resource waste, while studies on its life-cycle carbon reduction potential remain limited. Based on historical riding records, bicycle borrowing and return demand is predicted by incorporating spatial-temporal heterogeneity and external influencing factors, while quantifying life-cycle carbon emissions. A low-carbon-oriented multi-objective optimization model is further developed to integrate life-cycle assessment (LCA) of carbon emissions with bicycle deployment. Focusing on the FFBS system in Gulou District, Nanjing, the results reveal that, when enterprise market share is considered, user demand satisfaction reaches 80.87%, while production-related carbon emissions and enterprise costs decrease by 464.354 tons CO2-eq and 31.96%, respectively. These findings underscore carbon reduction benefits of controlling FFBS deployment scale and demonstrate the effectiveness of integrating demand forecasting, life-cycle carbon accounting, and deployment optimization.

Electric vehicle charging demand prediction is an important prerequisite for researching the interaction between electric vehicle (EVs), the power grid, and the transportation network. However, most existing work does not use real-world traffic data to analyze EV charging demand. Therefore, this paper proposes a data-driven model for EV charging demand prediction. Firstly, original EV travel trajectory data is used for data mining and integration modeling, including area selection, spatial grid modeling, trajectory data mapping, POI retrieval data identification, urban functional area clustering, and traffic network modeling. Through modeling and data processing, regenerated feature data such as functional area division, travel pattern distribution, and actual driving paths are obtained. Secondly, considering the mobility load characteristics of EVs, a single EV model is established, which contains driving characteristic parameters and charging characteristic parameters. And the evaluation model of virtual energy storage schedulable operation region of EV cluster is established. Finally, with a certain area as an example, path planning experiments and charging demand experiments in different scenarios were designed. And a day-ahead peak shaving strategy with EVs is proposed. The results show that the proposed model can effectively predict the spatial and temporal distribution characteristics of charging demand and load for different date types and functional areas. It also lays the theoretical foundation for subsequent research on EV charging control and guidance.

Capacity markets (CMs) have been widely analysed and implemented in various regions to enhance the capacity adequacy and supply security in power systems with high renewable penetration. This study compares the performance of two market designs, an energy market combined with a CM and an energy-only market, using a capacity expansion model that incorporates long-term energy storage (ES). This study contributes significantly to the debate on CM by quantifying the improvement in the system reliability. Through simulations conducted on provincial power systems in China, we demonstrate that introducing a CM significantly enhances the system reliability by providing a stable revenue stream that incentivises capacity investments. Additionally, the effectiveness of implementing a CM is analysed through simulations under various market conditions, thereby presenting various advantages. Furthermore, the results of this study indicate that the reliable availability of conventional technologies is essential for future power systems with high renewable energy penetration. The supply security cannot be ensured with an excessive penetration level of renewable energy sources, despite long-term ES. These findings present critical insights into the design of hybrid electricity markets for transitioning power systems.

In China, the inter-provincial market plays a crucial role in balancing regional electricity supply and demand. However, this market faces key challenges, including persistent market power concerns and insufficient consideration of the impact of the long-term market on the spot market. This paper proposes a novel market power mitigation mechanism (MPMM) in the inter-provincial hierarchical market (IPHM), incorporating both the long-term and spot markets. The MPMM is formulated as a set of clearing constraints and properly integrated into the IPHM clearing procedure, thereby achieving better harmonization of planning mechanisms with market-based principles compared to existing policies. A bi-level model with the MPMM constraint is developed to analyze strategic bidding of generation company. Finally, the proposed mechanism is implemented in the Shanxi-Jiangsu and Sichuan-Jiangsu inter-provincial systems to evaluate its rationality and validity. Simulation results show that in Shanxi, the market clearing price and social welfare are close to completely competitive levels. In Sichuan, similar improvements are observed in both wet and dry seasons, highlighting the mechanism's adaptability. These results demonstrate its potential to enhance market efficiency and mitigate market power, offering valuable insights for policymakers and market operators.

Battery Energy Storage Systems (BESS) play a crucial role in mitigating the volatility and intermittency of Renewable Energy Sources (RESs) and are widely deployed across the globe. To support the growth of BESS, governments have introduced various policies, including subsidies aimed at aligning BESS costs with benchmark RES costs. However, these measures can lead to strategic collusion between BESS and RES operators when BESS capacity is insufficient, resulting in the waste of social resources when surplus capacity exists. To address this, this paper presents an Optimal BESS Capacity Sizing (OBCS) framework based on day-ahead (DA) market clearing, designed to promote market operations that closely resemble a completely competitive without the need for policies that directly target strategic behavior control. We propose a collusive bidding analysis method to assess the strategic behavior within collusions. Building on this, we introduce an OBCS framework that leverages collusive bidding insights and existing BESS subsidies to regulate strategic behavior. Finally, the proposed OBCS method is applied to both test and real electricity markets, demonstrating that the operation cost of the DA market can be reduced by 24.29 % in abundant scenario and 13.62 % in scarce scenario compared to the initial state.

With the high penetration of renewable energy and the deregulation of electricity markets, future active distribution networks are anticipated to comprise areas with diverse ownership. Frequent power exchange between these areas leads to strong interarea influences, making it essential to account for voltage coupling in decentralized voltage prediction (DVP). To ensure the accuracy of DVP, collaborative modeling using power information from neighboring areas is imperative. However, potential privacy concerns pose significant challenges to data sharing and interarea collaboration. To address these challenges, this paper proposes a privacy-preserving collaborative prediction framework. First, a privacy-preserving multi-objective XGBoost algorithm is developed to mitigate curious behavior through homomorphic encryption and minimal information sharing. Then, an equitable incentive mechanism is introduced to allocate rewards based on the relative marginal contributions, which discourages potential adversarial behavior. The proposed framework further modularizes subproblems, enabling parallelization to improve computational efficiency. Finally, case studies demonstrate that the proposed framework can achieve prediction accuracy comparable to centralized methods while ensuring privacy preservation and collaboration reliability, highlighting its potential for broad adoption in multi-area systems.

In the context of ‘carbon peak’ and ‘carbon neutrality’, coordinating individual travel demand through multi-modal transportation and guiding travelers towards new shared public transportation (PT) modes is increasingly important. In this paper, we analyze the competitive and cooperative relationship between online car-hailing (OCH) services and metro systems in Nanning, China, and conduct aquestionnaire survey among different types of OCH users. A mixed choice model that considers psychological latent variables is constructed to investigate OCH users’ attitudes and cognitions toward customized buses (CBs). An improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed to identify potential carpooling station sets, and a hybrid genetic-ant colony algorithm (GACA) is designed to solve bi-level programming model for CB line optimization. Case study results indicate an 83.8% overall transfer rate from OCH users to CBs, with the optimized scheme achieving a 69.68% reduction in carbon emissions.

As extreme weather events become increasingly frequent and societal development places greater emphasis on a stable electricity supply, there is an increasing focus on the resilience of electrical distribution systems (EDSs). This is especially crucial in preventing cascading failures among interdependent critical infrastructures, such as medical, water, and telecommunication systems. Proactively preparing to cope with disruptive events causing a high risk of power outage is essential for improving the resilience of EDSs. A performance index, human well-being loss (HWL), is proposed to assess the disruption to people's quality of life during extreme weather events, considering the interdependence among critical infrastructures. Based on the proposed index, a novel resilience enhancement framework is proposed, which takes into account expected generation costs, economic losses due to power curtailment and HWL. Subsequently, a two-stage stochastic model for preventive disaster is presented, which optimises power rationing strategies and dispatch schedules. Through a series of case studies, the authors illustrate that the proposed framework can effectively reduce costs, alleviate abnormal operational hours of critical infrastructures and achieve a balance between economic, safety, and social benefits.

The zonal market (ZM) adopted in Europe, in contrast to the nodal market (NM), reconciles the inconsistency between physical networks and administrative management. However, the growing integration of renewable energy sources (RESs) has introduced zonal supply-demand imbalances that exacerbate congestion and the need to re-dispatch. Furthermore, different clearing mechanisms between the day-ahead and real-time markets provide further opportunities for collusive bidding, decreasing total social welfare (TSW). Thus, this paper is the first to propose a long-term congestion management (CM) framework through a market zone (MZ) configuration approach with CM assessment considering collusive bidding in the joint spot markets. More specifically, a topology-based location division (TLD) method is proposed to partition optimal MZs, ensuring the minimum number of MZs based on critical branches. Then, a bi-level evolutionary model is developed to analyze the collusive bidding of producers in the day-ahead and ancillary service markets. Finally, the established framework is applied to a 20-bus test and simplified European systems. Our simulation on the 20-bus system shows that compared with the initial ZM and NM, the congestion cost of the optimized ZM decreased by 90% and 33%, respectively, while the TSW increased by around 13% and 1%, respectively.

In the backdrop of advancing communication technology and the adoption of decarbonization initiatives, peer-to-peer (P2P) electricity trading has evolved into a consequential avenue for the reliable utilization of clean energy resources. Most efforts have focused on the design of P2P distributed mechanisms to ensure that the security constraints of the grid can be adhered to. However, ensuring the assurance of the correct operation of the distributed mechanisms is also essential but has received less attention. A common assumption is that all participants in the P2P market are honest and make reasonable bids at market prices. Such an assumption could be risky because the P2P market clearing process relies on a coordination process of market participants and the clearing outcome of the P2P market is susceptible to manipulation by dishonest participants. In this work, we propose a new architecture for the P2P market by adding a verification layer based on zero-knowledge proof technology to identify dishonest bidding information of market participants without collecting their private cost information. In addition, we introduce an asynchronous market mechanism, which can greatly guide the P2P market clearing results in a dishonest environment to be close to the theoretical optimal results. Case studies demonstrate the advantages of our approach in resisting dishonesty, preserving privacy, and enhancing market robustness, which can help build a more credible and resilient P2P market environment.

Electric vehicles have garnered substantial attention as an environmentally sustainable transportation alternative amid escalating global concerns regarding ecological preservation and energy resource management. While the proliferation of electric vehicles necessitates the development of efficient and secure charging infrastructure, the inherent communication-intensive nature of the charging processes has raised concerns regarding potential privacy vulnerabilities. Our paper introduces a privacy protection scheme specifically designed for electric vehicle charging reservations to address this issue. The primary goal of this scheme is to protect user privacy while maintaining operational efficiency and economic viability for charging providers. Our proposed solution ensures a secure and private environment for charging reservation transactions and subsequent deviation settlements by incorporating advanced technologies, including zero-knowledge proof, a consortium blockchain, and homomorphic encryption. The scheme encrypts charging reservation information and securely transmits it via a consortium blockchain, effectively shielding the sensitive data of all participating parties. Notably, the experimental findings establish the robustness of our scheme in terms of its security and privacy protection, aligning with the stringent demands of electric vehicle charging operations.

With the continuous improvement of distributed resource configuration capabilities and the continuous increase in the flexibility requirements of power systems, the integration of flexible resources on the distribution network (DN) side for coordinated dispatching is of great significance to improving the local consumption of distributed power sources and improving the real-time supply and demand balance of power systems. However, existing research rarely addresses distributed energy resources (DERs) in different distribution networks (DNs), which can participate in transmission network (TN) and DN coordination by considering DN topology to provide flexibility for various transmission-distribution interfaces (TDIs). To address this problem, this paper proposes a model for DER participation in transmission network operator (TSO) and distribution network operator (DSO) coordinated operation based on dynamic topology adjustment of DN, enabling DSOs to dynamically optimize DN topology and DER output based on TSO dispatch instructions. A two-stage deep reinforcement learning algorithm (TSDRL) is developed to optimize network topology and DER output, overcoming challenges in large-scale topology optimization and non-convex problem handling. The simulation results on the modified IEEE 14-bus and IEEE 118-bus systems demonstrate that the proposed method significantly improves the DN ability to respond to the flexibility requirements of the TN, enabling real-time decision-making.

With the development of renewable power systems, energy storage will participate in the power market as a major power source, so it is necessary to study the self-scheduling strategy for energy storage to participate in the day-ahead power market. At present, the intelligent quotation simulation technology of energy storage based on deep reinforcement learning algorithm has been developed rapidly, while most of the traditional reinforcement learning algorithms are aimed at optimizing the offer strategy of energy storage for a single time period, and it is difficult to optimize the one-day strategy of energy storage as a whole. Thus, this paper adopts the Soft Actor Critic algorithm with Hindsight experience replay to simulate the self-scheduling behavior of energy storage, and the simulation results show the effectiveness of the algorithm.