Under the constraint of the “dual-carbon” goals, power demand response has become a crucial means of tapping into demand-side resource adjustment potential and promoting efficient energy utilization. It plays a key role in the construction of the new-type power system. To scientifically measure its comprehensive benefits, this paper systematically examines the mechanisms and benefit types of various stakeholders including electricity users, grid companies, power generation companies, and society-and develops a comprehensive benefit evaluation index system covering three dimensions: operation, economy, and environment. Furthermore, the paper proposes a combined weighting mechanism integrating the entropy weight method and Analytic Hierarchy Process (AHP), and applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for benefit evaluation, achieving the unification of subjective and objective weights and ranking of multiple alternatives. Finally, using a regional power system as a case study, the paper quantifies the benefit changes under different demand response durations and conducts comparative evaluations. The results show that the environmental benefit has the highest weight, and the overall score significantly increases with the implementation years. Government guidance and user participation are key driving factors. The findings provide theoretical and practical references for policy development, resource allocation optimization, and the improvement of demand response mechanisms.

Given the strong coupling between electricity flow and carbon flow, promoting the low-carbon transformation of the energy sector is a crucial measure to actively responding to climate challenges. As a pivotal hub linking the electricity market with the carbon market, promoting electricity–carbon coordinated scheduling of Virtual Power Plants (VPPs) is of great significance in expediting the energy transition process. Based on the introduction of carbon potential, this manuscript constructs a VPP electricity–carbon coordinated scheduling model that incorporates various typical elements, including renewable energy units and demand response. Furthermore, this paper utilizes Brain Storm Optimization (BSO) to improve the Snow Ablation Optimizer (SAO) algorithm and applies the improved algorithm to solve the model developed in this manuscript. Finally, an analysis was conducted using a small-scale VPP project in eastern China, and the results are the following: Firstly, the SAO improved by BSO demonstrates a significant enhancement in solution efficiency. In particular, for the cases presented in this manuscript, the algorithm’s convergence speed increased by 42.85%. Secondly, under the multi-market conditions and with real-time carbon potential, VPPs will possess greater flexibility in scheduling optimization and stronger incentives to fully explore their emission reduction potential through collaborative electricity–carbon scheduling, thereby improving both economic and environmental performance. However, constrained by factors such as the currently low carbon price level, the extent of improvement in VPPs’ performance under real-time carbon potential, compared to fixed carbon potential, remains relatively limited, with a 1.07% increase in economic benefits and a 2.63% reduction in carbon emissions. Thirdly, an increase in carbon prices can incentivize VPPs to continuously tap into their emission reduction potential, but beyond a certain threshold (120 CNY/t in this case study), the marginal contribution of further carbon price increases to emission reductions will progressively decline. Specifically, for every 20-yuan increase in the carbon price, the carbon emission reduction rate of VPPs drops below 1%.

This study focuses on low-carbon retrofit pathways in the power sector and proposes an evaluation method for Carbon Capture, Utilization, and Storage (CCUS) technologies by integrating key operational parameters, forming a carbon allowance valuation framework based on the Putty-Clay Vintage (PCV) model. The model incorporates indicators such as unit availability, coal consumption, and carbon oxidation rate to establish a mapping between technical parameters and carbon emission intensity. A grey clustering method is applied to adjust for external macro factors, enhancing the model’s adaptability and generalizability. Validation using real-world data from a typical coal-fired power plant demonstrates strong technical sensitivity and engineering applicability. The proposed approach provides methodological support for CCUS and other low-carbon technologies, contributing to the technical advancement of carbon emission in the power industry.

As a fundamental and system-regulating power source, coal-fired power faces crucial low-carbon transition tasks under the “dual carbon” goals. Biomass blending, green ammonia co-firing, and carbon capture utilization and storage (CCUS) are three key technologies for achieving low-carbon transformation in coal power. To analyze the typical characteristics of these three low-carbon technologies, this paper constructs corresponding techno-economic evaluation models, using electricity cost and carbon emissions as assessment indicators to comprehensively evaluate the technical and economic features of these three low-carbon retrofitting schemes. Case studies on 660 MW coal-fired units demonstrate that from the perspectives of technological maturity and feasibility, biomass blending exhibits the most prominent economic performance when biomass resources are stably supplied with relatively low emission reduction ratios. CCUS technology can achieve large-scale carbon reduction based on surrounding geological conditions (such as oil displacement or storage requirements). Meanwhile, green ammonia co-firing, due to its current high costs, is more suitable for gradual implementation in regions rich in renewable energy through green electricity and hydrogen projects. This study provides a comprehensive techno-economic analysis of three key low carbon retrofitting pathways for coal-fired power, offering valuable insights for policymakers and industry stakeholders to make informed decisions in the energy transition. The findings contribute to optimizing low-carbon transformation strategies for coal power under China’s “dual carbon” goals.

The ongoing reforms in electricity markets have pushed energy producers and consumers to confront an increasingly uncertain market environment. The integrated energy systems (IES), designed to integrate a wide range of energy resources and carriers, have the potential to address the uncertainties of market trading. To this end, this paper innovatively introduces the Wasserstein metric-based distributionally robust optimization theory to construct a bidding model for IES participating in the spot power market. Firstly, the bidding mechanisms of the spot market across day-ahead, intraday and real-time market are analyzed, the decision-making keys for IES operators are proposed. Secondly, a data-driven Wasserstein metric ambiguity set is constructed to deal with price uncertainty, taking the minimization of expected cost and conditioned value-at-risk as objective, a distributionally robust optimization (DRO) bidding model is proposed for IES participating in the market. Thirdly, addressing the inherent complexity of the infinite-dimensional worst-case expectation problem, the proposed model is reformulated to a finite-dimensional mixed integer linear programming problem for computationally tractable, with rigorous reformulation process and final model presented in detail. Finally, taking the spot power market in Shandong Province, China, as an example, a case study is carried out to verify the feasibility and superiority of the proposed model. Comparison with stochastic programming and robust optimization show that the proposed model has better out-of-sample performance.

This study focuses on the economic feasibility of energy efficiency improvement technologies, renewable energy substitution technologies, and carbon capture and storage (CCS) technologies, exploring the economic viability and optimization pathways for carbon reduction at the user level. A user-side carbon reduction cost calculation method based on the Levelized Cost of Carbon Abatement (LCCA) is proposed, along with a detailed computational framework. A case study of an industrial park demonstrates that energy efficiency technologies have the lowest cost and are suitable for early-stage deployment. Among renewable energy options, photovoltaic power generation exhibits the best economic performance, while wind power requires cautious consideration due to its high investment costs. Although CCS technology demands substantial initial investment, its unit cost is only 11.79 RMB per ton of CO2, making it a critical solution for high-concentration industrial emissions. The study concludes that industrial parks should adopt a tailored technology mix based on their specific conditions to balance economic feasibility and environmental benefits, thereby achieving emission reduction targets.

With the goal of achieving carbon neutrality, promoting the clean and low-carbon transformation of energy assets, as exemplified by existing thermal power units, has emerged as a pivotal challenge in addressing climate change and achieving sustainable development. Arrangements and technologies such as the electricity–carbon–certificate multi-market, microgrids with direct green power connections, and carbon capture and storage (CCS) retrofitting provide favorable conditions for facing the aforementioned challenge. Based on an analysis of how liquid-storage CCS retrofitting affects the flexibility of thermal power units, this manuscript proposes a bi-level optimization model and solution method for capacity allocation for grid-connected microgrids, while considering CCS retrofits under multi-markets. This approach overcomes two key deficiencies in the existing research: first, neglecting the relationship between electricity–carbon coupling characteristics and unit flexibility and its potential impacts, and second, the significant deviation of scenarios constructed from real policy and market environments, which limits its ability to provide timely and relevant references. A case study in southern China demonstrates that first, multi-market implementation significantly boosts microgrids’ investment in and absolute consumption of renewable energy. However, its effect on reducing carbon emissions is limited, and renewable power curtailment may surge, potentially deviating from the original intent of carbon neutrality policies. In this case study, renewable energy installed capacity and consumption rose by 17.09% and 22.64%, respectively, while net carbon emissions decreased by only 3.32%, and curtailed power nearly doubled. Second, introducing liquid-storage CCS, which decouples the CO2 absorption and desorption processes, into the capacity allocation significantly enhances microgrid flexibility, markedly reduces the risk of overcapacity in renewable energy units, and enhances investment efficiency. In this case study, following CCS retrofits, renewable energy unit installed capacity decreased by 24%, while consumption dropped by only 7.28%, utilization hours increased by 22%, and the curtailment declined by 78.05%. Third, although CCS retrofitting can significantly reduce microgrid carbon emissions, factors such as current carbon prices, technological efficiency, and economic characteristics hinder large-scale adoption. In this case study, under multi-markets, CCS retrofitting reduced net carbon emissions by 86.16%, but the annualized total cost rose by 3.68%. Finally, based on the aforementioned findings, this manuscript discusses implications for microgrid development decision making, CCS industrialization, and market mechanisms from the perspectives of research directions, policy formulation, and practical work.

This study aims to address the issue of severe vibration in the catenary induced by heavy-haul railways - characterized by large transport volume and high axle load - when trains traverse bridge sections. Such vibrations significantly increase the risk of cumulative fatigue damage to catenary components. The research focuses on developing accurate methods for load identification and analysis. By precisely capturing and evaluating the dynamic loads sustained by the catenary during operation, this work provides essential data support and a theoretical basis for assessing the operational status of the system, predicting the service life of key components, and optimizing structural design. Research conclusions: (1) This study successfully developed an integrated online monitoring system capable of multi-parameter acquisition, wireless transmission, and intelligent analysis. The system adopts an integrated mast design, complies with IP65 protection standards, and features wide-temperature operational capability, ensuring stable performance in harsh railway environments. Its vehicle-triggered automatic activation and low-power sleep mechanism significantly enhance engineering application efficiency and reliability.(2) Through multi-sensor collaborative data acquisition and Kalman filter-based data fusion, this study accurately characterized the dynamic response of the catenary system. Field measurements revealed that the contact wire uplift fluctuates within ±5 mm under wind load when no train is present, while the peak uplift during pantograph passage ranges between 20 and 25 mm. This work represents the first quantitative characterization of the operational load spectrum for heavy-haul trains.(3) Utilizing deep learning algorithms, this research achieved intelligent recognition of pantograph abnormal states. Integrated with train number identification, a "one-pantograph-one-file" management system was established. The structured correlation of multi-source data and response spectrum analysis significantly improved the safety management level of the power supply system.(4) This study provides valuable references and a solid foundation for further investigation into vibration patterns and intelligent maintenance of heavy-haul railway catenary systems.

The analysis of how energy storage power plants contribute to the spot market is vital for developing energy storage projects. The development of new types of energy storage mainly based on electrochemical energy storage and pumped storage power plants in mechanical energy storage is an important step to enhance the regulation capacity, comprehensive efficiency and safety and security of energy and power systems as well as the operational benefits of the spot market. By establishing comprehensive models for both the lifecycle costs and potential revenues of energy storage, and analyzing the market operations benefits within a specific province using economic metrics like unit cost and unit income, valuable insights can be offered for guiding the development of energy storage projects and making investment decisions.

In order to control carbon emissions and improve the absorption capacity of wind and solar renewable energy, a multi-objective optimal operation model for microgrid participation in the electricity-carbon market was constructed, and the impact of the implementation of the stepped carbon trading mechanism and demand response strategy on the operation of the microgrid system was studied. Firstly, based on the stepped carbon trading mechanism and demand response strategy, the microgrid system operation architecture, including wind power, photovoltaic, internal combustion engine and other units, is designed; secondly, with the goal of minimizing the total system cost and carbon emissions, a multi-objective microgrid is constructed. The low-carbon economy optimization operation model and the multi-objective model are solved by the Epsilon constraint method; finally, a microgrid system in a park in the south is selected for example analysis. Improve system economy and environmental protection.

With the continuous development and improvement of Chinese electricity market, pumped storage power plants will face complex price mechanisms and transaction risks when participating in the electricity spot market. In order to protect the revenue of pumped storage power station, an optimization model of pumped storage bidding strategy considering the risks of the electricity spot market is proposed. Firstly, the price mechanism and transaction risk of pumped storage in electricity market are studied. Secondly, based on the conditional risk value, the risk of participating in the electricity spot market is quantified, and the risk-return matching mechanism is established. Based on electricity price prediction clustering to generate typical electricity price scenarios, a bidding strategy for pumped storage power stations to participate in spot-auxiliary service collaborative market considering risk factors is proposed. Finally, the influence of different risk preference levels and electricity price scenarios on the revenue of pumped storage power station is analyzed with examples. The results of the example show that the bidding strategy can stabilize the income fluctuation in different market scenarios and effectively avoid the risk of market price fluctuation. It can provide decision support for the pumped storage power station to participate in the bidding and capacity allocation strategy of the electric energy and auxiliary service market, and make the power station income more stable.

Integrated demand response (IDR) plays a crucial role in promoting multi-energy complementarity and source-load coordination within integrated energy systems (IES) by encouraging users to actively adjust their energy consumption through well-designed incentive mechanisms and strategies. However, existing user-side DR incentive strategies are often simplistic and ineffective in stimulating interactive engagement between supply and demand. Moreover, IES scheduling strategies that focus solely on power demand response fail to address the growing uncertainties on both sides of supply and demand and the increasingly complex structure of multi-energy markets under coordinated coupling.To address these challenges, this paper proposes an IDR incentive mechanism and develops an IES scheduling strategy model that considers the coupling and uncertainties of both supply and demand sides. This approach aims to ensure economic, safe, and efficient operation of the IES under various complex environments. Specifically, the design of a comprehensive incentive mechanism fully exploits the DR potential of users, while modeling the coupling and uncertainties of supply and demand to optimize DR behaviors and reduce the risk costs associated with uncertainties for integrated energy service providers. Simulation results demonstrate that the proposed bi-level game model effectively balances the interests of all stakeholders on both sides of supply and demand.

As a critical infrastructure to support the development of digital economy, the scale of data center (DC) power consumption has been growing continuously in recent years. The plug-in of a large number of DCs to the distribution network (DN) presents both opportunities and challenges for the secure, stable, and sustainable operation of the system. In order to address the planning issues arising from the integration of DCs into the DN, this paper proposes a two-stage (planning stage and operation stage) collaborative planning method for DCs and the DN while considering the source-demand uncertainty. Firstly, spatial-temporal demand response characteristic of DCs is explicitly elaborated, and based on the energy consumption model, a two-stage stochastic planning model considering conditional value at risk (CVaR) for DCs and the DN is constructed using probabilistic stochastic simulation, which obeys low-carbon and security, and satisfies the source-network-load constraints of the system. The siting and sizing for DCs and renewable generation, feeder expansion for the DN are determined to minimize the investment cost during the planning stage, while the power output for the devices considering uncertainties from load and renewable energy are optimized to minimize the operation cost during the operation stage. Secondly, the L-shaped decomposition algorithm is proposed to solve the two-stage stochastic optimization problem by reconstructing the problem into a master problem and subproblems, where the dual multiplies are calculated to generate feasibility cuts and optimality cuts to achieve iterative solving. Finally, numerical case studies on a 33-bus distribution system demonstrate that the participation of DCs in demand response can achieve a significant reduction in the investment and operation cost of the DN, and further promote the consumption of distributed renewable energy sources.

As a key infrastructure to support the development of digital economy, the scale of power consumption of data centers has been growing continuously in recent years. The plug-in of a large number of data center to the distribution grid is both an important opportunity and a serious challenge. In order to meet this challenge, data centers participate in the coordinated operation of the grid through demand response, thus promoting the coordinated development. In order to evaluate and analyze the comprehensive benefits of data centers participating in distribution grid demand response, this paper constructs a comprehensive benefit evaluation system for data center demand response that reflects the benefits of multiple agents. An evaluation index system containing 26 indexes is proposed from the technical, economic and environmental perspectives of distribution grid operator, data center operator, electricity consumer and data user, and a gray correlation improved TOPSIS evaluation method based on AHP-CRITIC combined weighting is proposed. Finally, a case study demonstrates that the higher the level of demand response participating in the data center, the better the optimization of collaborative operation.

Customer-side integrated energy system energy efficiency improvement is an effective path to achieve China's dual carbon goal. The current research on energy efficiency optimization of customer-side integrated energy systems is relatively single, and only model optimization is carried out unilaterally for high efficiency or combining high efficiency with economy. Based on this, this paper constructs a customer-side integrated energy system energy efficiency optimization operation model with three objective functions economy, environmental protection, and high efficiency. A typical industrial scenario containing electricity-heat-gas-cooling load is selected for example analysis, and the objective is solved by particle swarm algorithm and NSGA-II algorithm and compared for analysis, and the optimal total cost under the typical industrial scenario is calculated to be about 46 million RMB. It provides a reference for related parties to explore the optimization and enhancement of customer-side integrated energy system operation from multiple perspectives and also enriches the research results in the field of energy efficiency of customer-side integrated energy systems in China.

The new round of energy revolution is deepening under the impetus of Internet technology, and an integrated energy system as an important trend in the development of energy and electricity has become an initial decision. As different types of new technologies and models are gradually applied in the integrated energy system distribution network, the traditional distribution network technology can no longer fully adapt to the increasingly complex external environment. This paper considers the overall characteristics of the integrated energy system and its impact on the planning and operation of the distribution network based on the deep integration of the industrial Internet and the energy and power industry, studies the typical application model and synergy mechanism of active distribution network technology in the integrated energy system, and designs and constructs a typical model from multiple dimensions such as demand response, collaborative planning, energy management and monitoring and control of the distribution network, analyzes the overall trend and key areas of active distribution network technology in the future, and forms the development path of active distribution network technology in the integrated energy system under the industrial Internet, summarizes the key initiatives appropriate to the development of integrated energy systems in different development stages, and provides rationalization suggestions for the operation of distribution networks in the promotion of integrated energy systems.

In the context of accelerating the realization of the "carbon peaking and carbon neutrality" goals, the need for the coordinated development of the carbon electricity market to promote the clean and low-carbon transformation of energy is increasingly urgent. This paper studies the relationship between carbon electricity energy policy and carbon compliance. Firstly, the effectiveness of carbon electricity energy policy is analyzed quantitatively by introducing a policy quantitative model; then the carbon market trading volume is analyzed from two dimensions, and the correlation between the cumulative policy effectiveness and the turnover of carbon emission rights over the years was analyzed based on the Pearson correlation analysis, taking Hubei Province as an example; Finally, we proposed a more stable linkage mechanism between carbon electricity policy and carbon market compliance, aiming to optimize the formulation and adjustment of energy policy in the carbon electricity market, with a view to providing a reference for future research on the synergistic development of carbon electricity and energy.

Integrated energy system is an important approach to promote large-scale utilization of renewable energy. Under the context of energy market reformation and technology advancement, the economic operation of integrated energy system confronts new challenges, in terms of multiple uncertainties, multi-timescale characteristics of heterogeneous energy, and coordinated operation of hybrid energy storage system. To this end, this paper investigates the multi-timescale rolling optimization of integrated energy system with hybrid energy storage system considering the above challenges. Firstly, a basic framework of an integrated energy system with hybrid energy storage system (consisting of battery and hydrogen storage) is proposed, and the typical devices are modeled in detail. Secondly, the parameters and variables are divided into fast/slow timescale according to dispatch needs, and the multi-timescale problem of heterogeneous energy and the coordinated operation of the hybrid energy storage system can be solved simultaneously through two-stage optimization. Thirdly, the dispatch model is incorporated into the framework of model predictive control, the uncertainty of price, renewable energy and load can be effectively handled. Finally, a case study is conducted using an industrial park in the southern coastal region of China, the comparison with the rule-based method shows 60.65% reduction in the operating cost of hybrid energy storage system, the comparison with robust optimization shows 7.01% reduction in the total cost of the system, thus the feasibility and effectiveness of the proposed method are verified.

In the context of carbon neutrality and new power system construction, the large-scale access of renewable energy such as wind and solar brings great challenges to the safe and stable operation of the power system. This paper constructs a multi-agent, multi-objective, and multi-layer micro-grid collaborative planning model that comprehensively considers the transmission of multi-layer optimization problems based on the different objectives of multiple entities such as micro grid operators. This model achieves collaborative planning and optimization between renewable energy equipment, energy storage equipment, and network architecture at different time scales, and provides important technical solutions for improving the economic benefits of various entities and promoting the clean transformation of energy and power systems. Finally, this paper designs a simulation case based on the IEEE-33 node system to verify the effectiveness of the model. The results show that the coordinated configuration of renewable energy equipment and energy storage equipment in the micro-grid can significantly improve the absorptive capacity of renewable energy sources on the basis of ensuring the economic benefits of various entities, and thus provide important support for the construction of carbon neutrality and new power systems.

At present, the utilization hours of thermal power continue to decline, and renewable energy generation also has a serious socket phenomenon, so it is necessary to guide the construction of power side selection points through price signals, so as to promote the smooth integration of more power generation into the grid. This study focuses on the connection services provided by grid enterprises to power side and demand side, analyzes and summarizes the connection charging practices of different countries, designs a shallow connection cost recovery model according to energy strategy and the characteristics of supply and demand side in our country, and puts forward feasible suggestions for renewable energy generation connection charging, in order to promote the investment and construction of grid and the consumption of renewable energy.