Abstract:

As global carbon reduction efforts intensify, the rapid proliferation of electric vehicles (EVs) is accelerating the profound integration of traffic and power networks. However, the carbon-intensive nature of upstream power generation and the persistent emission challenges posed by conventional gasoline vehicles (GVs) remain fundamental bottlenecks restricting the overall decarbonization efficacy of the coupled network. To address these challenges, an energy-carbon integrated pricing methodology and a synergistic low-carbon scheduling model tailored for coupled traffic-power networks (CTPN) is proposed. Specifically, by leveraging carbon emission flow theory, the carbon footprints inherent in the power network are traced to the traffic side, enabling the formulation of spatiotemporally differentiated nodal carbon prices for EV charging. For GVs, a direct carbon pricing is implemented according to their emission models. Meanwhile, a power system model is constructed based on the alternating current optimal power flow equations, and an integrated energy-carbon price regulation system is established at the cross-network level to achieve coordinated low-carbon scheduling of the CTPN. Furthermore, an efficient iterative solution algorithm is designed to resolve the complex synergistic scheduling optimization problem. A case study demonstrates that the proposed mechanism accurately identifies carbon emission responsibilities and facilitates the scientific allocation of carbon-related costs among diverse stakeholders. Compared with conventional scheduling paradigms, the proposed scheme significantly enhances the decarbonization potential of the coupled network while effectively balancing the operational economy. Essential theoretical insights and technical support are provided for the low-carbon evolution of the modern urban energy internet.

Abstract:

With the integration of high shares of renewable energy into power systems and the deepening of market-oriented electricity reform, fixed cost recovery for conventional generating units and system capacity adequacy have become critical issues. Given this background, a staged capacity adequacy assurance mechanism adapted to the deep transition of the power supply structure is proposed, covering three evolutionary stages: fixed capacity compensation in the pilot stage, category-specific auction-based capacity compensation in the transition stage, and a unified capacity market in the mature stage. First, a fixed capacity accounting method based on expected cross-market revenues is developed to support fixed cost recovery for essential flexible generation resources in the initial stage. Then, a category-specific auction-based capacity compensation model is established according to the regulation characteristics of heterogeneous resources, guiding different types of flexible resources to bid independently. Finally, a centralized clearing mechanism for the unified capacity market is designed to coordinate long-term capacity investment decisions. It is demonstrated through case studies that the proposed staged mechanism supports effective fixed cost recovery for gen-eration companies, guides rational investment in flexible resources such as new-type energy storage, and ensures long-term system capacity adequacy and flexible regulation capability.

Abstract:

With the increasing uncertainty of source-load in the new power system, the allocation of reserve resources designed for short-term emergency power supply may be over-configured in long-term power system operation scenarios, resulting in a waste of grid planning resources, so proposing a two-stage reserve resource planning method that considers both long-term and short-term cycles is an important means to balance the supply security and operational economy of the power system. First, the key uncertainty factors that lead to redundant reserve resource planning are analyzed, including the uncertainties of new energy sources, loads, and line failures. A two-stage reserve planning model based on chance constraints for source-load-storage is then constructed. Subsequently, based on the decoupling characteristics of the two-stage planning problem, a single-stage solution method for the two-stage reserve resource planning model is proposed. The power supply guarantee constraint is embodied to further achieve the effective solution of the source-load-storage backup planning model. Finally, the effectiveness of the proposed method is verified in the modified IEEE-30 bus test system. The case study results show that, without significantly increasing the computation time, the proposed two-stage reserve resource planning method for source-load-storage can reduce the average power shortage by about 54.7%, lower the reserve planning cost by about 26.7%, and decrease the new energy curtailment rate by about 60.4%. Therefore, the proposed method can effectively enhance the system's power supply capability, improve the efficiency of new energy consumption, and promote the safe and economic operation of the power system.

Abstract:

Energy sharing has significant potential for improving system energy utilization, enhancing operational economic benefits, and mitigating impacts on the main grid. Focusing on multiple user microgrids, energy consumption characteristics and considers integrated electricity-heat demand response and multi-energy interaction are analyzed. A distributed coordinated operation model for multiple microgrids is proposed to support the green transition of user microgrids and energy-efficiency value-added services, while identifying high-energy-consuming equipment and energy-saving potential. The Nash bargaining method is adopted for cost settlement to ensure the willingness of microgrids to participate in coordinated operation, and the alternating direction method of multipliers is used to achieve privacy-preserving distributed coordination. Case studies verify that the proposed model can reduce operating costs, maintain coordinated operation among microgrids, and ensure the convergence and accuracy of the distributed algorithm. Moreover, the model provides energy-saving suggestions, electricity substitution recommendations, and energy information-sharing services, thereby promoting refined and low-carbon energy management and realizing energy-efficiency value-added services for users.

Abstract:

With the high penetration of renewable energy integrated into the grid, short-term power imbalances in power systems intensify, posing challenges to frequency security. Deploying energy storage is regarded as a critical means to address this challenge. In existing energy storage planning methods that consider frequency security, power fluctuations are usually presupposed when deriving frequency security constraints, leading to overly conservative results; moreover, the nonlinear frequency constraints become computationally complex under uncertainty. To this end, a multi-objective distributionally robust energy storage capacity planning method is proposed. Typical daily scenarios are generated through clustering, the nonlinear frequency security constraints are transformed into a mixed-integer programming formulation via scenario enumeration, and distributionally robust optimization is employed to handle power uncertainty. Pareto solution sets with frequency security satisfied at different confidence levels are provided through case studies, and the impacts of key factors such as frequency security constraints and green objective weights on energy storage configuration are further analyzed. It is demonstrated that the proposed method can effectively coordinate economic, green, and security objectives, offering decision support for energy storage planning in regional power systems.

Abstract:

To address the impact of large-scale renewable energy integration on the operation of electro-hydrogen coupling systems, and to resolve the subjectivity in uncertainty set selection and insufficient utilization of renewable energy probability information in traditional twostage robust optimization, a two-stage risk-robust scheduling method tailored for renewable power systems is proposed. First, an integrated model for renewable power system that encompasses dynamic hydrogen production, storage, and utilization is established. A multi-physics aware electrolyzer model that couples the thermodynamics and bubble dynamics is proposed to precisely characterize the dynamic variation of electrolytic efficiency. Second, a two-stage risk-robust generation-reserve coordination scheduling method for the renewable power system is developed based on robust optimization and conditional value at risk（CVaR）. In the first stage, the output baselines and reserve capacities for energy storage, controllable units, and hydrogen production systems are determined based on renewable energy forecasts, and wind curtailment and load shedding risks are quantified. In the second stage, operations are rescheduled based on actual renewable energy output and reserved reserves to meet all scenario demands within the acceptable range. Finally, the column-and-constraint generation（C&CG）algorithm is adopted to solve the optimization model, and the optimal uncertainty set is determined through collaborative two-stage solution. Case studies validate the effectiveness of the proposed model.

Abstract:

With the increasing proportion of power electronic equipment in the microgrid of the park, the problem of harmonic pollution is becoming more and more serious, which brings challenges to the safe and stable operation of the microgrid in the park. Flexible in-terconnection device (FID) based on power electronic devices can realize power mutual aid between different distribution stations in the campus microgrid, and can provide voltage and frequency support when the distribution station area fails. Firstly, the structure, mathematical model and operation mode of FID are introduced. Then, the FID control strategy with the function of harmonic current control in the distribution station area is proposed, and the harmonic current control in the distribution station area is realized on the basis of power mutual aid, which improves the power quality and FID equipment utilization rate. Finally, the effectiveness of the proposed control strategy is verified by MATLAB/Simulink simulation and StarSim hardware-in-the-loop semi-physical experiment.

Abstract:

Non-intrusive load identification decomposes the power usage of each appliance to achieve precise monitoring of appliance behavior by analyzing the total power consumption data of a household or enterprise. It is of great significance for energy conservation, reducing power costs, and promoting smart grids. When the current load identification methods deal with complex time series data, it is difficult to capture the scale diversity of the long-term and short-term dependencies of the data, resulting in limited identification accuracy. In response to the above problems, the multi-scale features of time series data are studied. Through a multi-scale feature extraction strategy, the information at different time scales in the data is effectively captured. Meanwhile, the potential attention mechanism is introduced into the extracted multi-scale features, enabling the model to focus on the key moments and important features in the data while reducing the computational complexity. Finally, experiments are conducted on the public dataset. The results show that, compared with other comparison methods, the proposed model achieves the best effect, verifying the validity of the model.

Abstract:

With the gradual deployment of integrated projects for wind solar green hydrogen synthesis and ammonia synthesis in China, electrolytic cells not only need to achieve effective tracking of wind solar power, but also must ensure the economic viability of downstream hydrogen production and ammonia synthesis, which poses higher requirements for operational strategies. Therefore, a multi-electrolysis cell operation strategy that balances wind and solar power tracking with the economic benefits of green hydrogen synthesis of ammonia is proposed. Firstly, a mathematical model for the integrated system of wind solar green hydrogen synthesis of ammonia is constructed. Then, considering the tracking effect of wind and solar power and the economy of green hydrogen synthesis of ammonia, based on multi-objective optimization methods, an optimization scheme for the coordinated operation strategy of multiple electrolytic cells is designed. A multi-objective optimization algorithm based on improved knee region (IKR) is proposed for this multi-objective optimization problem. Finally, the actual data of the wind solar hydrogen synthesis ammonia integrated system in Da'an City, Jilin Province is used for verification, and the results show that the proposed method can balance the power tracking ability and economy of the electrolytic cell, and improve the overall operating efficiency and revenue of the system.

Abstract:

With the large-scale integration of distributed renewable energy sources, virtual power plants have emerged as important carriers for aggregating distributed resources, and multi-agent coordinated operation through peer-to-peer trading has become a significant development trend. However, peer-to-peer trading among virtual power plants requires simultaneous consideration of coordinated optimization between electricity markets and green certificate markets, as well as compatibility with the secure operation of distribution networks. To address this challenge, a bi-level optimization model for virtual power plant electricity-green certificate collaborative peer-to-peer trading considering distribution network operational constraints is proposed. The upper-level model establishes a framework for virtual power plant operational optimization and electricity-green certificate collaborative peer-to-peer trading decision-making, while the lower-level model develops a distribution network market clearing mechanism, with nodal electricity prices serving as key variables connecting the upper and lower levels to achieve coordination between peer-to-peer trading decisions and distribution network security constraints. An electricity and green certificate collaborative peer-to-peer trading model is developed to achieve dual-commodity collaborative optimization, reducing virtual power plant green certificate acquisition costs and incentivizing renewable energy development. An adaptive alternating direction method of multipliers based on consensus is employed to handle distributed coordination problems among virtual power plants, combined with the bisection method to achieve efficient and stable solution of the bi-level model. Case studies demonstrate that the proposed method can effectively enhance peer-to-peer trading economic benefits while satisfying distribution network security constraints, providing technical support for virtual power plant participation in diversified electricity markets.

Abstract:

Against the background of the global energy transition and the rapid development of carbon markets, integrated energy systems (IESs) gradually become an important approach to supporting a low-carbon economy through multi-energy coordination and market interactions. However, existing studies do not fully address the asynchronous nature of inter-annual trading and multi-timescale decision-making for IESs in electricity, natural gas, and carbon markets. To address this issue, a multi-timescale optimization decision-making method for IESs considering multi-market asynchrony and uncertainty is proposed. At the annual scale, the proposed method optimizes electricity and natural gas contract trading strategies as well as inter-annual carbon quota banking strategies, and characterizes the inter-annual features of carbon trading according to carbon market compliance rules. At the daily scale, based on the annual-scale optimization results, the operation strategies of park-level energy devices and spot market trading schemes are further refined. By introducing the conditional value-at-risk (CVaR) method, the model quantifies and controls the risks caused by market price fluctuations and uncertainty in carbon emission verification, thereby ensuring the economic efficiency and robustness of the scheduling scheme. Case studies demonstrate the effectiveness of the proposed method in multi-market coordination and multi-timescale decision-making.

Abstract:

In recent years, the frequent occurrence of extreme disaster weather has greatly influenced the safe operation of distribution network. Therefore, it is an effective way to improve the resilience of distribution network to formulate reasonable planning strategy before the disasters. With the rapid growth of distributed power generation and electric vehicle users, a multi-flexible resource planning strategy is proposed to improve the resilience of distribution network in typhoon weather. Firstly, the typical typhoon disaster scenarios are screened by constructing the correlation model of typhoon wind speed and failure rate, combining the conditional generation adversarial network and information entropy. Secondly, the upper planning model aiming at the minimum annual comprehensive cost and the lower multi-objective optimization model taking into account the operating cost and performance loss area are established. The electric vehicle charging pile planning is innovatively incorporated into the model, and the distributed and mobile characteristics of electric vehicles are utilized to enhance the flexibility and elasticity of the power grid. Then, the optimization algorithm combining non-dominated sorting genetic algorithm and interior point method is used to solve the model. Finally, through the improved IEEE33-node distribution system, the results show that the proposed method can effectively reduce the network loss and load loss, and improve the overall resilience of the distribution network.

Abstract:

To mitigate power distribution network load superposition caused by heterogeneous private electric vehicle (EV) behaviors, a bi-layer optimal scheduling method integrating battery swapping entitlement profiling with a dynamic incentive “blind boxes” is proposed. Through a two-dimensional profiling system, users are categorized into three groups, and a “charging inertia” theory is employed to quantify the energy leverage effect during replenishment path shifts. A bidirectional dynamic incentive mechanism is designed to guide entitled users toward residential charging via non-linear psychological incentives while attracting paying users during off-peak hours. Based on this, a bi-layer model is established to maximize operator profits and minimize network power loss and voltage deviations. It is demonstrated through simulation using real-world data from Shanghai that user response thresholds are accurately identified by the strategy. Operator profits are increased, peak-to-valley differences are reduced, and nodal voltage fluctuations are significantly stabilized without compromising user experience.

Abstract:

As a typical flexible load resource, air conditioning (AC) loads can be aggregated by load aggregator to participate in demand response (DR) markets, effectively alleviating electricity supply pressure during summer peak periods. A hierarchical game-based optimal dispatch strategy for AC aggregators considering response characteristic-based grouping is proposed. First, the characteristics of AC devices are analyzed, and a relationship equation is derived to capture the correlation between average power consumption and temperature difference over long time intervals, thereby revealing the intrinsic relationship between minimum average power and the temperature difference range. Then, considering the heterogeneity of unit-level parameters, an affinity propagation (AP) clustering algorithm is employed to perform fine-grained grouping of AC loads. On this basis, a game-theoretic optimization model is established for AC aggregators. To address solution tractability, continuous intermediate variables are introduced during the solving process, and hierarchical optimization is adopted to ensure the uniqueness of the equilibrium solution. The simulation results demonstrate that the proposed air-conditioning operation equation effectively captures the deep relationship between the minimum hourly operating power of individual units and the temperature-difference interval, thereby reducing the dimensionality of decision variables. Based on the clustering results, the proposed game-theoretic hierarchical optimization strategy exploits the response potential of air-conditioning loads, reducing the peak-to-valley difference of system operation within the scheduling period to 27.22%.

Abstract:

To address the insufficient equipment coordination and inadequate consideration of distribution network conditions and uncertainty risks in the planning of photovoltaic-energy storage-charging stations (PECS), an economic and low-carbon coordinated configuration model for PECS is proposed considering uncertainty risks. A coordinated planning model is established by incorporating power prices and low-carbon demand response, PECS daily operation status and distribution network power flow constraints. Based on typical scenarios generated by K-means clustering, conditional value-at-risk is employed to quantify the PECS risk costs from photovoltaic output and charging load uncertainties. The constructed model is reformulated via product linearization and second-order conic relaxation into a unified model for accurate solution by commercial solvers. Simulation results verify that the proposed model improves the planning economy of the PECS, meets the high-efficiency and low-carbon demands of the distribution network operation, and balances economic costs, environmental benefits and risk costs.

Abstract:

With the rapid growth in electric vehicle ownership, the stochasticity and volatility of charging loads have posed significant challenges to the secure and stable operation of power grids. Therefore, accurate EV charging load forecasting has become a critical requirement for the development of new-type power systems. Existing studies often overlook the heterogeneity of service objects among different charging piles, resulting in the mixing of charging load characteristics associated with different vehicle usage types and limiting the ability of forecasting results to support grid risk assessment and decision-making. To address this issue, a probabilistic EV charging load forecasting method based on K-means clustering and long short-term memory networks (LSTM) is proposed. First, K-means clustering is employed to classify charging piles into five clusters according to their load patterns and dominant service vehicle types, namely private vehicles, buses, official vehicles, taxis, and other vehicles, thereby mitigating the feature-mixing effect. Subsequently, a LSTM-based forecasting model is constructed, in which a weighted quantile loss function is adopted instead of the conventional mean squared error loss to generate forecasting results within the 10%~90% quantile prediction interval. To validate the effectiveness of the proposed model, experiments are conducted using charging pile load data provided by a provincial power grid company. The results show that, after charging pile clustering, the average mean squared error of load forecasting is reduced by 16.6%. The proposed method can provide effective support for risk assessment and dispatch decision-making in power grid operation.

Abstract:

To improve the coordinated clearing efficiency of energy, frequency regulation and reserve markets, a day-ahead multi-market clearing method involving distributed photovoltaic-storage aggregators is proposed. Different from traditional sequential clearing and uniform regulation pricing, a comprehensive regulation performance index is introduced, and the regulation mileage bid is modified by the analytic hierarchy process so that high-performance regulation resources are prioritized. A coupling constraint between regulation and reserve capacities is established to avoid repeated ancillary-service capacity reservation. On this basis, a security-constrained unit commitment (SCUC) model is used to minimize the total cost, and a security-constrained economic dispatch (SCED) model is used to optimize dispatch and clearing prices. It is shown by case studies that system operation cost can be reduced and coordinated operation of photovoltaic-storage resources and thermal units can be improved.

Abstract:

The development of carbon-green certificate market trading mechanisms is a critical measure for advancing the green and low-carbon transformation of energy systems. In existing optimal resource capacity allocation studies for industrial parks, the impacts of such trading mechanisms and the flexible response of electrolytic aluminum loads are neglected, which compromises the economic efficiency and applicability of wind-photovoltaic-energy storage configuration schemes. To address this gap, an optimal Wind-PV-ESS capacity allocation method for industrial parks considering the above two factors is proposed. First, a demand response model for flexible electrolytic aluminum loads is established based on their production energy demand and regulation characteristics. Second, a ladder-type carbon-green certificate trading mechanism is investigated to support energy conservation and carbon reduction in these parks. Then, an integrated capacity allocation and scheduling model is constructed, with the park ' s annualized investment-operation cost as the objective and constraints on equipment investment, site availability, and operation fully considered. Finally, it is demonstrated through case studies that the proposed method effectively reduces annualized costs, achieves efficient new energy utilization, and lowers carbon emissions simultaneously.

Abstract:

A cross-provincial hydro-thermal ancillary service market mechanism is proposed to address the integration of conventional hydropower into regional peak regulation markets and its coordination with provincial markets. Hydropower feed-in tariffs are considered, together with the economic attributes and security-stability characteristics of different operational zones. A method for hydropower basic peak regulation is proposed, and an opportunity cost model for peak regulation is developed. For the generation right replacement market, transactions are categorized. The categorization is based on whether energy return is involved in hydro-coal peak regulation. The significant differences in deep peak regulation costs are considered, as well as the regional variations in provincial market price caps. Distinct trading products are defined for hydropower deep peak regulation and coal-fired deep peak regulation. A mechanism of categorized and tiered price caps with separate clearing is then proposed to achieve priority clearing for hydropower and differentiated price caps for hydro and coal units. Meanwhile, provincial price cap requirements are accommodated. Compatible compensation and cost allocation mechanisms are constructed. Through case verification, it is demonstrated that the deep peak regulation capability of hydropower is further harnessed. Hydro-coal peak regulation services are coordinated despite their cost differences. Market-based peak regulation optimization is enabled. System regulation capability is enhanced and overall costs are reduced. The exceeding of provincial peak regulation price caps by the cost of purchasing regional peak regulation resources is prevented.