To address the insufficient linkage between macro-level carbon policy signals and micro-level resource dispatching decisions in the low-carbon operation of virtual power plants under the carbon trading mechanism,this paper proposes a multi-market coupled low-carbon optimal dispatching strategy for virtual power plants under macro carbon constraint transmission. First,a macro carbon trading scenario generation method is developed based on the computable general equilibrium (CGE)model, which quantifies parameters such as carbon price,carbon quota,and energy price under different carbon emission reduction targets, thereby providing external policy boundaries for virtual power plant dispatching.Second,the macro parameters output from the upper layer are embedded into the lower-layer virtual power plant optimal dispatching model. Combined with the Minkowski-sum-based resource aggregation method,the comprehensive regulation capability of multiple types of resources, including electric vehicle clusters,thermostatically controlled loads,industrial loads,and residential flexible loads,is characterized. Furthermore,a multi-market coupled optimization model coordinating the electricity energy market,peak-shaving auxiliary service 
market,and carbon trading market is established to achieve the coordinated improvement of economic benefits and carbon emission reduction benefits for virtual power plants.Case study results show that the proposed strategy can effectively respond to carbon price fluctuations and carbon quota constraints,reduce carbon emissions while improving the comprehensive revenue of virtual power plants,and enhance renewable energy consumption capacity and the feasibility of dispatching schemes.

To address the impact of access voltage level selection on system security in photovoltaic (PV)bundled hydropower transmission systems,this paper investigates the differences in the effects of high-voltage side versus low-voltage side PV access on the internal equipment safety of hydropower stations and power grid stability.Taking a large-scale PV project in a hydropower station reservoir area as a case study,a system simulation model was constructed using PSD-BPA software.Two PV access schemes —high-voltage side (500 kV)and low-voltage side (15 .75 kV)—were established for comparative analysis through static short-circuit calculations and dynamic transient simulations.The results indicate that high-voltage side access significantly reduces the short-circuit current at the 15.75 kV and 35 kV generator buses,thereby increasing the safety margin of equipment.In the event of faults at the 500 kV and 35 kV buses,this scheme demonstrates smaller power angle swings,faster damping attenuation,and smoother voltage recovery.Consequently,the high-voltage side access mode effectively mitigates the risk of excessive short-circuit current associated with low-voltage side access through electrical isolation,enhances system transient stability,and represents the superior engineering implementation solution.

[Objective]To address the challenges of insufficient renewable energy accommodation, low carbon resource utilization efficiency, and inadequate coordinated operation in virtual power plants (VPPs) with a high penetration of renewable energy and waste-to-energy (WtE) power plants, a low-carbon optimal scheduling model incorporating power-to-gas (P2G) coordination, carbon capture, and carbon-green certificate trading is proposed.[Methods]A coordinated operation framework integrating carbon capture and storage (CCS), power-to-gas (P2G), and gas-fired generating units is established. Multiple energy resources, including thermal power units, waste-to-energy power plants, wind power, photovoltaic generation, and energy storage systems, are incorporated into a unified scheduling framework to realize the coordinated optimization of electricity, heat, gas, and carbon resources. Furthermore, a ladder-type carbon trading mechanism and green certificate trading revenue are introduced to achieve the coordinated optimal scheduling of multiple energy resources.[Results]Simulation results demonstrate that the proposed model effectively promotes carbon resource recycling. The ladder-type carbon trading mechanism strengthens carbon emission reduction incentives, increasing the emission reduction rate from 30.9% to 53.0% under the same carbon price. In addition, green certificate trading further improves renewable energy accommodation. Under the proposed coordinated mechanism, the total system operating cost is reduced from RMB 2.9050 million to RMB 2.4364 million, representing a 16.13% reduction, while significantly enhancing renewable energy utilization and the comprehensive economic and environmental performance of the system.[Conclusions]By integrating P2G coordination with carbon and green certificate trading mechanisms, the proposed model achieves the coordinated optimal scheduling of multiple energy resources. It effectively promotes carbon resource recycling while simultaneously improving economic performance and low-carbon operation, thereby providing an effective approach for the optimal scheduling of VPPs with high shares of renewable energy and waste-to-energy power plants.

Aiming at the challenges of source-grid-load-storage balance under high renewable penetration and the inadaptability of traditional adjustable-load theories, this paper systematically reviews the research progress of source-grid-load-storage systems for high-density land-based aquaculture under rigid load constraints. The system evolution is analyzed from three dimensions: technical integration, scenario adaptability, and scale expansion. A three-dimensional load rigidity classification system is constructed, and four core contradictions are extracted. Three typical configuration modes and four-level key supporting technologies are summarized. Results show that existing strategies can alleviate source-load imbalance and reliability pressure, but still have deficiencies in dynamic modeling, operational resilience, standardization, and coordinated control. Future directions are also prospected. This review provides theoretical support and technical reference for relevant system planning and optimal configuration.

To address the difficulty of simultaneously ensuring operational security and delivery reliability in flexibility service scheduling of distribution networks under multiple sources of uncertainty, an optimal scheduling method based on distributionally robust chance-constrained programming is proposed. A hierarchical model incorporating hard security constraints and distributionally robust chance constraints is established, in which the hard security constraints are used to impose operational security requirements on nodal voltage, branch capacity, and energy boundaries of energy storage within the prescribed uncertainty support and under the adopted model conditions, while the distributionally robust chance constraints are introduced to characterize the delivery risk of active and reactive flexibility services at the point of common coupling. Furthermore, linear decision rules are employed to describe real-time adjustment strategies, thereby enabling the coordinated optimal scheduling of controllable loads, energy storage, and reactive power resources of photovoltaic inverters. The resulting model is then reformulated as a second-order cone programming problem for efficient solution. Case studies on the IEEE 33-bus and IEEE 123-bus systems demonstrate that the proposed method can improve the delivery reliability of flexibility services and reduce out-of-sample violation risk while ensuring the secure operation of the distribution network, with satisfactory economic performance and engineering applicability.

Aiming at the problem of large load shedding caused by insufficient power supply capacity of rural distribution stations due to the superposition of photovoltaic output fluctuation and centralized operation of seasonal agricultural loads in rural distribution networks with high photovoltaic penetration, this paper proposes an optimal scheduling strategy for Interconnected Energy Storage Vehicles (IESV) oriented to power supply risk control in agricultural distribution networks. The system architecture of a rural distribution network based on IESV is constructed, and the mathematical model of IESV is established. The significant spatio-temporal fluctuation characteristics of agricultural loads are described, and on this basis, a two-stage scheduling strategy including pre-layout and dynamic dispatch is proposed. In the pre-layout stage, based on typical scenarios before the busy farming period, with the objectives of minimizing the configuration cost and load rate deviation penalty, the interconnected distribution station pairs are determined to reserve operation margin for the load peak during the busy farming period. On this basis, the dynamic dispatch stage is proposed to further cope with load fluctuations. Based on the topology formed by pre-layout, this stage optimizes the access locations and discharge power of IESV with the objective of minimizing the load shedding power. The two stages cooperate under the sequential optimization framework of "planning-operation" to realize the comprehensive optimization of operation cost and load shedding. Finally, the simulation based on the IEEE 33-node system verifies that the proposed strategy can significantly reduce the agricultural load shedding and the total system operation cost while controlling the power supply risk of agricultural distribution networks.

To address the impacts of multiple uncertainties on configuration schemes of integrated energy systems for data centers, a three-stage decision framework integrating information gap decision theory and bi-level optimization is proposed. First, a bi-level optimization model is established with levelized annual cost and annual CO₂ emissions as the two objectives. In the upper level, the NSGA-II algorithm is used to optimize equipment capacities, while in the lower level, mixed-integer linear programming is employed to minimize daily operating costs. Then, based on information gap decision theory analysis, the robustness radius and opportuneness radius are calculated to quantify each scheme's ability to withstand unfavorable uncertainties and its potential to capitalize on favorable uncertainties. Finally, according to the decision-maker's risk preference, a final recommended configuration is adjusted and generated. A simulation was conducted using a typical data center as a case study, and the results indicate that the risk-averse scheme achieves robustness at the cost of low-carbon performance, with carbon emissions increasing to 3.8 times that of the deterministic scheme; while the risk-opportunistic scheme achieves a dual reduction in both cost and emissions, with decreases of 4.3% and 16.5%, respectively.

In offshore fishery microgrid clusters operating under weak support, reliable probability distributions are often unavailable for PV generation and load forecasts, which challenges economic energy scheduling. This paper studies optimal operation considering source–load uncertainty. Starting from the fishery production process, a single-farm microgrid structure and key component models are established, and an interconnected microgrid cluster is formed via tie-lines. A deterministic dispatch model is developed considering tie-line capacity and transmission efficiency. Information-Gap Decision Theory (IGDT) is then introduced to build a robustness-oriented model that maximizes the admissible uncertainty radius to characterize the resilience boundary, and an opportunityoriented model that minimizes the uncertainty radius required to meet a target benefit to explore potential gains, providing a unified decision-making framework for different risk preferences. Case studies show that energy mutual aid reduces the total operating cost: compared with isolated operation, the cost decreases by about 3.9% under islanded mutual aid and by about 54.8% under a hybrid grid-connected mode. Moreover, the robustness-oriented strategy significantly enhances disturbance tolerance at the expense of limited economic performance and suppresses frequent diesel generator start–stop actions, while the opportunity-oriented strategy achieves better economic performance under controllable risk. The proposed method enables quantitative economic trade-offs for interconnected microgrid clusters without relying on precise probability distributions, and provides an interpretable decision-making approach for offshore fishery microgrid clusters and other weak-support scenarios.

To address the cost recovery challenges of new-type energy storage projects and to identify the key economic determinants across different application scenarios, a cost analysis framework and economic quantification methodology tailored to revenue models for new-type energy storage applied at the generation, grid, and demand sides are proposed. Unit energy/capacity cost characterization models are derived using the operational-period pricing method, which are compatible with revenue models across different application scenarios. Multidimensional revenue quantification models are established by incorporating market-based income alongside traditional income. Case studies and sensitivity analyses are conducted using indicators such as annualized net revenue, dynamic payback period, and internal rate of return. The results indicate that while mature electrochemical energy storage demonstrates better economic performance in grid-side independent energy storage, its financial viability is significantly impacted by electricity market products, and the potential discontinuation of peak-shaving markets would lead to a substantial decline in revenue. For generation-side renewable energy stations, increasing the storage duration of energy storage can increase its grid electricity and enhance grid compatibility, thereby enhancing both electricity revenue and "Two Rules" (the grid compliance and ancillary service compensation mechanisms) revenue. The economics of demand-side energy storage depend on tariff structures （single-rate vs. two-part） and load characteristics. For large industrial customers with stable loads under two-part tariffs, optimal sizing of storage power and energy capacity is required to avoid losses caused by increased demand charges. From the perspective of investors and operators, this study systematically outlines a cost mechanism and economic analysis approach for new-type energy storage across generation, grid, and demand sides that are aligned with revenue models. By comprehensively incorporating key cash flow elements such as loan repayments and tax liabilities, it enables accurate dynamic economic assessment, providing a practical theoretical basis and operational guidance for cost control and decision-making in market-oriented environments.

With the acceleration of new power system construction, distributed photovoltaic aggregators are able to participate in the day-ahead energy and reserve joint market trading and obtain profits. However, due to differences in their interests, distributed photovoltaic aggregators, market trading centers, and distribution system operators exhibit complex market trading game behaviors. Accordingly, a single-leader multi-follower mixed-integer Stackelberg game framework is constructed for distributed photovoltaic aggregators participating in the day-ahead electricity market. The upper-level model aims to maximize the profit of the distributed photovoltaic aggregator (leader) by optimizing bidding strategies, while the lower-level model involves the market trading center (follower 1) conducting day-ahead joint market clearing, and the distribution system operator (follower 2) performing security verification on the market clearing results based on discrete control measures such as transformer tap changers and capacitor switching. To solve this multi-agent game model, the Karush-Kuhn-Tucker conditions and the Big-M method are first used to equivalently transform the follower 1 problem. Subsequently, a data-driven bilevel reconstruction algorithm is employed to solve the leader-follower game model with continuous and discrete variables. Finally, the accuracy and effectiveness of the game model and its solution algorithm are validated using a practical transmission and distribution system in a certain region.

In recent years, the rapid development of Fuel-Cell Hybrid Electric Vehicles (FCHEV) has effectively alleviated the peak-shaving challenges in new power systems caused by the "anti-peak" characteristics of renewable energy, promoting energy-transportation coupling and low-carbon emission reduction. This paper focuses on the impact of renewable energy output uncertainty and the charging response of FCHEV on microgrid optimization scheduling. By utilizing FCHEV as a link between multiple regions of the microgrid, a two-stage robust game model for the microgrid is established. To address the issue of low solving efficiency caused by continuously adding constraints to the master problem during the iteration process of the C&CG algorithm, an improved iNC&CG algorithm is proposed. Finally, simulation results demonstrate that the proposed two-stage robust game model for the microgrid can achieve a balance of interests between the microgrid and FCHEV users, effectively cope with the impact of renewable energy output uncertainty. And the advantages of the improved algorithm in solving such large-scale problems is verified.

To address the problems of renewable energy accommodation and supply–demand imbalance caused by the dynamic evolution of load demand over the life cycle of western mining areas, a life-cycle dynamic configuration method for the energy system of underground coal mines is investigated. Based on the production organization in the construction, early-stage mining, mid-stage mining and late-stage mining periods, a multi-energy coupling framework for electricity–heat–cooling is constructed to reflect the differences between above-ground and underground loads. A multi-stage mixed-integer linear programming model is developed, which integrates photovoltaic (PV) generation, electrical energy storage, chillers and external power/heat supply. The objective is to minimize the total life-cycle cost consisting of investment, operation and maintenance, purchased energy and carbon emission costs by optimally sizing PV, energy storage and primary network equipment, and by representing the temporal evolution of source–load relationships through typical-day load profiles and year-by-year capacity expansion decisions. A typical western underground mine is used as a case study, and two scenarios are compared: traditional static one-shot configuration and life-cycle dynamic configuration. The results show that the dynamic configuration increases the average installed PV capacity and renewable penetration through staged PV and storage expansion in key years, while restraining the required capacity of primary network equipment and substantially reducing curtailment over the whole life cycle. Compared with the static configuration, the dynamic configuration reduces the total life-cycle cost by about 17.9% and the carbon emission cost by about 50.2%. The proposed life-cycle dynamic configuration method can satisfy secure energy supply for mining areas while balancing economic performance and low-carbon goals, and it provides a technical reference for planning clean energy systems in western mining areas and similar energy-intensive industrial parks.