Haiyang Jiang

Extreme cold events (ECEs) pose major challenges to the supply adequacy of integrated electricity–heat systems (IEHS). Traditional winter-adequacy assessments often rely on a single weather-severity metric, which may not fully reflect the heterogeneity of cold processes and the timing of supply–demand imbalances. This paper proposes an event-type-aware framework that distinguishes cold surges (CS) from long-term low-temperature (LTLT) events to assess their distinct impacts on IEHS. Multi-dimensional indicators are developed to characterize shortage patterns in terms of both heating-shortage severity and duration-based impacts. A case study of a Swedish regional system shows that, during extreme cold events, the coldest period does not necessarily pose the greatest supply challenge; instead, system stress depends on operating conditions and meteorology-driven power generation and heating demand. Specifically, CS tends to induce short but intense supply shocks, whereas LTLT imposes more persistent burdens on the system. These findings highlight the importance of incorporating event-type differences into system planning to better manage both rapid shocks and sustained stress under extreme cold conditions.

Incorporating demand-side resources (DRs) via virtual power plants into power system dispatch has become increasingly prevalent to increase power balancing flexibility. Nonetheless, diverse available time intervals for DRs impose substantial computational burdens for aggregation and may lead to infeasible dispatch after disaggregation. This work proposes an efficient reduction method for aggregating DRs with diverse time intervals and demand requirements while ensuring feasible dispatch. The method determines how to reduce the time interval ranges of all DRs to alleviate dispatch complexity. We reformulate an equivalent ILP problem with an available time interval matrix to reduce the problem scale. To further alleviate the computational burden, a fast approximation algorithm that combines interval clustering and weight selection is designed for large-scale DR aggregation. Numerical tests show that the proposed method maintains larger aggregated feasible regions to provide more flexibility than other inner-approximation methods, while maintaining feasible dispatch. Overall, the reformulated problem and fast approximation algorithm effectively reduce the computational time with a limited reduction in dispatch optimality.

Power systems worldwide are undergoing a transition toward a low-carbon future. The structure of low-carbon power systems and the pathways to achieve them remain uncertain due to the rapid evolution of low-carbon technologies. Moreover, advanced planning algorithms should be developed to address the high complexity of planning problems. A standard test system is required to evaluate various prospects of power system transition and planning optimization algorithms. This paper introduces a novel test system, PROSPECT-43, dedicated to the power system's transition towards the carbon-mitigation target. The test system represents a load center region with an AC-DC hybrid transmission network. The test system incorporates multiple emerging technologies such as low-carbon generation technology, transmission technology and new types of loads (including data centers and power-to-hydrogen industry), enabling researchers to explore the impact of individual technologies and their synergies. The test system provides comprehensive data necessary for long-term power system planning, including candidate units, load growth, the carbon emission budget, etc. This paper provides a detailed description of the test system from the perspectives of the transmission network, generation, storage, and load. In addition, benchmark low-carbon transition pathways are proposed for the test system.

With the increasing construction of generation bases using LCC-HVDC transmission in power systems, highproportion inverter-based resources (IBR) generation bases face overvoltage issues due to unavoidable commutation failure faults. This presents challenges in terms of system protection and stability. This paper proposes a power planning model for highIBR penetration generation bases with overvoltage constraints, ensuring that overvoltage limits are not exceeded at any bus while minimizing construction costs. The impacts of synchronous generators, synchronous condensers, and IBRs on overvoltage are explicitly modeled in an analytical model. This nonlinear constraint is conservatively reformulated into a linear form. Simulations of actual generation base planning verify the impact of overvoltage constraints on system operation and planning.

The increasing penetration of renewable energy leads to the frequent occurrences of zero-marginal-cost phenomenon in the spot market and causes missing money problems for renewable power producers (RPPs). Engagement in the forward market is a viable solution for RPPs to address this issue. This paper establishes a distributionally robust meanvariance model to optimize the RPPs' forward trading strategies. The model is further transformed into a tractable second-ordercone-programming one. Based on this model, RPPs are able to ensure their revenue sufficiency and mitigate revenue risks through the engagement in the forward market. We conduct case studies on RPPs' forward trading strategies under three typical market scenarios. In addition, we study the impacts of delivery mechanisms and the price of forward market on RPPs' trading strategies.

Poorly damped converter-driven oscillations are becoming increasingly prevalent as conventional synchronous generation (SG) is progressively replaced by inverter-based generation (IBG). Future power systems should strategically optimize the investment decisions of IBG and SG to prevent unstable dynamic interactions. However, different combinations of candidate generators would introduce varying oscillation modes, complicating the task of reserving sufficient oscillation damping for various critical modes during generation expansion. To address this challenge, this paper formulates a constraint-learning-based generation expansion planning framework. First, a cost-dominant sampling method is proposed to generate diverse generation mix scenarios, encompassing various on-off states and outputs of candidate generators. Next, stability constraints are learned for each generator and then grouped into an ensemble through the proposed physical-ensemble constraint learning method. These constraints are dynamically integrated into generation expansion and activated based on the generator's on-off status to maintain converter-driven stability across various generation mix scenarios. The feasibility and effectiveness of the proposed framework are validated on a modified IEEE standard test case and a real-world power grid with high IBG penetration. Case studies demonstrate that the proposed method outperforms state-of-the-art approaches by reducing computational burden while improving the proportion of stable states to over 95% in the operation simulations of the planned power systems.