This comprehensive review examines the key role and optimization dispatch of Technical Virtual Power Plants (TVPPs) in the new energy era. This study provides an overview of Virtual Power Plants (VPPs), including their definition, development history, and classification into Technical and Commercial VPPs. It then systematically analyzes optimization methods for TVPPs from five aspects: deterministic optimization, stochastic optimization, robust optimization, and bidding-integrated optimization. For each method, this review presents its mathematical models and solution algorithms. This review highlights the significance of TVPPs in enhancing power system flexibility, improving renewable energy integration, and providing ancillary services. Through methodological classification and comparative analysis, this review aims to provide valuable insights for the design, operation, and management of TVPPs in future power systems.

In the context of carbon peaking and carbon neutrality goals, the integration of a large number of renewable energy sources may trigger significant tidal changes, leading to transmission congestion, wind and light abandonment, power oscillation, voltage and frequency fluctuations, etc. A phase-shifting transformer is a cost-effective and reliable power flow control equipment, but its key parameters lack systematic design methods. Based on the equivalent model of phase-shifting transformers, detailed design principles and methods have been proposed, and the configuration method of winding turns has been improved to facilitate calculation and control strategy formulation. The regulation effect of the phase-shifting transformer designed through simulation of power flow regulation in a typical 500 kV network is simulated. By adjusting the phase-shifting transformer, the power flow of heavy load lines can be transferred to light load lines, optimizing the power flow distribution and improving the transmission capacity of the cross-section.

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method.

The high penetration of renewable energy introduces strong uncertainty into power system operating conditions, making it difficult for conventional planning methods to effectively satisfy N-1 security verification requirements. To address this issue, this paper proposes an N-1 security-constrained 8760-hour power flow generation method (N-1-SC-8760-PFGM), formulated as a mixed-integer nonlinear programming (MINLP) problem. To overcome the computational intractability of direct solution, an energy-power-reactive power co-optimization method is developed based on objective consistency and solution path coordination. By employing a consistency-oriented objective function, a pre-embedded N-1 constraint mechanism, and a “verification–augmentation–re-optimization” solution strategy, the proposed method overcomes the limitations of traditional sequential optimization approaches, such as stage-wise independent objectives, insufficient coordination of N-1 constraints, and the lack of iterative re-optimization mechanisms during operating state adjustments. Case studies on modified IEEE 39-bus and IEEE 300-bus systems demonstrate that the proposed method achieves superior convergence and computational efficiency compared with conventional decomposition-based methods, and can efficiently generate full-year power flow profiles that satisfy N-1 security constraints.