The large-scale integration of distributed photovoltaics and emerging loads has made limited distribution network hosting capacity a key physical bottleneck to the development of new-type power systems. Existing research on virtual power plants (VPPs) mostly focuses on commercial aggregation and optimal dispatch, lacking a systematic exploration of active support mechanisms for the underlying operational constraints of distribution networks. Stepping out of the single resource aggregation perspective, this paper proposes a four-stage evolutionary analysis framework for VPPs oriented towards enhancing distribution network hosting capacity, and systematically reviews international frontier practices from VPP 1.0 (aggregation arbitrage) to VPP 4.0 (hierarchical autonomy). Specifically, VPP 2.0 relies on the local autonomous control of smart inverters to mitigate voltage violations, effectively enhancing the static hosting capacity of distribution networks; VPP 3.0 introduces the dynamic operating envelope mechanism to decouple physical constraints from market optimization, deeply unlocking the spatiotemporal dynamic flexibility of the system; VPP 4.0 utilizes cellular energy systems and distributed collaborative algorithms to address the scalability bottleneck under the integration of hundreds of millions of nodes. On this basis, considering China’s practical national conditions—specifically the management system based on secondary substation areas (transformer service areas) and the massive scale of legacy non-smart assets, this paper proposes a three-stage localized evolution pathway encompassing physical capability upgrades, physical-market decoupling, and cellular autonomy at the secondary substation level. This research provides theoretical references and engineering guidelines for the transformation of VPPs in China from pure commercial aggregators to technical aggregators equipped with physical support capabilities for distribution networks.

Increasing penetration of renewable energy into power systems is the development trend of future energy systems. One of the main challenges is to plan the expansion scheme of transmission systems to accommodate uncertainties of wind power. In this letter, we propose a novel extreme scenarios (ESs) based data-adaptive probability uncertainty set for the transmission expansion planning problem. First, available historical data are utilized to identify data-adaptive ESs through the convex hull technology, and the probability uncertainty set with respect to the obtained ESs is then established, from which we draw the final expansion decision based on the worst-case distribution. The proposed distributionally robust transmission expansion planning (DRTEP) model can guarantee optimality of expected cost under the worst-case distribution, while ensuring feasibility of all possible wind power generation. Simulation studies are carried out on a modified IEEE RTS 24-bus system to verify the effectiveness of the proposed DRTEP model.

Under the “carbon peak and carbon neutrality” background, clean energy enters the market on a large scale, so it is very important to study the pricing theory of electricity. The bright spot of this paper is that the load curve is more practical. In the original version of electricity pricing theory based on continuous time commodity model, the load curve is supposed to be a monotonic function with time for simplicity of derivation, which is not realistic. To overcome this defect, load curves are supposed to be in shapes of a unimodal model and multimodal cases in this paper, which are more in conformity with the actual load curve in real-world power markets. Two study cases are used to demonstrate the rationality of the proposed models. These results together with the authors’ previous work provide a complete pricing scheme for continuous-time electricity commodity model.

This paper presents a robust interval economic dispatch (RIED) model for power systems with large-scale wind power integration. Differing from existing interval optimization (IO) approaches that merely rely on the upper and lower boundaries of random variables, the distribution information retained in the historical data is introduced to the IO method in this paper. Based on the available probability distribution function (PDF), wind power curtailment and load shedding are quantified as the operational risk and incorporated into the decision-making process. In this model, we need not rely on the forecasted value of wind power, which is randomly fluctuating and quite unpredictable. Furthermore, when the PDFs of wind power are taken into account, the resulting dispatch solution makes a good tradeoff between the generation cost and the operational risk. Finally, the RIED model yields an optimal dispatch solution for thermal units and the allowable intervals of wind power for the wind farms, which efficiently mitigates the uncertainty in wind power generation and provides more practical suggestions for system operators. Simulation studies are conducted on a modified IEEE-118 bus system and the results verify the effectiveness of the proposed RIED model.