Mixed load/generation profiles and non-convex optimization models pose significant computational challenges in hosting capacity allocation. On the use of constraint conversion and geometric programming, this paper presents an improved solving framework for optimal hosting capacity allocation, which advances in computational speed and solution quality. Specifically, by preliminarily calculating the feasible region in the combinational operational space of involved units, voltage and current variables from the original grid constraints can be omitted for model complexity reduction. Through subsequent model reformulation guided by geometric programming, the model convexity is retrieved to further accelerate the solving process and improve the solution quality. Relevant case studies reveal our method can significantly reduce computation time by up to 88.9% and improve the solution quality. These findings highlight the potential of this approach to enhance computation performance in capacity determination. We anticipate this work to open a new avenue for more comprehensive capacity allocation algorithms using geometric programming.

In contemporary heavy-load distribution networks, preceding feasibility assessment is imperative before incorporating additional energy units. However, the feasibility examination for massive combined operational scenarios of relevant units is computationally intensive with repetitive power flow calculations. To this end, this paper proposes a rapid assessment framework, the kernel of which is to learn from formerly examined scenarios, thus forming expansive feasible/infeasible regions to geometrically rule in/out subsequent scenarios. By sidestepping the power flow computation in most scenarios, we accelerate the assessment process. Furthermore, enlightened by heuristic hypersurface search, such prechecking efficiency can be boosted by resorting the scenario sequence. In a risk-averse manner, this framework can be conceptualized using the exact grid model, though it is initially designed for the convex grid model. Evidenced by testing on two different-scale distribution grids, the framework shows a significant assessment efficiency improvement and strict accuracy guarantee, where we observe at least 76.13% assessment time reduction and zero accuracy loss. We anticipate this work to be a starting point for more sophisticated geometry-accelerating feasibility assessment methods.

The model complexity in grid-related applications can be reduced by the concentration of distribution network constraints (DNC), which removes the original need for power flow calculations. To conquer the "curse of dimensionality" inherent in existing computational concentration methods, this letter proposes an analytical concentration scheme that utilizes Gaussian elimination and theoretical inner approximation. Based on the 16-dimensional numerical studies, this new scheme proves effective, which eventually enhances the applicability of physical constraint awareness in concrete grid management practices.

This paper proposes a novel attack identification mechanism for internet-of-things-based (IoT-based) converter-composed DC grids, where each agent collects its own and neighbours’ measurement data for output regulation to meet a preceding power-sharing consensus. Independent from model-free or average-model-based attack detection theories, this mechanism is mainly inspired by converter stitching behavior analysis. Correspondingly, when facing latent signal substitution or agent instigation attacks, through comparing estimated signals with received ones for signal source authentication, both self-sensors and neighbours will be inspected. Eventually, not only can such attacks be detected, but also will respective attack sources be identified. A simulation case of 4-agent 800V IoT-based DC grid on Simulink and a hardware case of 3-agent 90V IoT-based DC grid on dSpace testing platform were investigated. Experimental results revealed that the estimation ratio error kept lower than 3.9% and all attacks were successfully identified, verifying the effectiveness of the proposed mechanism.

The hosting capacity determines the grid ability to host integrated installations, where mutual-limitations among points of connection (POCs) are important and formulate a combinational feasible region. Intended to assess such interdependence for defining the region, this paper proposes a multidimensional hosting capacity region derivation scheme in a radial grid. As this scheme can be designed conservative, from a risk-averse viewpoint, it gains significant acceptance in the industrial sector. This multidimensional region not only exploits grid power delivery potential, but also aids in making grid congestion management decisions. A typical 10.5kV Dutch grid case has been tested accordingly, and corresponding results have confirmed the conservative property preservation of the region. The case study demonstrates that compared to the original hosting capacity concept, the region hypervolume gain ratio remains higher than 1.94. With proper measure selection, the estimated region occupation ratio can keep up to 92.50%. The congestion management decision computation time can also be reduced by 58.0% compared to that using the monolithic model. The effectiveness of the proposed scheme and its advantages in grid congestion management have been successfully confirmed.Â

This paper proposes a novel framework for modular multilevel converter (MMC) current harmonic emission estimation from medium voltage (MV) charging stations. The framework is enlightened by wave decomposition, exploiting an analytical derivation of the emission, without a time-domain model. Accordingly, voltage harmonic distribution in grids can be estimated with a frequency-domain model, using the estimated voltage-dependent current emission in the iterations. Compared to conventional time-domain simulation, such method not only enables analytical harmonic estimation integrated with a large load-flow model, but also benefits from the potential of distributed computation. A 10.5kV grid-tied single-phase half-bridge MMC simulation case on PLECS has been implemented and investigated for comparison. Corresponding results revealed that the estimated harmonic series kept the absolute average difference to the PLECS model lower than 0.02% and  a correlation ratio higher than 99.990% with the simulated ones. The effectiveness of the proposed estimation framework is successfully verified.