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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 article 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 10.5 kV 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.

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.

This chapter proposes an evolved concept of ”hosting capacity” using the term of ”feasible region” for installing additional loads or generations. Through converting the grid model into a more compact one, ”hosting capacity region” not only is promising to further exploit the grid potential for power delivery, but also benefits grid operation feasibility investigation with concise formulas. Facing the derived hosting capacity, originally complicated energy storage optimization problems can be represented algebraically, which is more efficient and friendly for computer processing. Case study based on a 10.5kV Dutch grid has been implemented, eventually demonstrating the validity of relevant assessment and optimization methods.

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.