Fast-acting reactive power support from distributed generations (DGs) is a promising approach for tackling rapid voltage fluctuations in distribution networks. However, the voltage regulation range via reactive power of DGs alone is narrow especially in distribution networks with high resistance-reactance ratio. In this paper, a randomized algorithm is proposed to improve the voltage profile in distribution networks via coordinated regulation of the active and reactive power of DGs. To this end, first the variables of the proposed quadratically constrained quadratic programming problem on voltage control are partitioned into disjoint subsets, each of which corresponds to a unique low-dimensional subproblem. Second, these subsets are updated serially in a randomized manner via solving their corresponding subproblems, which overcomes the requirement for system-wide coordination among participating agents and guarantees an optimal solution. Compared with the existing algorithms, the proposed algorithm is resilient to network reconfigurations and achieves a wider voltage regulation range. The effectiveness and convergence performance of the proposed algorithm is validated by the case studies.

Hydropower dominated systems often experience negative damping and severe power flow oscillations due to improperly tuned governor parameters, even with power system stabilizers. Conventional methods optimize these parameters in a single-machine infinite bus model, neglecting interactions between generations during primary frequency control. This article proposes a deep reinforcement learning based cooperative primary frequency controller using a goal representation heuristic dynamic programming model to address this challenge. The controller minimizes power flow oscillations and frequency deviation through cooperative action among hydrogenerations. Case studies on an IEEE 9 bus system demonstrate that the proposed method achieves superior damping of power oscillations and achieves a much shorter regulation time in the primary frequency control compared to the traditional PID controllers.

High penetration of renewable energy sources has intensified voltage fluctuations and violations in modern distribution networks. Although distributed control offers a scalable solution, existing methodologies frequently require the exchange of sensitive operational data. This critical limitation, which is intrinsic to distributed algorithms, raises significant privacy concerns. This paper proposes a novel privacy-preserving distributed voltage regulation scheme based on the framework of state-based potential games. Specifically, we first formulate a potential function that aligns the local objectives of individual buses with the voltage profile improvement goal. To reduce the risk of sensitive-data disclosure, we introduce a decoupling mechanism where buses only exchange information regarding coupling constraint violations rather than bus voltage or power injection data. Furthermore, a parallel update law is established, allowing buses to optimize their strategies independently. A key strength of the proposed scheme is its resilience to stochastic communication outages (SCOs). Numerical tests demonstrate that, in comparison to existing distributed voltage control methods, the proposed scheme not only reduces the risk of sensitive-data disclosure but also maintains highly consistent performance across diverse SCO scenarios. Finally, the effectiveness and convergence performance of the proposed voltage regulation scheme are validated by the case studies.