Solar Enablement Initiative in Australia: Report on Efficiently Identifying Critical Cases for Evaluating the Voltage Impact of Large PV Investment Machine Learning

The increasing quantity of PV generation connected to distribution networks is creating challenges in maintaining and controlling voltages in those distribution networks. Determining the maximum hosting capacity for new PV installations based on the historical data is an essential task for distribution networks. Analyzing all historical data in large distribution networks is impractical. Therefore, this paper focuses on how to time efficiently identify the critical cases for evaluating the voltage impacts of the new large PV applications in medium voltage (MV) distribution networks. A systematic approach is proposed to cluster medium voltage nodes based on electrical adjacency and time blocks. MV nodes are clustered along with the voltage magnitudes and time blocks. Critical cases of each cluster can be used for further power flow study. This method is scalable and can time efficiently identify cases for evaluating PV investment on medium voltage networks.

Meta-heuristic for non-homogeneous peak density spaces and implementation on 2 real-world parameter learning/tuning applications Artificial Intelligence

Observer effect in physics (/psychology) regards bias in measurement (/perception) due to the interference of instrument (/knowledge). Based on these concepts, a new meta-heuristic algorithm is proposed for controlling memory usage per localities without pursuing Tabu-like cut-off approaches. In this paper, first, variations of observer effect are explained in different branches of science from physics to psychology. Then, a metaheuristic algorithm is proposed based on observer effect concepts and the used metrics are explained. The derived optimizer performance has been compared between 1st, non-homogeneous-peaks-density functions, and 2nd, homogeneous-peaks-density functions to verify the algorithm outperformance in the 1st scheme. Finally, performance analysis of the novel algorithms is derived using two real-world engineering applications in Electroencephalogram feature learning and Distributed Generator parameter tuning, each of which having nonlinearity and complex multi-modal peaks distributions as its characteristics. Also, the effect of version improvement has been assessed. The performance analysis among other optimizers in the same context suggests that the proposed algorithm is useful both solely and in hybrid Gradient Descent settings where problem's search space is nonhomogeneous in terms of local peaks density.

Power System Restoration With Transient Stability

AAAI Conferences

We address the problem of power system restoration after a significant blackout. Prior work focus on optimization methods for finding high-quality restoration plans. Optimal solutions consist in a sequence of grid repairs and corresponding steady states. However, such approaches lack formal guarantees on the transient stability of restoration actions, a key property to avoid additional grid damage and cascading failures. In this paper, we show how to integrate transient stability in the optimization procedure by capturing the rotor dynamics of power generators. Our approach reasons about the differential equations describing the dynamics and their underlying transient states. The key contribution lies in modeling and solving optimization problems that return stable generators dispatch minimizing the difference with respect to steady states solutions. Computational efficiency is increased using preprocessing procedures along with traditional reduction techniques. Experimental results on existing benchmarks confirm the feasibility of the new approach.

Submodular Load Clustering with Robust Principal Component Analysis Machine Learning

Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas. Robust principal component analysis (R-PCA) firstly decomposes the annual load profiles into low-rank components and sparse components to extract key features. A novel submodular cluster center selection technique is then applied to determine the optimal cluster centers through constructed similarity graph. Following the selection results, load areas are efficiently assigned to different clusters for further load analysis and applications. Numerical results obtained from PJM load demonstrate the effectiveness of the proposed approach.

Clustering Stability: An Overview Machine Learning

A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are "most stable". In recent years, a series of papers has analyzed the behavior of this method from a theoretical point of view. However, the results are very technical and difficult to interpret for non-experts. In this paper we give a high-level overview about the existing literature on clustering stability. In addition to presenting the results in a slightly informal but accessible way, we relate them to each other and discuss their different implications.