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In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsupervised learning framework to train functions that can closely approximate optimal power flow (OPF) solutions. The primary aim is to establish specific conditions under which these learned functions can collectively guide the network towards desired configurations asymptotically, leveraging an incremental control approach. The flexibility of the proposed methodology allows to integrate fairness-driven components into the cost function associated with the OPF problem. This addition seeks to mitigate power curtailment disparities among DERs, thereby promoting equitable power injections across the network. To demonstrate the effectiveness of the proposed approach, power flow simulations are conducted using the IEEE 37-bus feeder. The findings not only showcase the guaranteed system stability but also underscore its improved overall performance. • We use the unsupervised learning approach to train controllers for DERs. • We establish explicit conditions on the DER controllers for closed-loop stability. • We introduce an equity-promoting penalty to promote learning equitable controllers. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for topology detection in distribution networks called the Time-series signature verification method for topology detection (TSV-Top). The TSV-Top analyzes data from phasor measurement units (PMU or $\mu$ PMU) installed on power distribution feeders. The TSV-Top relies on the fact that measurement data time series from power systems contain similar trends when network topology changes occur, and basically performs the projection of actual voltage phasorial patterns onto a library of signals associated with possible topology transictions of a given distribution network. The proposed algorithm is validated using a 33-bus testbed and the IEEE 123-bus feeder, and then compared with another state-of-the-art algorithm using the IEEE 37-bus feeder. [ABSTRACT FROM AUTHOR]

In this paper, we consider a voltage control problem in power distribution grids. The specific goal is that of keeping the voltages within preassigned operating limits by commanding the reactive power output of the microgenerators connected to the grid. We propose three strategies. The first two strategies are purely local, meaning that each microgenerator updates the amount of reactive power to be injected based only on local measurements of the voltages’ magnitude. Instead the third one is distributed, namely, the microgenerators, to perform the updating steps, require some additional information coming from the neighboring agents. The local strategies are simpler to be implemented, but they might fail in solving the voltage control problem. Instead, the distributed one requires the microgenerators to be endowed with communication capabilities, but it is effective in driving the voltages within the admissible intervals and, additionally, it exploits the cooperation among the agents to reach also a power losses minimization objective. Theoretical analysis and extensive numerical simulations are provided to confirm the arguments aforementioned. [ABSTRACT FROM PUBLISHER]

This paper proposes a technique to control distributed energy resources in low-voltage microgrids aiming at (i) allowing power flow control at the point of connection with the upstream grid, (ii) keeping voltage profiles within the operational limits. The first feature is crucial in smart low-voltage power systems. In fact, it enables both demand-responses, which is extremely valuable from the point of view of distribution system operators and for energy trading, and the autonomous operation of the microgrid. The latter can be achieved by regulating to zero the power exchanged with the main grid. The second feature allows to limit voltage increases due to active power injection by distributed energy resources and, thus, to limit stresses on the electrical infrastructure and the served loads, which is a concrete issue as renewables become widely deployed in the low-voltage scenario. The proposed approach is firstly described in detail, then a systematic analysis of its local and global properties is reported. All the obtained results are verified considering the IEEE 37 test feeder in realistic operating conditions. [ABSTRACT FROM AUTHOR]

We address the problem of multi-agent partition-based convex optimization which arises, for example, in robot localization problems and in regional state estimation in smart grids. More specifically, the global cost function is the sum of locally coupled cost functions that depend only on each agent variables and their neighbors' variables. Inspired by a generalized gradient descent strategy, namely the Block Jacobi iteration, we propose an algorithm amenable to a scalable distributed implementation, i.e., each agent eventually computes only the optimal values for its own variables via local communication with its neighbors. In particular, we provide sufficient conditions for global and semi-global exponential stability for the proposed algorithms even in the presence of lossy communications and asynchronous updates. The theoretical analysis relies on novel tools on Lyapunov theory based on separation of time scales and averaging theory for discrete-time systems. Finally, the proposed algorithm is numerically tested on the IEEE 123 nodes distribution feeder in the context of multi-area robust state estimation of smart grids in the presence of measurement outliers. [ABSTRACT FROM AUTHOR]