Xinyang Zhou

Pseudo-gradient based local voltage control in distribution networks

Voltage regulation is critical for distribution systems, and has become a much more challenging problem with the increasing proliferation of distributed renewable energy resources that cause frequent and rapid voltage fluctuations beyond what can be handled by the traditional voltage regulation methods. In this paper, motivated by the shortcomings of two previously proposed inverter-based local volt/var control algorithms, we design a pseudo-gradient based voltage control algorithm for the distribution network that does not constrain the allowable control functions while admits low implementation complexity. We characterize the convergence of the pseudo-gradient based control scheme, and compare it against the two previous algorithms in terms of the convergence condition and the convergence rate.

Local voltage control in distribution networks: A game-theoretic perspective

Inverter-based voltage regulation is gaining importance to alleviate emerging reliability and power-quality concerns related to distribution systems with high penetration of photovoltaic (PV) systems. This paper seeks contribution in the domain of reactive power compensation by establishing stability of local Volt/VAr controllers. In lieu of the approximate linear surrogate used in the existing work, the paper establishes existence and uniqueness of an equilibrium point using nonlinear AC power flow model. Key to this end is to consider a nonlinear dynamical system with non-incremental local Volt/VAr control, cast the Volt/VAr dynamics as a game, and leverage the fixed-point theorem as well as pertinent contraction mapping argument. Numerical examples are provided to complement the analytical results.

An incremental local algorithm for better voltage control in distribution networks

Motivated by the inability of the existing local voltage control schemes to stabilize the voltages to within the acceptable ranges, we propose a new local voltage control algorithm where each bus adjusts incrementally its reactive power in response to its voltage deviation from the nominal value. We show that the dynamical system with the proposed incremental control can be seen as a distributed algorithm for solving a well-defined optimization problem that minimizes the cost of voltage deviation, and give a sufficient condition under which the dynamical system converges. This optimization-based characterization of equilibrium and dynamical properties of the incremental local voltage control suggests a straightforward way to stabilize the distribution system to within the desired acceptable voltage ranges by simply specifying the control functions deadband as the acceptable voltage range. Numerical examples with a real world distribution circuit are provided to complement the theoretical analysis.

Panorama: A Framework to Support Collaborative Context Monitoring on Co-located Mobile Devices

A key challenge in wide adoption of sophisticated context-aware applications is the requirement of continuous sensing and context computing. This paper presents Panorama, a middleware that identifies collaboration opportunities to offload context computing tasks to nearby mobile devices as well as cloudlets/cloud. At the heart of Panorama is a multi-objective optimizer that takes into account different constraints such as access cost, computation capability, access latency, energy consumption and data privacy, and efficiently computes a collaboration plan optimized simultaneously for different objectives such as minimizing cost, energy and/or execution time. Panorama provides support for discovering nearby devices and cloudlets/cloud, computing an optimal collaboration plan, distributing computation to participating devices, and getting the results back. The paper provides an extensive evaluation of Panorama via two representative context monitoring applications over a set of Android devices and a cloudlet/cloud under different constraints.

An Incentive-Based Online Optimization Framework for Distribution Grids

This paper formulates a time-varying social-welfare maximization problem for distribution grids with distributed energy resources (DERs) and develops online distributed algorithms to identify (and track) its solutions. In the considered setting, network operator and DER-owners pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. The proposed algorithm affords an online implementation to enable tracking of the solutions in the presence of time-varying operational conditions and changing optimization objectives. It involves a strategy where the network operator collects voltage measurements throughout the feeder to build incentive signals for the DER-owners in real time; DERs then adjust the generated/consumed powers in order to avoid the violation of the voltage constraints while maximizing given objectives. The stability of the proposed schemes is analytically established and numerically corroborated.

Incentive-based voltage regulation in distribution networks

This paper considers distribution networks featuring distributed energy resources, and designs an incentive-based algorithm that allows the network operator and the end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. This social-welfare maximization problem is challenging due to the non-convexity. We first reformulate the problem as a convex task together with an incentive signal design strategy, and then propose a distributed algorithm for solving the reformulated problem. By doing so, we are able to achieve the solution of the original non-convex problem without exposure of any private information between end-customers and network operator. Stability of the proposed schemes is analytically established and numerically corroborated.

Demand shaping in cellular networks

Demand shaping is a promising way to mitigate the wireless cellular capacity shortfall in the presence of ever-increasing wireless data demand. In this paper, we formulate demand shaping as an optimization problem that minimizes the variation in aggregate traffic. We design a distributed and randomized offline demand shaping algorithm under complete traffic information and prove its almost sure convergence. We further consider a more realistic setting where the traffic information is incomplete but the future traffic can be predicted to a certain degree of accuracy. We design an online demand shaping algorithm that updates the schedules of deferrable applications each time when new information is available, based on solving at each timeslot an optimization problem over a shrinking horizon from the current time to the end of the day. We compare the performance of the online algorithm against the optimal offline algorithm and provide numerical examples to complement the theoretical analysis.