Jianfeng Mao

Distributed Secondary Voltage and Frequency Restoration Control of Droop-Controlled Inverter-Based Microgrids

In this paper, restorations for both voltage and frequency in the droop-controlled inverter-based islanded microgrid (MG) are addressed. A distributed finite-time control approach is used in the voltage restoration which enables the voltages at all the distributed generations (DGs) to converge to the reference value in finite time, and thus, the voltage and frequency control design can be separated. Then, a consensus-based distributed frequency control is proposed for frequency restoration, subject to certain control input constraints. Our control strategies are implemented on the local DGs, and thus, no central controller is required in contrast to existing control schemes proposed so far. By allowing these controllers to communicate with their neighboring controllers, the proposed control strategy can restore both voltage and frequency to their respective reference values while having accurate real power sharing, under a sufficient local stability condition established. An islanded MG test system consisting of four DGs is built in MATLAB to illustrate our design approach, and the results validate our proposed control strategy.

Distributed Economic Dispatch for Smart Grids With Random Wind Power

In this paper, we present a distributed economic dispatch (ED) strategy based on projected gradient and finite-time average consensus algorithms for smart grid systems. Both conventional thermal generators and wind turbines are taken into account in the ED model. By decomposing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution of the optimization problem in a distributed manner with limited communication among neighbors. It is theoretically shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. This scheme also brings some advantages, such as plug-and-play property. Different from most existing distributed methods, the private confidential information, such as gradient or incremental cost of each generator, is not required for the information exchange, which makes more sense in real applications. Besides, the proposed method not only handles quadratic, but also nonquadratic convex cost functions with arbitrary initial values. Several case studies implemented on six-bus power system, as well as the IEEE 30-bus power system, are discussed and tested to validate the proposed method.

Optimal Dynamic Voltage Scaling in Energy-Limited Nonpreemptive Systems with Real-Time Constraints

Dynamic voltage scaling is used in energy-limited systems as a means of conserving energy and prolonging their life. We consider a setting in which the tasks performed by such a system are nonpreemptive and aperiodic. Our objective is to control the processing rate over different tasks so as to minimize energy subject to hard real-time processing constraints. Under any given task scheduling policy, we prove that the optimal solution to the offline version of the problem can be efficiently obtained by exploiting the structure of optimal sample paths, leading to a new dynamic voltage scaling algorithm termed the critical task decomposition algorithm (CTDA). The efficiency of the algorithm rests on the existence of a set of critical tasks that decompose the optimal sample path into decoupled segments within which optimal processing times are easily determined. The algorithm is readily extended to an online version of the problem as well. Its worst-case complexity of both offline and online problems is O(N2)

Distributed Cooperative Secondary Control for Voltage Unbalance Compensation in an Islanded Microgrid

This paper presents a distributed cooperative control scheme for voltage unbalance compensation (VUC) in an islanded microgrid (MG). By letting each distributed generator (DG) share the compensation effort cooperatively, unbalanced voltage in sensitive load bus (SLB) can be compensated. The concept of contribution level (CL) for compensation is first proposed for each local DG to indicate its compensation ability. A two-layer secondary compensation architecture consisting of a communication layer and a compensation layer is designed for each local DG. A totally distributed strategy involving information sharing and exchange is proposed, which is based on finite-time average consensus and newly developed graph discovery algorithm. This strategy does not require the whole system structure as a prior and can detect the structure automatically. The proposed scheme not only achieves similar VUC performance to the centralized one, but also brings some advantages, such as communication fault tolerance and plug-and-play property. Case studies including communication failure, CL variation, and DG plug-and-play are discussed and tested to validate the proposed method.

Optimal dynamic voltage scaling in power-limited systems with real-time constraints

Dynamic voltage scaling is used in power-limited systems such as sensor networks as a means of conserving energy and prolonging their life. We consider a setting in which the tasks performed by such a system are nonpreemptive, aperiodic and have uncertain arrival times. Our objective is to control the processing rate over different tasks so as to minimize energy subject to hard real-time processing constraints. We prove that the solution to this problem reduces to two simpler problems which can be efficiently solved, leading to a new on-line dynamic voltage scaling algorithm. This algorithm is shown to have low complexity and, unlike similar state-of-the-art approaches, it involves no solution of nonlinear programming problems and is independent of the specific physical characteristics of the system. Both off-line and on-line versions of the algorithm are analyzed.

Optimal Control of Multi-Stage Discrete Event Systems With Real-Time Constraints

We consider discrete event systems involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task meeting its own constraint. When tasks are processed over a single stage, it has been shown that there are structural properties of the optimal state trajectory that lead to very efficient solutions of such problems. When tasks are processed over multiple stages and are subject to end-to-end real-time constraints, these properties no longer hold and no obvious extensions are known. We consider such a multi-stage problem with not only stage-dependent but also task-dependent cost functions over all tasks at each stage and derive several new optimality properties. These properties lead to the idea of introducing ldquovirtualrdquo deadlines at each stage except the last one, thus partially decoupling the stages so that the known efficient solutions for single-stage problems can be used. We prove that a sequence of solutions to single-stage problems with virtual deadlines updated at each step converges to the global optimal solution of the multi-stage problem. This leads to a virtual deadline algorithm (VDA) which is scalable in the number of processed tasks. We illustrate the scalability and efficiency of the VDA through numerical examples.

Optimal Control of Two-Stage Discrete Event Systems with Real-Time Constraints

We consider discrete event systems (DES) involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task meeting its own constraint. When tasks are processed over a single stage, it has been shown that there are structural properties of the optimal sample path that lead to very efficient solutions of such problems. When tasks are processed over multiple stages and are subject to end-to-end real-time constraints, these properties no longer hold and no obvious extensions are known. We consider a two-stage problem with homogeneous cost functions over all tasks at each stage and derive several new optimality properties. These properties lead to the idea of introducing “virtual” deadlines at the first stage, thus partially decoupling the stages so that the known efficient solutions for single-stage problems can be used. We prove that the solution obtained by an iterative virtual deadline algorithm (VDA) converges to the global optimal solution of the two-stage problem and illustrate the efficiency of the VDA through numerical examples.

Optimal admission control of discrete event systems with real-time constraints

The problem of optimally controlling the processing rate of tasks in discrete event systems (DES) with hard real-time constraints has been solved in (J. Mao, C.G. Cassandras, and Q.C. Zhao. Optimal dynamic voltage scaling in power-limited systems with real-time constraints. IEEE Trans, on Mobile Computing, 6(6):678-688, June 2007.) under the assumption that a feasible solution exists. Since this may not always be the case, we introduce in this paper an admission control scheme in which some tasks are removed with the objective of maximizing the number of remaining tasks which are all guaranteed feasibility. In the off-line case where task information is known, we derive several optimality properties and develop a computationally efficient algorithm for solving the admission control problem under certain conditions. In the on-line case, we derive necessary and sufficient conditions under which idling is optimal and define a metric for evaluating when and how long it is optimal to idle. Numerical examples are included to illustrate our results.

On-line Optimal Control of a Class of Discrete Event Systems with Real-Time Constraints

We consider Discrete Event Systems (DES) involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task meeting its own constraint. It has been shown that the off-line version of this problem can be efficiently solved by the Critical Task Decomposition Algorithm (CTDA) (Mao et al., IEEE Trans Mobile Comput 6(6):678–688, 2007). In the on-line version, random task characteristics (e.g., arrival times) are not known in advance. To bypass this difficulty, worst-case analysis may be used. This, however, does not make use of probability distributions and results in an overly conservative solution. In this paper, we develop a new approach which does not rely on worst-case analysis but provides a “best solution in probability” efficiently obtained by estimating the probability distribution of sample-path-optimal solutions. We introduce a condition termed “non-singularity” under which the best solution in probability leads to the on-line optimal control. Numerical examples are included to illustrate our results and show substantial performance improvements over worst-case analysis.

Optimal Admission Control of Discrete Event Systems with Real-Time Constraints

The problem of optimally controlling the processing rate of tasks in Discrete Event Systems with hard real-time constraints has been addressed in prior work under the assumption that a feasible solution exists. Since this cannot generally be the case, we introduce in this paper an admission control scheme in which some tasks are removed with the objective of maximizing the number of remaining tasks which are all guaranteed feasibility. We derive several optimality properties based on which we develop a computationally efficient algorithm for solving this admission control problem under certain conditions. Moreover, when no future task information is available, we derive necessary and sufficient conditions under which idling is optimal and define a metric for evaluating when and how long it is optimal to idle. Numerical examples are included to illustrate our results.