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An enhanced state estimator (SE) is proposed to incorporate the real-time underfrequency load shedding (UFLS) information in this paper. During contingencies, the historical SCADA measurements are not accurate enough while the PMU measurements may be inadequate for the SE. Considering that load shedding strategy is a common practice to prevent the system from collapse during contingencies, we can use the shedding information as a viable option to update/correct the SCADA measurements by employing proper techniques. The auxiliary binary variables and scheduled load shedding amount are introduced in the traditional weighted least square formulation so that a more accurate SE can be obtained. The alternating optimization method is utilized to solve the SE problem. Case studies are conducted on the IEEE 30- and 118-Bus system to illustrate the proposed methodology and demonstrate the evident improvements on the reconstructed states.

In this paper, we propose a novel optimization strategy for energy-efficient day-ahead energy management and economic power reserve capacity allocation for frequency regulation, by jointly utilizing battery energy storage systems (BESSs) and demand-responsive (DR) fans at the distribution level. Specifically, we consider a smart building with renewable energy integration. The building owns a BESS and is willing to take part in frequency regulation service by allocating a proportion of its BESS capacity as power reserve capacity and receiving monetary reservation fee in return. Due to the high cost of BESS, its limited capacity, and significant degradation caused by excessive charging and discharging, we consider DR fans as a viable low-cost capability of the smart building for energy cost saving and frequency regulation. Particularly, we consider modern fans of the heating, ventilation, and air conditioning (HVAC) systems that can vary their speeds (and thus energy consumption), subject to satisfying user comfort constraints. Simulations results based on the real renewable energy and load data show that our proposed joint optimization strategy is effective in reducing the total energy cost of the building and at the same time providing a higher power reserve capacity to the grid.

Recently the control methods based on disturbance rejection have largely renewed our understanding of automatic control, especially the role of feedforward control. With the renewed understanding, this work presents a feedforward and feedback approach for control of general dynamic systems under a state tracking framework. This is realized by introducing a fictitious system model and employing a proper state and disturbance estimator. The resultant control scheme embodies a close cooperation between feedforward and feedback controls: Feedforward control rejects the general disturbance and embeds a reference state trajectory, whilst feedback control cancels the fictitious dynamics and enforces desired tracking error dynamics. A notable advantage of the approach is that control designs of linear and nonlinear systems can follow a common routine and the closed-loop stability is guaranteed under standard conditions.

This paper presents an approach to assess the economics of customer-sited energy storage systems (ESSs) which are owned and operated by a customer. The ESSs can participate in frequency regulation and spinning reserve markets, and are used to help the customer consume available renewable energy and reduce electricity bill. A rolling-horizon approach is developed to optimize the service schedule, and the resulting costs and revenues are used to assess economics of the ESSs. The economic assessment approach is illustrated with case studies, from which we obtain some new observations on profitability of the customer- sited multi-use ESSs., Comment: 4 pages, 3 figures, presented in IEEE TENCON 2016

This work considers energy management in a grid-connected microgrid which consists of multiple conventional generators (CGs), renewable generators (RGs) and energy storage systems (ESSs). A two-stage optimization approach is presented to schedule the power generation, aimed at minimizing the long-term average operating cost subject to operational and service constraints. The first stage of optimization determines hourly unit commitment of the CGs via a day-ahead scheduling, and the second stage performs economic dispatch of the CGs, ESSs and energy trading via an hour-ahead scheduling. The combined solution meets the need of handling large uncertainties in the load demand and renewable generation, and provides an efficient solution under limited computational resource which meets both short-term and long-term quality-of-service requirements. The performance of the proposed strategy is evaluated by simulations based on real load demand and renewable generation data., 7 pages, 3 figures; to present in PSCC 2016, Italy

This paper considers energy management in a grid-connected microgrid which has multiple conventional generators (CGs), renewable generators and energy storage systems (ESSs). A robust two-stage optimization approach is presented to schedule the energy generation under uncertainties, aimed at minimizing the long-term average operating cost subject to realistic operational and service constraints. The first stage of optimization determines hourly unit commitment of the CGs via a day-ahead scheduling, and the second stage performs economic dispatch of the CGs, ESSs, and energy trading via a real-time scheduling. The combined solution meets the need of handling large uncertainties in the load demands and renewable generation, and provides an efficient solution under limited computational resource which approximately optimizes the long-term average operating cost while meeting the quality-of-service requirements. The performance of the proposed strategy is evaluated by simulations based on real load demand and renewable generation data. NRF (Natl Research Foundation, S’pore) EDB (Economic Devt. Board, S’pore)

The self-clocking principle (SCP) of Transmission Control Protocol (TCP) had been analyzed for a network implementing a per-flow buffering scheme. The ideal SCP is yet unknown for the Internet which implements a first-in–first-out buffering scheme. This paper derives an ideal SCP for the Internet by formulating the traffic transmission control as a typical control problem and then solving it by a control-theoretic approach. The ideal SCP reveals the defect of the SCP being deployed in the Internet that it is insufficient to avoid congestion by adjusting the packet effective window based on records of the outstanding packets of a single source; instead outstanding packets from other sources also have to be counted. The ideal SCP also reveals the difficulties of developing and implementing an effective self-clocking scheme for congestion control in the Internet.

The available methods for selection of controlled variables (CVs) using the concept of self-optimizing control have been developed under the restrictive assumption that the set of active constraints remains unchanged for all the allowable disturbances. To keep the input and output variables within their allowable bounds, the use of cascade controllers, and to track the optimal set of active input constraints, the use of split-range controllers is suggested in literature. In this paper, we propose a different strategy, where CVs are selected as linear combinations of measurements to minimize local average loss, while ensuring that all the constraints are satisfied over the allowable set of disturbances. This result is extended to select a few of the available measurements, whose combinations are used as CVs. The proposed approach offers simpler implementation of operational policy for processes with tight operational constraints. We use the case study of forced-circulation evaporator to illustrate the usefulness of the proposed method.

Energy-efficient dispatching of rail-guided vehicles (RGVs) is demanded in an automated air cargo terminal. The problem aims to minimize total energy cost of operating RGVs for transporting the cargos subject to real time constraints. This work focuses on solving an optimal dispatching problem with a single RGV having multiple capacity. A mixed-integer linear programming (MILP) model is developed for the problem, which takes account of various constraints, including loading, deadlock avoidance, precedence, time window and capacity constraints. Difficulties of identifying and representing the constraints are revealed. Computational studies performed on randomly generated instances are used to validate the model and demonstrate challenges of solving the problem to a large scale.

A problem of dispatching rail-guided vehicles (RGVs) for completing transportation requests arises in an automated air cargo terminal. The problem involves transportation requests as appear dynamically in a stochastic manner and aims to minimize total travel distance of each RGV. This work presents a model and a preliminary solution approach for the problem with a single RGV having unit capacity. Computational studies performed on randomly generated instances are used to evaluate the approach, and the performance is compared with that of a priority-based approach which mimics the one used in practice.

This thesis consists of two parts. The first part is devoted to analytically deriving proportional-integral-derivative (PID) tuning rules with different tuning methods and the second part is devoted to reporting some new results on self-optimizing control (SOC). The two parts are connected through the controlled variables (CVs) used in control. Firstly the problem of tuning PID controllers for integral plus time delay (IPTD) processes with specified gain and phase margins (GPMs) is approached and solved. Accurate expressions of GPMs in terms of the PID and process parameters are also obtained. Based on these results, simple PID tuning rules are then derived for typical process models. The new rules are shown to give improved disturbance rejection while maintaining the same peak sensitivities as compared to the well-known simple internal model control (SIMC) rules. We then present a systematic approach of combining two-degrees-of-freedom (2DOF) design with direct synthesis (DS) for designing controllers which give desired closed-loop transfer functions. Explicit PID tuning rules are obtained by approximating the ideal controllers appropriately as PID controllers or PID-C controllers (i.e., PID controllers in series with lead-lag compensators). Next we investigate the very recent closed-loop setpoint response (CSR) method for tuning PI controllers in an analytical manner. A common PI tuning rule is obtained without using explicit models for both IPTD and first-order plus time delay (FOPTD) processes. The rule has a form similar to a recent one concluded from numerical experiments and turns out to give satisfactory closed-loop performance for a broad range of processes. Conventionally, CVs are assumed to be known or given before a PID control design. The assumption, however, may neither be necessary nor be rational. It has been found in many applications that CVs need to be selected properly for maximizing product utility when a process is perturbed or the measurements are corrupted by noises. This has motivated the proposal of SOC for selecting CVs for near optimal operation. In the second part of the thesis, we firstly investigate the local solutions of available SOC further and then deal with two new problems arising in the SOC design. We give more complete analytical characterizations of the local solutions for SOC to minimize worst-case loss and average loss, respectively. The available solutions for SOC to minimize worst-case loss are extended in a more general form and the available solutions for SOC to minimize average loss are proved to be complete. The new results contribute to clarifying the relation between these two classes of solutions for SOC. DOCTOR OF PHILOSOPHY (EEE)

Two design methods for proportional-integral-derivative (PID) controllers are proposed based on the two-degrees-of-freedom (2-DOF) direct synthesis (DS) approach. One method specifies the closed-loop setpoint-to-output transfer function, and the other specifies the closed-loop disturbance-to-output transfer function. With appropriate DS, the 2-DOF controllers are approximated as a PID controller with setpoint weighting. Analytical expressions for the PID controllers are derived for common process models. The examples demonstrate that the proposed design methods result in very good control for a wide variety of processes, and that they can provide better performance over recent DS and internal model control methods when they are tuned to have the same degree of robustness as measured in terms of the peak sensitivity.

The available methods for selection of controlled variables (CVs) using the concept of self-optimizing control have been developed under the restrictive assumption that the set of active constraints remains unchanged for all the allowable disturbances and implementation errors. To track the changes in active constraints, the use of split-range controllers and parametric programming has been suggested in the literature. An alternate heuristic approach to maintain the variables within their allowable bounds involves the use of cascade controllers. In this work, we propose a different strategy, where CVs are selected as linear combinations of measurements to minimize the local average loss, while ensuring that all the constraints are satisfied over the allowable set of disturbances and implementation errors. This result is extended to select a subset of the available measurements, whose combinations can be used as CVs. In comparison with the available methods, the proposed approach offers simpler implementation of operational policy for processes with tight constraints. We use the case study of forced-circulation evaporator to illustrate the usefulness of the proposed method.

Limitations are revealed for the existing methods to derive relative gain array (RGA) for a MIMO process containing integrators and/or differentiators. This paper proposes a new method to overcome these limitations. The method can handle processes described by either transfer functions or state-space models. The effectiveness and simplicity of the proposed method are illustrated with examples where the existing methods fail to give valid RGAs.

Recently, eXplicit Control Protocol (XCP), Rate Control Protocol (RCP), and Adaptive Proportional-Integral Rate Control Protocol (API-RCP) have been proposed for congestion control, with the main objective of achieving fair and maximum bandwidth utilization. However, studies reveal that both RCP and XCP may suffer continuous oscillations due to misestimating the bottleneck link capacity, and API-RCP may experience oscillations because of its PI adaptivity scheme which involves switching nonlinearity. To avoid these problems, in this paper a way of designing congestion control based on the instantaneous queue sizes in the routers is proposed. The new scheme attains high link utilization and smooth dynamics by clamping the bottleneck queue at a desired size. And it maintains good fairness by allocating the bottleneck bandwidth equally to the competing flows. Simulations are performed to verify the effectiveness of the theoretical design.