Optimal Sizing of Voltage Control Devices for Distribution Circuit with Intermittent Load

We consider joint control of a switchable capacitor and a D-STATCOM for voltage regulation in a distribution circuit with intermittent load. The control problem is formulated as a two-timescale optimal power flow problem with chance constraints, which minimizes power loss while limiting the probability of voltage violations due to fast changes in load. The control problem forms the basis of an optimization problem which determines the sizes of the control devices by minimizing sum of the expected power loss cost and the capital cost. We develop computationally efficient heuristics to solve the optimal sizing problem and implement real-time control. Numerical experiments on a circuit with high-performance computing (HPC) load show that the proposed sizing and control schemes significantly improve the reliability of voltage regulation on the expense of only a moderate increase in cost.Comment: 10 pages, 7 figures, submitted to HICSS'1

Swing Dynamics as Primal-Dual Algorithm for Optimal Load Control

Frequency regulation and generation-load balancing are key issues in power transmission networks. Complementary to generation control, loads provide flexible and fast responsive sources for frequency regulation, and local frequency measurement capability of loads offers the opportunity of decentralized control. In this paper, we propose an optimal load control problem, which balances the load reduction (or increase) with the generation shortfall (or surplus), resynchronizes the bus frequencies, and minimizes a measure of aggregate disutility of participation in such a load control. We find that, a frequency-based load control coupled with the dynamics of swing equations and branch power flows serve as a distributed primal-dual algorithm to solve the optimal load control problem and its dual. Simulation shows that the proposed mechanism can restore frequency, balance load with generation and achieve the optimum of the load control problem within several seconds after a disturbance in generation. Through simulation, we also compare the performance of optimal load control with automatic generation control (AGC), and discuss the effect of their incorporation

Fast Load Control with Stochastic Frequency Measurement

Matching demand with supply and regulating frequency are key issues in power system operations. Flexibility and local frequency measurement capability of loads offer new regulation mechanisms through load control. We present a frequency-based fast load control scheme which aims to match total demand with supply while minimizing the global end-use disutility. Local frequency measurement enables loads to make decentralized decisions on their power from the estimates of total demand-supply mismatch. To resolve the errors in such estimates caused by stochastic frequency measurement errors, loads communicate via a neighborhood area network. Case studies show that the proposed load control can balance demand with supply and restore the frequency at the timescale faster than AGC, even when the loads use a highly simplified system model in their algorithms. Moreover, we discuss the tradeoff between communication and performance, and show with experiments that a moderate amount of communication significantly improves the performance

Experimental study of a counter-flow regenerative evaporative cooler

This paper aims to investigate the operational performance and impact factors of a counter-flow regenerative evaporative cooler (REC). This was undertaken through a dedicated experimental process. Temperature, humidity and flow rate of the air flows at the inlet, outlet and exhaust opening of the cooler were tested under various operational conditions, i.e., different inlet air conditions, feed water temperature and evaporation rate were also correspondingly measured. It was found that the wet-bulb effectiveness of the presented cooler ranged from 0.55 to 1.06 with Energy Efficiency Ratio (EER) rated from 2.8 to 15.5. The major experimental results were summarised below: 1) the wet-bulb effectiveness was significantly enhanced through either ways of increasing inlet wet-bulb depression or reducing intake air velocity, or alternatively by increasing working-to-intake air ratio; 2) the cooling capacity and EER of cooler was rapidly increased by means of increasing inlet wet-bulb depression or increasing intake air velocity, or reducing working-to-intake air ratio instead; 3) the effectiveness reduced by less 5% while feed water temperature increased from 18.9 to 23.1°C; 4) apparent acceleration in water evaporation rate was gained from increasing inlet wet-bulb depression or air velocity. The presented cooler showed 31% increase in wet-bulb effectiveness and 40% growth in EER compared to conventional indirect evaporative cooler. The research helped identifying the performance of a new REC with enhanced performance and thus contributed to development of energy efficient air conditioning technologies, which eventually lead to significant energy saving and carbon emissions reduction in air conditioning sector

A Joint Electricity and Carbon Pricing Method

The joint electricity and carbon pricing (JECP) problem is crucial for the low-carbon energy system transition. It is also challenging due to requirements such as providing incentives that can motivate market participants to follow the dispatch schedule and minimizing the impact on affected parties compared to when they were in the traditional electricity market. This letter proposes a novel JECP method based on partial carbon tax and primal-dual optimality conditions. Several nice properties of the proposed method are proven. Tests on different systems show its advantages over the two existing pricing methods.Comment: 4 pages, 3 figure

DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks

The traditional machine learning models to solve optimal power flow (OPF) are mostly trained for a given power network and lack generalizability to today's power networks with varying topologies and growing plug-and-play distributed energy resources (DERs). In this paper, we propose DeepOPF-U, which uses one unified deep neural network (DNN) to solve alternating-current (AC) OPF problems in different power networks, including a set of power networks that is successively expanding. Specifically, we design elastic input and output layers for the vectors of given loads and OPF solutions with varying lengths in different networks. The proposed method, using a single unified DNN, can deal with different and growing numbers of buses, lines, loads, and DERs. Simulations of IEEE 57/118/300-bus test systems and a network growing from 73 to 118 buses verify the improved performance of DeepOPF-U compared to existing DNN-based solution methods.Comment: 3 pages, 2 figure