Abdus Salam

Energy use is on the rise due to an increase in the number of households and general consumptions. It is important to estimate and forecast the number of houses and the resultant energy consumptions to address the effective and efficient use of energy in future planning. In this paper, the number of houses in Brunei Darussalam is estimated by using Spline interpolation and forecasted by using two methods, namely an autoregressive integrated moving average (ARIMA) model and nonlinear autoregressive (NAR) neural network. The NAR model is more accurate in forecasting the number of houses as compared to the ARIMA model. The energy required for water heating and other appliances is investigated and are found to be 21.74% and 78.26% of the total energy used, respectively. Through analysis, it is demonstrated that 9 m² solar heater and 90 m² of solar panel can meet these energy requirements.

A comprehensive method is presented in this work to locate faults in distribution systems with distributed generators (DGs). A two-level model is developed for this purpose with both telecommunication and telemetering data employed, so as to make good use of fused information for attaining a more credible optimization solution under scenarios with alarm distortions of feeder terminal units (FTUs) or loss during communication. First, at the upper level, an analytic model is developed to search all potential faulted sections/candidates based on the telecommunication data. Then, on the lower level, a model is presented using the telemetering data to identify the most likely fault location from the candidates provided by the upper model. The essential features of the two-level diagnosis model are demonstrated through a number of case studies. Simulation results have shown that the proposed approach is capable of not only locating the faulted section(s) in a distribution system with DGs but also identifying false and/or missing alarms.

When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems.

With the ever-intensive utilization of distributed generators (DGs) and smart devices, distribution networks are evolving from a hierarchal structure to a distributed structure, which imposes significant challenges to network operators in system dispatch. A distributed energy-management method for a networked microgrid (NM) is proposed to coordinate a large number of DGs for maintaining secure and economic operations in the electricity-market environment. A second-order conic programming model is used to formulate the energy-management problem of an NM. Network decomposition was first carried out, and then a distributed solution for the established optimization model through invoking alternating-direction method of multipliers (ADMM). A modified IEEE 33-bus power system was finally utilized to demonstrate the performance of distributed energy management in an NM.

With ever-growing interconnections of various kinds of energy sources, the coupling between a power distribution system (PDS) and a district heating system (DHS) has been progressively intensified. Thus, it is becoming more and more important to take the PDS and the DHS as a whole in energy flow analysis. Given this background, a steady state model of DHS is first presented with hydraulic and thermal sub-models included. Structurally, the presented DHS model is composed of three major parts, i.e., the straight pipe, four kinds of local pipes, and the radiator. The impacts of pipeline parameters and the environment temperature on heat losses and pressure losses are then examined. The term “heat-power flow” is next defined, and the optimal heat-power flow (OHPF) model formulated as a quadratic planning problem, in which the objective is to minimize energy losses, including the heat losses and active power losses, and both the operational constraints of PDS and DHS are respected. The developed OHPF model is solved by the well-established IPOPT (Interior Point OPTimizer) commercial solver, which is based on the YALMIP/MATLAB toolbox. Finally, two sample systems are served for demonstrating the characteristics of the proposed models.