• Energy Research

This paper develops new practical rule-based energy management systems (EMSs) for typical grid-connected houses with solar photovoltaic (PV) and battery by considering different rates for purchasing and selling electricity. The EMSs are developed to supply the household’s loads and reduce operating costs of the system based on different options of flat and time-of-use (ToU) rates for buying and selling electricity prices. Four different options are evaluated and compared in this study: (1) Flat-Flat, (2) ToU-Flat, (3) Flat-ToU, and (4) ToU-ToU. The operation cost is calculated based on the electricity exchange with the main grid, the equivalent cost of PV generation, as well as the degradation cost of battery storage. The operation of the grid-connected house with rooftop solar PV and battery is evaluated for a sunny week in summer and a cloudy week in winter to investigate the proper performance for high and low generations of PV. While the developed rule-based EMS are generic and can be applied for any case studies, a grid-connected house in Australia is examined. For this purpose, real data of solar radiation, air temperature, electricity consumption, and electricity rates are used. It is found that the ToU-Flat option has the lowest operating cost for the customers.

This paper develops a novel demand side management (DSM) approach to incorporate in optimal sizing of solar photovoltaic (PV), wind turbine (WT), and battery storage (BS) for a standalone household. The DSM strategy is based on the state-of-charge level of battery and day-ahead forecasts of solar insolation and wind speed. The core of the DSM is a fuzzy logic method which decides for efficient load shifting and/or load curtailment. The day-ahead forecasting errors, obtained by an artificial neural network technique, are considered not only in the DSM strategy but also in maintaining an operating reserve. The battery capacity degradation is calculated using the Rainflow counting algorithm to obtain a realistic battery model and estimate its lifetime. A typical household in South Australia (SA) is considered as a case study. Three different configurations (PV-BS, WT-BS, and PV-WT-BS) of the electricity supply system are optimized using the proposed method. It is found that the PV-WT-BS system is the best configuration that provides the lowest cost of electricity for both with and without applying the proposed DSM strategy. Comparison of the results of the best system configuration with an actual system in SA and two recently published articles indicates that the proposed method is very effective in lowering the electricity cost with zero-emission.

This paper presents the design process, detailed analysis, and prototyping of a novel-structured line-start solid-rotor-based axial-flux permanent-magnet (AFPM) motor capable of autostarting with solid-rotor rings. The preliminary design is a slotless double-sided AFPM motor with four poles for high torque density and stable operation. Two concentric unilevel-spaced raised rings are added to the inner and outer radii of the rotor discs for smooth line-start of the motor. The design allows the motor to operate at both starting and synchronous speeds. The basic equations for the solid rings of the rotor of the proposed AFPM motor are discussed. Nonsymmetry of the designed motor led to its 3-D time-stepping finite-element analysis (FEA) via Vector Field Opera 14.0, which evaluates the design parameters and predicts the transient performance. To verify the design, a prototype 1-hp four-pole three-phase line-start AFPM synchronous motor is built and is used to test the performance in real time. There is a good agreement between experimental and FEA-based computed results. It is found that the prototype motor maintains high starting torque and good synchronization.

This paper presents the design and analysis of an inside-out axial-∞ux permanent-magnet (AFPM) synchronous machine optimized by genetic algorithm (GA) based sizing equation, flnite element analysis (FEA) and flnite volume analysis (FVA). The preliminary design is a 2-pole-pair slotted TORUS AFPM machine. The designed motor comprises sinusoidal back-EMF waveforms, maximum power density and the best heat removal. The GA is used to optimize the dimensions of the machine in order to achieve the highest power density. Electromagnetic fleld analysis of the candidate machines from GA with various dimensions is then put through FEA in order to obtain various motor characteristics. Based on the results from GA and FEA, new candidates are introduced and then put through FVA for thermal behavior evaluation of the designed motors. Techniques like modifying the winding conflguration and skewing the permanent magnets are also investigated to attain the most sinusoidal back-EMF waveform and reduced cogging torque. The performance of the designed 1kW, 3-phase, 50Hz, 4-pole AFPM synchronous machine is tested in simulation using FEA software. It is found that the simulation results fully agree with the designed technical speciflcations. It is also found from FVA results that the motor temperature reaches

This paper presents a two-stage optimization technique in sizing various components of standalone hybrid electricity systems with time-of-use (ToU) incentive demand response program. In the first stage of optimization, the minimum levelized cost of electricity (LCOE) is determined without using demand response. The result of the first stage (LCOE) is used as a base rate to develop a ToU demand response for incentive payment in the second stage of optimization. In developing the incentive payment, three periods of a day (off-peak, shoulder, and peak) with different payment rates of electricity are considered. Five different standalone system configurations are developed using various combinations of diesel generators, wind generators, solar photovoltaics, battery energy storages, and flywheels. The proposed two-stage optimization technique is then applied to all five configurations of a remote area South Australian community. Real yearly data of electricity consumption, solar radiation, wind speed, and air temperature, as well as real market price of the components are used in the optimization. It has been found that the hybrid standalone system consisting of diesel, solar, wind, and battery has the minimum overall cost of electricity.

This paper presents a two-stage adaptive robust optimization approach to develop an optimal bidding strategy for a grid-connected solar photovoltaic (PV) plant with a coupled energy storage system (ESS). This study models the power flow through system elements as well as the exact interactions between the system and upstream network. The uncertainties of solar radiation, affecting the PV generation and market prices are characterized by bounded intervals in polyhedral uncertainty sets. A robust optimization is formed as a min-max-min problem characterizing both “here-and-now” and “wait-and-see” variables. This tri-level robust optimization is solved through a decomposition approach, where it is recast into a min master problem and a max-min subproblem. Unlike previous conventional robust optimization models, that utilise duality for solving the inner subproblem, a block coordinate decent (BCD) methodology is used in this study. Accordingly, instead of conducting duality theory, the subproblem is solved over a first-order Taylor series approximation of uncertainties. This results in a moderate computation/mathematical burden. Moreover, there is no need to linearize the dualized problem anymore, as no duality is conducted. Using BCD methodology in solving the robust optimization model also allows modelling binary variables as recourse actions, which differentiates this approach to conventional dual-based robust optimization models. An illustrative example is provided to demonstrate the performance of the proposed bidding strategy model. Refereed/Peer-reviewed

Refereed/Peer-reviewed Consideration of the overload (OL) performance of electric machines designed for EVs enables increasing the power density of the propulsion system. This paper aims to show the characteristics and advantages of the optimal IMs which have the capability of handling OL. A subdomain model (SDM) with the capability of the saturation prediction is developed and validated using experimental data. A lumped thermal model is developed to predict the transient temperature variation of the IMs. The thermal model is validated using the Motor-CAD transient thermal analysis. The fast speed and accuracy of the applied SDM allows to select twelve variables in a large search space for the optimization purpose. Initially, an optimization procedure is proposed to design three IMs over three different driving cycles. The optimal designs are validated from the electromagnetic and thermal aspects by the finite element analysis. IMs are then designed optimally with consideration of the OL capability. A transient thermal analysis is carried out to validate the designs. The optimal designs with and without consideration of OL are compared in terms of the machine parameters and geometry to understand how dimensions and equivalent circuit parameters of the IMs vary according to the driving cycles. The comparison allows more intuition about the consideration of OL capability in design.

This paper investigates a comparative study for practical optimal sizing of rooftop solar photovoltaic (PV) and battery energy storage systems (BESSs) for grid-connected houses (GCHs) by considering flat and time-of-use (TOU) electricity rate options. Two system configurations, PV only and PV-BESS, were optimally sized by minimizing the net present cost of electricity for four options of electricity rates. A practical model was developed by considering grid constraints, daily supply of charge of electricity, salvation value and degradation of PV and BESS, actual annual data of load and solar, and current market price of components. A rule-based energy management system was examined for GCHs to control the power flow among PV, BESS, load, and grid. Various sensitivity analyses are presented to examine the impacts of grid constraint and electricity rates on the cost of electricity and the sizes of the components. Although the capacity optimization model is generally developed for any case study, a grid-connected house in Australia is considered as the case system in this paper. It is found that the TOU-Flat option for the PV-BESS configuration achieved the lowest NPC compared to other configuration and options. The optimal capacities of rooftop PV and BESS were obtained as 9 kW and 6 kWh, respectively, for the PV-BESS configuration with TOU-Flat according to two performance metrices: net present cost and cost of electricity.