Thermo-economic multi-objective optimization for a solar-dish Brayton system using NSGA-II and decision making

A 100 kW regenerative Brayton heat engine driven by the hybrid of fossil fuel and solar energy was considered for optimization based on multiple criteria. A thermodynamic model of such hybrid system was developed so that the power output, thermal efficiency and dimensionless thermo-economic performance with the imperfect performance of parabolic dish solar collector, the external irreversibility of Brayton heat engine and conductive thermal bridging loss could be obtained. Evolutionary algorithm based on NSGA-II (Elitist Non-dominated Sorting Genetic Algorithm) was employed to optimize triple-objective and dual-objective functions, where the temperatures of hot reservoir, cold reservoir and working fluid, the effectiveness of hot-side heat exchanger, cold-side heat exchanger and regenerator were considered as design variables. Using decision makings, including Shannon Entropy, LINMAP and TOPSIS methods, the final optimal solutions were selected from Pareto frontier obtained by NSGA-II. The results show that there exists an appropriate working fluid temperature to cause optimal solution under each given condition. The comparisons of triple-objective and dual-objective optimization with single-objective optimization indicate that multi-objective optimization can yield the more suitable results due to the lower deviation index from the ideal solution. In the analysis of triple-objective optimization, an expected result is obtained that the optimal values of the power out, efficiency and dimensionless thermo-economic performance of solar-dish Brayton system (68.65 kW, 0.2331 and 0.3077) are 22.6%, 34.9% and 18.4% respectively less than that of convectional Brayton heat engine. Finally, a range of functional relationship between the optimized objectives in Pareto frontier is fitted to provide more detailed insight into the optimal design of solar-dish Brayton system.     

Switching Power Supplies with Ferrite Inductors in Sustainable Saturation Operation

This paper investigates the Sustainable Saturation Operation (SSO) of Ferrite Core Power Inductors (FCPIs) in Switch Mode Power Supplies (SMPSs). A ferrite inductor is considered in SSO if its current ripple, power losses and temperature rise are acceptable and reliable for both the device and the SMPS, despite the inductance drop determined by the core saturation. An algorithm is discussed, which identifies SSO-compliant FCPIs with minimum size and volume, given the SMPS specifications about the allowed power losses, temperature rise and peak-to-peak current ripple of the inductor. The experimental results relevant to a 465 kHz/3.3 V/1.5 A buck converter show that SSO-compliant inductors allow to increase the SMPS power density, while preserving the overall converter efficiency. Despite the proposed low power application, the findings relevant to the utilization of power inductors in partial saturation have general conceptual valence and similar investigations can be prospectively re-assessed for few kW output power DC/DC converters.     

Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization

The main objective of the short-term hydrothermal generation scheduling (SHGS) problem is to determine the optimal strategy for hydro and thermal generation in order to minimize the fuel cost of thermal plants while satisfying various operational and physical constraints. Usually, SHGS is assumed for a 1 day or a 1 week planing time horizon. It is viewed as a complex non-linear, non-convex and non-smooth optimization problem considering valve point loading (VPL) effect related to the thermal power plants, transmission loss and other constraints. In this paper, a modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) is proposed to solve the SHGS problem. In the proposed approach, the particles in swarm are grouped in a number of neighborhoods and every particle learns from any particle which exists in current neighborhood. The neighborhood memberships are changed with a refreshing operation which occurs at refreshing periods. It causes the information exchange to be made with all particles in the swarm. It is found that mentioned improvement increases both of the exploration and exploitation abilities in comparison with the conventional PSO. The presented approach is applied to three different multi-reservoir cascaded hydrothermal test systems. The results are compared with other recently proposed methods. Simulation results clearly show that the MDNLPSO method is capable of obtaining a better solution.     

Particle swarm optimization algorithm for capacitor allocation problem in distribution systems with wind turbine generators

This paper aims at adopting the Particle Swarm Optimization (PSO) technique to find the near-optimal solutions for the capacitor allocation problem in distribution systems for the modified IEEE 16-bus distribution system connected to wind energy generation based on a cost function. The proper allocation and the optimized number of capacitors have led to adequate power losses reduction and voltage profile enhancement. Because of the wind power generation variations due to the nature of wind speed intermittency and the lack of reactive power compensation, the problem under study have been presented involving a nonlinear fitness function. In order to solve it, the corresponding mathematical tools have to be used. The formulated fitness cost function has consisted of four terms: cost of real power loss, capacitor installation cost, voltage constraint penalty, and capacitor constraint penalty. PSO technique has been used to obtain the near-optimum solution to the proposed problem. Simulation results demonstrate the efficiency of the proposed fitness cost function when applied to the system under study. Furthermore, the application of PSO to the modified IEEE 16-bus system has shown better results in terms of power losses cost and voltage profile enhancement compared to Genetic Algorithm (GA). In order to verify the successful adaptation of PSO toward attaining adequate near-optimal capacitor allocations in distribution systems, this metaheuristic technique has been employed to the large-scale IEEE 30-bus system. The proposed PSO technique has provided adequate results while modifying the objective function and constraints to include the power factor and transmission line capacities for normal and contingency (N-1) operating conditions.     

Performance of printable supercapacitors in an RF energy harvesting circuit

We report the fabrication of a supercapacitor on a plastic substrate with mass-production-compatible methods and its characterisation using galvanostatic and voltammetric methods. The supercapacitor is prepared in ambient conditions using activated carbon and an aqueous, non-acidic electrolyte. The obtained capacitances are 0.45 F and 0.21 F for device sizes of 4 cm² and 2 cm², respectively. Additionally, we demonstrate the utilisation of the supercapacitor in an autonomous energy harvesting and storage system. The RF energy harvester comprises a printed loop antenna and a half-wave organic diode rectifier operating at 13.56 MHz frequency. The harvested energy is stored in two supercapacitors connected in series to increase the maximum operating voltage. In order to power a device such as a sensor or a small indicator display, voltage regulation is needed. A voltage regulator, implemented as an application specific integrated circuit (ASIC), was designed for this purpose, and fabricated commercially. We demonstrate the ability of the harvester storage unit to power the regulator for hours with a constant regulator output voltage and power. The effect of supercapacitor charging time on the actual supercapacitor charging state is also discussed, as a slower charging rate is found to have a significant effect on the output of the supercapacitor.     

Congestion management solution under secure bilateral transactions in hybrid electricity market for hydro-thermal combination

The deregulation of power system has created an environment of competitiveness among different market players and the transmission lines are forced to operate near to their thermal or stability limits. It is a challenge with System Operators (SO) to ensure a secure and reliable transmission of power under these conditions. This paper proposes a rescheduling based congestion management strategy in hybrid (pool + bilateral) electricity market structure for a combination of hydro and thermal units. The proposed congestion management problem has been formulated as mixed integer nonlinear programming (MINLP) problem with an objective to minimize the congestion management cost by suitably rescheduling the hydro and thermal units based on their up and down generation cost bids. The hydro units having lowest operational cost and fast startup time have been used to alleviate the congestion by considering non-concave piecewise linear performance curves for them. The secure bilateral transactions have been ensured while rescheduling of the generators for alleviating the congestion. The performance of the proposed model has been demonstrated by solving the congestion management problem on modified IEEE-24 bus system.     

Optimal design of a robust discrete parallel FP + FI + FD controller for the Automatic Voltage Regulator system

The purpose of this paper is to design a good tracking controller for the generator Automatic Voltage Regulator (AVR) system. A fuzzy logic-based controller that is called Fuzzy P + Fuzzy I + Fuzzy D (FP + FI + FD) controller has been designed optimally and applied to AVR system. In the proposed method, optimal tuning of controller parameters is very important to achieve the desired level of robust performance. Thus, a hybrid of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) (HGAPSO) technique has been used to find a better fuzzy system control. The motivation for using this hybrid method is to increase disturbance rejection effort, reduce fuzzy system efforts and take large parametric uncertainties into account. The developed FP + FI + FD control strategy leads to a flexible controller with simple structure that is easy to implement. The simulation results have been compared with the conventional Proportional–Integral–Derivative (PID) and fuzzy PID controllers. Three cases of simulation have been performed, case 1: comparing the tracking capability of the controllers, case 2: comparing the disturbance rejection capability of the controller and case 3: evaluating the performance of the controllers assuming that amplifier and exciter system parameters have 50% uncertainty. The simulation results shows that the proposed parallel FP + FI + FD controller has good performance from the perspective of overshoot/undershoot, settling time, and rise time in comparison with both conventional and fuzzy PID controllers.     

Methods used for evaluation of actual power generating thermal cycles and comparing them

In this study, thermodynamic optimization criteria used for assessing thermal engines are investigated and compared. The Purpose of this is to determine the most advantageous criteria. An irreversible Carnot cycle is analyzed by using five different methods and results are compared. According to calculations, the ecological function criterion (ECF) is defined as the most convenient optimization method. Although, its work output is less than the maximum work criteria and maximum available work (MAW), it has advantageous in terms of entropy generation and first law efficiency. In addition, ecological coefficient of performance (ECOP) and exergetic performance criteria (EPC) values provide minimum entropy generation and maximum efficiency at their maximum, however, their work output is very small. ECF obtains its maximum values at x = 0.488 (377.175 kW) for endoreversible cycle and at x = 0.477 (329.812 kW) for irreversible cycle. For these reasons, ECF is suggested as the best optimization criteria.     

Energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle based on fuzzy logic

In order to enhance the fuel economy of hybrid vehicle and increase the mileage of continuation of journey, a fuzzy logic control is utilized to design energy management strategies for fuel cell/battery (FC + B) hybrid vehicle and fuel cell/battery/ultra-capacitor (FC + B + UC) hybrid vehicle. The models of hybrid vehicle for FC + B and FC + B + UC structure are developed by electric vehicle simulation software ADVISOR which uses a hybrid backward/forward approach. The results demonstrate that the proposed control strategy can satisfy the power requirement for four standard driving cycles and achieve the power distribution among various power sources. The comprehensive comparisons with the power tracking control strategy which is wide adopted in ADVISOR verify that the proposed control strategy has better rationality and validity in terms of fuel economy and dynamic property in four standard driving cycles. Therefore, the proposed strategy will provide a novel approach for the advanced energy management system of hybrid vehicle.     

A new technique for optimal allocation and sizing of capacitors and setting of LTC

An iterative based strategy is proposed for finding the optimal rating and location of fixed and switched capacitors in distribution networks. The substation Load Tap Changer tap is also set during this procedure. A Modified Discrete Particle Swarm Optimization is employed in the proposed strategy. The objective function is composed of the distribution line loss cost and the capacitors investment cost. The line loss is calculated using estimation of the load duration curve to multiple levels. The constraints are the bus voltage and the feeder current which should be maintained within their standard range.For validation of the proposed method, two case studies are tested. The first case study is the semi-urban 37-bus distribution system which is connected at bus 2 of the Roy Billinton Test System which is located in the secondary side of a 33/11 kV distribution substation. The second case is a 33 kV distribution network based on the modification of the 18-bus IEEE distribution system. The results are compared with prior publications to illustrate the accuracy of the proposed strategy.     

Evolutionary computation based multi-objective pole shape optimization of switched reluctance machine

This paper presents the application of elitist Non-dominated Sorting Genetic Algorithm version II (NSGA-II) to determine optimum pole shape design for performance enhancement of Switched Reluctance Machine (SRM). In SRM, torque output and torque ripple are sensitive to stator and rotor pole arcs and their selection is a vital part of SRM design process. The problem of determining optimal pole arc is formulated as a multi-objective optimization problem with the objective of maximizing average torque, minimizing torque ripple and copper loss. In order to account for the geometry as well as for the nonlinearity of material utilized, the Finite Element Method (FEM) is used to determine the performance of the machine. The proposed optimization technique is applied to determine optimal pole shape of an 8/6, four-phase, 5 HP, 1500 rpm SRM. The results show the effectiveness of the proposed approach and confirm the application of NSGA-II as a promising tool for solving SRM design problems. The results obtained by NSGA-II are compared and validated with classical multi-objective approach based on weighted sum method using Differential Evolution (DE) algorithm.     

Development and optimization of a moving-magnet tubular linear permanent magnet motor for use in a reciprocating compressor of household refrigerators

This paper presents the development, validation and design optimization of a moving-magnet tubular linear permanent magnet motor (TLPMM) with a trapezoidal permanent magnet shape. The design optimization was implemented by a two-dimensional Finite-Element Analysis (2-D FEA) and the validation was established by using Matlab software. The proposed motor has been designed to produce 85 W output power which is enough to operate the reciprocating compressor of a household refrigerator system. The purpose of the optimization is to achieve a maximum efficiency and minimum losses, where the angle of PMs, split-ratio and T_mr/T_p after optimized, the motor produced the highest efficiency by 93.8%.     

Experimental analysis and optimization of key parameters of ZVS mode and its application in the proposed LLC converter designed for distributed power system application

This paper deals with experimental analysis of zero-voltage switching mode targeting high-frequency operation of chosen MOSFET type. After selection of specific type of transistor (IPW60R165CP) the experimental investigation has been made by changing parameters (e.g. dead-time, auxiliary capacitance of MOSFET, transistor current), that are influencing the ZVS commutation process. For these purposes we constructed the universal testing device, which is capable to secure realistic conditions of various types of commutation modes (hard switching, zero-voltage switching, zero-current switching). Afterwards the best settings of commutation mode have been utilized in proposed LLC converter suited for distributed power system application. Prototype is operating in ZVS region with optimized parameters. Switching frequency is from 130 kHz (input voltage 325 Vdc) to 210 kHz (input voltage 415 Vdc) with the output power of 1500 W. It is clear from the results that experimental analysis of the ZVS commutation mode brings expectation of transistor behavior which was totally confirmed also in the case of experimental analysis of LLC resonant converter.     

Significant performance improvement for micro-thermoelectric energy generator based on system analysis

This study presents a high-performance micro-thermoelectric generator (μ-TEG) optimized based on a system analysis. The system analysis indicates the thermal matching requirement for thermocouples dimension and array density to maximize the output power. With the measured performance of a reference device, the complicated thermal properties of various application environments can be easily derived, which are necessary parameters for thermal matching. The effect has been further proved by a CMOS-MEMS fabricated μ-TEG module for different applications. The wristwatch-TEG application produces 0.32 μW output power, realizing an improvement of three orders of magnitudes for the reported wristwatch-TEG device of the similar materials.     

Stochastic mid-term generation scheduling incorporated with wind power

A challenge now facing system operator is how to schedule optimally the generation units in a wind integrated power system over a one year time horizon considering the effects of wind forecasting and variability; also, regarding the effects of load uncertainty. By the same token, this paper first develops a new formulation for Stochastic Mid-term Generation Scheduling (SMGS). In the formulation, 2m + 1 point estimate method is developed to accurately estimate the output variables of Mid-term Generation Scheduling (MGS) problem. Then, the formulation is combined with adaptive modified gravitational search algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. It is noteworthy to say that the classical methods considered certain wind information in the deterministic solution of the MGS problem which is not the realistic approach. However, this study improves modeling of wind–thermal system in the MGS problem by considering possible uncertainties when scheduling the generators of power system. The proposed model is capable of taking uncertainty of load and wind into account. The proposed method is applied on two test cases and the numerical results confirmed the efficiency and stability of the proposed algorithm.     

Hydro unit commitment and loading problem for day-ahead operation planning problem

We describe a new model for the hydro unit commitment and loading (HUCL) problem that has been developed to be used as a support tool for day-ahead operation in the Brazilian system. The objective is to determine the optimal unit commitment and generation schedules for cascaded plants with multiple units and a head-dependent hydropower model. In this paper, we propose a new mathematical model for the hydropower function where the mechanical and electrical losses in the turbine-generator are included. We model the HUCL problem as a nonlinear mixed 0–1 programming problem and solve it with a strategy that includes a two-phase approach based on dual decomposition. The computational tool allows the model to effectively schedule hydro units for the problem in the Brazilian regulatory framework. Application of the approach is demonstrated by determining a 24-time step HUCL schedule for four cascaded plants with 4170 MW of installed capacity.     

Effect of varying concentration and temperature on steady and dynamic parameters of low concentration photovoltaic energy system

Low-concentration photovoltaic (LCPV) system has huge potential for further cost reduction of solar photovoltaic (PV) power as compared to flat panel PV. The dependence of steady state and dynamic parameters on concentration and temperature is crucial to extract maximum power from solar photovoltaic system. This article aims to present the effect of varying concentration and temperature on steady state and dynamic parameters of LCPV system under actual test conditions (ATC). The rate of change in I_SC with solar irradiation i.e., dI_SC/dG is found as 0.25 A/W assuming ≈±1 °C change in module temperature. The effect of temperature on inherent material properties responsible for photo-conversion efficiency is studied using impedance spectroscopy technique. A linear response of series resistance of LCPV module is observed with respect to change in module temperature, i.e. dR_S/dT from 297 to 333 K is in the range of 1.15–1.20 Ω with a rate of 1 mΩ/K. From real-time analysis of LCPV system open-circuit voltage found decreasing from 21 to 20.6 V with temperature coefficient of voltage ≈−0.061 V/K. The dynamic resistance has a positive coefficient of module temperature i.e., dr_d/dT given by 0.49 Ω/K.     

Improving electric load forecasts using network committees

Accurate daily peak load forecasts are important for secure and profitable operation of modern power utilities, with deregulation and competition demanding ever-increasing accuracies. Machine learning techniques including neural and abductive networks have been used for this purpose. Network committees have been proposed for improving regression and classification accuracy in many disciplines, but are yet to be widely applied to load forecasting. This paper presents a formal approach to apply the technique using historical load and temperature data spanning multiple years, with individual committee members trained on different years. Correlation among data for successive years is investigated and methods to enhance independence between member models for improving committee performance are described. Both neural and abductive networks implementations are presented and compared. An abductive network three-member committee was developed on data for three successive years and evaluated on the fourth year. Compared to a monolithic model trained on the same full three-year data, the committee reduces the mean absolute percentage error from 2.52% to 2.19%. The corresponding reduction in the mean of the absolute error from 70 MW to 61 MW is statistically significant at the 95% confidence level.     

Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller

This article presents the automatic generation control of an unequal three area thermal system. Single stage reheat turbines and generation rate constraints of 3%/min are considered in each control area. Controllers such as Integral (I), Proportional – Integral (PI), Proportional – Integral – Derivative (PID), and Proportional – Integral – Derivative Plus Second Order Derivative (PID + DD) are treated as secondary controllers separately. A nature inspired optimization technique called Ant Lion Optimizer (ALO) algorithm is used for simultaneous optimization of the controller gains. Comparison of dynamic responses of frequencies and tie line powers corresponding to ALO optimized I, PI, PID and PID + DD controller reveal the better performance of PID + DD controller in terms of lesser settling time, peak overshoots as well as reduced oscillations. Robustness of the optimum gains of best controller obtained at nominal conditions is evaluated using sensitivity analysis. Analysis exposed that the optimum PID + DD controller gains obtained at nominal are robust and not necessary to reset again for changes in loading, parameter like inertia constant (H), size and position of disturbance. Furthermore, the performance of PID + DD controller is found better as compared to PID controller against random loading pattern condition.     

Collaborative distributed sun-tracking control system for building integration with minimal plant area and maximum energy-conversion efficiency

This paper presents a collaborative distributed sun-tracking control system for a novel Fixed Mirror Solar Concentrator (FMSC) structure, which increases the energy-conversion efficiency of the FMSC and reduces the space between solar collectors units, a positive aspect for in-building integration. The improved FMSC uses solar concentration collectors suited for mid-range thermal applications (90–200 °C) and is designed for easy installation in buildings because of its relatively small extension. The proposed solar orientation system (ORSYS) relies on a two-step algorithm to increase the energy captured by the receiver, which provides tolerance to common logical and mechanical errors in the estimation of the receiver position. ORSYS is implemented as a CAN-based distributed system, extended with web-interface features for supervision and configuration of the overall system. ORSYS also includes a coordination algorithm that allows adjacent collectors to share the physical space between them, thus reducing the total plant area. Experimental evaluation has been performed using an industrial-scale solar collector prototype, showing its feasibility and efficiency in terms of energy conversion in real environments.