In this paper, we propose a side-absorption concentrated module with diffractive grating as a spectral-beam-splitter to divide sunlight into visible and infrared parts. The separate solar energy can be applied to different energy conversion devices or diverse applications, such as hybrid PV/T solar systems and other hybrid-collecting solar systems. Via the optimization of the geometric parameters of the diffractive grating, such as the grating period and height, the visible and the infrared bands can dominate the first and the zeroth diffraction orders, respectively. The designed grating integrated with the lens and the light-guide forms the proposed module, which is able to export visible and infrared light individually. This module is demonstrated in the form of an array consisting of seven units, successfully out-coupling the spectral-split beams by separate planar ports. Considering the whole solar spectrum, the simulated and measured module efficiencies of this module were 45.2% a... [more]

Two-dimensional design and analysis issues on the meridional surface, which is important in the preliminary design procedure of compressors, are highly dependent on the accuracy of empirical models, such as the prediction of total pressure loss model and turning flow angle. Most of the widely used models are derived or improved from experimental data of some specific cascades with low-loading and low-speed airfoil types. These models may work for most conventional compressors but are incapable of accurately estimating the performance for some specific cases like transonic compressors. The errors made by these models may mislead the final design results. Therefore, surrogate models are developed in this work to reduce the errors and replace the conventional empirical models in the through-flow calculation procedure. A group of experimental data considering a two-stage transonic compressor is used to generate the airfoils database for training the surrogate models. Sensitivity analysis i... [more]

In this paper, design and optimization method of a three-phase dual-active-bridge DC/DC converter is discussed. Three single phase transformers connected in star-star configuration were designed with large leakage inductance aiming to eliminate the need for external inductors. Switching frequency, peak flux density, number of turns, number of layers, etc., were optimized using non-linear programming technique for minimizing the overall converter loss. Experimental results on a 10 kW prototype show that the optimized converter can operate efficiently an efficacy of up to 98.65% and a low-temperature rise of less than 70 degrees Celsius on both transformers and semiconductor devices.

In Japan, residents of apartments are generally contracted to receive low voltage electricity from electric utilities. In recent years, there has been an increasing number of high voltage batch power receiving contracts for condominiums. In this research, a high voltage batch receiving contractor introduces a demand−response in a low voltage power receiving contract, which maximizes the profit of a high voltage batch receiving contractor and minimizes the electricity charge of residents by utilizing battery storage, electric vehicles (EV), and heat pumps. A multi-objective optimization algorithm calculates a Pareto solution for the relationship between two objective trade-offs in the MATLAB ® environment.

One of the main tasks of the Power System Operators is to ensure a proper, safe, and trouble-free operation and development of power grids. Growth of power system is inseparably linked to a connection of both renewable and classical new energy sources. For network operators and potential investors, it is essential to know the place and volume of generation capacity that can be connected to the grid. A publication of such data is currently a legal obligation for many operators. This paper proposes a method of determining the bus connection capacity in power grid of any type with the use of black-box optimization. Calculations and analyses were performed with a full, nonlinear model of the analyzed network. Obtained results show the effectiveness of this method for both single and multiple nodes in any configuration. All the constraints relevant for the proper and safe system operation, such as bus voltages, line loads, and short-circuit currents, both in a steady-state and (n-1) conting... [more]

Western China has good conditions for constructing large-scale photovoltaic (PV) power stations; however, such power plants with large fluctuations and strong randomness suffer from the long-distance power transmission problem, which needs to be solved. For large-scale PV power stations that do not have the conditions for simultaneous hydropower and PV power, this study examined long-distance delivery mode and energy storage optimization. The objective was to realize the long-distance transmission of electrical energy and maximize the economic value of the energy storage and PV power storage. For a large-scale PV power station, the energy storage optimization was modelled under a given long-distance delivery mode, and the economic evaluation system quantified using the net present value (NPV) of the battery was based on the energy dispatch optimization model. By contrast, a lithium battery performance model was developed. Therefore, further analysis of the economics of the energy stora... [more]

This paper presents the Hybrid Renewable Energy System (HYRES), a powerful tool to contribute to the viability analysis of energy systems involving renewable generators. HYRES considers various input parameters related to climatic conditions, statistical reliability, and economic views; in addition to offering multi-objective optimizations using Genetic Algorithms (GAs) that have a better cost-benefit ratio than mono-objective optimization, which is the technique used in several commercial systems like HOMER, a worldwide leader in microgrid modeling. The use of intelligent techniques in HYRES allows optimal sizing of hybrid renewable systems with wind and solar energy generators adapted to different conditions and case studies. The elements that affect the system design like buying and selling energy from/to the grid and the use of storage units can be included in system configuration according to the need. Optimization approaches are selectable and include Initial Cost, Life Cycle Cos... [more]

In this paper, the optimal site and size selection of wind turbines (WTs) is presented considering the maximum allowable capacity constraint with the objective of loss reduction and voltage profile improvement of distribution grids based on particle swarm optimization (PSO as a multi-objective problem using weighted coefficients method. The optimal site, size, and power factor of the WTs are determined using PSO. The proposed method is implemented on 84- and 32-bus standard grids. In this study, PSO algorithm is applied to determine the size, site, and power factor of WTs considering their maximum size constraint (with constraint, variant size) and also not considering their maximum size constraint (without constraint, constant size). The simulation results showed that the PSO is effective to find the site, size, and power factor of WTs optimally in the single and multi-objective problem. The results of this method showed that the power loss is reduced more and voltage profile improved... [more]

Several window functions are currently applied to improve the performance of the discrete Fourier transform (DFT) harmonic detection method. These window functions exhibit poor accuracy in measuring the harmonic contents of a signal with high-order and weak-amplitude components when the power frequency fluctuates within a small range. In this paper, a minimum side-lobe optimization window function that is aimed at overcoming the abovementioned issue is proposed. Moreover, an improved DFT harmonic detection algorithm based on the six-term minimum side-lobe optimization window and four-spectrum-line interpolation method is proposed. In this context, the minimum side-lobe optimization window is obtained by optimizing the conventional cosine window function according to the optimization rules, and the characteristics of the new proposed window are provided to analyze its performance. Then, the proposed optimization window function is employed to improve the DFT harmonic detection algorithm... [more]

With the increasing attention of the clean and efficient use of energy, the regional integrated energy system (RIES), as an efficient measure to improve energy efficiency, is tending to play an important role in the field of energy supply. The configuration of multiple energy storage equipment in the RIES can greatly improve the economy of the system, which is an important research direction of RIES planning. However, at present the research on the configuration optimization of electric-gas-thermal multi-energy storage devices in RIES is insufficient. Under this background, a method for configuring the rated capacity and power of various energy storage devices in the RIES under both off-grid and grid-connected operating modes was proposed in this paper, and the configuration optimization model was also established. Firstly, the RIES was divided into four parts: Energy supply, energy conversion, energy storage and the load. Based on the energy hub concept, the four parts were modeled re... [more]

This study examines the effect of mixing on the performance of anaerobic digestion of cow manure in Chinese dome digesters (CDDs) at ambient temperatures (27−32 °C) in comparison with impeller mixed digesters (STRs) and unmixed digesters (UMDs) at the laboratory scale. The CDD is a type of household digester used in rural and pre-urban areas of developing countries for cooking. They are mixed by hydraulic variation during gas production and gas use. Six digesters (two of each type) were operated at two different influent total solids (TS) concentration, at a hydraulic retention time (HRT) of 30 days for 319 days. The STRs were mixed at 55 rpm, 10 min/hour; the unmixed digesters were not mixed, and the Chinese dome digesters were mixed once a day releasing the stored biogas under pressure. The reactors exhibited different specific biogas production and treatment efficiencies at steady state conditions. The STR 1 exhibited the highest methane (CH4) production and treatment efficiency (vo... [more]

This paper reports on the optimum conditions for simultaneous hydrogen and butyric acid production from microalgae (Chlorella sp.) using enriched anaerobic mixed cultures as inoculum. The fermentation was objectively carried out under acidogenic conditions to achieve butyric acid for further ABE fermentation in solventogenesis stage. The main effects of initial pH (5 and 7), temperature (35 °C and 55 °C), and substrate concentration (40, 60, 80, and 100 g-VS/L) for hydrogen and butyric acid production were evaluated by using batch fermentation experiment. The major effects on hydrogen and butyric acid production are pH and temperature. The highest production of hydrogen and butyric acid was observed at pH 7 and temperature 35 °C. Using initial Chlorella sp. concentration of 80 g-VS/L or 100 g-VS/L at pH 7 and temperature 35 °C could produce hydrogen with an average yield of 22 mL-H2/g-VS along with high butyric acid production yield of 0.05 g/g-VS, suggesting that microalgae (Chlorella... [more]

Train operation strategy optimization is a multi-objective optimization problem affected by multiple conditions and parameters, and it is difficult to solve it by using general optimization methods. In this paper, the parallel structure and double-population strategy are used to improve the general optimization algorithm. One population evolves by genetic algorithm (GA), and the other population evolves by particle swarm optimization (PSO). In order to make these two populations complement each other, an immigrant strategy is proposed, which can give full play to the overall advantages of parallel structure. In addition, GA and PSO is also improved, respectively. For GA, its convergence speed is improved by adjusting the selection pressure adaptively based on the current iteration number. Elite retention strategy (ERS) is introduced into GA, so that the best individual in each iteration can be saved and enter the next iteration process. In addition, the opposition-based learning (OBL)... [more]

Utilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality t... [more]

Electronic Capacitive Voltage Transformer (ECVT), connecting the transmission system and measuring device, is the key equipment of digital substation in the distribution network. Its frequency characteristics and the rapid transient response performance exert decisive effects on whether it is in keeping with the power quality requirements for wide frequency domain and high precision detection. Many detection methods that require special testing equipment have limitations of high-cost and complexity in project implementation. We establish the mathematical model of ECVT, which is utilized to study its integrative characteristics based on the definitions of frequency response and transient voltage error. Besides, the harmonic and transient disturbance detection performances are analyzed comprehensively with the model by simulation. Then, we propose a multi-objective parameter optimization design model with minimization of transient voltage decay duration, maximization of frequency bandwid... [more]

Electric vehicle fleets and smart grids are two growing technologies. These technologies have provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, a comparison of several evolutionary algorithms—namely genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution—is shown, in order to evaluate the proposed architecture. The proposed solution is presented as a means to prevent overload of the power grid.

The bearingless switched reluctance motor (BSRM) integrates the switched reluctance motor (SRM) with the magnetic bearings, which avoids mechanical bearings-loss and makes it promising in high-speed applications. In this paper, a comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on finite-element method (FEM) is proposed. At first, the 2-D electromagnetic model of a fabricated initial design prototype is built and solved by the open-source FEM software, Elmer. The iron loss model in Elmer based on the Fourier series is modified by a transient iron loss model with less computation time. Besides, a simplified lumped-parameter (LP) thermal model of the BSRM is applied to estimate the temperature rise of BSRM in the steady state. Then, the comprehensive framework for the multi-physics multi-objective optimal design of BSRMs based on FEM is proposed. The objectives, constraints, and decision variables for optimization are determined. The multi-objec... [more]

Solving the Unit Commitment problem is an important step in optimally dispatching the available generation and involves two stages—deciding which generators to commit, and then deciding their power output (economic dispatch). The Unit Commitment problem is a mixed-integer combinational optimization problem that traditional optimization techniques struggle to solve, and metaheuristic techniques are better suited. Dragonfly algorithm (DA) and particle swarm optimization (PSO) are two such metaheuristic techniques, and recently a hybrid (DA-PSO), to make use of the best features of both, has been proposed. The original DA-PSO optimization is unable to solve the Unit Commitment problem because this is a mixed-integer optimization problem. However, this paper proposes a new and improved DA-PSO optimization (referred to as iDA-PSO) for solving the unit commitment and economic dispatch problems. The iDA-PSO employs a sigmoid function to find the optimal on/off status of units, which is the mi... [more]

The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solvin... [more]

In order to reduce the energy consumption of deep-sea self-sustaining profile buoy (DSPB) and extend its running time, a stage quantitative oil draining control mode has been proposed in this paper. System parameters have been investigated including oil discharge resolution (ODR), judgment threshold of the floating speed and frequency of oil draining on the energy consumption of the system. The single-objective optimization model with the total energy consumption of DSPB’s ascent stage as the objective function has been established by combining the DSPB’s floating kinematic model. At the same time, as the static working current of the DSPB can be further optimized, a multi-objective energy consumption optimization model with the floating time and the energy consumption of the oil pump motor as objective functions has been established. The non-dominated sorted genetic algorithm-II (NSGA-II) has been employed to optimized the energy consumption model in the ascent stage of the DSPB. The... [more]

This paper presents two economic criteria for guiding the energy storage system (ESS) sizing in grid-connected microgrids. The internal power output model and the economic operation model of ESS are firstly established. Then, the combination of heuristic adjustment strategy and hybrid particle swarm optimization algorithm are introduced to solve the optimal operation model of ESS. Then according to the ESS life model and cost-benefit analysis, a static investment economic criterion which is easy and simple to be calculated is proposed to demonstrate the economic feasibility of ESS investment programs in the short term. Considering the time value of currency, a dynamic investment economic criterion is proposed later for long-term investment projects. Furthermore, the ESS sizing boundary of achieving profits could be also obtained according to the criteria which can indicate the economic attractiveness or resistance to ESS investors in the microgrid. A case study has verified its effecti... [more]

Continuous advances in computer hardware and software are enabling researchers to address optimization solutions using computational resources, as can be seen in the large number of optimization approaches that have been applied to the energy field [...]

In power systems protection, the optimal coordination of directional overcurrent relays (DOCRs) is of paramount importance. The coordination of DOCRs in a multi-loop power system is formulated as an optimization problem. The main objective of this paper is to develop the whale optimization algorithm (WOA) for the optimal coordination of DOCRs and minimize the sum of the operating times of all primary relays. The WOA is inspired by the bubble-net hunting strategy of humpback whales which leads toward global minima. The proposed algorithm has been applied to six IEEE test systems including the IEEE three-bus, eight-bus, nine-bus, 14-bus, 15-bus, and 30-bus test systems. Furthermore, the results obtained using the proposed WOA are compared with those obtained by other up-to-date algorithms. The obtained results show the effectiveness of the proposed WOA to minimize the relay operating time for the optimal coordination of DOCRs.

Upstream oil production using dual string completion, i.e., two tubing inside a well casing, is common due to its cost advantage. High pressure gas is employed to lift the oil to the surface when there is insufficient reservoir energy to overcome the liquids static head in the tubing. However, gas lifting for this type of completion can be complicated. This is due to the operating condition where total gas is injected into the common annulus and then allowed to be distributed among the two strings without any surface control. High uncertainties often result from the methods used to determine the split factor—the ratio between the gas lift rate to one string over the total gas injected. A hybrid model which combined three platforms: the Visual Basics for Application programme, PROSPER (a nodal analysis tool) and Excel spreadsheet, is proposed for the estimation of the split factor. The model takes into consideration two important parameters, i.e., the lift gas pressure gradient along th... [more]

Recently, power companies apply optimal algorithms for short-term load forecasting, especially the daily load. However, in Vietnam, the load forecasting of the power system has not focused on this solution. Optimal algorithms and can help experts improve forecasting results including accuracy and the time required for forecasting. To achieve both goals, the combinations of different algorithms are still being studied. This article describes research using a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This combination limits the weakness of the convergence speed of GA as well as the weakness of PSO that it easily falls into local optima (thereby reducing accuracy). This new hybrid algorithm was applied to the Southern Power Corporation’s (SPC—a large Power company in Vietnam) daily load forecasting. The results show the algorithm’s potential to provide a solution. The most accurate result was for the forecasting of a normal wo... [more]