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]

A groundswell of opinion in utilizing environmentally friendly energy technologies has been put forth worldwide. In this paper, we consider an energy generation plant distribution and allocation problem under uncertainty to get the utmost out of available developments, as well as to control costs and greenhouse emissions. Different clean and traditional energy technologies are considered in this paper. In particular, we present a risk-averse stochastic mixed-integer linear programming (MILP) model to minimize the total expected costs and control the risk of CO2 emissions exceeding a certain budget. We employ the conditional value-at-risk (CVaR) model to represent risk preference and risk constraint of emissions. We prove that our risk-averse model can be equivalent to the traditional risk-neutral model under certain conditions. Moreover, we suggest that the risk-averse model can provide solutions generating less CO2 than traditional models. To handle the computational difficulty in unc... [more]

The economic load dispatch (ELD) problem is a complex optimization problem in power systems. The main task for this optimization problem is to minimize the total fuel cost of generators while also meeting the conditional constraints of valve-point loading effects, prohibited operating zones, and nonsmooth cost functions. In this paper, a novel grey wolf optimization (GWO), abbreviated as NGWO, is proposed to solve the ELD problem by introducing an independent local search strategy and a noninferior solution neighborhood independent local search technique to the original GWO algorithm to achieve the best problem solution. A local search strategy is added to the standard GWO algorithm in the NGWO, which is called GWOI, to search the local neighborhood of the global optimal point in depth and to guarantee a better candidate. In addition, a noninferior solution neighborhood independent local search method is introduced into the GWOI algorithm to find a better solution in the noninferior so... [more]

The growth in energy demand, coupled with declining fossil fuel resources and the onset of climate change, has resulted in increased interest in renewable energy, particularly from biomass. Co-firing, which is the joint use of coal and biomass to generate electricity, is seen to be a practical immediate solution for reducing coal use and the associated emissions. However, biomass is difficult to manage because of its seasonal availability and variable quality. This study proposes a biomass co-firing supply chain optimization model that simultaneously minimizes costs and environmental emissions through goal programming. The economic costs considered include retrofitting investment costs, together with fuel, transport, and processing costs, while environmental emissions may come from transport, treatment, and combustion activities. This model incorporates the consideration of feedstock quality and its impact on storage, transportation, and pre-treatment requirements, as well as conversio... [more]

In this study, the performance of a current sensor based on giant magnetostrictive materials (GMM) and fiber Bragg grating (FBG) has been improved by optimizing the spectral characteristics of gratings. By analyzing the influence of FBG on the current sensor characteristics, three key parameters (gate region length, refractive index modulation depth, and toe cutting system) are selected for optimization. The optimal grating parameters are determined to improve the linearity and sensitivity of sensor output. Experimental tests reveal that after grating optimization, the current sensor shows excellent performance parameters, including a linearity of 0.9942, sensitivity of 249.75 mV/A, and good stability in the temperature range of 0−60 °C. This research can provide a reference for improving the grating design and performance of existing GMM-FBG current sensors.

Recently, renewable energy resources (RESs) and electric vehicles (EVs), in addition to other distributed energy resources (DERs), have gained high popularity in power systems applications. These resources bring quite a few advantages for power systems—reducing carbon emission, increasing efficiency, and reducing power loss. However, they also bring some disadvantages for the network because of their intermittent behavior and their high number in the grid which makes the optimal management of the system a tough task. Virtual power plants (VPPs) are introduced as a promising solution to make the most out of these resources by aggregating them as a single entity. On the other hand, VPP’s optimal management depends on its accuracy in modeling stochastic parameters in the VPP body. In this regard, an efficient approach for a VPP is a method that can overcome these intermittent resources. In this paper, a comprehensive study has been investigated for the optimal management of a VPP by model... [more]

In this paper, the optimization issue of electric vehicle charging station layout (EVCSL) for tourist attractions is addressed, and an improved PSO is used to solve the optimization issue. Specifically, the improved particle swarm optimization (PSO) is proposed to obtain an appreciative planning solution of EVCSL, and dynamic weight adjustment strategy and integration into the two-population genetic mode are proposed to improve the optimization quality for PSO. Simulation results show that the proposed improvement strategies can increase the optimization quality for PSO effectively so that a more appreciative planning solution of EVCSL can be obtained.

The rooftop photovoltaic (PV) system that uses a power optimization device at the module level (MLPE) has been theoretically proven to have an advantage over other types in case of reducing the effect of partial shading. Unfortunately, there is still a lack of studies about the energy production of such a system in real working conditions with the impact of partial shading conditions (PSC). In this study, we evaluated the electrical energy production of the PV systems which use two typical configurations of power optimization at the PV panel level, a DC optimizer and a microinverter, using their real datasets working under PSC. Firstly, we compared the energy utilization ratio of the monthly energy production of these systems to the reference ones generated from PVWatt software to evaluate the effect of PSC on energy production. Secondly, we conducted a linear decline model to estimate the annual degradation rate of PV systems during a 6-year period to evaluate the effect of PSC on the... [more]

Hybrid concentrated solar thermal power (CSP) and photovoltaic (PV) plants are gaining relevance because they combine their advantages: easy installation and low cost of PV plus dispatchability of CSP. This paper presents results of a techno-economic modelling of this hybrid approach for sites in Jordan, Tunisia and Algeria. Local boundary conditions such as meteorology, cost and electricity demand have been considered to determine the best configurations for these three sites. Different CSP technologies with thermal energy storage have been selected. Hybridization with natural gas has also been included. The optimization is done towards minimizing the LCOE while covering the electrical demand 24/7. Results are presented for different CO2 emissions ranges, as the use of fossil fuel has a strong impact on the LCOE and for environmental reasons, it may be preferred to be kept to a minimum. For most of the cases analyzed, the fraction of energy from PV that leads to minimum LCOE is lower... [more]

The leakage inductance of the transformer in a dual active bridge (DAB) dc−dc converter directly impacts the ac current waveforms and the power factor; thus, it can be considered a design requirement for the transformer. In the existing literature, a choice is made to either ensure soft switching in nominal power or to minimize the RMS current of the transformer. The inductance is typically obtained using optimization procedures. Implementing these optimizations is time-consuming, which can be avoided if a closed-form equation is derived for the optimum leakage inductance. In this paper, analytical formulas are derived to estimate the desired leakage inductance such that the highest RMS value of the current in the operation region of a DAB is kept to its minimum value. The accuracy and sensitivity of the analytical solutions are evaluated. It is shown that in a large design domain, the solution for the YY-connected MFT has a less than 3% error compared to the results obtained from an o... [more]

Solid oxide fuel cell (SOFC) performance degradation analysis and optimization studies are important prerequisites for its commercialization. Reviewing and summarizing SOFC performance degradation studies can help researchers identify research gaps and increase investment in weak areas. In this study, to help researchers purposely improve system performance, degradation mechanism analysis, degradation performance prediction, and degradation performance optimization studies are sorted out. In the review, it is found that the degradation mechanism analysis studies can help to improve the system structure. Degradation mechanism analysis studies can be performed at the stack level and system level, respectively. Degradation performance prediction can help to take measures to mitigate degradation in advance. The main tools of prediction study can be divided into model-based, data-based, electrochemical impedance spectroscopy-based, and image-based approaches. Degradation performance optimiz... [more]