Aiming at the economic dispatch problem for an interconnected system with wind power integration, and in order to realize the goals of system economy and improvement of the cross-regional consumption level of wind energy, a decentralized coordination dispatch model is established in this paper. In this model, a DC tie-line is cut by the branch cutting method and used as a coupling variable. A virtual upper-level dispatch center is established, and the economic dispatch problem to be solved is decomposed into a master optimization problem for the upper-level dispatch center and subsidiary optimization problems for the lower-level dispatch centers. For solving this model, an improved Harris hawks optimization (HHO) algorithm called the chaotic mutation Harris hawks optimization (CMHHO) algorithm is proposed. In the CMHHO algorithm, tent mapping and the “DE/pbad-to-pbest/1” strategy are introduced, and a new nonlinear escape energy factor adjustment is proposed. Through an algorithm compa... [more]

Biodiesel has been established as a promising alternative fuel to petroleum diesel. This study offers a promising energy conversion platform to valorise high acidity waste cooking oil (WCO) into biodiesel in a single-step reaction via supercritical methanol. Carbon dioxide (CO2) has been used as a co-solvent in the reaction with a catalytic effect to enhance the production of biodiesel. This work provides an in-depth assessment of the yield of four fatty acids methyl esters (FAME) from their correspondent triglycerides and fatty acids. The effects of four independent process variables, i.e., methanol to oil (M:O) molar ratio, temperature, pressure, and time, have been investigated using Response Surface Methodology (RSM). Four quadratic models have been developed between process variables and the yield of FAMEs. The statistical validation of the predicted models has been performed using analysis of variance (ANOVA). Numerical optimisation has been employed to predict the optimal condit... [more]

Hydrogen has been identified as a very promising vector for energy storage, especially for heavy mobility applications. For this reason, France is making significant investments in this field, and use cases need to be evaluated as they are sprouting. In this paper, the relevance of H2 in two storage applications is studied: a domestic renewable electricity production system connected to the grid and a collective hydrogen production for the daily bus refill. The investigation consists of the sizing of the system and then the evaluation of its performance according to several criteria depending on case. Optimizations are made using Bayesian and gradient-based methods. Several variations around a central case are explored for both cases to give insights on the impact of the different parameters (location, pricing, objective, etc.) on the performance of the system.Our results show that domestic power-to-power applications (case 1) do not seem to be competitive with electrochemical storage.... [more]

A large-scale two-dimensional computational fluid dynamics study is conducted in order to maximise the power output and smoothness of power delivery of a free-stream water wheel, a low-impact hydropower device. Based on models and methods developed in previous research, the study uses a genetic algorithm to optimise the geometry of a wheel with a given radius and depth, maximising two objective functions simultaneously. After convergence and suitable post-processing, a single optimal design is identified, featuring eight shortened blades that become fully immersed at the nadir point. The design results in a 71% reduction in blade material and a 113% increase in the work ratio while improving the hydraulic power by 8% compared to the previous best design. These characteristics are applied retroactively to a broad family of designs, resulting in significant improvements in performance. Analysis of the resulting designs indicates that when either the hydraulic power coefficient, rotor pow... [more]

Advances in communication technologies and protocols among vehicles, charging stations, and controllers have enabled the application of scheduling techniques to prioritize EV fleet charging. From the perspective of users, residential EV charging must particularly address cost-effective solutions to use energy more efficiently and preserve the lifetime of the battery—the most expensive element of an EV. Considering this matter, this research addresses a residential EV charging scheduling model including battery degradation aspects when discharging. Due to the non-linear characteristics of charging and battery degradation, we consider a mixed integer non-linearly constrained formulation with the aim of scheduling the charging and discharging of EVs to satisfy the following goals: prioritizing charging, reducing charging costs and battery degradation, and limiting the power demand requested to the distribution transformer. The results shows that, when EVs are discharged before charging up... [more]

The paper investigated the problem of selecting/finding the optimal process conditions for gas condensate wells. The well process conditions imply a set of parameters that characterize its operation. The optimization of process conditions provides for the efficient operation of an oil and gas field while meeting the defined boundary and initial conditions, and allows for the process/production goal to be achieved. This paper proposed using the tree-structured Parzen estimator (TPE), which allows for the results from previous iterations to be considered, in order to identify the most promising region of conditions, thereby increasing the optimization efficiency. The movement of multiphase fluid inside the pipeline system (also in the borehole) must be calculated to solve the process optimization problem. The optimization module was integrated into the hydraulic and unsteady state multiphase flow calculations inside the well and the pipeline. The platform created allows for the process c... [more]

The growth of renewable energy facilities worldwide creates new challenges for sustainable regional development. Unregulated investment flows in the green energy sector cause disparities in the deployment of various renewable energy technologies, worsen the ability to balance national energy systems, etc. This article is the first comprehensive study that offers a methodology for multifactor modeling of investment flows in regional green energy deployment considering the priorities of national, regional, and local authorities within the sustainable development concept. The proposed methodological approaches help (1) determine the types of renewable energy technologies for priority development in the region, (2) select specific green energy projects to receive budgetary support on territories, and (3) form the optimal mechanism for budget financing distribution on regional development of renewable energy technologies. The modeling factors include natural conditions and resource base of... [more]

In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compa... [more]

A new and efficient analytical optimization methodology for the design of rotor features is developed and used in interior permanent magnet motors (IPM). The analytical methodology is based on an extended winding function theory to include the IPM rotor’s primary and secondary reluctance paths and the non-homogeneous airgap of the rotor sculpt features. The shape and placement of the rotor features, derived from the analytical-based optimization process, show the improvement in torque average and torque ripple of the IPM machine at a fraction of computational effort. The analytical optimization results are validated with finite element analysis via an exhaustive search.

Renewable energy sources (RES) are becoming more attractive due to the global demand for the utilization of clean energy and their easy accessibility. The integration of renewable energy technologies into power systems has been made easy, such that RES can be incorporated into small distribution systems or power grids. This integration of RES has negative impacts on power quality, system reliability, and network security. This could affect the RES performance and causes power quality-related problems such as harmonics, voltage flickers, swell, and sag. This paper presents a generation and transmission expansion planning model with the integration of large-scale RES considering harmonic emissions constraints. This method uses a weighted sum approach to combine the multi-objective optimization problems, thereby minimizing the total costs, active power loss, and harmonic power loss. An analytical technique was developed to estimate and quantify the harmonic emissions from the RES componen... [more]

Microgrid (MG) is a combination of distributed generators (DGs), energy storage systems (ESSs), and loads connected to distribution network that can either be in islanded mode or grid-tied mode. Similarly, a multi-microgrid (MMG) system is a number of interconnected MGs connected with a larger and complex distribution network. Recently, the MMG energy management has created new challenges due to the inherent intermittency, uncertainty, and probabilistic nature of renewable based DGs output and varying load demands. To ensure the efficient operation and optimal energy management in the MMGs, this work proposes a two-stage, a day-ahead, simultaneous energy management strategy (EMS) of the MMG system as well as the MG system. At the first stage, each MG assumes a day-ahead predicted load demand and DGs output. At the second stage, through EMS, the energy scheduling, minimization of the main grid dependency, and maximization of the stored energy in the ESS are managed simultaneously. Four... [more]

In this paper, a survey is presented on the use of optimization models for the integration of electric vehicles (EVs) and charging stations (CSs) in the energy system, paying particular attention both to planning problems (i.e., those problems related to long term decisions such as the siting and sizing of CSs), and operational management problems (i.e., the optimal scheduling of EVs in smart grids, microgrids and buildings taking into account vehicle-to-grid (V2G) capabilities). Moreover, specific attention was dedicated to decision problems that couple transportation and electrical networks, such as the energy demand assessment for a vehicle over a path and routing and charging decision problems for goods and people transportation. Finally, an effort was dedicated to highlighting the integration and the use of EVs in very recent regulation frameworks, with specific reference to the participation in the balancing market through the figure of an aggregator and the inclusion in the mana... [more]

This paper proposes an approach for the accurate and efficient parameter identification of lithium-ion battery packs using only drive cycle data obtained from hybrid or electric vehicles. The approach was experimentally validated using data collected from a BMW i8 hybrid vehicle. The dual polarization model was used, and a new open circuit voltage equation was proposed based on a simplification of the combined model, with the aim of reducing the number of parameters to be identified. The parameter identification was performed using NEDC data collected on a rolling road dynamometer; the results showed that the proposed model improved the accuracy of terminal voltage estimation, reducing the peak voltage error from 2.16% using the Nernst model to 1.28%. Furthermore, the robustness of these models in maintaining accuracy when new drive cycles were used was evaluated by comparing WLTC simulations with experimental measurements. The proposed model showed improved robustness, with a reductio... [more]

Distribution networks have undergone a series of changes, with the insertion of distributed energy resources, such as distributed generation, energy storage systems, and demand response, allowing the consumers to produce energy and have an active role in distribution systems. Thus, it is possible to form microgrids. From the active grid’s point of view, it is necessary to plan the operation considering the distributed resources and the microgrids connected to it, aiming to ensure the maintenance of grid economy and operational safety. So, this paper presents the proposition of a hierarchical model for planning the daily operation of active distribution grids with microgrids. In this case, the entire grid operation is optimized considering the results from the microgrid optimization itself. If none of the technical constraints, for example voltage levels, are reached, the grid is optimized, however, if there are some violations in the constraints feedback is sent to the internal microgr... [more]

Economic load dispatch (ELD) provides significant benefits to the operation of the power system. It appears to be a complex nonconvex optimization problem subject to several equal and unequal constraints. The greedy sine-cosine nonhierarchical gray wolf optimizer (G-SCNHGWO) is introduced in this study to solve complex nonconvex ELD optimization problems efficiently and robustly. The sine and cosine functions assist the search agents of the grey wolf optimizer (GWO) algorithm in avoiding trapping in a local optimum. In addition, the greedy nonhierarchical concept is integrated into GWO to enrich the optimization power of the conventional GWO algorithm. Simulations are implemented to validate the capability of the suggested algorithm in solving the different ELD problems. According to the results, the algorithm demonstrates very suitable performance compared to other state-of-the-art methods.

Voltage control in modern electric power distribution systems has become challenging due to the increasing penetration of distributed energy resources (DER). The current state-of-the-art voltage control is based on static/pre-determined DER volt-var curves. Static volt-var curves do not provide sufficient flexibility to address the temporal and spatial aspects of the voltage control problem in a power system with a large number of DER. This paper presents a simple, scalable, and robust distributed optimization framework (DOF) for optimizing voltage control. The proposed framework allows for data-driven distributed voltage optimization in a power distribution system. This method enhances voltage control by optimizing volt-var curve parameters of inverters in a distributed manner based on a cellular computational network (CCN) representation of the power distribution system. The cellular optimization approach enables the system-wide optimization. The cells to be optimized may be prioriti... [more]

In a competitive electricity market, generation capacities can exactly cover their full costs. However, the real market deviates from this ideal in some aspects. One is the concern of non-existent or insufficient scarcity prices. We present an iterative method in a linear optimization model to investigate the profitability of assets in the absence of scarcity prices and how the system changes when this risk is incorporated into investors’ expectations. Therefore, we use a two-step optimization of capacity planning and unit commitment. Iteratively, mark-ups at the height of uncovered costs are added to investment costs. This typically leads to a system with better investment profitability while keeping the system cost increase low. The methodology is applied to a simplified brownfield generation system, targeting CO2-free power generation within 25 years. In a model with annual foresight of actors, iterations result in a generation system with significantly lower (or even no) uncovered... [more]

While the contribution of renewable energy technologies to the energy system is increasing, so is its level of complexity. In addition to new types of consumer systems, the future system will be characterized by volatile generation plants that will require storage technologies. Furthermore, a solid interconnected system that enables the transit of electrical energy can reduce the need for generation and storage systems. Therefore, appropriate methods are needed to analyze energy production and consumption interactions within different system constellations. Energy system models can help to understand and build these future energy systems. However, although various energy models already exist, none of them can cover all issues related to integrating renewable energy systems. The existing research gap is also reflected in the fact that current models cannot model the entire energy system for very high shares of renewable energies with high temporal resolution (15 min or 1-h steps) and hi... [more]

The reduction of CO2 emissions has become a global concern. In this regard, the EU intends to cut CO2 emissions by 55% by 2030 compared to those of 1990. The utilization of shallow geothermal energy (SGE) in EU countries is considered the most effective measure for decarbonizing heating and cooling. SGE systems utilize heat energy collected from the earth’s crust to provide secure, clean, and ubiquitous energy. This paper provides a literature review on the use of SGE for heating and cooling purposes. The latest advances in materials, new innovative structures, and techno-economic optimization approaches have been discussed in detail. Shallow geothermal energy’s potential is first introduced, and the innovative borehole structures to improve performance and reduce installation cost is outlined. This is followed by an extensive survey of different types of conventional and thermally enhanced collectors and grouts. Attention is mainly given to the techno-economic analysis and optimizatio... [more]

Digitally controlled Dual-Input Doherty Power Amplifiers (DIDPAs) are becoming increasingly popular due to the flexible input signal splitting between the main and auxiliary stages. Nevertheless, the presence of many degrees of freedom, e.g., input amplitude split and phase displacement as well as biasing for multiple stages, often involves inefficient trial-and-error procedures to reach a suitable PA performance. This article presents automated parameter setting based on coordinate descent or Bayesian optimizations, demonstrating an improvement in the performance in terms of RF output power and power-added efficiency (PAE) in the presence of broadband-modulated signals, yet maintaining suitable linear behavior for, e.g., communications applications.

Rapid transit systems play a significant role in supporting rapid social and economic development in large cities all over the world. However, the systems consume a large amount of energy, which brings increasing environmental concerns. A number of energy-saving technologies have been studied on railways. However, few of the outcomes have been tested and evaluated in practice. This paper presents the development of a timetable optimization and trial test on a metro line to reach the full potential of the train regenerative braking system. To achieve this purpose, a timetable optimization algorithm has been developed, and a trial test of the optimal timetable has been arranged on a metro line for a whole day. In the test, all the trains running in the network were organized to operate in accordance with the optimal timetable. The trial test results indicate that by applying the optimal timetable, the regenerative braking energy utilization can be improved, thereby reducing the overall n... [more]

As a new energy source, gas hydrates have attracted worldwide attention, but their exploration and development face enormous challenges. Thus, it has become increasingly crucial to identify hydrate distribution accurately. Electrical resistivity tomography (ERT) can be used to detect the distribution of hydrate deposits. An ERT inversion network (ERTInvNet) based on a deep neural network (DNN) is proposed, with strong learning and memory capabilities to solve the ERT nonlinear inversion problem. 160,000 samples about hydrate distribution are generated by numerical simulation, of which 10% are used for testing. The impact of different deep learning parameters (such as loss function, activation function, and optimizer) on the performance of ERT inversion is investigated to obtain a more accurate hydrate distribution. When the Logcosh loss function is enabled in ERTInvNet, the average correlation coefficient (CC) and relative error (RE) of all samples in the test sets are 0.9511 and 0.109... [more]

A turbulent jet ignition engine enables operation with lean mixtures, decreasing nitrogen oxide (NOX) emissions up to 92%, while the engine efficiency can be increased compared to conventional spark-ignition engines. The geometry of the pre-chamber and engine operating parameters play the most important role in the performance of turbulent jet ignition engines and, therefore, must be optimized. The initial experimental and 3D CFD results of a single-cylinder engine fueled by gasoline were used for the calibration of a 0D/1D simulation model. The 0D/1D simulation model was upgraded to capture the effects of multiple flame propagations, and the evolution of the turbulence level was described by the new K-k-ε turbulence model, which considers the strong turbulent jets occurring in the main chamber. The optimization of the pre-chamber volume, the orifice diameter, the injected fuel mass in the pre-chamber and the spark timing was made over 9 different operating points covering the variatio... [more]

This paper presents a technical overview for Switched Reluctance Generators (SRG) in Wind Energy Conversion System (WECS) applications. Several topics are discussed, such as the main structures and topologies for SRG converters in WECS, and the optimization control methods to improve the operational efficiency of SRGs in wind power generation systems. A comprehensive overview including the main characteristics of each SRG converter topology and control techniques were discussed. The analysis presented can also serve as a foundation for more advanced versions of SRG control techniques, providing a necessary basis to spur more and, above all, motivate the younger researchers to study magnetless electric machines, and pave the way for higher growth of wind generators based on SRGs.

The optimal allocation of protective devices is a serious issue in an electrical power system; in order to reduce the possibility of faults, the protection devices should be optimally placed. The paper presents a continuous genetic algorithm (CGA) for the optimal allocation of directional relays for the efficient energy minimization in a radial distribution system (DG). The algorithm is flexible to use for the changes and improvements in the optimal location for a DG unit and can optimize the energy consumption in the radial distribution system. The proposed algorithm has been implemented on IEEE 33 and 69-bus system using MATLAB (R2014b, MathWorks). Low energy consumption is a common design objective in an energy-constrained distribution system. Engineers, power utilities, and network operators can profit from the proposed methodology to enhance the use of DG in distribution networks.