In this work, the solar-thermal-chemical integrated design for a methane dry reforming reactor with cavity-type solar absorption was numerically performed. Combined with a multiphysical reactor model, the gradient optimization algorithm was used to find optimal radiation flux distribution with fixed total incident solar energy for maximizing overall hydrogen yield, defined as the ratio of molar flow of exported hydrogen to imported methane, which can be applied for guiding the optical property design of solar adsorption surface. The comprehensive performances of the reactor under the conditions of original solar flux and optimal solar flux were analyzed and compared. The results show that for the inlet volume flow rate of 8−14 L·min−1, the hydrogen production rate was increased by up to 5.10%, the energy storage efficiency was increased by up to 5.55%, and the methane conversion rate was increased by up to 6.01%. Finally, the local absorptivities of the solar-absorptive coating on the... [more]

This paper proposes a home energy management system able to achieve optimized load scheduling for the operation of appliances within a given household. The system, based on the genetic algorithm, provides recommendations for the user to improve the way the energy needs of the home are handled. These recommendations not only take into account the dynamic pricing of electricity, but also the optimization for solar energy usage as well as user comfort. Historical data regarding the times at which the appliances have been used is leveraged through a statistical method to integrate the user’s preference into the algorithm. Based on real life appliance consumption data collected from a household in Morocco, three scenarios are established to assess the performance of the proposed system with each scenario having different parameters. Running the scenarios on the developed MATLAB script shows a cost saving of up to 63.48% as compared to a base scenario for a specific day. These results demons... [more]

Given the concern for maintaining voltage stability in power systems integrated with renewable power systems due to a mismatch in generation and demand, there remains a need to invoke flexible alternating current transmission system (FACTS) devices in the distribution network. The present paper deals with identifying the locations of placement of a static var compensator in an experimental IEEE 14-bus system; the voltage drop in different buses in an IEEE 14-bus system is calculated by the standard formula. The total voltage drop in the network (TVDN) is also calculated as a reference. The ranking of buses in order of decreasing voltage drop is made, and the weak buses are identified as those showing the maximum or near-maximum voltage drop for the installation of a Static Var Compensator (SVC). The optimum usable size is calculated using a Genetic Algorithm approach to optimize the installation cost. After size optimization, installing a 2 MW solar generator is considered for the weak... [more]

Renewable energy penetration increases Smart Grid (SG) instability. A power balance between consumption and production can mitigate this instability. For this, intelligent and optimizing techniques can be used to properly combine and manage storage devices like Electric Vehicle Batteries (EVBs) with Demand-Side Management (DSM) strategies. The EVB helps distribution networks with auxiliary services, backup power, reliability, demand response, peak shaving, lower renewable power production’s climate unpredictability, etc. In this paper, a new energy management system based on Artificial Neural Networks (ANNs) is developed to maximize the performance of islanded SG-connected EVBs. The proposed ANN controller can operate at specified periods based on the demand curve and EVB charge level to implement a peak load shaving (PLS) DSM strategy. The intelligent controller’s inputs include the time of day and the EVB’s State of Charge (SOC). After the controller detects a peak demand, it alerts... [more]

Determining the operation scenarios of renewable energies is important for power system dispatching. This paper proposes a renewable scenario generation method based on the hybrid genetic algorithm with variable chromosome length (HGAVCL). The discrete wavelet transform (DWT) is used to divide the original data into linear and fluctuant parts according to the length of time scales. The HGAVCL is designed to optimally divide the linear part into different time sections. Additionally, each time section is described by the autoregressive integrated moving average (ARIMA) model. With the consideration of temporal correlation, the Copula joint probability density function is established to model the fluctuant part. Based on the attained ARIMA model and joint probability density function, a number of data are generated by the Monte Carlo method, and the time autocorrelation, average offset rate, and climbing similarity indexes are established to assess the data quality of generated scenarios... [more]

Compressed natural gas (CNG) main stations are critical components of the urban energy infrastructure for CNG distribution. Due to its high electrification and significant power consumption, researching the economic operation of the CNG main station in demand response (DR)-based electricity pricing environments is crucial. In this paper, the dehydration process is considered in the CNG main station energy consumption model to enhance its participation in DR. A bilevel economic dispatch model for the CNG main station is proposed, considering critical peak pricing. The upper-level and lower-level models represent the energy cost minimization problems of the pre-system and rear-system, respectively, with safety operation constraints. The bilevel programming model is solved using a genetic algorithm combined with a bilevel programming method, which has better efficiency and convergence. The proposed optimization scheme has better control performance and stability, reduces the daily electri... [more]

Zinc−bromine redox flow battery (ZBFB) is one of the most promising candidates for large-scale energy storage due to its high energy density, low cost, and long cycle life. However, numerical simulation studies on ZBFB are limited. The effects of operational parameters on battery performance and battery design strategy remain unclear. Herein, a 2D transient model of ZBFB is developed to reveal the effects of electrolyte flow rate, electrode thickness, and electrode porosity on battery performance. The results show that higher positive electrolyte flow rates can improve battery performance; however, increasing electrode thickness or porosity causes a larger overpotential, thus deteriorating battery performance. On the basis of these findings, a genetic algorithm was performed to optimize the batter performance considering all the operational parameters. It is found that the battery energy efficiency can reach 79.42% at a current density of 20 mA cm−2. This work is helpful to understand... [more]

Renewable energy sources have been characterized by a persistent and rapid proliferation, which has resulted in a notable reduction in grid inertia over an extended period. There is a widely held belief that the primary source of inertia within the grid stems from generation-side conventional units. However, in power consumption, a significant number of induction motors are present, which can inherently offer rotational inertia by virtue of their kinetic energy. To investigate the influence of induction motors on grid inertia, in this paper, we propose two types of models, i.e., a detailed grid model and a dynamic equivalent model that considers multiple induction motors. Specifically, the detailed grid model with multiple induction motors is first established. However, the detailed model requires the specific parameters of induction motors, which are hard to acquire in large systems. Moreover, the accuracy of the model is unsatisfactory. To fill these gaps, the dynamic equivalent mode... [more]

Permanent magnet machines with segmented stator cores are affected by additional harmonic components of the cogging torque which cannot be minimized by conventional methods adopted for one-piece stator machines. In this study, a novel approach is proposed to minimize the cogging torque of such machines. This approach is based on the design of multiple independent shapes of the tooth tips through a topological optimization. Theoretical studies define a design formula that allows to choose the number of independent shapes to be designed, based on the number of stator core segments. Moreover, a computationally-efficient heuristic approach based on genetic algorithms and artificial neural network-based surrogate models solves the topological optimization and finds the optimal tooth tips shapes. Simulation studies with the finite element method validates the design formula and the effectiveness of the proposed method in suppressing the additional harmonic components. Moreover, a comparison... [more]

Energy storage system (ESS) has been advocated as one of the key elements for the future energy system by the fast power regulation and energy transfer capabilities. In particular, for distribution networks with high penetration of renewables, ESS plays an important role in bridging the gap between the supply and demand, maximizing the benefits of renewables and providing various types of ancillary services to cope the intermittences and fluctuations, consequently improving the resilience, reliability and flexibility. To solve the voltage fluctuations caused by the high permeability of renewables in distribution networks, an optimal capacity allocation strategy of ESS is proposed in this paper. Taking the life cycle cost, arbitrage income and the benefit of reducing network losses into consideration, a bilevel optimization model of ESS capacity allocation is established, the coordination between active/reactive power of associate power conversion system is considered, and the large sca... [more]

This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings considering component interconnections. The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. The proposed heuristic algorithm targets simple embedded hardware with limited computation and memory and, taking as inputs the hourly thermal and electrical demand estimated from daily load profiles, computes a dispatch of the energy vectors including the CHP. The main idea of the heuristic is to have a procedure that initially decomposes the three energy vectors’ requests: electrical, thermal, and hot water. Then, the latter are later combined and dispatched c... [more]

Energy efficiency and data security of smart grids are one of the major concerns in the context of implementing modern approaches in smart cities. For the intelligent management of energy systems, wireless sensor networks and advanced metering infrastructures have played an essential role in the transformation of traditional cities into smart communities. In this paper, a smart city energy model is proposed in which prosumer communities were built by interconnecting energy self-sufficient households to generate, consume and share clean energy on a decentralized trading platform by integrating blockchain technology with a smart microgrid. The efficiency and stability of the grid network were improved by using several wireless sensor nodes that manage a massive amount of data in the network. However, long communication distances between sensor nodes and the base station can greatly consume the energy of sensors and decrease the network lifespan. Therefore, bio-inspired algorithm approach... [more]

The increasing penetration of electrical vehicles (EVs), on the way to decarbonizing the transportation sector, presents several challenges and opportunities for the end users, the distribution grid, and the electricity markets. Uncontrollable EV charging may increase peak demand and impact the grid stability and reliability, especially in the case of non-interconnected microgrids such as the distribution grids of small islands. On the other hand, if EVs are considered as flexible loads and distributed storage, they may offer Vehicle to Grid (V2G) services and contribute to demand-side management through smart charging and discharging. In this work, we present a study on the penetration of EVs and the flexibility they may offer for services to the grid, using a genetic algorithm for optimum valley filling and peak shaving for the case of a non-interconnected island where the electricity demand is several times higher during the summer due to the influx of tourists. Test cases have been... [more]

This paper presents a new approach based on the optimization of the blade pitching strategy of offshore wind turbines in order to maximize the global energy output considering the Gaussian wake model and including the effect of added turbulence. A genetic algorithm is proposed as an optimization tool in the process of finding the optimal setting of the wind turbines, which aims to determine the individual pitch of each turbine so that the overall losses due to the wake effect are minimised. The integration of the Gaussian model, including the added turbulence effect, for the evaluation of the wakes provides a step forward in the development of strategies for optimal operation of offshore wind farms, as it is one of the state-of-the-art analytical wake models that allow the evaluation of the energy output of the project in a more reliable way. The proposed methodology has been tested through the execution of a set of test cases that show the ability of the proposed tool to maximize the... [more]

In this paper, a digital hydraulic variable control method based on multi-way valve spool displacement feedback is proposed, which combines the advantages of low cost and high reliability of the loader fixed displacement pump hydraulic system, and the distinguished energy saving effect of the loader variable pump hydraulic system. Based on the principle of hydraulic resistance, the mathematical model of flow-pressure of multi-way valve was established. Meanwhile, the theoretical structure of a valve spool suitable for digital hydraulic system was deduced. On this basis, an improvement scheme of the valve spool was proposed in combination with machining feasibility. The simulation model of multi-way valve was established, the correctness of which was verified by experiment with the significance test method, and the valve port parameters of the multi-way valve were optimized by genetic algorithm. The simulation model before and after optimization was analyzed by AMESim software. The simu... [more]

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River.... [more]

The scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of pro... [more]

In this paper the D-decomposition technique is investigated as a source of non-linear boundaries used with the Genetic Algorithm (GA) search of a PI voltage compensator gains of the boost converter operating in Continuous Conduction Mode (CCM). The well known and appreciated boost converter has been chosen as a test object due to its right-half plane zero in the control-to-output (c2o) voltage transfer function. The D-decomposition, as a technique relying on the frequency sweeping, clearly indicates not only the global stability but, in its extended version, regions satisfying the required gain (GM) and phase (PM) margins. Such results are in form of easy to interpret functions KI=f(KP). The functions are easy to convert to the GA constraints. The GA search, with three different performance indexes as the fitness functions, is applied to a control structure with time delays basing on identified c2o voltage transfer functions. The identification took place in an experiment and in simula... [more]

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton−Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained resul... [more]

This study is concerned with the application of two major kinds of optimisation algorithms on the hydraulic power take-off (HPTO) model for the wave energy converters (WECs). In general, the HPTO unit’s performance depends on the configuration of its parameters such as hydraulic cylinder size, hydraulic accumulator capacity and pre-charge pressure and hydraulic motor displacement. Conventionally, the optimal parameters of the HPTO unit need to be manually estimated by repeating setting the parameters’ values during the simulation process. However, such an estimation method can easily be exposed to human error and would subsequently result in an inaccurate selection of HPTO parameters for WECs. Therefore, an effective approach of using the non-evolutionary Non-Linear Programming by Quadratic Lagrangian (NLPQL) and evolutionary Genetic Algorithm (GA) algorithms for determining the optimal HPTO parameters was explored in the present study. A simulation−optimisation of the HPTO model was p... [more]

This paper refers to the issue that mainly appears in distribution grids, where renewable energy sources (RES) are widely installed. In such grids, one of the main problems is the coordination of energy production time with demand time, especially if photovoltaic energy sources are present. To face this problem, battery energy storage units (ESU) can be installed. In recent years, more and more attention has been paid to optimizing the use of ESU. This paper contains a simple description of available solutions for the application of ESU as well as an original proposal for selecting the optimal location and control of ESU. The ESU selection method is based on the use of a genetic algorithm and the ESU control method utilizes the fuzzy logic. The combination of the aforementioned methods/algorithms of ESU application is named an integrated algorithm. The performance of the proposed algorithm was validated by multivariate computer simulations with the use of the real low-voltage grid mode... [more]

Support vector machine (SVM), which serves as one kind of artificial intelligence technique, has been widely employed in transformer fault diagnosis when involving dissolved gas analysis (DGA). However, when using SVM, it is easy to misclassify samples which are located near the decision boundary, resulting in a decrease in the accuracy of fault diagnosis. Given this issue, this paper proposed a genetic algorithm (GA) optimized probabilistic SVM (GAPSVM) integrated with the fuzzy three-ratio (FTR) method, in which the GAPSVM can judge whether a sample is near the decision boundary according to its output probabilities and diagnose the samples which are not near the decision boundary. Then, FTR is used to diagnose the samples which are near the decision boundary. Combining GAPSVM and FTR, the integrated model can accurately diagnose samples near the decision boundary of SVM. In addition, to avoid redundant and erroneous features, this paper also used GA to select the optimal DGA feature... [more]

This paper is interested in studying a system consisting of a wind turbine operating at variable wind speeds, and a two-feed asynchronous machine (DFIG) connected to the grid by a stator and fed by a transducer at the side of the rotor. The conductors are separately controlled for active and reactive power flow between the stator (DFIG) and the grid. The proposed controllers generate reference voltages for the rotor to ensure that the active and reactive power reaches the required reference values, to ensure effective tracking of the optimum operating point and to obtain the maximum electrical power output. Dynamic analysis of the system is performed under variable wind speeds. This analysis is based on active and reactive energy control. The new work in this paper is to introduce theories of genetic algorithms into the control strategy used in the switching chain of wind turbines in order to improve performance and efficiency. Simulation results applied to genetic algorithms give grea... [more]

This paper proposes a methodology based on a genetic algorithms (GA) to calibrate the parameters of a chlorine decay model in a water distribution system (WDS). The proposed methodology first contemplates that a GA is implemented using historical measurements of chlorine concentration at some sensed nodes to calibrate the unknown values corresponding to both the bulk and wall reaction coefficients. Once both parameters are estimated, the optimal-fit chlorine decay model is used to predict the decay of chlorine concentration in the water at each node for any concentration input at the pumping station. Then, a second GA-based algorithm is implemented to obtain the minimal chlorine concentration needed at the input to ensure that every node in the system meets the official normativity requirements for free chlorine in a WDS. The proposed methodology performed satisfactorily for a WDS simulated in EPANET with a GA implemented in MATLAB, both for the estimation of the reaction coefficients... [more]

With the emergence of the concept of green manufacturing, more manufacturers have attached importance to energy consumption indicators. The process planning and shop scheduling procedures involved in manufacturing processes can both independently achieve energy savings, however independent optimization approaches limit the optimization space. In order to achieve a better optimization effect, the optimization of energy savings for integrated process planning and scheduling (IPPS) was studied in this paper. A mathematical model for multi-objective optimization of IPPS was established to minimize the total energy consumption, makespan, and peak power of the job shop. A hierarchical multi-strategy genetic algorithm based on non-dominated sorting (NSHMSGA) was proposed to solve the problem. This algorithm was based on the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) framework, in which an improved hierarchical coding method is used, containing a variety of genetic operators with diffe... [more]