Chlorine demand as a disinfectant for water utility impacts on unintended energy consumption from electrolysis manufacture; thus, diminishing the chlorine consumption also reduces the environmental impact and energy consumption. Problems of disinfectant distribution and uniformity in Water Distribution Networks (WDN) are associated with the exponential urban growth and the physical and biochemical difficulties within the network. This study optimizes Chlorine Booster Stations (CBS) location on a network with two main objectives; (1) to deliver minimal Free Residual Chlorine (FRC) throughout all demand nodes according to country regulations, and (2) to reduce day chlorine mass concentration supplied in the system by applying an hour time pattern in CBS, consequently associated economic, energy and environmental impacts complying with regulatory standards. The application is demonstrated on a real-world WDN modeled from Guanajuato, Mexico. The resulting optimal location and disinfectant... [more]

Under-frequency load shedding (UFLS) prevents a power grid from a blackout when a severe contingency occurs. UFLS schemes can be classified into two categories—event-based and response-driven. A response-driven scheme utilizes 81L relays with pre-determined settings while an event-based scheme develops a pre-specified look-up table. In this work, an event-based UFLS scheme is presented for use in an offshore standalone power grid with renewables to avoid cascading outages due to low frequency protection of wind power generators and photovoltaic arrays. Possible “N-1” and “N-2” forced outages for peak and off-peak load scenarios in summer and winter are investigated. For each forced outage event, the total shed load is minimized and the frequency nadir is maximized using particle swarm optimization (PSO). In order to reduce the computation time, initialization and parallel computing are implemented using MATLAB/Simulink because all forced outage events and all particles in PSO are mutua... [more]

The small modular dual fluid reactor is a novel variant of the Generation IV molten salt reactor and liquid metal fast reactor. In the primary circuit, molten salt or liquid eutectic metal (U-Pu-Cr) is employed as fuel, and liquid lead works as the coolant in the secondary circuit. To design the control system of such an advanced reactor, the uncertainties of the employed computer model and the physicochemical properties of the materials must be considered. In this paper, a one-dimensional model of a core is established based on the equivalent parameters achieved via the coupled three-dimensional model, taking into account delayed neutron precursor drifting, and a power control system is developed. The performance of the designed controllers is assessed, taking into account the model and property uncertainties. The achieved results show that the designed control system is able to maintain the stability of the system and regulate the power as expected. Among the considered uncertain par... [more]

A de-loaded real power control strategy is proposed to decrease the real power output and increase the reactive power output of a grid-connected offshore wind farm in order to improve the voltage profile when the wind farm is subject to a grid fault. A simplified linear model of the wind farm is first derived and a fixed-gain proportional-integral (PI) real power controller is designed based on the pole-zero cancellation method. To improve the dynamic voltage response when the system is subject to a major disturbance such as a three-phase fault in the grid, a self-tuning controller based on particle swarm optimization (PSO) is proposed to adapt the PI controller gains based on the on-line measured system variables. Digital simulations using MATLAB/SIMULINK were performed on an offshore wind farm connected to the power grid in central Taiwan in order to validate the effectiveness of the proposed PSO controller. It is concluded from the simulation results that a better dynamic voltage re... [more]

Accurate and timely wind power forecasting is essential for achieving large-scale wind power grid integration and ensuring the safe and stable operation of the power system. For overcoming the inaccuracy of wind power forecasting caused by randomness and volatility, this study proposes a hybrid convolutional neural network (CNN) model (GA−PSO−CNN) integrating genetic algorithm (GA) and a particle swarm optimization (PSO). The model can establish feature maps between factors affecting wind power such as wind speed, wind direction, and temperature. Moreover, a mix-encoding GA−PSO algorithm is introduced to optimize the network hyperparameters and weights collaboratively, which solves the problem of subjective determination of the optimal network in the CNN and effectively prevents local optimization in the training process. The prediction effectiveness of the proposed model is verified using data from a wind farm in Ningxia, China. The results show that the MAE, MSE, and MAPE of the prop... [more]

The tunneling work belongs to the group operation of semi-closed space, and the work is difficult with a high risk coefficient. It is an urgent requirement of coal mining to achieve unmanned and intelligent tunneling work. The path rectification planning of roadheaders is a necessary step before roadway cutting. In the traditional dynamic modeling analysis of roadhead tracks, problems such as compaction resistance, bulldozing resistance, steering resistance, tunnel dip angle, ditching, and obstacle-crossing capacity are not considered. In order to approximate the kinematic and dynamic parameters of a roadheader’s deviation correction under actual working conditions, this paper establishes kinematic and dynamic models of a roadheader’s path rectification at low speeds and under complex working conditions, and calculates the obstacle-crossing ability of roadheaders in the course of path rectification by modes based on roadway conditions, crawler resistance, and driving performance of the... [more]

The use of hybrid renewable energy systems (HRES) has become the best option for supplying electricity to sites remote from the central power system because of its sustainability, environmental friendliness, and its low cost of energy compared to many conventional sources such as diesel generators. Due to the intermittent nature of renewable energy resources, there is a need however for an energy storage system (ESS) to store the surplus energy and feed the energy deficit. Most renewable sources used battery storage systems (BSS), a green hydrogen storage system (GHSS), and a diesel generator as a backup for these sources. Batteries are very expensive and have a very short lifetime, and GHSS have a very expensive initial cost and many security issues. In this paper, a system consisting of wind turbines and a photovoltaic (PV) array with a pumped hydro energy storage (PHES) system as the main energy storage to replace the expensive and short lifetime batteries is proposed. The proposed... [more]

Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization (MPSO) along with equilibrium optimizer (EO), termed as MPSO-EO. The proposed approach is tested firstly on 15 benchmark test functions, and then it is implemented to solve the UCP under two test systems. The results are basically compared to that of standard EO and previously applied optimization techniques in solving the UCP. In test system 1, the load demand is deterministic. The proposed technique is in the best three solutions for the 10-unit system with cost savings of 309.95 USD over standard EO and for the 20-unit system it shows the best results over all algorithms in comparison with cost savings of 1951.5 USD over standard EO. In test system 2, the load demand is considered stocha... [more]

The growing demand for electricity runs counter to European-level goals, which include activities aimed at sustainable development and environmental protection. In this context, efficient consumption of electricity attracts much research interest nowadays. One environment friendly solution to meet increased demand lies in the deployment of Renewable Energy Sources (RES) in the network and in mobilizing the active participation of consumers in reducing the peak of demand, thus smoothing the overall load curve. This paper addresses the issue of efficient and economical use of electricity from the Demand Side Management (DSM) perspective and presents an implementation of a fully-parameterized and explicitly constrained incentive-based demand response program The program uses the Particle Swarm Optimization algorithm and demonstrates the potential advantages of integrating RES while supporting two-way communication between energy production and consumption and two-way power exchange betwee... [more]

Well-logging is an important formation characterization and resource evaluation method in oil and gas exploration and development. However, there has been a shortage of well-logging data because Well-logging can only be measured by expensive and time-consuming field tests. In this study, we aimed to find effective machine learning techniques for well-logging data prediction, considering the temporal and spatial characteristics of well-logging data. To achieve this goal, the convolutional neural network (CNN) and the long short-term memory (LSTM) neural networks were combined to extract the spatial and temporal features of well-logging data, and the particle swarm optimization (PSO) algorithm was used to determine hyperparameters of the optimal CNN-LSTM architecture to predict logging curves in this study. We applied the proposed CNN-LSTM-PSO model, along with support vector regression, gradient-boosting regression, CNN-PSO, and LSTM-PSO models, to forecast photoelectric effect (PE) log... [more]

Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conduct... [more]

Charge scheduling can mitigate against issues arising from excessive electric vehicle (EV) charging loads and is commonly implemented using time-of-use pricing. A charge scheduling strategy to suit vertically structured power systems without relying on time-of-use pricing has not yet been reported, despite being needed by industry. Therefore, a novel charge scheduling strategy to meet this need is proposed in this paper. Key aspects include the provision of a decision-making framework that accommodates for the considerations of transmission and distribution network operators, and the allowance for dynamically changing charging loads through timely forecast updates with reduced communication requirements. A case study based on the Indonesian Java-Bali power system is undertaken to demonstrate the strategy’s effectiveness. Different and realistic EV uptake scenarios are considered, using probabilistic modeling, survey work, and a Monte Carlo modeling approach. Even under slow EV charging... [more]

Polarization is a universal phenomenon that occurs inside lithium-ion batteries especially during operation, and whether it can be accurately characterized affects the accuracy of the battery management system. Model-based approaches are commonly adopted in studies of the characterization of polarization. Towards the application of the battery management system, a lumped diffusion model with three parameters was adopted. In addition, a joint algorithm composed of the Particle Swarm Optimization algorithm and the Levenberg-Marquardt method is proposed to identify model parameters. Verification experiments showed that this proposed algorithm can significantly improve the accuracy of model output voltages compared to the Particle Swarm Optimization algorithm alone and the Levenberg-Marquardt method alone. Furthermore, to verify the real-time performance of the proposed method, a hardware implementation platform was built, and this system’s performance was tested under actual operating con... [more]

The microgrid has two main steady-state modes: grid-connected mode and islanded mode. The microgrid needs a high-performance controller to reduce the overshoot value that affects the efficiency of the network. However, the high voltage value causes the inverter to stop. Thus, an improved power-sharing response to the transfer between these two modes must be insured. More important points to study in a microgrid are the current sharing and power (active or reactive) sharing, besides the match percentage of power sharing among parallel inverters and the overshoot of both active and reactive power. This article aims to optimize the power response in addition to voltage and frequency stability, in order to make this network’s performance more robust against external disturbance. This can be achieved through a self-tuning control method using an optimization algorithm. Here, the optimized droop control is provided by the H-infinity (H∞) method improved with the artificial bee colony algorit... [more]

An improved mathematical model and an improved particle swarm optimization (IPSO) are proposed for the complex design parameters and conflicting design goals of the indoor luminaire layout (ILL) problem. The ILL problem is formulated as a nonlinear constrained mixed-variable optimization problem that has four decision variables. For a general lighting scheme (GLS), the number and location of luminaires can be uniquely determined by optimizing four decision variables, which avoid using program loops to determine the number of luminaires. We improve the particle swarm optimization (PSO) in three aspects: (1) up-down probabilistic rounding (UDPR) method proposed to solve mixed integer, (2) improving the velocity of the best global particle, and (3) using nonlinear inertia weights with random items. The IPSO has better optimization results in an office study compared with the PSO and genetic algorithm (GA). The results are validated by DIALux simulation software, and a maximum deviation of... [more]

The green innovations in the energy sector are smart solutions to meet the excessive power requirements through renewable energy resources (RERs). These resources have forwarded the revolutionary relief in control of carbon dioxide gaseous emissions from traditional energy resources. The use of RERs in a heuristic manner is necessary to meet the demand side management in microgrids (MGs). The pricing scheme limitations hinder the profit maximization of MG and their customers. In addition, recent pricing schemes lack mechanistic underpinning. Therefore, a dynamic electricity pricing scheme through linear regression is designed for RERs to maximize the profit of load customers (changeable and unchangeable) in MG. The demand response optimization problem is solved through the particle swarm optimization (PSO) technique. The proposed dynamic electricity pricing scheme is evaluated under two different scenarios. The simulation results verified that the proposed dynamic electricity pricing s... [more]

The state of health and remaining useful life of lithium-ion batteries are key indicators for the normal operation of electrical devices. To address the problem of the capacity of lithium-ion batteries being difficult to measure online, in this paper, we propose an online method based on particle swarm optimization and support vector regression to estimation the state of health and remaining useful life. First, a novel health indicator is extracted from the discharge voltage to characterize the capacity of lithium-ion batteries. Then, based on the capacity degradation characteristics, support vector regression is used to predict the remaining useful life of these batteries, and particle swarm optimization is selected to optimize the parameters of the support vector regression, which effectively enhances the predictive performance of the model. Validated for the NASA battery aging dataset, when training with the first 40% of the dataset, the maximum error of the predicted remaining usef... [more]

Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (... [more]

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experime... [more]

The aim of this study is to improve the three-phase unbalanced voltage at the secondary side of a distribution transformer. The proposed method involves compensation sources injecting three different single-phase currents into the connected point of a grid. The computations of optimal single-phase currents are performed using the circuit analysis method and particle swarm optimization algorithm. An unbalanced three-phase power distribution system model is constructed, including a transformer Δ−Δ connection, V−V connection, load balance, load unbalance combination, and three single-phase compensation current sources. The results show that the voltage unbalance rate of the electricity user side is improved to less than 1%, and the three-phase total compensation apparent power is approximately 0 VA. In the future, the application of the model as an auxiliary service could be achieved by adding an energy storage system.

Recent research reveals that multi-phase motors in electric propulsion systems are highly recommended due to their improved reliability and efficiency over traditional three phase motors. This research presented a comparison of optimal model design of a six phase squirrel cage induction motor (IM) for electric propulsion by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A six phase squirrel cage induction motor is designed and simulated by ANSYS Motor-CAD. In order to find the best fit method, simulation results are compared and applied to the motors for electric propulsion, considering the influence of design upon the motor performance. The six-phase squirrel cage induction motor is more energy efficient, reliable and cost effective for the electric propulsion compared to the conventional three phase motor. In this study, first the initial parameters of the six phase squirrel cage induction motor have been determined and then these parameters have been compared wi... [more]

Load and supply parameters may be uncertain in microgrids (MGs) due for instance to the intermittent nature of renewable energy sources among others. Guaranteeing reliable and stable MGs despite parameter uncertainties is crucial for their correct operation. Their stability and dynamical features are directly related to the controllers’ parameters and power-sharing coefficients. Hence, to maintain power good quality within the desirable range of system parameters and to have a satisfactory response to sudden load changes, careful selection of the controllers and power-sharing coefficients are necessary. In this paper, a simple design approach for the optimal design of controllers’ parameters is presented in an islanded MG. To that aim, an optimization problem is formulated based on a small-signal state-space model and solved by three different optimization techniques including particle swarm optimization (PSO), genetic algorithm (GA), and a proposed approach based on the combination of... [more]

In order to realize the sustainable development of energy, the combination of new energy power generation technology and the traditional offshore platform has excellent research prospects. The access to new energy sources can provide a powerful supplement to the power grid of the offshore platform, but will also create new challenges for the planning, operation, and control of the power grid of the platform; hence, it is very important to optimize the reactive power of the offshore platform with new study, a mathematical model was first built for the reactive power optimization of offshore platform power systems with new energy sources, and the Taguchi method was then used to optimize the parameters and population of particle swarm optimization, thereby addressing a defect in particle swarm optimization, namely, that it can easily fall into local optimal solutions. Finally, the algorithm proposed in this paper was applied to solve the reactive power optimization problem of the offshore... [more]

The valorization of chicken manure via pyrolysis can give biowaste a second life to generate value and contribute to the circular economy. In the present study, the thermal degradation and pyrolysis characteristics of chicken manure pyrolysis were investigated via thermogravimetric analyses (TGA) coupled with optimization methods. Thermogravimetric data were obtained for the samples at five heating rates of 5, 10, 20, 30 and 50 °C/min over a range of temperature under inert conditions. The manure devolatilization process was initiated at between 328 and 367 °C to overcome the global activation energy barrier. The determined activation energy of the manure via Flynn−Wall−Ozawa (FWO), Kissinger−Akahira−Sunose (KAS), Friedman and Kissinger methods was in the range of 167.5−213.9 kJ/mol. By using the particle swarm optimization (PSO) method, the pyrolytic kinetic parameters of the individual component present in the manure were calculated, in which the activation energy for cellulose (227.... [more]

Gas and downhole water sink-assisted gravity drainage (GDWS-AGD) is a new process of enhanced oil recovery (EOR) in oil reservoirs underlain by large bottom aquifers. The process is capital intensive as it requires the construction of dual-completed wells for oil production and water drainage and additional multiple vertical gas-injection wells. The costs could be substantially reduced by eliminating the gas-injection wells and using triple-completed multi-functional wells. These wells are dubbed triple-completion-GDWS-AGD (TC-GDWS-AGD). In this work, we design and optimize the TC-GDWS-AGD oil recovery process in a fictitious oil reservoir (Punq-S3) that emulates a real North Sea oil field. The design aims at maximum oil recovery using a minimum number of triple-completed wells with a gas-injection completion in the vertical section of the well, and two horizontal well sections—the upper section for producing oil (from above the oil/water contact) and the lower section for draining wat... [more]